diff --git a/api/v2alpha1/python/setup.py b/api/v2alpha1/python/setup.py
index f08cd595fc..a6a98656cf 100644
--- a/api/v2alpha1/python/setup.py
+++ b/api/v2alpha1/python/setup.py
@@ -15,7 +15,7 @@
import setuptools
NAME = 'kfp-pipeline-spec'
-VERSION = '0.2.2'
+VERSION = '0.3.0'
setuptools.setup(
name=NAME,
@@ -26,7 +26,7 @@
url='https://github.com/kubeflow/pipelines',
packages=setuptools.find_namespace_packages(include=['kfp.*']),
python_requires='>=3.7.0,<3.13.0',
- install_requires=['protobuf>=3.13.0,<4'],
+ install_requires=['protobuf>=4.21.1,<5'],
include_package_data=True,
license='Apache 2.0',
)
diff --git a/backend/Dockerfile b/backend/Dockerfile
index c65b897da4..08fee1822f 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -59,6 +59,7 @@ ARG COMMIT_SHA=unknown
ENV COMMIT_SHA=${COMMIT_SHA}
ARG TAG_NAME=unknown
ENV TAG_NAME=${TAG_NAME}
+ENV LOG_LEVEL info
WORKDIR /bin
@@ -82,4 +83,4 @@ RUN sed -E "s#/(blob|tree)/master/#/\1/${COMMIT_SHA}/#g" -i /config/sample_confi
EXPOSE 8888
# Start the apiserver
-CMD /bin/apiserver --config=/config --sampleconfig=/config/sample_config.json -logtostderr=true
+CMD /bin/apiserver --config=/config --sampleconfig=/config/sample_config.json -logtostderr=true --logLevel=${LOG_LEVEL}
diff --git a/backend/Dockerfile.persistenceagent b/backend/Dockerfile.persistenceagent
index c16403637e..0b493041cc 100644
--- a/backend/Dockerfile.persistenceagent
+++ b/backend/Dockerfile.persistenceagent
@@ -49,7 +49,8 @@ ENV TTL_SECONDS_AFTER_WORKFLOW_FINISH 86400
# NUM_WORKERS indicates now many worker goroutines
ENV NUM_WORKERS 2
+ENV LOG_LEVEL info
ENV EXECUTIONTYPE Workflow
-CMD persistence_agent --logtostderr=true --namespace=${NAMESPACE} --ttlSecondsAfterWorkflowFinish=${TTL_SECONDS_AFTER_WORKFLOW_FINISH} --numWorker ${NUM_WORKERS} --executionType ${EXECUTIONTYPE}
+CMD persistence_agent --logtostderr=true --namespace=${NAMESPACE} --ttlSecondsAfterWorkflowFinish=${TTL_SECONDS_AFTER_WORKFLOW_FINISH} --numWorker ${NUM_WORKERS} --executionType ${EXECUTIONTYPE} --logLevel=${LOG_LEVEL}
diff --git a/backend/Dockerfile.scheduledworkflow b/backend/Dockerfile.scheduledworkflow
index 6ff9f8b1d7..69bb642611 100644
--- a/backend/Dockerfile.scheduledworkflow
+++ b/backend/Dockerfile.scheduledworkflow
@@ -46,5 +46,6 @@ COPY --from=builder /tmp/NOTICES /third_party/NOTICES
ENV NAMESPACE ""
ENV EXECUTIONTYPE Workflow
+ENV LOG_LEVEL info
-CMD /bin/controller --logtostderr=true --namespace=${NAMESPACE} --executionType ${EXECUTIONTYPE}
+CMD /bin/controller --logtostderr=true --namespace=${NAMESPACE} --executionType ${EXECUTIONTYPE} --logLevel=${LOG_LEVEL}
diff --git a/backend/src/agent/persistence/main.go b/backend/src/agent/persistence/main.go
index 20ab4a791d..05c6ef634e 100644
--- a/backend/src/agent/persistence/main.go
+++ b/backend/src/agent/persistence/main.go
@@ -30,6 +30,7 @@ import (
var (
masterURL string
+ logLevel string
kubeconfig string
initializeTimeout time.Duration
timeout time.Duration
@@ -48,6 +49,7 @@ var (
)
const (
+ logLevelFlagName = "logLevel"
kubeconfigFlagName = "kubeconfig"
masterFlagName = "master"
initializationTimeoutFlagName = "initializeTimeout"
@@ -91,6 +93,16 @@ func main() {
log.Fatalf("Error building schedule clientset: %s", err.Error())
}
+ if logLevel == "" {
+ logLevel = "info"
+ }
+
+ level, err := log.ParseLevel(logLevel)
+ if err != nil {
+ log.Fatal("Invalid log level:", err)
+ }
+ log.SetLevel(level)
+
clientParam := util.ClientParameters{QPS: float64(cfg.QPS), Burst: cfg.Burst}
execInformer := util.NewExecutionInformerOrFatal(util.CurrentExecutionType(), namespace, time.Second*30, clientParam)
@@ -136,6 +148,7 @@ func main() {
func init() {
flag.StringVar(&kubeconfig, kubeconfigFlagName, "", "Path to a kubeconfig. Only required if out-of-cluster.")
flag.StringVar(&masterURL, masterFlagName, "", "The address of the Kubernetes API server. Overrides any value in kubeconfig. Only required if out-of-cluster.")
+ flag.StringVar(&logLevel, logLevelFlagName, "", "Defines the log level for the application.")
flag.DurationVar(&initializeTimeout, initializationTimeoutFlagName, 2*time.Minute, "Duration to wait for initialization of the ML pipeline API server.")
flag.DurationVar(&timeout, timeoutFlagName, 1*time.Minute, "Duration to wait for calls to complete.")
flag.StringVar(&mlPipelineAPIServerName, mlPipelineAPIServerNameFlagName, "ml-pipeline", "Name of the ML pipeline API server.")
diff --git a/backend/src/apiserver/main.go b/backend/src/apiserver/main.go
index 599c0694b7..c75ad6a51d 100644
--- a/backend/src/apiserver/main.go
+++ b/backend/src/apiserver/main.go
@@ -43,6 +43,7 @@ import (
"github.com/kubeflow/pipelines/backend/src/apiserver/template"
"github.com/kubeflow/pipelines/backend/src/common/util"
"github.com/prometheus/client_golang/prometheus/promhttp"
+ log "github.com/sirupsen/logrus"
"github.com/spf13/viper"
"google.golang.org/grpc"
"google.golang.org/grpc/reflection"
@@ -54,6 +55,7 @@ const (
)
var (
+ logLevelFlag = flag.String("logLevel", "", "Defines the log level for the application.")
rpcPortFlag = flag.String("rpcPortFlag", ":8887", "RPC Port")
httpPortFlag = flag.String("httpPortFlag", ":8888", "Http Proxy Port")
configPath = flag.String("config", "", "Path to JSON file containing config")
@@ -92,6 +94,17 @@ func main() {
}
}
+ logLevel := *logLevelFlag
+ if logLevel == "" {
+ logLevel = "info"
+ }
+
+ level, err := log.ParseLevel(logLevel)
+ if err != nil {
+ log.Fatal("Invalid log level:", err)
+ }
+ log.SetLevel(level)
+
go startRpcServer(resourceManager)
startHttpProxy(resourceManager)
diff --git a/backend/src/crd/controller/scheduledworkflow/main.go b/backend/src/crd/controller/scheduledworkflow/main.go
index a1239d9ab0..05a10f0fb3 100644
--- a/backend/src/crd/controller/scheduledworkflow/main.go
+++ b/backend/src/crd/controller/scheduledworkflow/main.go
@@ -35,6 +35,7 @@ import (
)
var (
+ logLevel string
masterURL string
kubeconfig string
namespace string
@@ -77,6 +78,16 @@ func main() {
cfg.QPS = float32(clientQPS)
cfg.Burst = clientBurst
+ if logLevel == "" {
+ logLevel = "info"
+ }
+
+ level, err := log.ParseLevel(logLevel)
+ if err != nil {
+ log.Fatal("Invalid log level:", err)
+ }
+ log.SetLevel(level)
+
kubeClient, err := kubernetes.NewForConfig(cfg)
if err != nil {
log.Fatalf("Error building kubernetes clientset: %s", err.Error())
@@ -149,6 +160,7 @@ func initEnv() {
func init() {
initEnv()
+ flag.StringVar(&logLevel, "logLevel", "", "Defines the log level for the application.")
flag.StringVar(&kubeconfig, "kubeconfig", "", "Path to a kubeconfig. Only required if out-of-cluster.")
flag.StringVar(&masterURL, "master", "", "The address of the Kubernetes API server. Overrides any value in kubeconfig. Only required if out-of-cluster.")
flag.StringVar(&namespace, "namespace", "", "The namespace name used for Kubernetes informers to obtain the listers.")
diff --git a/backend/src/v2/driver/driver.go b/backend/src/v2/driver/driver.go
index 15edc8adfa..1e67ab9c43 100644
--- a/backend/src/v2/driver/driver.go
+++ b/backend/src/v2/driver/driver.go
@@ -17,9 +17,7 @@ import (
"context"
"encoding/json"
"fmt"
- "path"
"strconv"
- "strings"
"time"
"github.com/golang/glog"
@@ -1062,7 +1060,9 @@ func provisionOutputs(pipelineRoot, taskName string, outputsSpec *pipelinespec.C
outputs.Artifacts[name] = &pipelinespec.ArtifactList{
Artifacts: []*pipelinespec.RuntimeArtifact{
{
- Uri: generateOutputURI(pipelineRoot, name, taskName),
+ // Do not preserve the query string for output artifacts, as otherwise
+ // they'd appear in file and artifact names.
+ Uri: metadata.GenerateOutputURI(pipelineRoot, []string{taskName, name}, false),
Type: artifact.GetArtifactType(),
Metadata: artifact.GetMetadata(),
},
@@ -1078,12 +1078,6 @@ func provisionOutputs(pipelineRoot, taskName string, outputsSpec *pipelinespec.C
return outputs
}
-func generateOutputURI(root, artifactName string, taskName string) string {
- // we cannot path.Join(root, taskName, artifactName), because root
- // contains scheme like gs:// and path.Join cleans up scheme to gs:/
- return fmt.Sprintf("%s/%s", strings.TrimRight(root, "/"), path.Join(taskName, artifactName))
-}
-
var accessModeMap = map[string]k8score.PersistentVolumeAccessMode{
"ReadWriteOnce": k8score.ReadWriteOnce,
"ReadOnlyMany": k8score.ReadOnlyMany,
diff --git a/backend/src/v2/metadata/client.go b/backend/src/v2/metadata/client.go
index adfca08766..d6f37183a7 100644
--- a/backend/src/v2/metadata/client.go
+++ b/backend/src/v2/metadata/client.go
@@ -260,6 +260,26 @@ func (e *Execution) FingerPrint() string {
return e.execution.GetCustomProperties()[keyCacheFingerPrint].GetStringValue()
}
+// GenerateOutputURI appends the specified paths to the pipeline root.
+// It may be configured to preserve the query part of the pipeline root
+// by splitting it off and appending it back to the full URI.
+func GenerateOutputURI(pipelineRoot string, paths []string, preserveQueryString bool) string {
+ querySplit := strings.Split(pipelineRoot, "?")
+ query := ""
+ if len(querySplit) == 2 {
+ pipelineRoot = querySplit[0]
+ if preserveQueryString {
+ query = "?" + querySplit[1]
+ }
+ } else if len(querySplit) > 2 {
+ // this should never happen, but just in case.
+ glog.Warningf("Unexpected pipeline root: %v", pipelineRoot)
+ }
+ // we cannot path.Join(root, taskName, artifactName), because root
+ // contains scheme like gs:// and path.Join cleans up scheme to gs:/
+ return fmt.Sprintf("%s/%s%s", strings.TrimRight(pipelineRoot, "/"), path.Join(paths...), query)
+}
+
// GetPipeline returns the current pipeline represented by the specified
// pipeline name and run ID.
func (c *Client) GetPipeline(ctx context.Context, pipelineName, runID, namespace, runResource, pipelineRoot string) (*Pipeline, error) {
@@ -272,7 +292,7 @@ func (c *Client) GetPipeline(ctx context.Context, pipelineName, runID, namespace
keyNamespace: stringValue(namespace),
keyResourceName: stringValue(runResource),
// pipeline root of this run
- keyPipelineRoot: stringValue(strings.TrimRight(pipelineRoot, "/") + "/" + path.Join(pipelineName, runID)),
+ keyPipelineRoot: stringValue(GenerateOutputURI(pipelineRoot, []string{pipelineName, runID}, true)),
}
runContext, err := c.getOrInsertContext(ctx, runID, pipelineRunContextType, metadata)
glog.Infof("Pipeline Run Context: %+v", runContext)
diff --git a/backend/src/v2/metadata/client_test.go b/backend/src/v2/metadata/client_test.go
index d384ab20aa..86a16fe772 100644
--- a/backend/src/v2/metadata/client_test.go
+++ b/backend/src/v2/metadata/client_test.go
@@ -143,7 +143,7 @@ func Test_GetPipeline_Twice(t *testing.T) {
// The second call to GetPipeline won't fail because it avoid inserting to MLMD again.
samePipeline, err := client.GetPipeline(ctx, "get-pipeline-test", runId, namespace, runResource, pipelineRoot)
fatalIf(err)
- if (pipeline.GetCtxID() != samePipeline.GetCtxID()) {
+ if pipeline.GetCtxID() != samePipeline.GetCtxID() {
t.Errorf("Expect pipeline context ID %d, actual is %d", pipeline.GetCtxID(), samePipeline.GetCtxID())
}
}
@@ -214,6 +214,60 @@ func Test_GetPipelineConcurrently(t *testing.T) {
wg.Wait()
}
+func Test_GenerateOutputURI(t *testing.T) {
+ // Const define the artifact name
+ const (
+ pipelineName = "my-pipeline-name"
+ runID = "my-run-id"
+ pipelineRoot = "minio://mlpipeline/v2/artifacts"
+ pipelineRootQuery = "?query=string&another=query"
+ )
+ tests := []struct {
+ name string
+ queryString string
+ paths []string
+ preserveQueryString bool
+ want string
+ }{
+ {
+ name: "plain pipeline root without preserveQueryString",
+ queryString: "",
+ paths: []string{pipelineName, runID},
+ preserveQueryString: false,
+ want: fmt.Sprintf("%s/%s/%s", pipelineRoot, pipelineName, runID),
+ },
+ {
+ name: "plain pipeline root with preserveQueryString",
+ queryString: "",
+ paths: []string{pipelineName, runID},
+ preserveQueryString: true,
+ want: fmt.Sprintf("%s/%s/%s", pipelineRoot, pipelineName, runID),
+ },
+ {
+ name: "pipeline root with query string without preserveQueryString",
+ queryString: pipelineRootQuery,
+ paths: []string{pipelineName, runID},
+ preserveQueryString: false,
+ want: fmt.Sprintf("%s/%s/%s", pipelineRoot, pipelineName, runID),
+ },
+ {
+ name: "pipeline root with query string with preserveQueryString",
+ queryString: pipelineRootQuery,
+ paths: []string{pipelineName, runID},
+ preserveQueryString: true,
+ want: fmt.Sprintf("%s/%s/%s%s", pipelineRoot, pipelineName, runID, pipelineRootQuery),
+ },
+ }
+ for _, tt := range tests {
+ t.Run(tt.name, func(t *testing.T) {
+ got := metadata.GenerateOutputURI(fmt.Sprintf("%s%s", pipelineRoot, tt.queryString), tt.paths, tt.preserveQueryString)
+ if diff := cmp.Diff(got, tt.want); diff != "" {
+ t.Errorf("GenerateOutputURI() = %v, want %v\nDiff (-want, +got)\n%s", got, tt.want, diff)
+ }
+ })
+ }
+}
+
func Test_DAG(t *testing.T) {
t.Skip("Temporarily disable the test that requires cluster connection.")
diff --git a/components/google-cloud/Dockerfile b/components/google-cloud/Dockerfile
index de45cf9735..9960664755 100644
--- a/components/google-cloud/Dockerfile
+++ b/components/google-cloud/Dockerfile
@@ -44,7 +44,7 @@ RUN pip3 install -U "fsspec>=0.7.4" "gcsfs>=0.6.0" "pandas<=1.3.5" "scikit-learn
RUN pip3 install -U google-cloud-notebooks
# Install main package
-RUN pip3 install "git+https://github.com/kubeflow/pipelines.git@google-cloud-pipeline-components-2.6.0#egg=google-cloud-pipeline-components&subdirectory=components/google-cloud"
+RUN pip3 install "git+https://github.com/kubeflow/pipelines.git@google-cloud-pipeline-components-2.8.0#egg=google-cloud-pipeline-components&subdirectory=components/google-cloud"
# Note that components can override the container entry ponint.
ENTRYPOINT ["python3","-m","google_cloud_pipeline_components.container.v1.aiplatform.remote_runner"]
diff --git a/components/google-cloud/LICENSE b/components/google-cloud/LICENSE
index 7a4a3ea242..733f4ec26e 100644
--- a/components/google-cloud/LICENSE
+++ b/components/google-cloud/LICENSE
@@ -199,4 +199,40 @@
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
- limitations under the License.
\ No newline at end of file
+ limitations under the License.
+
+
+ ------------------
+
+ Copyright 2008 Google Inc. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+ * Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following disclaimer
+in the documentation and/or other materials provided with the
+distribution.
+ * Neither the name of Google Inc. nor the names of its
+contributors may be used to endorse or promote products derived from
+this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Code generated by the Protocol Buffer compiler is owned by the owner
+of the input file used when generating it. This code is not
+standalone and requires a support library to be linked with it. This
+support library is itself covered by the above license.
diff --git a/components/google-cloud/RELEASE.md b/components/google-cloud/RELEASE.md
index bfed0dc04f..d4a9a162b8 100644
--- a/components/google-cloud/RELEASE.md
+++ b/components/google-cloud/RELEASE.md
@@ -1,7 +1,17 @@
## Upcoming release
-* Fix `v1.automl.training_job.AutoMLImageTrainingJobRunOp` `ModuleNotFoundError`
-* Append `tune-type` to existing labels when uploading models tuned by `preview.llm.rlhf_pipeline` instead of overriding them.
+* Use `large_model_reference` for `model_reference_name` when uploading models from `preview.llm.rlhf_pipeline` instead of hardcoding value as `text-bison@001`.
+* Disable caching when resolving model display names for RLHF-tuned models so a unique name is generated on each `preview.llm.rlhf_pipeline` run.
+* Upload the tuned adapter to Model Registry instead of model checkpoint from `preview.llm.rlhf_pipeline`.
+
+## Release 2.8.0
+* Release AutoSxS pipeline to preview.
+* Apply latest GCPC image vulnerability resolutions (base OS and software updates).
+## Release 2.7.0
+* Fix `v1.automl.training_job.AutoMLImageTrainingJobRunOp` `ModuleNotFoundError`.
+* Append `tune-type` to existing labels when uploading models tuned by `preview.llm.rlhf_pipeline` instead of overriding them.
+* Use `llama-2-7b` for the base reward model when tuning `llama-2-13b` with the `preview.llm.rlhf_pipeline`
+* Apply latest GCPC image vulnerability resolutions (base OS and software updates).
## Release 2.6.0
* Bump supported KFP versions to kfp>=2.0.0b10,<=2.4.0
diff --git a/components/google-cloud/docs/source/versions.json b/components/google-cloud/docs/source/versions.json
index 8e0cbc0109..a3f7f92a2c 100644
--- a/components/google-cloud/docs/source/versions.json
+++ b/components/google-cloud/docs/source/versions.json
@@ -1,4 +1,14 @@
[
+ {
+ "version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.8.0",
+ "title": "2.8.0",
+ "aliases": []
+ },
+ {
+ "version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.7.0",
+ "title": "2.7.0",
+ "aliases": []
+ },
{
"version": "https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-2.6.0",
"title": "2.6.0",
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/arbiter_preprocess.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/arbiter_preprocess.py
new file mode 100644
index 0000000000..344e8fe6db
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/arbiter_preprocess.py
@@ -0,0 +1,137 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""KFP Container component for preprocessing predictions for the Arbiter."""
+
+import os
+from typing import Dict, List
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components import utils as gcpc_utils
+from google_cloud_pipeline_components._implementation.llm import utils
+from kfp import dsl
+
+
+def _resolve_image() -> str:
+ """Determines the image URI to create a container from."""
+ return (
+ os.environ.get('AUTOSXS_IMAGE_OVERRIDE')
+ or utils.get_default_image_uri('autosxs'))
+
+
+# pylint: disable=unused-argument,dangerous-default-value
+@dsl.container_component
+def arbiter_preprocess(
+ evaluation_dataset: str,
+ id_columns: List[str],
+ response_column_a: str,
+ response_column_b: str,
+ task: str,
+ is_bp_output_a: bool,
+ is_bp_output_b: bool,
+ autorater_prompt_parameters: Dict[str, Dict[str, str]],
+ preprocessed_evaluation_dataset: dsl.Output[dsl.Dataset], # pylint: disable=unused-argument # pytype: disable=unsupported-operands
+ preprocessed_evaluation_dataset_uri: dsl.OutputPath(str), # pylint: disable=unused-argument # pytype: disable=invalid-annotation
+ gcp_resources: dsl.OutputPath(str), # pytype: disable=invalid-annotation
+ prediction_uris_a: str = '',
+ prediction_uris_b: str = '',
+ model_a_prompt_parameters: Dict[str, Dict[str, str]] = {},
+ model_b_prompt_parameters: Dict[str, Dict[str, str]] = {},
+ human_preference_column: str = '',
+) -> dsl.ContainerSpec: # pylint: disable=g-doc-args
+ """Preprocesses predictions tables for the AutoSxS Arbiter.
+
+ Args:
+ evaluation_dataset: GCS or BigQuery URIs representing a dataset of prompts
+ and responses.
+ id_columns: The columns which distinguish unique evaluation examples.
+ response_column_a: The column containing responses for model a.
+ response_column_b: The column containing responses for model a.
+ task: Task to evaluate.
+ output_path: Path to write the path where preprocessed predictions are
+ stored.
+ is_bp_output_a: If True, the prediction URIs will be parsed as if they came
+ from Vertex Batch Prediction, where response_column_a represents a field
+ in the model output containing the response. If False, the expected format
+ will be a table containing all model_prompt_parameters and the
+ response_column.
+ is_bp_output_b: If True, the prediction URIs will be parsed as if they came
+ from Vertex Batch Prediction, where response_column_b represents a field
+ in the model output containing the response. If False, the expected format
+ will be a table containing all model_prompt_parameters and the
+ response_column.
+ prediction_uris: A list of GCS or BigQuery URIs representing a dataset of
+ prompts and responses for model a.
+ prediction_uris: A list of GCS or BigQuery URIs representing a dataset of
+ prompts and responses for model b.
+ model_a_prompt_parameters: Map of model A prompt template parameters to
+ columns or templates.
+ model_b_prompt_parameters: Map of model B prompt template parameters to
+ columns or templates.
+ autorater_prompt_parameters: Map of autorater prompt template parameters to
+ columns or templates.
+ human_preference_column: The column containing ground truths. The default
+ value is an empty string if not be provided by users.
+
+ Returns:
+ preprocessed_evaluation_dataset: Dataset of the table containing the inputs
+ expected by the Arbiter.
+ preprocessed_evaluation_dataset_uri: URI of the table containing the inputs
+ expected by the Arbiter.
+ gcp_resources: Tracker for GCP resources created by this component.
+ """
+ return gcpc_utils.build_serverless_customjob_container_spec(
+ project=_placeholders.PROJECT_ID_PLACEHOLDER,
+ location=_placeholders.LOCATION_PLACEHOLDER,
+ custom_job_payload=utils.build_payload(
+ display_name='arbiter_preprocess',
+ machine_type='n1-standard-4',
+ image_uri=_resolve_image(),
+ args=[
+ '--', # Used to mark the start of component flags.
+ 'arbiter_preprocess',
+ f'--evaluation_dataset={evaluation_dataset}',
+ f'--prediction_uris_a={prediction_uris_a}',
+ f'--prediction_uris_b={prediction_uris_b}',
+ (
+ '--id_columns='
+ "{{$.inputs.parameters['id_columns'].json_escape[0]}}"
+ ),
+ (
+ '--autorater_prompt_parameters='
+ "{{$.inputs.parameters['autorater_prompt_parameters']"
+ '.json_escape[0]}}'
+ ),
+ (
+ '--model_a_prompt_parameters='
+ "{{$.inputs.parameters['model_a_prompt_parameters']"
+ '.json_escape[0]}}'
+ ),
+ (
+ '--model_b_prompt_parameters='
+ "{{$.inputs.parameters['model_b_prompt_parameters']"
+ '.json_escape[0]}}'
+ ),
+ f'--response_column_a={response_column_a}',
+ f'--response_column_b={response_column_b}',
+ f'--human_preference_column={human_preference_column}',
+ f'--task={task}',
+ f'--is_batch_prediction_output_a={is_bp_output_a}',
+ f'--is_batch_prediction_output_b={is_bp_output_b}',
+ f'--output_dir={dsl.PIPELINE_ROOT_PLACEHOLDER}',
+ f'--preprocessed_evaluation_dataset_uri={preprocessed_evaluation_dataset_uri}',
+ '--executor_input={{$.json_escape[1]}}',
+ ],
+ ),
+ gcp_resources=gcp_resources,
+ )
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/autosxs_arbiter.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/autosxs_arbiter.py
new file mode 100644
index 0000000000..6269b41307
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/autosxs_arbiter.py
@@ -0,0 +1,107 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""KFP Container component that performs AutoSxS."""
+
+import os
+from typing import Any, Dict, List
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components import utils as gcpc_utils
+from google_cloud_pipeline_components._implementation.llm import utils
+from kfp import dsl
+
+
+def _resolve_image() -> str:
+ """Determines the image URI to create a container from."""
+ return (
+ os.environ.get('AUTOSXS_IMAGE_OVERRIDE')
+ or utils.get_default_image_uri('autosxs'))
+
+
+def _get_prediction_endpoint_overrides() -> str:
+ """Used for integration tests to override the prediction endpoint."""
+ return os.environ.get('PREDICTION_ENDPOINT_OVERRIDES', '')
+
+
+@dsl.container_component
+def autosxs_arbiter(
+ inference_output_uri: str,
+ id_columns: List[str],
+ task: str,
+ judgments: dsl.Output[dsl.Dataset], # pylint: disable=unused-argument # pytype: disable=unsupported-operands
+ judgments_uri: dsl.OutputPath(str), # pytype: disable=invalid-annotation
+ error_messages: dsl.Output[dsl.Dataset], # pylint: disable=unused-argument # pytype: disable=unsupported-operands
+ gcp_resources: dsl.OutputPath(str),
+ metadata: dsl.OutputPath(str),
+ human_preference_column: str = '',
+ judgments_format: str = 'jsonl',
+ bigquery_destination_prefix: str = '',
+ experimental_args: Dict[str, Any] = {},
+) -> dsl.ContainerSpec: # pylint: disable=g-doc-args
+ """Evaluate two models using an autorater.
+
+ Args:
+ inference_output_uri: Directory of model A's inference output.
+ id_columns: The columns which distinguish unique evaluation examples.
+ human_preference_column: Human preference column included in our inference
+ output.
+ task: Evaluation task in the form {task}@{version}. task can be one of
+ "summarization", "question_answer". Version is an integer with 3 digits or
+ "latest". Ex: summarization@001 or question_answer@latest.
+ judgments_format: The format to write judgments to. Can be either 'json' or
+ 'bigquery'.
+ bigquery_destination_prefix: BigQuery table to write judgments to if the
+ specified format is 'bigquery'.
+ experimental_args: Experimentally released arguments. Subject to change.
+
+ Returns:
+ judgments: Individual judgments used to calculate the win rates.
+ judgments_uri: URI of the Judgments Artifact.
+ error_messages: Error messages of failed samples of each evaluation example.
+ gcp_resources: Tracker for GCP resources created by this component.
+ metadata: Computed runtime metrics metadata from this component.
+ """
+ return gcpc_utils.build_serverless_customjob_container_spec(
+ project=_placeholders.PROJECT_ID_PLACEHOLDER,
+ # Hardcode location to us-central1 for text-bison availability.
+ location='us-central1',
+ custom_job_payload=utils.build_payload(
+ display_name='autosxs_arbiter',
+ machine_type='n1-standard-4',
+ image_uri=_resolve_image(),
+ args=[
+ '--', # Used to mark the start of component flags.
+ 'arbiter',
+ f'--inference_output_uri={inference_output_uri}',
+ f'--human_preference_column={human_preference_column}',
+ f'--task={task}',
+ f'--prediction_endpoint_overrides={_get_prediction_endpoint_overrides()}',
+ f'--output_dir={dsl.PIPELINE_ROOT_PLACEHOLDER}',
+ f'--judgments_uri={judgments_uri}',
+ f'--judgments_format={judgments_format}',
+ f'--bigquery_destination_prefix={bigquery_destination_prefix}',
+ (
+ '--id_columns='
+ "{{$.inputs.parameters['id_columns'].json_escape[0]}}"
+ ),
+ (
+ '--experimental_args='
+ "{{$.inputs.parameters['experimental_args'].json_escape[0]}}"
+ ),
+ '--executor_input={{$.json_escape[1]}}',
+ f'--metadata_path={metadata}',
+ ],
+ ),
+ gcp_resources=gcp_resources,
+ )
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/autosxs_metrics_computer.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/autosxs_metrics_computer.py
new file mode 100644
index 0000000000..ede9a816f9
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/autosxs_metrics_computer.py
@@ -0,0 +1,66 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""KFP Container component for computing AutoSXS metrics."""
+
+import os
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components import utils as gcpc_utils
+from google_cloud_pipeline_components._implementation.llm import utils
+from kfp import dsl
+
+
+def _resolve_image() -> str:
+ """Determines the image URI to create a container from."""
+ return os.environ.get(
+ 'AUTOSXS_IMAGE_OVERRIDE'
+ ) or utils.get_default_image_uri('autosxs')
+
+
+@dsl.container_component
+def autosxs_metrics_computer(
+ judgments_dir: str,
+ has_human_preference: bool,
+ autosxs_metrics: dsl.Output[dsl.Metrics], # pylint: disable=unused-argument # pytype: disable=unsupported-operands
+ gcp_resources: dsl.OutputPath(str), # pytype: disable=invalid-annotation
+) -> dsl.ContainerSpec: # pylint: disable=g-doc-args
+ """Compute AutoSXS metrics using judgments outputs from Arbiter.
+
+ Args:
+ judgments_dir: Path where store the Judgments.
+ has_human_preference: Boolean value. True if users provided human preference
+ data, otherwise false.
+
+ Returns:
+ autosxs_metrics: Autosxs win rate metrics and human alignment metrics.
+ gcp_resources: Tracker for GCP resources created by this component.
+ """
+ return gcpc_utils.build_serverless_customjob_container_spec(
+ project=_placeholders.PROJECT_ID_PLACEHOLDER,
+ # Hardcode location to us-central1 for text-bison availability.
+ location='us-central1',
+ custom_job_payload=utils.build_payload(
+ display_name='autosxs_metrics_computer',
+ machine_type='n1-standard-4',
+ image_uri=_resolve_image(),
+ args=[
+ '--', # Used to mark the start of component flags.
+ 'autosxs_metrics',
+ f'--judgments_dir={judgments_dir}',
+ f'--has_human_preference={has_human_preference}',
+ '--executor_input={{$.json_escape[1]}}',
+ ],
+ ),
+ gcp_resources=gcp_resources,
+ )
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/deployment_graph.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/deployment_graph.py
index 3010724da1..bdc436ffef 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/deployment_graph.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/deployment_graph.py
@@ -61,10 +61,14 @@ def pipeline(
upload_location=upload_location
).set_display_name('Resolve Regional Endpoint')
- display_name = function_based.resolve_model_display_name(
- large_model_reference=large_model_reference,
- model_display_name=model_display_name,
- ).set_display_name('Resolve Model Display Name')
+ display_name = (
+ function_based.resolve_model_display_name(
+ large_model_reference=large_model_reference,
+ model_display_name=model_display_name,
+ )
+ .set_caching_options(False)
+ .set_display_name('Resolve Model Display Name')
+ )
reference_model_metadata = function_based.resolve_reference_model_metadata(
large_model_reference=large_model_reference,
@@ -81,7 +85,7 @@ def pipeline(
regional_endpoint=regional_endpoint.output,
artifact_uri=adapter_artifact.output,
model_display_name=display_name.output,
- model_reference_name='text-bison@001',
+ model_reference_name=large_model_reference,
upload_model=upload_model.output,
tune_type='rlhf',
).set_display_name('Upload Model')
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/env.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/env.py
index 8ba7080937..c7511d222c 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/env.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/env.py
@@ -16,7 +16,7 @@
def get_private_image_tag() -> str:
- return os.getenv('PRIVATE_IMAGE_TAG', '20231031_0507_RC00')
+ return os.getenv('PRIVATE_IMAGE_TAG') or '20231213_0507_RC00'
def get_use_test_machine_spec() -> bool:
@@ -26,14 +26,24 @@ def get_use_test_machine_spec() -> bool:
# Variables associated with private images:
CLOUD_ML_REGION = os.getenv('CLOUD_ML_REGION', 'europe-west4')
-PRIVATE_ARTIFACT_REGISTRY_PROJECT: str = os.getenv(
- 'PRIVATE_ARTIFACT_REGISTRY_PROJECT', 'vertex-ai-restricted'
+PRIVATE_ARTIFACT_REGISTRY_PROJECT: str = (
+ os.getenv(
+ 'PRIVATE_ARTIFACT_REGISTRY_PROJECT',
+ )
+ or 'vertex-ai-restricted'
)
-PRIVATE_ARTIFACT_REGISTRY_LOCATION: str = os.getenv(
- 'PRIVATE_ARTIFACT_REGISTRY_LOCATION', 'us'
+PRIVATE_ARTIFACT_REGISTRY_LOCATION: str = (
+ os.getenv(
+ 'PRIVATE_ARTIFACT_REGISTRY_LOCATION',
+ )
+ or 'us'
+)
+PRIVATE_ARTIFACT_REGISTRY: str = (
+ os.getenv('PRIVATE_ARTIFACT_REGISTRY') or 'rlhf'
+)
+PRIVATE_IMAGE_NAME_PREFIX: str = (
+ os.getenv('PRIVATE_IMAGE_NAME_PREFIX') or 'rlhf_'
)
-PRIVATE_ARTIFACT_REGISTRY: str = os.getenv('PRIVATE_ARTIFACT_REGISTRY', 'rlhf')
-PRIVATE_IMAGE_NAME_PREFIX: str = os.getenv('PRIVATE_IMAGE_NAME_PREFIX', 'rlhf_')
PRIVATE_IMAGE_TAG: str = get_private_image_tag()
# Dataset variables:
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/function_based.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/function_based.py
index b793121280..9e4bae85ca 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/function_based.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/function_based.py
@@ -13,7 +13,7 @@
# limitations under the License.
"""Python function-based components used in KFP pipelies."""
import functools
-from typing import List, NamedTuple, Optional
+from typing import Any, Dict, List, NamedTuple, Optional
from google_cloud_pipeline_components import _image
from google_cloud_pipeline_components._implementation.llm import env
@@ -302,8 +302,8 @@ def resolve_reference_model_metadata(
'llama-2-13b': reference_model_metadata(
large_model_reference='LLAMA_2_13B',
reference_model_path='gs://vertex-rlhf-restricted/pretrained_models/llama/t5x_llama_2_13b/',
- reward_model_reference='LLAMA_2_13B',
- reward_model_path='gs://vertex-rlhf-restricted/pretrained_models/llama/t5x_llama_2_13b/',
+ reward_model_reference='LLAMA_2_7B',
+ reward_model_path='gs://vertex-rlhf-restricted/pretrained_models/llama/t5x_llama_2_7b/',
is_supported=True,
),
'llama-2-7b-chat': reference_model_metadata(
@@ -316,8 +316,8 @@ def resolve_reference_model_metadata(
'llama-2-13b-chat': reference_model_metadata(
large_model_reference='LLAMA_2_13B_CHAT',
reference_model_path='gs://vertex-rlhf-restricted/pretrained_models/llama/t5x_llama_2_13b_chat/',
- reward_model_reference='LLAMA_2_13B',
- reward_model_path='gs://vertex-rlhf-restricted/pretrained_models/llama/t5x_llama_2_13b/',
+ reward_model_reference='LLAMA_2_7B',
+ reward_model_path='gs://vertex-rlhf-restricted/pretrained_models/llama/t5x_llama_2_7b/',
is_supported=True,
),
}
@@ -495,3 +495,80 @@ def resolve_instruction(
"""
instruction = instruction or ''
return instruction if 'chat' not in large_model_reference.lower() else ''
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def resolve_num_microbatches(large_model_reference: str) -> int:
+ """Resolves the number of microbatches to use during training.
+
+ Args:
+ large_model_reference: Base model tuned by the pipeline.
+
+ Returns:
+ Number of microbatches to break the total batch size into during training.
+ """
+ if 'llama' in large_model_reference.lower():
+ return 2
+ return 0
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def read_file(path: str) -> str:
+ """Reads the contents of the given file."""
+ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+ import re
+ # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+
+ path = re.sub('^gs://', '/gcs/', path)
+ with open(path, 'r') as f:
+ return f.read()
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def get_usage_metric(metadata: Dict[str, Any], key: str) -> bool: # pytype: disable=unsupported-operands
+ """Extracts a single usage metric from metadata."""
+ return metadata[key]
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def dump_dict(value: Dict[Any, Any]) -> str:
+ """Dumps the given dict to a JSON string."""
+ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+ import json
+ # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+
+ return json.dumps(value).replace('"', '\\"')
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def dump_list(value: List[Any]) -> str:
+ """Dumps the given dict to a JSON string."""
+ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+ import json
+ # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+
+ return json.dumps(value).replace('"', '\\"')
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def identity(
+ x: str,
+) -> str:
+ return x
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def get_uri(artifact: dsl.Input[dsl.Artifact], is_dir: bool = False) -> str: # pytype: disable=unsupported-operands
+ """Extracts the URI from an artifact."""
+ # pylint: disable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+ import os
+ # pylint: enable=g-import-not-at-top,import-outside-toplevel,redefined-outer-name,reimported
+
+ if is_dir:
+ return os.path.join(artifact.uri, '*')
+ return artifact.uri
+
+
+@dsl.component(base_image=_image.GCPC_IMAGE_TAG, install_kfp_package=False)
+def get_empty_string() -> str:
+ return ''
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcement_learning_graph.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcement_learning_graph.py
index c58ef44671..e610882b4b 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcement_learning_graph.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcement_learning_graph.py
@@ -117,6 +117,11 @@ def pipeline(
accelerator_type=machine_spec.outputs['accelerator_type'],
accelerator_count=machine_spec.outputs['accelerator_count'],
).set_display_name('Resolve Reinforcer Image URI')
+ num_microbatches = function_based.resolve_num_microbatches(
+ large_model_reference=reference_model_metadata.outputs[
+ 'large_model_reference'
+ ]
+ ).set_display_name('Resolve Number of Microbatches')
rl_model = (
reinforcer.Reinforcer(
project=project,
@@ -145,6 +150,7 @@ def pipeline(
learning_rate_multiplier=reinforcement_learning_rate_multiplier,
kl_coeff=kl_coeff,
lora_dim=lora_dim,
+ num_microbatches=num_microbatches.output,
)
.set_display_name('Reinforcer')
.set_caching_options(False)
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcer.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcer.py
index 82d0ab2543..8865a21396 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcer.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reinforcer.py
@@ -43,6 +43,7 @@ def Reinforcer( # pylint: disable=invalid-name
learning_rate_multiplier: float = 1.0,
kl_coeff: float = 0.1,
lora_dim: int = 0,
+ num_microbatches: int = 0,
) -> kfp.dsl.ContainerSpec: # pylint: disable=g-doc-args
"""Trains a model using reinforcement learning.
@@ -53,8 +54,8 @@ def Reinforcer( # pylint: disable=invalid-name
input_reward_model_path: Path to the reward model to use during
reinforcement learning.
input_dataset_path: Path to training dataset.
- train_steps: Number of training steps. These are the number of steps
- on top of any steps used to train the base model.
+ train_steps: Number of training steps. These are the number of steps on top
+ of any steps used to train the base model.
targets_length: Maximum decoder steps. Outputs will be at most this length.
accelerator_type: Type of TPU accelerator. Can be either TPU_V2 or TPU_V3.
accelerator_count: Number of TPU accelerators.
@@ -75,6 +76,9 @@ def Reinforcer( # pylint: disable=invalid-name
then use full-tuning.
learning_rate_multiplier: Constant multiplied by the base learning rate used
to adjust the learning rate during reinforcement learning.
+ num_microbatches: Number of microbatches to break the total batch size into
+ during training. If <= 1, the model is trained on the full batch size
+ directly.
Returns:
output_model_path: Path to the trained model checkpoint.
@@ -110,6 +114,7 @@ def Reinforcer( # pylint: disable=invalid-name
f'--learning_rate_multiplier={learning_rate_multiplier}',
f'--kl_coeff={kl_coeff}',
f'--lora_dim={lora_dim}',
+ f'--num_microbatches={num_microbatches}',
],
),
gcp_resources=gcp_resources,
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_graph.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_graph.py
index 68893976f0..c8825ab21b 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_graph.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_graph.py
@@ -118,6 +118,11 @@ def pipeline(
accelerator_type=machine_spec.outputs['accelerator_type'],
accelerator_count=machine_spec.outputs['accelerator_count'],
).set_display_name('Resolve Reward Model Image URI')
+ num_microbatches = function_based.resolve_num_microbatches(
+ large_model_reference=reference_model_metadata.outputs[
+ 'reward_model_reference'
+ ]
+ ).set_display_name('Resolve Number of Microbatches')
reward_model = (
reward_model_trainer.RewardModelTrainer(
project=project,
@@ -141,6 +146,7 @@ def pipeline(
batch_size=batch_size,
learning_rate_multiplier=reward_model_learning_rate_multiplier,
lora_dim=lora_dim,
+ num_microbatches=num_microbatches.output,
)
.set_display_name('Reward Model Trainer')
.set_caching_options(False)
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_trainer.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_trainer.py
index cdf0bba4db..f32904e1f5 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_trainer.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/reward_model_trainer.py
@@ -39,6 +39,7 @@ def RewardModelTrainer( # pylint: disable=invalid-name
batch_size: int = 64,
learning_rate_multiplier: float = 1.0,
lora_dim: int = 0,
+ num_microbatches: int = 0,
) -> kfp.dsl.ContainerSpec: # pylint: disable=g-doc-args
"""Trains a reward model.
@@ -47,8 +48,8 @@ def RewardModelTrainer( # pylint: disable=invalid-name
location: Location used to run the job.
input_model_path: Path to the base model to fine tune.
input_dataset_path: Path to dataset to use to train a reward model.
- train_steps: Number of training steps. These are the number of steps
- on top of any steps used to train the base model.
+ train_steps: Number of training steps. These are the number of steps on top
+ of any steps used to train the base model.
accelerator_type: Type of TPU accelerator. Can be either TPU_V2 or TPU_V3.
accelerator_count: Number of TPU accelerators.
large_model_reference: Predefined model used to create the ``input_model``.
@@ -64,6 +65,9 @@ def RewardModelTrainer( # pylint: disable=invalid-name
then use full-tuning.
learning_rate_multiplier: Constant multiplied by the base learning rate used
to adjust the learning rate when training a reward model.
+ num_microbatches: Number of microbatches to break the total batch size into
+ during training. If <= 1, the model is trained on the full batch size
+ directly.
Returns:
output_model: Trained reward model.
@@ -98,6 +102,7 @@ def RewardModelTrainer( # pylint: disable=invalid-name
f'{kfp.dsl.PIPELINE_TASK_ID_PLACEHOLDER}'
),
f'--lora_dim={lora_dim}',
+ f'--num_microbatches={num_microbatches}',
],
),
gcp_resources=gcp_resources,
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/supervised_fine_tuner.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/supervised_fine_tuner.py
index 7254ce2407..76bdf2d183 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/supervised_fine_tuner.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/supervised_fine_tuner.py
@@ -39,6 +39,7 @@ def SupervisedFineTuner( # pylint: disable=invalid-name
batch_size: int = 64,
learning_rate_multiplier: float = 1.0,
lora_dim: int = 0,
+ num_microbatches: int = 0,
) -> kfp.dsl.ContainerSpec: # pylint: disable=g-doc-args
"""Performs supervised fine tuning.
@@ -65,6 +66,9 @@ def SupervisedFineTuner( # pylint: disable=invalid-name
then use full-tuning.
learning_rate_multiplier: Constant multiplied by the base learning rate used
to adjust the learning rate during supervised fine tuning.
+ num_microbatches: Number of microbatches to break the total batch size into
+ during training. If <= 1, the model is trained on the full batch size
+ directly.
Returns:
output_model_path: Fine-tuned model path.
@@ -99,6 +103,7 @@ def SupervisedFineTuner( # pylint: disable=invalid-name
f'{kfp.dsl.PIPELINE_TASK_ID_PLACEHOLDER}'
),
f'--lora_dim={lora_dim}',
+ f'--num_microbatches={num_microbatches}',
],
),
gcp_resources=gcp_resources,
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/task_preprocess.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/task_preprocess.py
new file mode 100644
index 0000000000..c04b6aff5a
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/llm/task_preprocess.py
@@ -0,0 +1,97 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Component for preprocessing the evaluation dataset into prediction inputs."""
+
+import os
+from typing import Any, Dict, List
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components import utils as gcpc_utils
+from google_cloud_pipeline_components._implementation.llm import utils
+from kfp import dsl
+
+
+def _resolve_image() -> str:
+ """Determines the image URI to create a container from."""
+ return (
+ os.environ.get('AUTOSXS_IMAGE_OVERRIDE')
+ or utils.get_default_image_uri('autosxs'))
+
+
+# pylint: disable=dangerous-default-value,g-bare-generic,unused-argument
+@dsl.container_component
+def task_preprocess(
+ evaluation_dataset: str,
+ id_columns: List[str],
+ task: str,
+ model_prompt_parameters: Dict[str, Dict[str, str]],
+ prediction_inputs: dsl.OutputPath(List[str]), # pytype: disable=invalid-annotation
+ gcp_resources: dsl.OutputPath(str), # pytype: disable=invalid-annotation
+ metadata: dsl.OutputPath(Dict[str, Any]), # pytype: disable=invalid-annotation
+ response_column: str,
+ human_preference_column: str = '',
+) -> dsl.ContainerSpec: # pylint: disable=g-doc-args
+ """Preprocesses evaluation dataset into prediction inputs.
+
+ Args:
+ evaluation_dataset: GCS or BigQuery URIs representing a dataset of prompts
+ and responses.
+ id_columns: The columns which distinguish unique evaluation examples.
+ task: Evaluation task in the form {task}@{version}. task can be one of
+ "summarization", "question_answer". Version is an integer with 3 digits or
+ "latest". Ex: summarization@001 or question_answer@latest.
+ model_prompt_parameters: Map of model prompt template parameters to columns
+ or templates.
+ response_column: Either an existing column containing predefined responses,
+ or the name of the model output column containing responses.
+ human_preference_column: The column containing ground truths. Only required
+ when users want to check the autorater alignment against human preference.
+
+ Returns:
+ prediction_inputs_path: Path to write the path where preprocessed
+ predictions are stored.
+ gcp_resources: Tracker for GCP resources created by this component.
+ metadata_path: Path to write the object that stores computed metrics
+ metadata for the task preprocess component.
+ """
+ return gcpc_utils.build_serverless_customjob_container_spec(
+ project=_placeholders.PROJECT_ID_PLACEHOLDER,
+ location=_placeholders.LOCATION_PLACEHOLDER,
+ custom_job_payload=utils.build_payload(
+ display_name='task_preprocess',
+ machine_type='n1-standard-4',
+ image_uri=_resolve_image(),
+ args=[
+ '--', # Used to mark the start of component flags.
+ 'task_preprocess',
+ f'--evaluation_dataset={evaluation_dataset}',
+ f'--staging_dir={dsl.PIPELINE_ROOT_PLACEHOLDER}',
+ f'--task={task}',
+ f'--prediction_inputs_path={prediction_inputs}',
+ (
+ '--id_columns='
+ "{{$.inputs.parameters['id_columns'].json_escape[0]}}"
+ ),
+ (
+ '--model_prompt_parameters='
+ "{{$.inputs.parameters['model_prompt_parameters']"
+ '.json_escape[0]}}'
+ ),
+ f'--metadata_path={metadata}',
+ f'--response_column={response_column}',
+ f'--human_preference_column={human_preference_column}',
+ ],
+ ),
+ gcp_resources=gcp_resources,
+ )
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/component.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/component.py
index 9e3cda2a4b..fa3c01a28e 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/component.py
@@ -31,6 +31,7 @@ def chunking(
input_text_gcs_dir: str,
output_bq_destination: str,
output_text_gcs_dir: str,
+ output_error_file_path: str,
generation_threshold_microseconds: str,
display_name: str = 'chunking',
machine_type: str = 'n1-standard-8',
@@ -48,6 +49,7 @@ def chunking(
output_bq_destination: The BigQuery table URI where the component will write
chunks to.
output_text_gcs_dir: The GCS folder to hold intermediate data.
+ output_error_file_path: The path to the file containing chunking error.
generation_threshold_microseconds: only files created on/after this
generation threshold will be processed, in microseconds.
display_name: The name of the chunking job/component.
@@ -85,6 +87,7 @@ def chunking(
f'--input_text_gcs_dir={input_text_gcs_dir}',
f'--output_bq_destination={output_bq_destination}',
f'--output_text_gcs_dir={output_text_gcs_dir}',
+ f'--output_error_file_path={output_error_file_path}',
f'--generation_threshold_microseconds={generation_threshold_microseconds}',
f'--gcp_resources={gcp_resources}',
'--executor_input={{$.json_escape[1]}}',
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/feature_store_grounding_pipeline.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/feature_store_grounding_pipeline.py
new file mode 100644
index 0000000000..b95eb63e1b
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/chunking/feature_store_grounding_pipeline.py
@@ -0,0 +1,96 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature Store grounding pipeline."""
+
+from google_cloud_pipeline_components._implementation.model_evaluation.chunking.component import chunking as ChunkingOp
+from google_cloud_pipeline_components.types.artifact_types import VertexModel
+from google_cloud_pipeline_components.v1 import bigquery
+from google_cloud_pipeline_components.v1.batch_predict_job import ModelBatchPredictOp
+import kfp
+
+_PIPELINE_NAME = 'feature-store-grounding-pipeline'
+
+
+@kfp.dsl.pipeline(name=_PIPELINE_NAME)
+def feature_store_grounding_pipeline(
+ project: str,
+ location: str,
+ input_text_gcs_dir: str,
+ bigquery_bp_input_uri: str,
+ bigquery_bp_output_uri: str,
+ output_text_gcs_dir: str,
+ output_error_file_path: str,
+ model_name: str,
+ generation_threshold_microseconds: str = '0',
+ machine_type: str = 'e2-highmem-16',
+ service_account: str = '',
+ encryption_spec_key_name: str = '',
+):
+ """The Feature Store grounding pipeline.
+
+ Args:
+ project: Required. The GCP project that runs the pipeline components.
+ location: Required. The GCP region that runs the pipeline components.
+ input_text_gcs_dir: the GCS directory containing the files to chunk.
+ bigquery_bp_input_uri: The URI to a bigquery table as the input for the
+ batch prediction component. The chunking component will populate data to
+ this uri first before batch prediction.
+ bigquery_bp_output_uri: The URI to a bigquery table as the output for the
+ batch prediction component.
+ output_text_gcs_dir: The GCS folder to hold intermediate data for chunking.
+ output_error_file_path: The path to the file containing chunking error.
+ model_name: The path for model to generate embeddings, example,
+ 'publishers/google/models/textembedding-gecko@latest'
+ generation_threshold_microseconds: only files created on/after this
+ generation threshold will be processed, in microseconds.
+ machine_type: The machine type to run chunking component in the pipeline.
+ service_account: Service account to run the pipeline.
+ encryption_spec_key_name: Customer-managed encryption key options for the
+ CustomJob. If this is set, then all resources created by the CustomJob
+ will be encrypted with the provided encryption key.
+ """
+
+ get_vertex_model_task = kfp.dsl.importer(
+ artifact_uri=(
+ f'https://{location}-aiplatform.googleapis.com/v1/{model_name}'
+ ),
+ artifact_class=VertexModel,
+ metadata={'resourceName': model_name},
+ )
+ get_vertex_model_task.set_display_name('get-vertex-model')
+
+ chunking_task = ChunkingOp(
+ project=project,
+ location=location,
+ input_text_gcs_dir=input_text_gcs_dir,
+ output_bq_destination=bigquery_bp_input_uri,
+ output_text_gcs_dir=output_text_gcs_dir,
+ output_error_file_path=output_error_file_path,
+ generation_threshold_microseconds=generation_threshold_microseconds,
+ machine_type=machine_type,
+ service_account=service_account,
+ encryption_spec_key_name=encryption_spec_key_name,
+ )
+
+ batch_predict_task = ModelBatchPredictOp(
+ job_display_name='feature-store-grounding-batch-predict-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}',
+ project=project,
+ location=location,
+ model=get_vertex_model_task.outputs['artifact'],
+ bigquery_source_input_uri=bigquery_bp_input_uri,
+ bigquery_destination_output_uri=bigquery_bp_output_uri,
+ service_account=service_account,
+ encryption_spec_key_name=encryption_spec_key_name,
+ )
+ batch_predict_task.after(chunking_task)
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/import_evaluation/component.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/import_evaluation/component.py
index 2d4976d1ed..235f9b28a0 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/import_evaluation/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/import_evaluation/component.py
@@ -31,6 +31,7 @@ def model_evaluation_import(
gcp_resources: dsl.OutputPath(str),
evaluation_resource_name: dsl.OutputPath(str),
metrics: Optional[Input[Metrics]] = None,
+ row_based_metrics: Optional[Input[Metrics]] = None,
problem_type: Optional[str] = None,
classification_metrics: Optional[Input[ClassificationMetrics]] = None,
forecasting_metrics: Optional[Input[ForecastingMetrics]] = None,
@@ -59,6 +60,8 @@ def model_evaluation_import(
model: Vertex model resource that will be the parent resource of the
uploaded evaluation.
metrics: Path of metrics generated from an evaluation component.
+ row_based_metrics:
+ Path of row_based_metrics generated from an evaluation component.
problem_type: The problem type of the metrics being imported to the
VertexModel. `classification`, `regression`, `forecasting`,
`text-generation`, `question-answering`, and `summarization` are the
@@ -106,6 +109,13 @@ def model_evaluation_import(
metrics.metadata["explanation_gcs_path"],
],
),
+ dsl.IfPresentPlaceholder(
+ input_name="row_based_metrics",
+ then=[
+ "--row_based_metrics",
+ row_based_metrics.uri,
+ ],
+ ),
dsl.IfPresentPlaceholder(
input_name="explanation",
then=[
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/llm_evaluation/component.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/llm_evaluation/component.py
index 99f9316667..6375cf0203 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/llm_evaluation/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/llm_evaluation/component.py
@@ -29,6 +29,7 @@
def model_evaluation_text_generation(
gcp_resources: OutputPath(str),
evaluation_metrics: Output[Metrics],
+ row_based_metrics: Output[Metrics],
project: str,
location: str,
evaluation_task: str = 'text-generation',
@@ -38,6 +39,7 @@ def model_evaluation_text_generation(
joined_predictions_gcs_source: dsl.Input[Artifact] = None,
predictions_gcs_source: dsl.Input[Artifact] = None,
ground_truth_gcs_source: str = '',
+ enable_row_based_metrics: bool = False,
display_name: str = 'model-evaluation-text-generation',
machine_type: str = 'e2-highmem-16',
service_account: str = '',
@@ -60,8 +62,7 @@ def model_evaluation_text_generation(
target_field_name: The full name path of the features target field in the
predictions file. Formatted to be able to find nested columns, delimited
by `.`. Alternatively referred to as the ground truth (or
- ground_truth_column) field. If not set, defaulted to
- `inputs.ground_truth`.
+ ground_truth_column) field. If not set, defaulted to `inputs.output_text`.
prediction_field_name: The full name path of the prediction field in the
prediction file. Formatted to be able to find nested columns, delimited by
`.`. If not set, defaulted to `predictions.content`.
@@ -106,11 +107,14 @@ def model_evaluation_text_generation(
created.
Returns:
- evaluation_metrics: `Metrics` artifact representing the language model
- evaluation metrics.
gcp_resources: Serialized gcp_resources proto tracking the custom job.
For more details, see
https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.
+ evaluation_metrics: `Metrics` artifact representing the language model
+ evaluation metrics.
+ row_based_metrics: `Metrics` artifact representing the language model
+ evaluation metrics of each instance. This is only available if
+ enable_row_based_metrics is set to True.
"""
return gcpc_utils.build_serverless_customjob_container_spec(
project=project,
@@ -128,6 +132,8 @@ def model_evaluation_text_generation(
f'--predictions_gcs_source={predictions_gcs_source.uri}',
f'--ground_truth_gcs_source={ground_truth_gcs_source}',
f'--evaluation_metrics_output_path={evaluation_metrics.path}',
+ f'--enable_row_based_metrics={enable_row_based_metrics}',
+ f'--row_based_metrics_output_path={row_based_metrics.path}',
'--executor_input={{$.json_escape[1]}}',
],
service_account=service_account,
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/model_inference/component.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/model_inference/component.py
index 0cef9ca6e6..63cce68a1f 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/model_inference/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/model_inference/component.py
@@ -17,6 +17,7 @@
from google_cloud_pipeline_components import utils as gcpc_utils
from google_cloud_pipeline_components._implementation.model_evaluation import LLMEvaluationTextGenerationOp
from google_cloud_pipeline_components._implementation.model_evaluation import utils
+from google_cloud_pipeline_components._implementation.model_evaluation import version
from kfp.dsl import Artifact
from kfp.dsl import container_component
from kfp.dsl import Metrics
@@ -31,17 +32,17 @@
@container_component
def model_inference_component_internal(
gcp_resources: OutputPath(str),
- gcs_output_directory: Output[Artifact],
+ gcs_output_path: Output[Artifact],
project: str,
location: str,
client_api_key_path: str,
prediction_instances_source_uri: str,
- output_inference_gcs_prefix: str,
inference_platform: str = 'openai_chat_completions',
model_id: str = 'gpt-3.5-turbo',
request_params: Dict[str, Any] = {},
- max_request_per_second: float = 3,
- max_tokens_per_minute: float = 100,
+ max_request_per_minute: float = 3,
+ max_tokens_per_minute: float = 10000,
+ query_field_name: str = '',
display_name: str = 'third-party-inference',
machine_type: str = 'e2-highmem-16',
service_account: str = '',
@@ -54,12 +55,23 @@ def model_inference_component_internal(
Args:
gcp_resources (str): Serialized gcp_resources proto tracking the custom
job.
- model_inference_output_gcs_uri: The storage URI pointing toward a GCS
- location to store CSV for third party inference.
+ gcs_output_path: The storage URI pointing toward a GCS location to store
+ CSV for third party inference.
project: Required. The GCP project that runs the pipeline component.
location: Required. The GCP region that runs the pipeline component.
client_api_key_path: The GCS URI where client API key.
- output_inference_gcs_prefix: GCS file prefix for writing output.
+ prediction_instances_source_uri: GCS file path to prediction requests.
+ inference_platform: Name of the inference platform.
+ model_id: Name of the model to send requests against.
+ request_params: Parameters to confirgure requests.
+ max_request_per_minute: Maximum number of requests can be sent in a
+ minute.
+ max_tokens_per_minute: float = 10000,
+ query_field_name: The full name path of the features prompt field in the
+ request file. Formatted to be able to find nested columns, delimited by
+ `.`. Alternatively referred to as the ground truth (or
+ ground_truth_column) field. If not set, defaulted to
+ `inputs.ground_truth`.
display_name: display name of the pipeline.
machine_type: The machine type of this custom job. If not set, defaulted
to `e2-highmem-16`. More details:
@@ -88,7 +100,7 @@ def model_inference_component_internal(
Returns:
gcp_resources (str): Serialized gcp_resources proto tracking the custom
job.
- model_inference_output_gcs_uri: The storage URI pointing toward a
+ gcs_output_path: The storage URI pointing toward a
GCS location to store CSV for third party inference.
"""
return gcpc_utils.build_serverless_customjob_container_spec(
@@ -97,21 +109,20 @@ def model_inference_component_internal(
custom_job_payload=utils.build_custom_job_payload(
display_name=display_name,
machine_type=machine_type,
- image_uri=_IMAGE_URI,
+ image_uri=version.LLM_EVAL_IMAGE_TAG, # for local test and validation, use _IMAGE_URI.
args=[
f'--3p_model_inference={True}',
f'--project={project}',
f'--location={location}',
f'--prediction_instances_source_uri={prediction_instances_source_uri}',
f'--inference_platform={inference_platform}',
- f'--output_inference_gcs_prefix={output_inference_gcs_prefix}',
f'--model_id={model_id}',
f'--request_params={request_params}',
f'--client_api_key_path={client_api_key_path}',
- f'--max_request_per_second={max_request_per_second}',
+ f'--max_request_per_minute={max_request_per_minute}',
f'--max_tokens_per_minute={max_tokens_per_minute}',
- # f'--gcs_output_directory={gcs_output_directory}',
- f'--gcs_output_directory={gcs_output_directory.path}',
+ f'--query_field_name={query_field_name}',
+ f'--gcs_output_path={gcs_output_path.path}',
'--executor_input={{$.json_escape[1]}}',
],
service_account=service_account,
@@ -129,12 +140,12 @@ def model_inference_component(
location: str,
client_api_key_path: str,
prediction_instances_source_uri: str,
- output_inference_gcs_prefix: str,
inference_platform: str = 'openai_chat_completions',
model_id: str = 'gpt-3.5-turbo',
request_params: Dict[str, Any] = {},
- max_request_per_second: float = 3,
- max_tokens_per_minute: float = 100,
+ query_field_name: str = 'prompt',
+ max_request_per_minute: float = 3,
+ max_tokens_per_minute: float = 10000,
display_name: str = 'third-party-inference',
machine_type: str = 'e2-highmem-16',
service_account: str = '',
@@ -143,7 +154,7 @@ def model_inference_component(
encryption_spec_key_name: str = '',
) -> NamedTuple(
'outputs',
- gcs_output_directory=Artifact,
+ gcs_output_path=Artifact,
):
"""Component to run Third Party Model Inference.
@@ -151,7 +162,18 @@ def model_inference_component(
project: Required. The GCP project that runs the pipeline component.
location: Required. The GCP region that runs the pipeline component.
client_api_key_path: The GCS URI where client API key.
- output_inference_gcs_prefix: GCS file prefix for writing output.
+ prediction_instances_source_uri: GCS file path to prediction requests.
+ inference_platform: Name of the inference platform.
+ model_id: Name of the model to send requests against.
+ request_params: Parameters to confirgure requests.
+ query_field_name: The full name path of the features prompt field in the
+ request file. Formatted to be able to find nested columns, delimited by
+ `.`. Alternatively referred to as the ground truth (or
+ ground_truth_column) field. If not set, defaulted to
+ `inputs.ground_truth`.
+ max_request_per_minute: Maximum number of requests can be sent in a
+ minute.
+ max_tokens_per_minute: float = 10000,
display_name: display name of the pipeline.
machine_type: The machine type of this custom job. If not set, defaulted
to `e2-highmem-16`. More details:
@@ -179,12 +201,12 @@ def model_inference_component(
Returns:
NamedTuple:
- model_inference_output_gcs_uri: CSV file output containing third
+ gcs_output_path: CSV file output containing third
party prediction results.
"""
outputs = NamedTuple(
'outputs',
- gcs_output_directory=Artifact,
+ gcs_output_path=Artifact,
)
inference_task = model_inference_component_internal(
@@ -195,10 +217,10 @@ def model_inference_component(
inference_platform=inference_platform,
model_id=model_id,
request_params=request_params,
- max_request_per_second=max_request_per_second,
+ max_request_per_minute=max_request_per_minute,
max_tokens_per_minute=max_tokens_per_minute,
- output_inference_gcs_prefix=output_inference_gcs_prefix,
display_name=display_name,
+ query_field_name=query_field_name,
machine_type=machine_type,
service_account=service_account,
network=network,
@@ -207,7 +229,7 @@ def model_inference_component(
)
return outputs(
- gcs_output_directory=inference_task.outputs['gcs_output_directory'],
+ gcs_output_path=inference_task.outputs['gcs_output_path'],
)
@@ -217,13 +239,13 @@ def model_inference_and_evaluation_component(
location: str,
client_api_key_path: str,
prediction_instances_source_uri: str,
- output_inference_gcs_prefix: str,
- target_field_name: str = '',
inference_platform: str = 'openai_chat_completions',
model_id: str = 'gpt-3.5-turbo',
request_params: Dict[str, Any] = {},
- max_request_per_second: float = 3,
- max_tokens_per_minute: float = 100,
+ target_field_name: str = 'ground_truth',
+ query_field_name: str = 'prompt',
+ max_request_per_minute: float = 3,
+ max_tokens_per_minute: float = 10000,
display_name: str = 'third-party-inference',
machine_type: str = 'e2-highmem-16',
service_account: str = '',
@@ -232,7 +254,7 @@ def model_inference_and_evaluation_component(
encryption_spec_key_name: str = '',
) -> NamedTuple(
'outputs',
- gcs_output_directory=Artifact,
+ gcs_output_path=Artifact,
evaluation_metrics=Metrics,
):
"""Component tun Third Party Model Inference and evaluation.
@@ -241,7 +263,23 @@ def model_inference_and_evaluation_component(
project: Required. The GCP project that runs the pipeline component.
location: Required. The GCP region that runs the pipeline component.
client_api_key_path: The GCS URI where client API key.
- output_inference_gcs_prefix: GCS file prefix for writing output.
+ prediction_instances_source_uri: GCS file path to prediction requests.
+ inference_platform: Name of the inference platform.
+ model_id: Name of the model to send requests against.
+ request_params: Parameters to confirgure requests.
+ target_field_name: The full name path of the features target field in the
+ predictions file. Formatted to be able to find nested columns, delimited
+ by `.`. Alternatively referred to as the ground truth (or
+ ground_truth_column) field. If not set, defaulted to
+ `inputs.ground_truth`.
+ query_field_name: The full name path of the features prompt field in the
+ request file. Formatted to be able to find nested columns, delimited by
+ `.`. Alternatively referred to as the ground truth (or
+ ground_truth_column) field. If not set, defaulted to
+ `inputs.ground_truth`.
+ max_request_per_minute: Maximum number of requests can be sent in a
+ minute.
+ max_tokens_per_minute: float = 10000,
display_name: display name of the pipeline.
machine_type: The machine type of this custom job. If not set, defaulted
to `e2-highmem-16`. More details:
@@ -269,12 +307,12 @@ def model_inference_and_evaluation_component(
Returns:
NamedTuple:
- model_inference_output_gcs_uri: CSV file output containing third
+ gcs_output_path: CSV file output containing third
party prediction results.
"""
outputs = NamedTuple(
'outputs',
- gcs_output_directory=Artifact,
+ gcs_output_path=Artifact,
evaluation_metrics=Metrics,
)
@@ -286,9 +324,9 @@ def model_inference_and_evaluation_component(
inference_platform=inference_platform,
model_id=model_id,
request_params=request_params,
- max_request_per_second=max_request_per_second,
+ max_request_per_minute=max_request_per_minute,
max_tokens_per_minute=max_tokens_per_minute,
- output_inference_gcs_prefix=output_inference_gcs_prefix,
+ query_field_name=query_field_name,
display_name=display_name,
machine_type=machine_type,
service_account=service_account,
@@ -297,28 +335,18 @@ def model_inference_and_evaluation_component(
encryption_spec_key_name=encryption_spec_key_name,
)
- if inference_platform == 'openai_chat_completions':
- prediction_field_name = 'predictions.0.message.content'
- elif inference_platform == 'anthropic_predictions':
- prediction_field_name = 'predictions'
- else:
- prediction_field_name = ''
-
eval_task = LLMEvaluationTextGenerationOp(
project=project,
location=location,
evaluation_task='text-generation',
- target_field_name=target_field_name,
- prediction_field_name=prediction_field_name,
+ target_field_name='.'.join(['instance', str(target_field_name)]),
predictions_format='jsonl',
- joined_predictions_gcs_source=inference_task.outputs[
- 'gcs_output_directory'
- ],
+ joined_predictions_gcs_source=inference_task.outputs['gcs_output_path'],
machine_type=machine_type,
encryption_spec_key_name=encryption_spec_key_name,
)
return outputs(
- gcs_output_directory=inference_task.outputs['gcs_output_directory'],
+ gcs_output_path=inference_task.outputs['gcs_output_path'],
evaluation_metrics=eval_task.outputs['evaluation_metrics'],
)
diff --git a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/version.py b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/version.py
index 8873adb429..87748f269b 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/version.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_implementation/model_evaluation/version.py
@@ -14,7 +14,7 @@
"""Version constants for model evaluation components."""
_EVAL_VERSION = 'v0.9.4'
-_LLM_EVAL_VERSION = 'v0.4'
+_LLM_EVAL_VERSION = 'v0.5'
_EVAL_IMAGE_NAME = 'gcr.io/ml-pipeline/model-evaluation'
_LLM_EVAL_IMAGE_NAME = 'gcr.io/ml-pipeline/llm-model-evaluation'
diff --git a/components/google-cloud/google_cloud_pipeline_components/_placeholders.py b/components/google-cloud/google_cloud_pipeline_components/_placeholders.py
index 44dd3cae8a..409b30c695 100644
--- a/components/google-cloud/google_cloud_pipeline_components/_placeholders.py
+++ b/components/google-cloud/google_cloud_pipeline_components/_placeholders.py
@@ -13,6 +13,17 @@
# limitations under the License.
"""Placeholders for use in component authoring."""
-# prefer not using placeholder suffix like KFP does for reduce verbosity
+# prefer not using PIPELINE_TASK_ prefix like KFP does for reduced verbosity
PROJECT_ID_PLACEHOLDER = "{{$.pipeline_google_cloud_project_id}}"
LOCATION_PLACEHOLDER = "{{$.pipeline_google_cloud_location}}"
+
+
+# omit placeholder type annotation to avoid dependency on KFP SDK internals
+# placeholder is type kfp.dsl.placeholders.Placeholder
+def json_escape(placeholder, level: int) -> str:
+ if level not in {0, 1}:
+ raise ValueError(f"Invalid level: {level}")
+ # Placeholder implements __str__
+ s = str(placeholder)
+
+ return s.replace("}}", f".json_escape[{level}]}}}}")
diff --git a/components/google-cloud/google_cloud_pipeline_components/container/_implementation/model_evaluation/import_model_evaluation.py b/components/google-cloud/google_cloud_pipeline_components/container/_implementation/model_evaluation/import_model_evaluation.py
index d82b8340e2..06fcf9fb80 100644
--- a/components/google-cloud/google_cloud_pipeline_components/container/_implementation/model_evaluation/import_model_evaluation.py
+++ b/components/google-cloud/google_cloud_pipeline_components/container/_implementation/model_evaluation/import_model_evaluation.py
@@ -56,6 +56,9 @@ def _make_parent_dirs_and_return_path(file_path: str):
prog='Vertex Model Service evaluation importer', description=''
)
parser.add_argument('--metrics', dest='metrics', type=str, default='')
+parser.add_argument(
+ '--row_based_metrics', dest='row_based_metrics', type=str, default=''
+)
parser.add_argument(
'--classification_metrics',
dest='classification_metrics',
@@ -274,6 +277,11 @@ def main(argv):
'pipeline_job_resource_name': parsed_args.pipeline_job_resource_name,
'evaluation_dataset_type': parsed_args.dataset_type,
'evaluation_dataset_path': dataset_paths or None,
+ 'row_based_metrics_path': (
+ parsed_args.row_based_metrics
+ if parsed_args.row_based_metrics
+ else None
+ ),
}.items()
if value
}
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/__init__.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/__init__.py
index 917eb0e145..6843d095b5 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/__init__.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/__init__.py
@@ -11,18 +11,27 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
+
"""Experimental AutoML forecasting components."""
import os
from google_cloud_pipeline_components.preview.automl.forecasting.forecasting_ensemble import automl_forecasting_ensemble as ForecastingEnsembleOp
from google_cloud_pipeline_components.preview.automl.forecasting.forecasting_stage_1_tuner import automl_forecasting_stage_1_tuner as ForecastingStage1TunerOp
from google_cloud_pipeline_components.preview.automl.forecasting.forecasting_stage_2_tuner import automl_forecasting_stage_2_tuner as ForecastingStage2TunerOp
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_learn_to_learn_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_sequence_to_sequence_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_temporal_fusion_transformer_forecasting_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.forecasting.utils import get_time_series_dense_encoder_forecasting_pipeline_and_parameters
from kfp import components
__all__ = [
- 'ForecastingStage1TunerOp',
'ForecastingEnsembleOp',
+ 'ForecastingStage1TunerOp',
'ForecastingStage2TunerOp',
+ 'get_learn_to_learn_forecasting_pipeline_and_parameters',
+ 'get_sequence_to_sequence_forecasting_pipeline_and_parameters',
+ 'get_temporal_fusion_transformer_forecasting_pipeline_and_parameters',
+ 'get_time_series_dense_encoder_forecasting_pipeline_and_parameters',
'learn_to_learn_forecasting_pipeline',
'sequence_to_sequence_forecasting_pipeline',
'temporal_fusion_transformer_forecasting_pipeline',
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py
index 22b4a96eb9..0d894282ce 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/forecasting/utils.py
@@ -244,6 +244,7 @@ def get_learn_to_learn_forecasting_pipeline_and_parameters(
temporal_total_weight: float = 0.0,
group_temporal_total_weight: float = 0.0,
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Returns l2l_forecasting pipeline and formatted parameters.
Args:
@@ -251,45 +252,28 @@ def get_learn_to_learn_forecasting_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle",
- "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or
- "minimize-quantile-loss".
- transformations: Dict mapping auto and/or type-resolutions to feature
- columns. The supported types are: auto, categorical, numeric, text, and
- timestamp.
- train_budget_milli_node_hours: The train budget of creating this model,
- expressed in milli node hours i.e. 1,000 value in this field means 1 node
- hour.
+ optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle", "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or "minimize-quantile-loss".
+ transformations: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
+ train_budget_milli_node_hours: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: The column that indicates the time.
- time_series_identifier_columns: The columns which distinguish different time
- series.
- time_series_identifier_column: [Deprecated] The column which distinguishes
- different time series.
- time_series_attribute_columns: The columns that are invariant across the
- same time series.
- available_at_forecast_columns: The columns that are available at the
- forecast time.
- unavailable_at_forecast_columns: The columns that are unavailable at the
- forecast time.
+ time_series_identifier_columns: The columns which distinguish different time series.
+ time_series_identifier_column: [Deprecated] The column which distinguishes different time series.
+ time_series_attribute_columns: The columns that are invariant across the same time series.
+ available_at_forecast_columns: The columns that are available at the forecast time.
+ unavailable_at_forecast_columns: The columns that are unavailable at the forecast time.
forecast_horizon: The length of the horizon.
context_window: The length of the context window.
- evaluated_examples_bigquery_path: The bigquery dataset to write the
- predicted examples into for evaluation, in the format
- `bq://project.dataset`.
+ evaluated_examples_bigquery_path: The bigquery dataset to write the predicted examples into for evaluation, in the format `bq://project.dataset`.
window_predefined_column: The column that indicate the start of each window.
window_stride_length: The stride length to generate the window.
window_max_count: The maximum number of windows that will be generated.
- holiday_regions: The geographical regions where the holiday effect is
- applied in modeling.
+ holiday_regions: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: Number of parallel trails for stage 1.
- stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS
- URI.
+ stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: Number of parallel trails for stage 2.
num_selected_trials: Number of selected trails.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: The predefined_split column name.
training_fraction: The training fraction.
validation_fraction: The validation fraction.
@@ -298,50 +282,34 @@ def get_learn_to_learn_forecasting_pipeline_and_parameters(
dataflow_service_account: The full service account name.
dataflow_subnetwork: The dataflow subnetwork.
dataflow_use_public_ips: `True` to enable dataflow public IPs.
- feature_transform_engine_bigquery_staging_full_dataset_id: The full id of
- the feature transform engine staging dataset.
- feature_transform_engine_dataflow_machine_type: The dataflow machine type of
- the feature transform engine.
- feature_transform_engine_dataflow_max_num_workers: The max number of
- dataflow workers of the feature transform engine.
- feature_transform_engine_dataflow_disk_size_gb: The disk size of the
- dataflow workers of the feature transform engine.
- evaluation_batch_predict_machine_type: Machine type for the batch prediction
- job in evaluation, such as 'n1-standard-16'.
- evaluation_batch_predict_starting_replica_count: Number of replicas to use
- in the batch prediction cluster at startup time.
- evaluation_batch_predict_max_replica_count: The maximum count of replicas
- the batch prediction job can scale to.
- evaluation_dataflow_machine_type: Machine type for the dataflow job in
- evaluation, such as 'n1-standard-16'.
+ feature_transform_engine_bigquery_staging_full_dataset_id: The full id of the feature transform engine staging dataset.
+ feature_transform_engine_dataflow_machine_type: The dataflow machine type of the feature transform engine.
+ feature_transform_engine_dataflow_max_num_workers: The max number of dataflow workers of the feature transform engine.
+ feature_transform_engine_dataflow_disk_size_gb: The disk size of the dataflow workers of the feature transform engine.
+ evaluation_batch_predict_machine_type: Machine type for the batch prediction job in evaluation, such as 'n1-standard-16'.
+ evaluation_batch_predict_starting_replica_count: Number of replicas to use in the batch prediction cluster at startup time.
+ evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to.
+ evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'.
evaluation_dataflow_max_num_workers: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow.
study_spec_parameters_override: The list for overriding study spec.
- stage_1_tuner_worker_pool_specs_override: The dictionary for overriding
- stage 1 tuner worker pool spec.
- stage_2_trainer_worker_pool_specs_override: The dictionary for overriding
- stage 2 trainer worker pool spec.
- enable_probabilistic_inference: If probabilistic inference is enabled, the
- model will fit a distribution that captures the uncertainty of a
- prediction. If quantiles are specified, then the quantiles of the
- distribution are also returned.
- quantiles: Quantiles to use for probabilistic inference. Up to 5 quantiles
- are allowed of values between 0 and 1, exclusive. Represents the quantiles
- to use for that objective. Quantiles must be unique.
+ stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec.
+ stage_2_trainer_worker_pool_specs_override: The dictionary for overriding stage 2 trainer worker pool spec.
+ enable_probabilistic_inference: If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. If quantiles are specified, then the quantiles of the distribution are also returned.
+ quantiles: Quantiles to use for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Represents the quantiles to use for that objective. Quantiles must be unique.
encryption_spec_key_name: The KMS key name.
model_display_name: Optional display name for model.
model_description: Optional description.
run_evaluation: `True` to evaluate the ensembled model on the test split.
- group_columns: A list of time series attribute column names that define the
- time series hierarchy.
- group_total_weight: The weight of the loss for predictions aggregated over
- time series in the same group.
- temporal_total_weight: The weight of the loss for predictions aggregated
- over the horizon for a single time series.
- group_temporal_total_weight: The weight of the loss for predictions
- aggregated over both the horizon and time series in the same hierarchy
- group.
+ group_columns: A list of time series attribute column names that define the time series hierarchy.
+ group_total_weight: The weight of the loss for predictions aggregated over time series in the same group.
+ temporal_total_weight: The weight of the loss for predictions aggregated over the horizon for a single time series.
+ group_temporal_total_weight: The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group.
+
+ Returns:
+ Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = _get_base_forecasting_parameters(
project=project,
location=location,
@@ -470,6 +438,7 @@ def get_time_series_dense_encoder_forecasting_pipeline_and_parameters(
temporal_total_weight: float = 0.0,
group_temporal_total_weight: float = 0.0,
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Returns timeseries_dense_encoder_forecasting pipeline and parameters.
Args:
@@ -477,45 +446,28 @@ def get_time_series_dense_encoder_forecasting_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle",
- "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or
- "minimize-quantile-loss".
- transformations: Dict mapping auto and/or type-resolutions to feature
- columns. The supported types are: auto, categorical, numeric, text, and
- timestamp.
- train_budget_milli_node_hours: The train budget of creating this model,
- expressed in milli node hours i.e. 1,000 value in this field means 1 node
- hour.
+ optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle", "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or "minimize-quantile-loss".
+ transformations: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
+ train_budget_milli_node_hours: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: The column that indicates the time.
- time_series_identifier_columns: The columns which distinguish different time
- series.
- time_series_identifier_column: [Deprecated] The column which distinguishes
- different time series.
- time_series_attribute_columns: The columns that are invariant across the
- same time series.
- available_at_forecast_columns: The columns that are available at the
- forecast time.
- unavailable_at_forecast_columns: The columns that are unavailable at the
- forecast time.
+ time_series_identifier_columns: The columns which distinguish different time series.
+ time_series_identifier_column: [Deprecated] The column which distinguishes different time series.
+ time_series_attribute_columns: The columns that are invariant across the same time series.
+ available_at_forecast_columns: The columns that are available at the forecast time.
+ unavailable_at_forecast_columns: The columns that are unavailable at the forecast time.
forecast_horizon: The length of the horizon.
context_window: The length of the context window.
- evaluated_examples_bigquery_path: The bigquery dataset to write the
- predicted examples into for evaluation, in the format
- `bq://project.dataset`.
+ evaluated_examples_bigquery_path: The bigquery dataset to write the predicted examples into for evaluation, in the format `bq://project.dataset`.
window_predefined_column: The column that indicate the start of each window.
window_stride_length: The stride length to generate the window.
window_max_count: The maximum number of windows that will be generated.
- holiday_regions: The geographical regions where the holiday effect is
- applied in modeling.
+ holiday_regions: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: Number of parallel trails for stage 1.
- stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS
- URI.
+ stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: Number of parallel trails for stage 2.
num_selected_trials: Number of selected trails.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: The predefined_split column name.
training_fraction: The training fraction.
validation_fraction: The validation fraction.
@@ -524,51 +476,34 @@ def get_time_series_dense_encoder_forecasting_pipeline_and_parameters(
dataflow_service_account: The full service account name.
dataflow_subnetwork: The dataflow subnetwork.
dataflow_use_public_ips: `True` to enable dataflow public IPs.
- feature_transform_engine_bigquery_staging_full_dataset_id: The full id of
- the feature transform engine staging dataset.
- feature_transform_engine_dataflow_machine_type: The dataflow machine type of
- the feature transform engine.
- feature_transform_engine_dataflow_max_num_workers: The max number of
- dataflow workers of the feature transform engine.
- feature_transform_engine_dataflow_disk_size_gb: The disk size of the
- dataflow workers of the feature transform engine.
- evaluation_batch_predict_machine_type: Machine type for the batch prediction
- job in evaluation, such as 'n1-standard-16'.
- evaluation_batch_predict_starting_replica_count: Number of replicas to use
- in the batch prediction cluster at startup time.
- evaluation_batch_predict_max_replica_count: The maximum count of replicas
- the batch prediction job can scale to.
- evaluation_dataflow_machine_type: Machine type for the dataflow job in
- evaluation, such as 'n1-standard-16'.
+ feature_transform_engine_bigquery_staging_full_dataset_id: The full id of the feature transform engine staging dataset.
+ feature_transform_engine_dataflow_machine_type: The dataflow machine type of the feature transform engine.
+ feature_transform_engine_dataflow_max_num_workers: The max number of dataflow workers of the feature transform engine.
+ feature_transform_engine_dataflow_disk_size_gb: The disk size of the dataflow workers of the feature transform engine.
+ evaluation_batch_predict_machine_type: Machine type for the batch prediction job in evaluation, such as 'n1-standard-16'.
+ evaluation_batch_predict_starting_replica_count: Number of replicas to use in the batch prediction cluster at startup time.
+ evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to.
+ evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'.
evaluation_dataflow_max_num_workers: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow.
study_spec_parameters_override: The list for overriding study spec.
- stage_1_tuner_worker_pool_specs_override: The dictionary for overriding
- stage 1 tuner worker pool spec.
- stage_2_trainer_worker_pool_specs_override: The dictionary for overriding
- stage 2 trainer worker pool spec.
- enable_probabilistic_inference: If probabilistic inference is enabled, the
- model will fit a distribution that captures the uncertainty of a
- prediction. If quantiles are specified, then the quantiles of the
- distribution are also returned.
- quantiles: Quantiles to use for probabilistic inference. Up to 5 quantiles
- are allowed of values between 0 and 1, exclusive. Represents the quantiles
- to use for that objective. Quantiles must be unique.
+ stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec.
+ stage_2_trainer_worker_pool_specs_override: The dictionary for overriding stage 2 trainer worker pool spec.
+ enable_probabilistic_inference: If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. If quantiles are specified, then the quantiles of the distribution are also returned.
+ quantiles: Quantiles to use for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Represents the quantiles to use for that objective. Quantiles must be unique.
encryption_spec_key_name: The KMS key name.
model_display_name: Optional display name for model.
model_description: Optional description.
run_evaluation: `True` to evaluate the ensembled model on the test split.
- group_columns: A list of time series attribute column names that define the
- time series hierarchy.
- group_total_weight: The weight of the loss for predictions aggregated over
- time series in the same group.
- temporal_total_weight: The weight of the loss for predictions aggregated
- over the horizon for a single time series.
- group_temporal_total_weight: The weight of the loss for predictions
- aggregated over both the horizon and time series in the same hierarchy
- group.
- """
+ group_columns: A list of time series attribute column names that define the time series hierarchy.
+ group_total_weight: The weight of the loss for predictions aggregated over time series in the same group.
+ temporal_total_weight: The weight of the loss for predictions aggregated over the horizon for a single time series.
+ group_temporal_total_weight: The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group.
+ Returns:
+ Tuple of pipeline_definition_path and parameter_values.
+ """
+ # fmt: on
parameter_values = _get_base_forecasting_parameters(
project=project,
location=location,
@@ -690,6 +625,7 @@ def get_temporal_fusion_transformer_forecasting_pipeline_and_parameters(
model_description: Optional[str] = None,
run_evaluation: bool = True,
):
+ # fmt: off
"""Returns tft_forecasting pipeline and formatted parameters.
Args:
@@ -697,44 +633,27 @@ def get_temporal_fusion_transformer_forecasting_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle",
- "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or
- "minimize-quantile-loss".
- transformations: Dict mapping auto and/or type-resolutions to feature
- columns. The supported types are: auto, categorical, numeric, text, and
- timestamp.
- train_budget_milli_node_hours: The train budget of creating this model,
- expressed in milli node hours i.e. 1,000 value in this field means 1 node
- hour.
+ optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle", "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or "minimize-quantile-loss".
+ transformations: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
+ train_budget_milli_node_hours: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: The column that indicates the time.
- time_series_identifier_columns: The columns which distinguish different time
- series.
- time_series_identifier_column: [Deprecated] The column which distinguishes
- different time series.
- time_series_attribute_columns: The columns that are invariant across the
- same time series.
- available_at_forecast_columns: The columns that are available at the
- forecast time.
- unavailable_at_forecast_columns: The columns that are unavailable at the
- forecast time.
+ time_series_identifier_columns: The columns which distinguish different time series.
+ time_series_identifier_column: [Deprecated] The column which distinguishes different time series.
+ time_series_attribute_columns: The columns that are invariant across the same time series.
+ available_at_forecast_columns: The columns that are available at the forecast time.
+ unavailable_at_forecast_columns: The columns that are unavailable at the forecast time.
forecast_horizon: The length of the horizon.
context_window: The length of the context window.
- evaluated_examples_bigquery_path: The bigquery dataset to write the
- predicted examples into for evaluation, in the format
- `bq://project.dataset`.
+ evaluated_examples_bigquery_path: The bigquery dataset to write the predicted examples into for evaluation, in the format `bq://project.dataset`.
window_predefined_column: The column that indicate the start of each window.
window_stride_length: The stride length to generate the window.
window_max_count: The maximum number of windows that will be generated.
- holiday_regions: The geographical regions where the holiday effect is
- applied in modeling.
+ holiday_regions: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: Number of parallel trails for stage 1.
- stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS
- URI.
+ stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: Number of parallel trails for stage 2.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: The predefined_split column name.
training_fraction: The training fraction.
validation_fraction: The validation fraction.
@@ -743,34 +662,28 @@ def get_temporal_fusion_transformer_forecasting_pipeline_and_parameters(
dataflow_service_account: The full service account name.
dataflow_subnetwork: The dataflow subnetwork.
dataflow_use_public_ips: `True` to enable dataflow public IPs.
- feature_transform_engine_bigquery_staging_full_dataset_id: The full id of
- the feature transform engine staging dataset.
- feature_transform_engine_dataflow_machine_type: The dataflow machine type of
- the feature transform engine.
- feature_transform_engine_dataflow_max_num_workers: The max number of
- dataflow workers of the feature transform engine.
- feature_transform_engine_dataflow_disk_size_gb: The disk size of the
- dataflow workers of the feature transform engine.
- evaluation_batch_predict_machine_type: Machine type for the batch prediction
- job in evaluation, such as 'n1-standard-16'.
- evaluation_batch_predict_starting_replica_count: Number of replicas to use
- in the batch prediction cluster at startup time.
- evaluation_batch_predict_max_replica_count: The maximum count of replicas
- the batch prediction job can scale to.
- evaluation_dataflow_machine_type: Machine type for the dataflow job in
- evaluation, such as 'n1-standard-16'.
+ feature_transform_engine_bigquery_staging_full_dataset_id: The full id of the feature transform engine staging dataset.
+ feature_transform_engine_dataflow_machine_type: The dataflow machine type of the feature transform engine.
+ feature_transform_engine_dataflow_max_num_workers: The max number of dataflow workers of the feature transform engine.
+ feature_transform_engine_dataflow_disk_size_gb: The disk size of the dataflow workers of the feature transform engine.
+ evaluation_batch_predict_machine_type: Machine type for the batch prediction job in evaluation, such as 'n1-standard-16'.
+ evaluation_batch_predict_starting_replica_count: Number of replicas to use in the batch prediction cluster at startup time.
+ evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to.
+ evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'.
evaluation_dataflow_max_num_workers: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow.
study_spec_parameters_override: The list for overriding study spec.
- stage_1_tuner_worker_pool_specs_override: The dictionary for overriding
- stage 1 tuner worker pool spec.
- stage_2_trainer_worker_pool_specs_override: The dictionary for overriding
- stage 2 trainer worker pool spec.
+ stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec.
+ stage_2_trainer_worker_pool_specs_override: The dictionary for overriding stage 2 trainer worker pool spec.
encryption_spec_key_name: The KMS key name.
model_display_name: Optional display name for model.
model_description: Optional description.
run_evaluation: `True` to evaluate the ensembled model on the test split.
+
+ Returns:
+ Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
# TFT should only have 1 selected trial to freeze the ensemble size at 1.
excluded_parameters = _RETAIL_MODEL_DISABLED_OPTIONS.union({
'num_selected_trials',
@@ -891,6 +804,7 @@ def get_sequence_to_sequence_forecasting_pipeline_and_parameters(
model_description: Optional[str] = None,
run_evaluation: bool = True,
):
+ # fmt: off
"""Returns seq2seq forecasting pipeline and formatted parameters.
Args:
@@ -898,45 +812,28 @@ def get_sequence_to_sequence_forecasting_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle",
- "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or
- "minimize-quantile-loss".
- transformations: Dict mapping auto and/or type-resolutions to feature
- columns. The supported types are: auto, categorical, numeric, text, and
- timestamp.
- train_budget_milli_node_hours: The train budget of creating this model,
- expressed in milli node hours i.e. 1,000 value in this field means 1 node
- hour.
+ optimization_objective: "minimize-rmse", "minimize-mae", "minimize-rmsle", "minimize-rmspe", "minimize-wape-mae", "minimize-mape", or "minimize-quantile-loss".
+ transformations: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
+ train_budget_milli_node_hours: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: The column that indicates the time.
- time_series_identifier_columns: The columns which distinguish different time
- series.
- time_series_identifier_column: [Deprecated] The column which distinguishes
- different time series.
- time_series_attribute_columns: The columns that are invariant across the
- same time series.
- available_at_forecast_columns: The columns that are available at the
- forecast time.
- unavailable_at_forecast_columns: The columns that are unavailable at the
- forecast time.
+ time_series_identifier_columns: The columns which distinguish different time series.
+ time_series_identifier_column: [Deprecated] The column which distinguishes different time series.
+ time_series_attribute_columns: The columns that are invariant across the same time series.
+ available_at_forecast_columns: The columns that are available at the forecast time.
+ unavailable_at_forecast_columns: The columns that are unavailable at the forecast time.
forecast_horizon: The length of the horizon.
context_window: The length of the context window.
- evaluated_examples_bigquery_path: The bigquery dataset to write the
- predicted examples into for evaluation, in the format
- `bq://project.dataset`.
+ evaluated_examples_bigquery_path: The bigquery dataset to write the predicted examples into for evaluation, in the format `bq://project.dataset`.
window_predefined_column: The column that indicate the start of each window.
window_stride_length: The stride length to generate the window.
window_max_count: The maximum number of windows that will be generated.
- holiday_regions: The geographical regions where the holiday effect is
- applied in modeling.
+ holiday_regions: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: Number of parallel trails for stage 1.
- stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS
- URI.
+ stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: Number of parallel trails for stage 2.
num_selected_trials: Number of selected trails.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: The predefined_split column name.
training_fraction: The training fraction.
validation_fraction: The validation fraction.
@@ -945,35 +842,28 @@ def get_sequence_to_sequence_forecasting_pipeline_and_parameters(
dataflow_service_account: The full service account name.
dataflow_subnetwork: The dataflow subnetwork.
dataflow_use_public_ips: `True` to enable dataflow public IPs.
- feature_transform_engine_bigquery_staging_full_dataset_id: The full id of
- the feature transform engine staging dataset.
- feature_transform_engine_dataflow_machine_type: The dataflow machine type of
- the feature transform engine.
- feature_transform_engine_dataflow_max_num_workers: The max number of
- dataflow workers of the feature transform engine.
- feature_transform_engine_dataflow_disk_size_gb: The disk size of the
- dataflow workers of the feature transform engine.
- evaluation_batch_predict_machine_type: Machine type for the batch prediction
- job in evaluation, such as 'n1-standard-16'.
- evaluation_batch_predict_starting_replica_count: Number of replicas to use
- in the batch prediction cluster at startup time.
- evaluation_batch_predict_max_replica_count: The maximum count of replicas
- the batch prediction job can scale to.
- evaluation_dataflow_machine_type: Machine type for the dataflow job in
- evaluation, such as 'n1-standard-16'.
+ feature_transform_engine_bigquery_staging_full_dataset_id: The full id of the feature transform engine staging dataset.
+ feature_transform_engine_dataflow_machine_type: The dataflow machine type of the feature transform engine.
+ feature_transform_engine_dataflow_max_num_workers: The max number of dataflow workers of the feature transform engine.
+ feature_transform_engine_dataflow_disk_size_gb: The disk size of the dataflow workers of the feature transform engine.
+ evaluation_batch_predict_machine_type: Machine type for the batch prediction job in evaluation, such as 'n1-standard-16'.
+ evaluation_batch_predict_starting_replica_count: Number of replicas to use in the batch prediction cluster at startup time.
+ evaluation_batch_predict_max_replica_count: The maximum count of replicas the batch prediction job can scale to.
+ evaluation_dataflow_machine_type: Machine type for the dataflow job in evaluation, such as 'n1-standard-16'.
evaluation_dataflow_max_num_workers: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: The disk space in GB for dataflow.
study_spec_parameters_override: The list for overriding study spec.
- stage_1_tuner_worker_pool_specs_override: The dictionary for overriding
- stage 1 tuner worker pool spec.
- stage_2_trainer_worker_pool_specs_override: The dictionary for overriding
- stage 2 trainer worker pool spec.
+ stage_1_tuner_worker_pool_specs_override: The dictionary for overriding stage 1 tuner worker pool spec.
+ stage_2_trainer_worker_pool_specs_override: The dictionary for overriding stage 2 trainer worker pool spec.
encryption_spec_key_name: The KMS key name.
model_display_name: Optional display name for model.
model_description: Optional description.
run_evaluation: `True` to evaluate the ensembled model on the test split.
- """
+ Returns:
+ Tuple of pipeline_definition_path and parameter_values.
+ """
+ # fmt: on
parameter_values = _get_base_forecasting_parameters(
project=project,
location=location,
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/__init__.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/__init__.py
index 4268da69ff..bf8f42e43f 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/__init__.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/__init__.py
@@ -22,6 +22,12 @@
from google_cloud_pipeline_components.preview.automl.tabular.feature_transform_engine import feature_transform_engine as FeatureTransformEngineOp
from google_cloud_pipeline_components.preview.automl.tabular.tabnet_hyperparameter_tuning_job import tabnet_hyperparameter_tuning_job as TabNetHyperparameterTuningJobOp
from google_cloud_pipeline_components.preview.automl.tabular.tabnet_trainer import tabnet_trainer as TabNetTrainerOp
+from google_cloud_pipeline_components.preview.automl.tabular.utils import get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.tabular.utils import get_tabnet_trainer_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.tabular.utils import get_wide_and_deep_hyperparameter_tuning_job_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.tabular.utils import get_wide_and_deep_trainer_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.tabular.utils import get_xgboost_hyperparameter_tuning_job_pipeline_and_parameters
+from google_cloud_pipeline_components.preview.automl.tabular.utils import get_xgboost_trainer_pipeline_and_parameters
from google_cloud_pipeline_components.preview.automl.tabular.wide_and_deep_hyperparameter_tuning_job import wide_and_deep_hyperparameter_tuning_job as WideAndDeepHyperparameterTuningJobOp
from google_cloud_pipeline_components.preview.automl.tabular.wide_and_deep_trainer import wide_and_deep_trainer as WideAndDeepTrainerOp
from google_cloud_pipeline_components.preview.automl.tabular.xgboost_hyperparameter_tuning_job import xgboost_hyperparameter_tuning_job as XGBoostHyperparameterTuningJobOp
@@ -30,15 +36,21 @@
__all__ = [
'AutoFeatureEngineeringOp',
+ 'DistillationStageFeatureTransformEngineOp',
'FeatureSelectionOp',
- 'WideAndDeepHyperparameterTuningJobOp',
- 'WideAndDeepTrainerOp',
+ 'FeatureTransformEngineOp',
'TabNetHyperparameterTuningJobOp',
'TabNetTrainerOp',
- 'FeatureTransformEngineOp',
- 'DistillationStageFeatureTransformEngineOp',
+ 'WideAndDeepHyperparameterTuningJobOp',
+ 'WideAndDeepTrainerOp',
'XGBoostHyperparameterTuningJobOp',
'XGBoostTrainerOp',
+ 'get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters',
+ 'get_tabnet_trainer_pipeline_and_parameters',
+ 'get_wide_and_deep_hyperparameter_tuning_job_pipeline_and_parameters',
+ 'get_wide_and_deep_trainer_pipeline_and_parameters',
+ 'get_xgboost_hyperparameter_tuning_job_pipeline_and_parameters',
+ 'get_xgboost_trainer_pipeline_and_parameters',
]
tabnet_trainer_pipeline = components.load_component_from_file(
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/utils.py b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/utils.py
index 97e6b370a1..8bc2e0be2d 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/utils.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/automl/tabular/utils.py
@@ -1187,6 +1187,7 @@ def get_wide_and_deep_trainer_pipeline_and_parameters(
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the Wide & Deep training pipeline.
Args:
@@ -1194,16 +1195,12 @@ def get_wide_and_deep_trainer_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- prediction_type: The type of prediction the model is to produce.
- 'classification' or 'regression'.
+ prediction_type: The type of prediction the model is to produce. 'classification' or 'regression'.
learning_rate: The learning rate used by the linear optimizer.
- dnn_learning_rate: The learning rate for training the deep part of the
- model.
+ dnn_learning_rate: The learning rate for training the deep part of the model.
transform_config: Path to v1 TF transformation configuration.
- dataset_level_custom_transformation_definitions: Dataset-level custom
- transformation definitions in string format.
- dataset_level_transformations: Dataset-level transformation configuration in
- string format.
+ dataset_level_custom_transformation_definitions: Dataset-level custom transformation definitions in string format.
+ dataset_level_transformations: Dataset-level transformation configuration in string format.
run_feature_selection: Whether to enable feature selection.
feature_selection_algorithm: Feature selection algorithm.
materialized_examples_format: The format for the materialized examples.
@@ -1213,101 +1210,61 @@ def get_wide_and_deep_trainer_pipeline_and_parameters(
training_fraction: Training fraction.
validation_fraction: Validation fraction.
test_fraction: Test fraction.
- tf_transform_execution_engine: The execution engine used to execute TF-based
- transformations.
- tf_auto_transform_features: List of auto transform features in the
- comma-separated string format.
- tf_custom_transformation_definitions: TF custom transformation definitions
- in string format.
+ tf_transform_execution_engine: The execution engine used to execute TF-based transformations.
+ tf_auto_transform_features: List of auto transform features in the comma-separated string format.
+ tf_custom_transformation_definitions: TF custom transformation definitions in string format.
tf_transformations_path: Path to TF transformation configuration.
- optimizer_type: The type of optimizer to use. Choices are "adam", "ftrl" and
- "sgd" for the Adam, FTRL, and Gradient Descent Optimizers, respectively.
+ optimizer_type: The type of optimizer to use. Choices are "adam", "ftrl" and "sgd" for the Adam, FTRL, and Gradient Descent Optimizers, respectively.
max_steps: Number of steps to run the trainer for.
max_train_secs: Amount of time in seconds to run the trainer for.
- l1_regularization_strength: L1 regularization strength for
- optimizer_type="ftrl".
- l2_regularization_strength: L2 regularization strength for
- optimizer_type="ftrl".
- l2_shrinkage_regularization_strength: L2 shrinkage regularization strength
- for optimizer_type="ftrl".
+ l1_regularization_strength: L1 regularization strength for optimizer_type="ftrl".
+ l2_regularization_strength: L2 regularization strength for optimizer_type="ftrl".
+ l2_shrinkage_regularization_strength: L2 shrinkage regularization strength for optimizer_type="ftrl".
beta_1: Beta 1 value for optimizer_type="adam".
beta_2: Beta 2 value for optimizer_type="adam".
- hidden_units: Hidden layer sizes to use for DNN feature columns, provided in
- comma-separated layers.
- use_wide: If set to true, the categorical columns will be used in the wide
- part of the DNN model.
- embed_categories: If set to true, the categorical columns will be used
- embedded and used in the deep part of the model. Embedding size is the
- square root of the column cardinality.
+ hidden_units: Hidden layer sizes to use for DNN feature columns, provided in comma-separated layers.
+ use_wide: If set to true, the categorical columns will be used in the wide part of the DNN model.
+ embed_categories: If set to true, the categorical columns will be used embedded and used in the deep part of the model. Embedding size is the square root of the column cardinality.
dnn_dropout: The probability we will drop out a given coordinate.
- dnn_optimizer_type: The type of optimizer to use for the deep part of the
- model. Choices are "adam", "ftrl" and "sgd". for the Adam, FTRL, and
- Gradient Descent Optimizers, respectively.
- dnn_l1_regularization_strength: L1 regularization strength for
- dnn_optimizer_type="ftrl".
- dnn_l2_regularization_strength: L2 regularization strength for
- dnn_optimizer_type="ftrl".
- dnn_l2_shrinkage_regularization_strength: L2 shrinkage regularization
- strength for dnn_optimizer_type="ftrl".
+ dnn_optimizer_type: The type of optimizer to use for the deep part of the model. Choices are "adam", "ftrl" and "sgd". for the Adam, FTRL, and Gradient Descent Optimizers, respectively.
+ dnn_l1_regularization_strength: L1 regularization strength for dnn_optimizer_type="ftrl".
+ dnn_l2_regularization_strength: L2 regularization strength for dnn_optimizer_type="ftrl".
+ dnn_l2_shrinkage_regularization_strength: L2 shrinkage regularization strength for dnn_optimizer_type="ftrl".
dnn_beta_1: Beta 1 value for dnn_optimizer_type="adam".
dnn_beta_2: Beta 2 value for dnn_optimizer_type="adam".
enable_profiler: Enables profiling and saves a trace during evaluation.
- cache_data: Whether to cache data or not. If set to 'auto', caching is
- determined based on the dataset size.
+ cache_data: Whether to cache data or not. If set to 'auto', caching is determined based on the dataset size.
seed: Seed to be used for this run.
- eval_steps: Number of steps to run evaluation for. If not specified or
- negative, it means run evaluation on the whole validation dataset. If set
- to 0, it means run evaluation for a fixed number of samples.
+ eval_steps: Number of steps to run evaluation for. If not specified or negative, it means run evaluation on the whole validation dataset. If set to 0, it means run evaluation for a fixed number of samples.
batch_size: Batch size for training.
- measurement_selection_type: Which measurement to use if/when the service
- automatically selects the final measurement from previously reported
- intermediate measurements. One of "BEST_MEASUREMENT" or
- "LAST_MEASUREMENT".
- optimization_metric: Optimization metric used for
- `measurement_selection_type`. Default is "rmse" for regression and "auc"
- for classification.
- eval_frequency_secs: Frequency at which evaluation and checkpointing will
- take place.
+ measurement_selection_type: Which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. One of "BEST_MEASUREMENT" or "LAST_MEASUREMENT".
+ optimization_metric: Optimization metric used for `measurement_selection_type`. Default is "rmse" for regression and "auc" for classification.
+ eval_frequency_secs: Frequency at which evaluation and checkpointing will take place.
data_source_csv_filenames: The CSV data source.
data_source_bigquery_table_path: The BigQuery data source.
- bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for
- storing intermediate tables.
+ bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for storing intermediate tables.
weight_column: The weight column name.
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
- worker_pool_specs_override: The dictionary for overriding training and
- evaluation worker pool specs. The dictionary should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
+ worker_pool_specs_override: The dictionary for overriding training and evaluation worker pool specs. The dictionary should be of format https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
run_evaluation: Whether to run evaluation steps during training.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
if isinstance(tf_auto_transform_features, list):
tf_auto_transform_features = {'auto': tf_auto_transform_features}
@@ -1778,6 +1735,7 @@ def get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters(
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the TabNet HyperparameterTuningJob pipeline.
Args:
@@ -1785,24 +1743,15 @@ def get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- prediction_type: The type of prediction the model is to produce.
- "classification" or "regression".
- study_spec_metric_id: Metric to optimize, possible values: [ 'loss',
- 'average_loss', 'rmse', 'mae', 'mql', 'accuracy', 'auc', 'precision',
- 'recall'].
- study_spec_metric_goal: Optimization goal of the metric, possible values:
- "MAXIMIZE", "MINIMIZE".
- study_spec_parameters_override: List of dictionaries representing parameters
- to optimize. The dictionary key is the parameter_id, which is passed to
- training job as a command line argument, and the dictionary value is the
- parameter specification of the metric.
+ prediction_type: The type of prediction the model is to produce. "classification" or "regression".
+ study_spec_metric_id: Metric to optimize, possible values: [ 'loss', 'average_loss', 'rmse', 'mae', 'mql', 'accuracy', 'auc', 'precision', 'recall'].
+ study_spec_metric_goal: Optimization goal of the metric, possible values: "MAXIMIZE", "MINIMIZE".
+ study_spec_parameters_override: List of dictionaries representing parameters to optimize. The dictionary key is the parameter_id, which is passed to training job as a command line argument, and the dictionary value is the parameter specification of the metric.
max_trial_count: The desired total number of trials.
parallel_trial_count: The desired number of trials to run in parallel.
transform_config: Path to v1 TF transformation configuration.
- dataset_level_custom_transformation_definitions: Dataset-level custom
- transformation definitions in string format.
- dataset_level_transformations: Dataset-level transformation configuration in
- string format.
+ dataset_level_custom_transformation_definitions: Dataset-level custom transformation definitions in string format.
+ dataset_level_transformations: Dataset-level transformation configuration in string format.
run_feature_selection: Whether to enable feature selection.
feature_selection_algorithm: Feature selection algorithm.
materialized_examples_format: The format for the materialized examples.
@@ -1812,71 +1761,43 @@ def get_tabnet_hyperparameter_tuning_job_pipeline_and_parameters(
training_fraction: Training fraction.
validation_fraction: Validation fraction.
test_fraction: Test fraction.
- tf_transform_execution_engine: The execution engine used to execute TF-based
- transformations.
- tf_auto_transform_features: List of auto transform features in the
- comma-separated string format.
- tf_custom_transformation_definitions: TF custom transformation definitions
- in string format.
+ tf_transform_execution_engine: The execution engine used to execute TF-based transformations.
+ tf_auto_transform_features: List of auto transform features in the comma-separated string format.
+ tf_custom_transformation_definitions: TF custom transformation definitions in string format.
tf_transformations_path: Path to TF transformation configuration.
enable_profiler: Enables profiling and saves a trace during evaluation.
- cache_data: Whether to cache data or not. If set to 'auto', caching is
- determined based on the dataset size.
+ cache_data: Whether to cache data or not. If set to 'auto', caching is determined based on the dataset size.
seed: Seed to be used for this run.
- eval_steps: Number of steps to run evaluation for. If not specified or
- negative, it means run evaluation on the whole validation dataset. If set
- to 0, it means run evaluation for a fixed number of samples.
- eval_frequency_secs: Frequency at which evaluation and checkpointing will
- take place.
+ eval_steps: Number of steps to run evaluation for. If not specified or negative, it means run evaluation on the whole validation dataset. If set to 0, it means run evaluation for a fixed number of samples.
+ eval_frequency_secs: Frequency at which evaluation and checkpointing will take place.
data_source_csv_filenames: The CSV data source.
data_source_bigquery_table_path: The BigQuery data source.
- bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for
- storing intermediate tables.
+ bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for storing intermediate tables.
weight_column: The weight column name.
- max_failed_trial_count: The number of failed trials that need to be seen
- before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides
- how many trials must fail before the whole job fails.
- study_spec_algorithm: The search algorithm specified for the study. One of
- "ALGORITHM_UNSPECIFIED", "GRID_SEARCH", or "RANDOM_SEARCH".
- study_spec_measurement_selection_type: Which measurement to use if/when the
- service automatically selects the final measurement from previously
- reported intermediate measurements. One of "BEST_MEASUREMENT" or
- "LAST_MEASUREMENT".
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
- worker_pool_specs_override: The dictionary for overriding training and
- evaluation worker pool specs. The dictionary should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
+ max_failed_trial_count: The number of failed trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many trials must fail before the whole job fails.
+ study_spec_algorithm: The search algorithm specified for the study. One of "ALGORITHM_UNSPECIFIED", "GRID_SEARCH", or "RANDOM_SEARCH".
+ study_spec_measurement_selection_type: Which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. One of "BEST_MEASUREMENT" or "LAST_MEASUREMENT".
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
+ worker_pool_specs_override: The dictionary for overriding training and evaluation worker pool specs. The dictionary should be of format https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
run_evaluation: Whether to run evaluation steps during training.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
if isinstance(tf_auto_transform_features, list):
tf_auto_transform_features = {'auto': tf_auto_transform_features}
@@ -2060,6 +1981,7 @@ def get_wide_and_deep_hyperparameter_tuning_job_pipeline_and_parameters(
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the Wide & Deep algorithm HyperparameterTuningJob pipeline.
Args:
@@ -2067,24 +1989,15 @@ def get_wide_and_deep_hyperparameter_tuning_job_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- prediction_type: The type of prediction the model is to produce.
- "classification" or "regression".
- study_spec_metric_id: Metric to optimize, possible values: [ 'loss',
- 'average_loss', 'rmse', 'mae', 'mql', 'accuracy', 'auc', 'precision',
- 'recall'].
- study_spec_metric_goal: Optimization goal of the metric, possible values:
- "MAXIMIZE", "MINIMIZE".
- study_spec_parameters_override: List of dictionaries representing parameters
- to optimize. The dictionary key is the parameter_id, which is passed to
- training job as a command line argument, and the dictionary value is the
- parameter specification of the metric.
+ prediction_type: The type of prediction the model is to produce. "classification" or "regression".
+ study_spec_metric_id: Metric to optimize, possible values: [ 'loss', 'average_loss', 'rmse', 'mae', 'mql', 'accuracy', 'auc', 'precision', 'recall'].
+ study_spec_metric_goal: Optimization goal of the metric, possible values: "MAXIMIZE", "MINIMIZE".
+ study_spec_parameters_override: List of dictionaries representing parameters to optimize. The dictionary key is the parameter_id, which is passed to training job as a command line argument, and the dictionary value is the parameter specification of the metric.
max_trial_count: The desired total number of trials.
parallel_trial_count: The desired number of trials to run in parallel.
transform_config: Path to v1 TF transformation configuration.
- dataset_level_custom_transformation_definitions: Dataset-level custom
- transformation definitions in string format.
- dataset_level_transformations: Dataset-level transformation configuration in
- string format.
+ dataset_level_custom_transformation_definitions: Dataset-level custom transformation definitions in string format.
+ dataset_level_transformations: Dataset-level transformation configuration in string format.
run_feature_selection: Whether to enable feature selection.
feature_selection_algorithm: Feature selection algorithm.
materialized_examples_format: The format for the materialized examples.
@@ -2094,71 +2007,43 @@ def get_wide_and_deep_hyperparameter_tuning_job_pipeline_and_parameters(
training_fraction: Training fraction.
validation_fraction: Validation fraction.
test_fraction: Test fraction.
- tf_transform_execution_engine: The execution engine used to execute TF-based
- transformations.
- tf_auto_transform_features: List of auto transform features in the
- comma-separated string format.
- tf_custom_transformation_definitions: TF custom transformation definitions
- in string format.
+ tf_transform_execution_engine: The execution engine used to execute TF-based transformations.
+ tf_auto_transform_features: List of auto transform features in the comma-separated string format.
+ tf_custom_transformation_definitions: TF custom transformation definitions in string format.
tf_transformations_path: Path to TF transformation configuration.
enable_profiler: Enables profiling and saves a trace during evaluation.
- cache_data: Whether to cache data or not. If set to 'auto', caching is
- determined based on the dataset size.
+ cache_data: Whether to cache data or not. If set to 'auto', caching is determined based on the dataset size.
seed: Seed to be used for this run.
- eval_steps: Number of steps to run evaluation for. If not specified or
- negative, it means run evaluation on the whole validation dataset. If set
- to 0, it means run evaluation for a fixed number of samples.
- eval_frequency_secs: Frequency at which evaluation and checkpointing will
- take place.
+ eval_steps: Number of steps to run evaluation for. If not specified or negative, it means run evaluation on the whole validation dataset. If set to 0, it means run evaluation for a fixed number of samples.
+ eval_frequency_secs: Frequency at which evaluation and checkpointing will take place.
data_source_csv_filenames: The CSV data source.
data_source_bigquery_table_path: The BigQuery data source.
- bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for
- storing intermediate tables.
+ bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for storing intermediate tables.
weight_column: The weight column name.
- max_failed_trial_count: The number of failed trials that need to be seen
- before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides
- how many trials must fail before the whole job fails.
- study_spec_algorithm: The search algorithm specified for the study. One of
- "ALGORITHM_UNSPECIFIED", "GRID_SEARCH", or "RANDOM_SEARCH".
- study_spec_measurement_selection_type: Which measurement to use if/when the
- service automatically selects the final measurement from previously
- reported intermediate measurements. One of "BEST_MEASUREMENT" or
- "LAST_MEASUREMENT".
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
- worker_pool_specs_override: The dictionary for overriding training and
- evaluation worker pool specs. The dictionary should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
+ max_failed_trial_count: The number of failed trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many trials must fail before the whole job fails.
+ study_spec_algorithm: The search algorithm specified for the study. One of "ALGORITHM_UNSPECIFIED", "GRID_SEARCH", or "RANDOM_SEARCH".
+ study_spec_measurement_selection_type: Which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. One of "BEST_MEASUREMENT" or "LAST_MEASUREMENT".
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
+ worker_pool_specs_override: The dictionary for overriding training and evaluation worker pool specs. The dictionary should be of format https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
run_evaluation: Whether to run evaluation steps during training.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
if isinstance(tf_auto_transform_features, list):
tf_auto_transform_features = {'auto': tf_auto_transform_features}
@@ -2359,6 +2244,7 @@ def get_tabnet_trainer_pipeline_and_parameters(
dataflow_use_public_ips: bool = True,
encryption_spec_key_name: str = '',
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the TabNet training pipeline.
Args:
@@ -2366,14 +2252,11 @@ def get_tabnet_trainer_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- prediction_type: The type of prediction the model is to produce.
- "classification" or "regression".
+ prediction_type: The type of prediction the model is to produce. "classification" or "regression".
learning_rate: The learning rate used by the linear optimizer.
transform_config: Path to v1 TF transformation configuration.
- dataset_level_custom_transformation_definitions: Dataset-level custom
- transformation definitions in string format.
- dataset_level_transformations: Dataset-level transformation configuration in
- string format.
+ dataset_level_custom_transformation_definitions: Dataset-level custom transformation definitions in string format.
+ dataset_level_transformations: Dataset-level transformation configuration in string format.
run_feature_selection: Whether to enable feature selection.
feature_selection_algorithm: Feature selection algorithm.
materialized_examples_format: The format for the materialized examples.
@@ -2383,111 +2266,64 @@ def get_tabnet_trainer_pipeline_and_parameters(
training_fraction: Training fraction.
validation_fraction: Validation fraction.
test_fraction: Test fraction.
- tf_transform_execution_engine: The execution engine used to execute TF-based
- transformations.
- tf_auto_transform_features: List of auto transform features in the
- comma-separated string format.
- tf_custom_transformation_definitions: TF custom transformation definitions
- in string format.
+ tf_transform_execution_engine: The execution engine used to execute TF-based transformations.
+ tf_auto_transform_features: List of auto transform features in the comma-separated string format.
+ tf_custom_transformation_definitions: TF custom transformation definitions in string format.
tf_transformations_path: Path to TF transformation configuration.
max_steps: Number of steps to run the trainer for.
max_train_secs: Amount of time in seconds to run the trainer for.
- large_category_dim: Embedding dimension for categorical feature with large
- number of categories.
- large_category_thresh: Threshold for number of categories to apply
- large_category_dim embedding dimension to.
+ large_category_dim: Embedding dimension for categorical feature with large number of categories.
+ large_category_thresh: Threshold for number of categories to apply large_category_dim embedding dimension to.
yeo_johnson_transform: Enables trainable Yeo-Johnson power transform.
- feature_dim: Dimensionality of the hidden representation in feature
- transformation block.
- feature_dim_ratio: The ratio of output dimension (dimensionality of the
- outputs of each decision step) to feature dimension.
+ feature_dim: Dimensionality of the hidden representation in feature transformation block.
+ feature_dim_ratio: The ratio of output dimension (dimensionality of the outputs of each decision step) to feature dimension.
num_decision_steps: Number of sequential decision steps.
- relaxation_factor: Relaxation factor that promotes the reuse of each feature
- at different decision steps. When it is 1, a feature is enforced to be
- used only at one decision step and as it increases, more flexibility is
- provided to use a feature at multiple decision steps.
- decay_every: Number of iterations for periodically applying learning rate
- decaying.
+ relaxation_factor: Relaxation factor that promotes the reuse of each feature at different decision steps. When it is 1, a feature is enforced to be used only at one decision step and as it increases, more flexibility is provided to use a feature at multiple decision steps.
+ decay_every: Number of iterations for periodically applying learning rate decaying.
decay_rate: Learning rate decaying.
gradient_thresh: Threshold for the norm of gradients for clipping.
- sparsity_loss_weight: Weight of the loss for sparsity regularization
- (increasing it will yield more sparse feature selection).
+ sparsity_loss_weight: Weight of the loss for sparsity regularization (increasing it will yield more sparse feature selection).
batch_momentum: Momentum in ghost batch normalization.
- batch_size_ratio: The ratio of virtual batch size (size of the ghost batch
- normalization) to batch size.
- num_transformer_layers: The number of transformer layers for each decision
- step. used only at one decision step and as it increases, more flexibility
- is provided to use a feature at multiple decision steps.
- num_transformer_layers_ratio: The ratio of shared transformer layer to
- transformer layers.
- class_weight: The class weight is used to computes a weighted cross entropy
- which is helpful in classify imbalanced dataset. Only used for
- classification.
- loss_function_type: Loss function type. Loss function in classification
- [cross_entropy, weighted_cross_entropy, focal_loss], default is
- cross_entropy. Loss function in regression: [rmse, mae, mse], default is
- mse.
- alpha_focal_loss: Alpha value (balancing factor) in focal_loss function.
- Only used for classification.
- gamma_focal_loss: Gamma value (modulating factor) for focal loss for focal
- loss. Only used for classification.
+ batch_size_ratio: The ratio of virtual batch size (size of the ghost batch normalization) to batch size.
+ num_transformer_layers: The number of transformer layers for each decision step. used only at one decision step and as it increases, more flexibility is provided to use a feature at multiple decision steps.
+ num_transformer_layers_ratio: The ratio of shared transformer layer to transformer layers.
+ class_weight: The class weight is used to computes a weighted cross entropy which is helpful in classify imbalanced dataset. Only used for classification.
+ loss_function_type: Loss function type. Loss function in classification [cross_entropy, weighted_cross_entropy, focal_loss], default is cross_entropy. Loss function in regression: [rmse, mae, mse], default is mse.
+ alpha_focal_loss: Alpha value (balancing factor) in focal_loss function. Only used for classification.
+ gamma_focal_loss: Gamma value (modulating factor) for focal loss for focal loss. Only used for classification.
enable_profiler: Enables profiling and saves a trace during evaluation.
- cache_data: Whether to cache data or not. If set to 'auto', caching is
- determined based on the dataset size.
+ cache_data: Whether to cache data or not. If set to 'auto', caching is determined based on the dataset size.
seed: Seed to be used for this run.
- eval_steps: Number of steps to run evaluation for. If not specified or
- negative, it means run evaluation on the whole validation dataset. If set
- to 0, it means run evaluation for a fixed number of samples.
+ eval_steps: Number of steps to run evaluation for. If not specified or negative, it means run evaluation on the whole validation dataset. If set to 0, it means run evaluation for a fixed number of samples.
batch_size: Batch size for training.
- measurement_selection_type: Which measurement to use if/when the service
- automatically selects the final measurement from previously reported
- intermediate measurements. One of "BEST_MEASUREMENT" or
- "LAST_MEASUREMENT".
- optimization_metric: Optimization metric used for
- `measurement_selection_type`. Default is "rmse" for regression and "auc"
- for classification.
- eval_frequency_secs: Frequency at which evaluation and checkpointing will
- take place.
+ measurement_selection_type: Which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. One of "BEST_MEASUREMENT" or "LAST_MEASUREMENT".
+ optimization_metric: Optimization metric used for `measurement_selection_type`. Default is "rmse" for regression and "auc" for classification.
+ eval_frequency_secs: Frequency at which evaluation and checkpointing will take place.
data_source_csv_filenames: The CSV data source.
data_source_bigquery_table_path: The BigQuery data source.
- bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for
- storing intermediate tables.
+ bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for storing intermediate tables.
weight_column: The weight column name.
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
- worker_pool_specs_override: The dictionary for overriding training and
- evaluation worker pool specs. The dictionary should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
+ worker_pool_specs_override: The dictionary for overriding training and evaluation worker pool specs. The dictionary should be of format https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
run_evaluation: Whether to run evaluation steps during training.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
if isinstance(tf_auto_transform_features, list):
tf_auto_transform_features = {'auto': tf_auto_transform_features}
@@ -2838,6 +2674,7 @@ def get_xgboost_trainer_pipeline_and_parameters(
dataflow_use_public_ips: Optional[bool] = None,
encryption_spec_key_name: Optional[str] = None,
):
+ # fmt: off
"""Get the XGBoost training pipeline.
Args:
@@ -2845,79 +2682,50 @@ def get_xgboost_trainer_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- objective: Specifies the learning task and the learning objective. Must be
- one of [reg:squarederror, reg:squaredlogerror,
- reg:logistic, reg:gamma, reg:tweedie, reg:pseudohubererror,
- binary:logistic, multi:softprob].
- eval_metric: Evaluation metrics for validation data represented as a
- comma-separated string.
+ objective: Specifies the learning task and the learning objective. Must be one of [reg:squarederror, reg:squaredlogerror, reg:logistic, reg:gamma, reg:tweedie, reg:pseudohubererror, binary:logistic, multi:softprob].
+ eval_metric: Evaluation metrics for validation data represented as a comma-separated string.
num_boost_round: Number of boosting iterations.
- early_stopping_rounds: Activates early stopping. Validation error needs to
- decrease at least every early_stopping_rounds round(s) to continue
- training.
+ early_stopping_rounds: Activates early stopping. Validation error needs to decrease at least every early_stopping_rounds round(s) to continue training.
base_score: The initial prediction score of all instances, global bias.
- disable_default_eval_metric: Flag to disable default metric. Set to >0 to
- disable. Default to 0.
+ disable_default_eval_metric: Flag to disable default metric. Set to >0 to disable. Default to 0.
seed: Random seed.
seed_per_iteration: Seed PRNG determnisticly via iterator number.
- booster: Which booster to use, can be gbtree, gblinear or dart. gbtree and
- dart use tree based model while gblinear uses linear function.
+ booster: Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.
eta: Learning rate.
- gamma: Minimum loss reduction required to make a further partition on a leaf
- node of the tree.
+ gamma: Minimum loss reduction required to make a further partition on a leaf node of the tree.
max_depth: Maximum depth of a tree.
min_child_weight: Minimum sum of instance weight(hessian) needed in a child.
- max_delta_step: Maximum delta step we allow each tree's weight estimation to
- be.
+ max_delta_step: Maximum delta step we allow each tree's weight estimation to be.
subsample: Subsample ratio of the training instance.
colsample_bytree: Subsample ratio of columns when constructing each tree.
colsample_bylevel: Subsample ratio of columns for each split, in each level.
colsample_bynode: Subsample ratio of columns for each node (split).
reg_lambda: L2 regularization term on weights.
reg_alpha: L1 regularization term on weights.
- tree_method: The tree construction algorithm used in XGBoost. Choices:
- ["auto", "exact", "approx", "hist", "gpu_exact", "gpu_hist"].
+ tree_method: The tree construction algorithm used in XGBoost. Choices: ["auto", "exact", "approx", "hist", "gpu_exact", "gpu_hist"].
scale_pos_weight: Control the balance of positive and negative weights.
- updater: A comma separated string defining the sequence of tree updaters to
- run.
- refresh_leaf: Refresh updater plugin. Update tree leaf and nodes's stats if
- True. When it is False, only node stats are updated.
- process_type: A type of boosting process to run. Choices:["default",
- "update"]
- grow_policy: Controls a way new nodes are added to the tree. Only supported
- if tree_method is hist. Choices:["depthwise", "lossguide"]
+ updater: A comma separated string defining the sequence of tree updaters to run.
+ refresh_leaf: Refresh updater plugin. Update tree leaf and nodes's stats if True. When it is False, only node stats are updated.
+ process_type: A type of boosting process to run. Choices:["default", "update"]
+ grow_policy: Controls a way new nodes are added to the tree. Only supported if tree_method is hist. Choices:["depthwise", "lossguide"]
sampling_method: The method to use to sample the training instances.
monotone_constraints: Constraint of variable monotonicity.
- interaction_constraints: Constraints for interaction representing permitted
- interactions.
- sample_type: [dart booster only] Type of sampling algorithm.
- Choices:["uniform", "weighted"]
- normalize_type: [dart booster only] Type of normalization algorithm,
- Choices:["tree", "forest"]
+ interaction_constraints: Constraints for interaction representing permitted interactions.
+ sample_type: [dart booster only] Type of sampling algorithm. Choices:["uniform", "weighted"]
+ normalize_type: [dart booster only] Type of normalization algorithm, Choices:["tree", "forest"]
rate_drop: [dart booster only] Dropout rate.'
- one_drop: [dart booster only] When this flag is enabled, at least one tree
- is always dropped during the dropout (allows Binomial-plus-one or
- epsilon-dropout from the original DART paper).
- skip_drop: [dart booster only] Probability of skipping the dropout procedure
- during a boosting iteration.
- num_parallel_tree: Number of parallel trees constructed during each
- iteration. This option is used to support boosted random forest.
- feature_selector: [linear booster only] Feature selection and ordering
- method.
- top_k: The number of top features to select in greedy and thrifty feature
- selector. The value of 0 means using all the features.
- max_cat_to_onehot: A threshold for deciding whether XGBoost should use
- one-hot encoding based split for categorical data.
+ one_drop: [dart booster only] When this flag is enabled, at least one tree is always dropped during the dropout (allows Binomial-plus-one or epsilon-dropout from the original DART paper).
+ skip_drop: [dart booster only] Probability of skipping the dropout procedure during a boosting iteration.
+ num_parallel_tree: Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.
+ feature_selector: [linear booster only] Feature selection and ordering method.
+ top_k: The number of top features to select in greedy and thrifty feature selector. The value of 0 means using all the features.
+ max_cat_to_onehot: A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data.
max_leaves: Maximum number of nodes to be added.
max_bin: Maximum number of discrete bins to bucket continuous features.
- tweedie_variance_power: Parameter that controls the variance of the Tweedie
- distribution.
- huber_slope: A parameter used for Pseudo-Huber loss to define the delta
- term.
- dataset_level_custom_transformation_definitions: Dataset-level custom
- transformation definitions in string format.
- dataset_level_transformations: Dataset-level transformation configuration in
- string format.
+ tweedie_variance_power: Parameter that controls the variance of the Tweedie distribution.
+ huber_slope: A parameter used for Pseudo-Huber loss to define the delta term.
+ dataset_level_custom_transformation_definitions: Dataset-level custom transformation definitions in string format.
+ dataset_level_transformations: Dataset-level transformation configuration in string format.
run_feature_selection: Whether to enable feature selection.
feature_selection_algorithm: Feature selection algorithm.
max_selected_features: Maximum number of features to select.
@@ -2926,52 +2734,37 @@ def get_xgboost_trainer_pipeline_and_parameters(
training_fraction: Training fraction.
validation_fraction: Validation fraction.
test_fraction: Test fraction.
- tf_auto_transform_features: List of auto transform features in the
- comma-separated string format.
- tf_custom_transformation_definitions: TF custom transformation definitions
- in string format.
+ tf_auto_transform_features: List of auto transform features in the comma-separated string format.
+ tf_custom_transformation_definitions: TF custom transformation definitions in string format.
tf_transformations_path: Path to TF transformation configuration.
data_source_csv_filenames: The CSV data source.
data_source_bigquery_table_path: The BigQuery data source.
- bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for
- storing intermediate tables.
+ bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for storing intermediate tables.
weight_column: The weight column name.
training_machine_type: Machine type.
training_total_replica_count: Number of workers.
training_accelerator_type: Accelerator type.
training_accelerator_count: Accelerator count.
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
run_evaluation: Whether to run evaluation steps during training.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = {}
if isinstance(tf_auto_transform_features, list):
tf_auto_transform_features = {'auto': tf_auto_transform_features}
@@ -3158,6 +2951,7 @@ def get_xgboost_hyperparameter_tuning_job_pipeline_and_parameters(
dataflow_use_public_ips: Optional[bool] = None,
encryption_spec_key_name: Optional[str] = None,
):
+ # fmt: off
"""Get the XGBoost HyperparameterTuningJob pipeline.
Args:
@@ -3165,31 +2959,18 @@ def get_xgboost_hyperparameter_tuning_job_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- objective: Specifies the learning task and the learning objective. Must be
- one of [reg:squarederror, reg:squaredlogerror,
- reg:logistic, reg:gamma, reg:tweedie, reg:pseudohubererror,
- binary:logistic, multi:softprob].
- study_spec_metric_id: Metric to optimize. For options, please look under
- 'eval_metric' at
- https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters.
- study_spec_metric_goal: Optimization goal of the metric, possible values:
- "MAXIMIZE", "MINIMIZE".
+ objective: Specifies the learning task and the learning objective. Must be one of [reg:squarederror, reg:squaredlogerror, reg:logistic, reg:gamma, reg:tweedie, reg:pseudohubererror, binary:logistic, multi:softprob].
+ study_spec_metric_id: Metric to optimize. For options, please look under 'eval_metric' at https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters.
+ study_spec_metric_goal: Optimization goal of the metric, possible values: "MAXIMIZE", "MINIMIZE".
max_trial_count: The desired total number of trials.
parallel_trial_count: The desired number of trials to run in parallel.
- study_spec_parameters_override: List of dictionaries representing parameters
- to optimize. The dictionary key is the parameter_id, which is passed to
- training job as a command line argument, and the dictionary value is the
- parameter specification of the metric.
- eval_metric: Evaluation metrics for validation data represented as a
- comma-separated string.
- disable_default_eval_metric: Flag to disable default metric. Set to >0 to
- disable. Default to 0.
+ study_spec_parameters_override: List of dictionaries representing parameters to optimize. The dictionary key is the parameter_id, which is passed to training job as a command line argument, and the dictionary value is the parameter specification of the metric.
+ eval_metric: Evaluation metrics for validation data represented as a comma-separated string.
+ disable_default_eval_metric: Flag to disable default metric. Set to >0 to disable. Default to 0.
seed: Random seed.
seed_per_iteration: Seed PRNG determnisticly via iterator number.
- dataset_level_custom_transformation_definitions: Dataset-level custom
- transformation definitions in string format.
- dataset_level_transformations: Dataset-level transformation configuration in
- string format.
+ dataset_level_custom_transformation_definitions: Dataset-level custom transformation definitions in string format.
+ dataset_level_transformations: Dataset-level transformation configuration in string format.
run_feature_selection: Whether to enable feature selection.
feature_selection_algorithm: Feature selection algorithm.
max_selected_features: Maximum number of features to select.
@@ -3198,61 +2979,40 @@ def get_xgboost_hyperparameter_tuning_job_pipeline_and_parameters(
training_fraction: Training fraction.
validation_fraction: Validation fraction.
test_fraction: Test fraction.
- tf_auto_transform_features: List of auto transform features in the
- comma-separated string format.
- tf_custom_transformation_definitions: TF custom transformation definitions
- in string format.
+ tf_auto_transform_features: List of auto transform features in the comma-separated string format.
+ tf_custom_transformation_definitions: TF custom transformation definitions in string format.
tf_transformations_path: Path to TF transformation configuration.
data_source_csv_filenames: The CSV data source.
data_source_bigquery_table_path: The BigQuery data source.
- bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for
- storing intermediate tables.
+ bigquery_staging_full_dataset_id: The BigQuery staging full dataset id for storing intermediate tables.
weight_column: The weight column name.
- max_failed_trial_count: The number of failed trials that need to be seen
- before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides
- how many trials must fail before the whole job fails.
+ max_failed_trial_count: The number of failed trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many trials must fail before the whole job fails.
training_machine_type: Machine type.
training_total_replica_count: Number of workers.
training_accelerator_type: Accelerator type.
training_accelerator_count: Accelerator count.
- study_spec_algorithm: The search algorithm specified for the study. One of
- 'ALGORITHM_UNSPECIFIED', 'GRID_SEARCH', or 'RANDOM_SEARCH'.
- study_spec_measurement_selection_type: Which measurement to use if/when the
- service automatically selects the final measurement from previously
- reported intermediate measurements. One of "BEST_MEASUREMENT" or
- "LAST_MEASUREMENT".
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
+ study_spec_algorithm: The search algorithm specified for the study. One of 'ALGORITHM_UNSPECIFIED', 'GRID_SEARCH', or 'RANDOM_SEARCH'.
+ study_spec_measurement_selection_type: Which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. One of "BEST_MEASUREMENT" or "LAST_MEASUREMENT".
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
run_evaluation: Whether to run evaluation steps during training.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = {}
if isinstance(tf_auto_transform_features, list):
tf_auto_transform_features = {'auto': tf_auto_transform_features}
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/llm/rlhf/component.py b/components/google-cloud/google_cloud_pipeline_components/preview/llm/rlhf/component.py
index d0de9131ab..d9f3f0f80b 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/llm/rlhf/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/llm/rlhf/component.py
@@ -128,7 +128,7 @@ def rlhf_pipeline(
)
llm_model_handler = deployment_graph.pipeline(
- output_adapter_path=rl_model_pipeline.outputs['output_model_path'],
+ output_adapter_path=rl_model_pipeline.outputs['output_adapter_path'],
large_model_reference=large_model_reference,
model_display_name=model_display_name,
deploy_model=deploy_model,
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/__init__.py b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/__init__.py
index 6cf10f1d07..1f09e1e009 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/__init__.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/__init__.py
@@ -18,9 +18,11 @@
from google_cloud_pipeline_components.preview.model_evaluation.evaluation_llm_text_generation_pipeline import evaluation_llm_text_generation_pipeline
from google_cloud_pipeline_components.preview.model_evaluation.feature_attribution_component import feature_attribution as ModelEvaluationFeatureAttributionOp
from google_cloud_pipeline_components.preview.model_evaluation.feature_attribution_graph_component import feature_attribution_graph_component as FeatureAttributionGraphComponentOp
+from google_cloud_pipeline_components.preview.model_evaluation.model_based_llm_evaluation.autosxs.autosxs_pipeline import autosxs_pipeline
from google_cloud_pipeline_components.preview.model_evaluation.model_bias_component import detect_model_bias as DetectModelBiasOp
__all__ = [
+ 'autosxs_pipeline',
'evaluation_llm_classification_pipeline',
'evaluation_llm_text_generation_pipeline',
'ModelEvaluationFeatureAttributionOp',
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_classification_pipeline.py b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_classification_pipeline.py
index 994eac238a..6d00a33028 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_classification_pipeline.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_classification_pipeline.py
@@ -62,11 +62,11 @@ def evaluation_llm_classification_pipeline( # pylint: disable=dangerous-default
"""The LLM Text Classification Evaluation pipeline.
Args:
- project: The GCP project that runs the pipeline components.
- location: The GCP region that runs the pipeline components.
- target_field_name: The target field's name. Formatted to be able to find nested columns, delimited by `.`. Prefixed with 'instance.' on the component for Vertex Batch Prediction.
- batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
- batch_predict_gcs_destination_output_uri: The Google Cloud Storage location of the directory where the output is to be written to.
+ project: Required. The GCP project that runs the pipeline components.
+ location: Required. The GCP region that runs the pipeline components.
+ target_field_name: Required. The target field's name. Formatted to be able to find nested columns, delimited by `.`. Prefixed with 'instance.' on the component for Vertex Batch Prediction.
+ batch_predict_gcs_source_uris: Required. Google Cloud Storage URI(-s) to your instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
+ batch_predict_gcs_destination_output_uri: Required. The Google Cloud Storage location of the directory where the output is to be written to.
model_name: The Model name used to run evaluation. Must be a publisher Model or a managed Model sharing the same ancestor location. Starting this job has no impact on any existing deployments of the Model and their resources.
evaluation_task: The task that the large language model will be evaluated on. The evaluation component computes a set of metrics relevant to that specific task. Currently supported Classification tasks is: `text-classification`.
evaluation_class_labels: The JSON array of class names for the target_field, in the same order they appear in the batch predictions input file.
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_text_generation_pipeline.py b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_text_generation_pipeline.py
index d861d97fa0..0002cdd5e9 100644
--- a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_text_generation_pipeline.py
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/evaluation_llm_text_generation_pipeline.py
@@ -40,6 +40,7 @@ def evaluation_llm_text_generation_pipeline( # pylint: disable=dangerous-defaul
batch_predict_instances_format: str = 'jsonl',
batch_predict_predictions_format: str = 'jsonl',
batch_predict_model_parameters: Dict[str, str] = {},
+ enable_row_based_metrics: bool = False,
machine_type: str = 'e2-highmem-16',
service_account: str = '',
network: str = '',
@@ -55,10 +56,10 @@ def evaluation_llm_text_generation_pipeline( # pylint: disable=dangerous-defaul
models, performing the following generative tasks: `summarization`, `question-answering`, and `text-generation`.
Args:
- project: The GCP project that runs the pipeline components.
- location: The GCP region that runs the pipeline components.
- batch_predict_gcs_source_uris: Google Cloud Storage URI(-s) to your eval dataset instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
- batch_predict_gcs_destination_output_uri: The Google Cloud Storage location of the directory where the eval pipeline output is to be written to.
+ project: Required. The GCP project that runs the pipeline components.
+ location: Required. The GCP region that runs the pipeline components.
+ batch_predict_gcs_source_uris: Required. Google Cloud Storage URI(-s) to your eval dataset instances data to run batch prediction on. The instances data should also contain the ground truth (target) data, used for evaluation. May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. For more details about this input config, see https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.batchPredictionJobs#InputConfig.
+ batch_predict_gcs_destination_output_uri: Required. The Google Cloud Storage location of the directory where the eval pipeline output is to be written to.
model_name: The Model name used to run evaluation. Must be a publisher Model or a managed Model sharing the same ancestor location. Starting this job has no impact on any existing deployments of the Model and their resources.
evaluation_task: The task that the large language model will be evaluated on. The evaluation component computes a set of metrics relevant to that specific task. Currently supported tasks are: `summarization`, `question-answering`, `text-generation`.
input_field_name: The field name of the input eval dataset instances that contains the input prompts to the LLM.
@@ -74,7 +75,7 @@ def evaluation_llm_text_generation_pipeline( # pylint: disable=dangerous-defaul
Returns:
evaluation_metrics: Metrics Artifact for LLM Text Generation.
- evaluation_resource_name: If run on an user's managed VertexModel, the imported evaluation resource name. Empty if run on a publisher model.
+ evaluation_resource_name: If run on a user's managed VertexModel, the imported evaluation resource name. Empty if run on a publisher model.
"""
# fmt: on
outputs = NamedTuple(
@@ -123,6 +124,7 @@ def evaluation_llm_text_generation_pipeline( # pylint: disable=dangerous-defaul
evaluation_task=evaluation_task,
target_field_name=f'instance.{target_field_name}',
predictions_format=batch_predict_predictions_format,
+ enable_row_based_metrics=enable_row_based_metrics,
joined_predictions_gcs_source=batch_predict_task.outputs[
'gcs_output_directory'
],
@@ -134,6 +136,9 @@ def evaluation_llm_text_generation_pipeline( # pylint: disable=dangerous-defaul
import_evaluation_task = ModelImportEvaluationOp(
metrics=eval_task.outputs['evaluation_metrics'],
+ row_based_metrics=eval_task.outputs['row_based_metrics']
+ if enable_row_based_metrics
+ else None,
model=get_vertex_model_task.outputs['artifact'],
problem_type=evaluation_task,
dataset_type=batch_predict_predictions_format,
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/__init__.py b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/__init__.py
new file mode 100644
index 0000000000..eefbfd0f96
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/__init__.py
@@ -0,0 +1,20 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Model based LLM evaluation preview components."""
+
+from google_cloud_pipeline_components.preview.model_evaluation.model_based_llm_evaluation.autosxs.autosxs_pipeline import autosxs_pipeline
+
+__all__ = [
+ 'autosxs_pipeline',
+]
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/autosxs/__init__.py b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/autosxs/__init__.py
new file mode 100644
index 0000000000..aa8704bef8
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/autosxs/__init__.py
@@ -0,0 +1,13 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/autosxs/autosxs_pipeline.py b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/autosxs/autosxs_pipeline.py
new file mode 100644
index 0000000000..a0a9f7b7a1
--- /dev/null
+++ b/components/google-cloud/google_cloud_pipeline_components/preview/model_evaluation/model_based_llm_evaluation/autosxs/autosxs_pipeline.py
@@ -0,0 +1,219 @@
+# Copyright 2023 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Optimization AI Inference and AutoSxS pipeline function."""
+
+from typing import Any, Dict, List
+
+from google_cloud_pipeline_components import _placeholders
+from google_cloud_pipeline_components._implementation.llm import arbiter_preprocess
+from google_cloud_pipeline_components._implementation.llm import autosxs_arbiter
+from google_cloud_pipeline_components._implementation.llm import autosxs_metrics_computer
+from google_cloud_pipeline_components._implementation.llm import function_based
+from google_cloud_pipeline_components._implementation.llm import task_preprocess
+from google_cloud_pipeline_components.types import artifact_types
+from google_cloud_pipeline_components.v1 import batch_predict_job
+from kfp import dsl
+
+
+# pylint: disable=no-value-for-parameter
+@dsl.pipeline(
+ name='predictions-pipeline',
+ description='Runs the prediction pipeline for one of the two SxS models.',
+)
+def _get_predictions(
+ name: str,
+ project: str,
+ location: str,
+ model: str,
+ model_parameters: Dict[str, str],
+ prediction_inputs: List[str],
+ is_model_inference: bool,
+) -> str:
+ """Makes predictions for a given model."""
+ with dsl.If(is_model_inference == True, name='Inference Required'): # pylint: disable=singleton-comparison
+ get_vertex_model_task = dsl.importer(
+ artifact_uri=(
+ f'https://{location}-aiplatform.googleapis.com/v1/{model}'
+ ),
+ artifact_class=artifact_types.VertexModel,
+ metadata={'resourceName': model},
+ ).set_display_name('Import Vertex Model Artifact')
+
+ batch_predict_task = batch_predict_job.ModelBatchPredictOp(
+ project=project,
+ location=location,
+ model=get_vertex_model_task.outputs['artifact'],
+ job_display_name=(
+ f'autosxs-{name}-{{$.pipeline_job_uuid}}-{{$.pipeline_task_uuid}}'
+ ),
+ gcs_source_uris=prediction_inputs,
+ instances_format='jsonl',
+ predictions_format='jsonl',
+ gcs_destination_output_uri_prefix=(
+ f'{dsl.PIPELINE_ROOT_PLACEHOLDER}/{dsl.PIPELINE_TASK_ID_PLACEHOLDER}'
+ f'/{name}_predictions'
+ ),
+ model_parameters=model_parameters,
+ )
+ prediction_uris_from_inference = function_based.get_uri(
+ artifact=batch_predict_task.outputs['gcs_output_directory'],
+ is_dir=True,
+ )
+
+ with dsl.Else(name='Responses Provided'): # pylint: disable=singleton-comparison
+ prediction_uris_inference_provided = function_based.get_empty_string()
+
+ prediction_uris = dsl.OneOf(
+ prediction_uris_from_inference.output,
+ prediction_uris_inference_provided.output,
+ )
+
+ # We can't directly output dsl.OneOf, so we need to use identity.
+ return function_based.identity(x=prediction_uris).output
+
+
+# pylint: disable=dangerous-default-value,g-bare-generic,unused-argument
+@dsl.pipeline(
+ name='autosxs-template',
+ description='Determines the SxS winrate between two models.',
+)
+def autosxs_pipeline(
+ evaluation_dataset: str,
+ task: str,
+ id_columns: List[str],
+ model_a: str = '',
+ model_b: str = '',
+ autorater_prompt_parameters: Dict[str, Dict[str, str]] = {},
+ model_a_prompt_parameters: Dict[str, Dict[str, str]] = {},
+ model_b_prompt_parameters: Dict[str, Dict[str, str]] = {},
+ response_column_a: str = '',
+ response_column_b: str = '',
+ model_a_parameters: Dict[str, str] = {},
+ model_b_parameters: Dict[str, str] = {},
+ human_preference_column: str = '',
+ project: str = _placeholders.PROJECT_ID_PLACEHOLDER,
+ location: str = _placeholders.LOCATION_PLACEHOLDER,
+ judgments_format: str = 'jsonl',
+ bigquery_destination_prefix: str = '',
+ experimental_args: Dict[str, Any] = {},
+):
+ # fmt: off
+ """Evaluates two models side-by-side using an arbiter model.
+
+ Args:
+ evaluation_dataset: A BigQuery table or comma-separated list of GCS paths to a JSONL dataset containing evaluation examples.
+ task: Evaluation task in the form `{task}@{version}`. task can be one of `[summarization, question_answer]`. Version is an integer with 3 digits or "latest". Ex: `summarization@001` or `question_answer@latest`.
+ id_columns: The columns which distinguish unique evaluation examples.
+ model_a: A fully-qualified model resource name (`projects/{project}/locations/{location}/models/{model}@{version}`) or publisher model resource name (`publishers/{publisher}/models/{model}`). This parameter is optional if Model A responses are specified.
+ model_b: A fully-qualified model resource name (`projects/{project}/locations/{location}/models/{model}@{version}`) or publisher model resource name (`publishers/{publisher}/models/{model}`). This parameter is optional if Model B responses are specified.
+ autorater_prompt_parameters: Map of autorater prompt parameters to columns or templates. The expected parameters are: `inference_instruction` (details on how to perform a task) and `inference_context` (content to reference to perform the task). As an example, `{'inference_context': {'column': 'my_prompt'}}` uses the evaluation dataset's `my_prompt` column for the AutoRater's context.
+ model_a_prompt_parameters: Map of Model A prompt template parameters to columns or templates. This parameter is optional if Model A predictions are predefined. Example - `{'prompt': {'column': 'my_prompt'}}` uses the evaluation dataset's `my_prompt` column for the prompt parameter named `prompt`.
+ model_b_prompt_parameters: Map of Model B prompt template parameters to columns or templates. This parameter is optional if Model B predictions are predefined. Example - `{'prompt': {'column': 'my_prompt'}}` uses the evaluation dataset's `my_prompt` column for the prompt parameter named `prompt`.
+ response_column_a: Either the name of a column in the evaluation dataset containing predefined predictions, or the name of the column in the Model A output containing predictions. If no value is provided, the correct model output column name will attempt to be inferred.
+ response_column_b: Either the name of a column in the evaluation dataset containing predefined predictions, or the name of the column in the Model B output containing predictions. If no value is provided, the correct model output column name will attempt to be inferred.
+ model_a_parameters: The parameters that govern the predictions from model A, such as temperature or maximum output tokens.
+ model_b_parameters: The parameters that govern the predictions from model B, such as temperature or maximum output tokens.
+ human_preference_column: The column containing ground truth winners for each example. Providing this parameter adds additional metrics for checking the AutoRater alignment with human preferences.
+ project: Project used to run custom jobs. Default is the same project used to run the pipeline.
+ location: Location used to run custom jobs. Default is the same location used to run the pipeline.
+ judgments_format: The format to write judgments to. Can be either `[json, bigquery]`.
+ bigquery_destination_prefix: BigQuery table to write judgments to if the specified format is 'bigquery'.
+ experimental_args: Experimentally released arguments. Subject to change.
+ """
+ # fmt: on
+ prediction_inputs_a = task_preprocess.task_preprocess(
+ evaluation_dataset=evaluation_dataset,
+ task=task,
+ model_prompt_parameters=model_a_prompt_parameters,
+ response_column=response_column_a,
+ human_preference_column=human_preference_column,
+ id_columns=id_columns,
+ ).set_display_name('Preprocess Model A Inputs')
+
+ prediction_inputs_b = task_preprocess.task_preprocess(
+ evaluation_dataset=evaluation_dataset,
+ task=task,
+ model_prompt_parameters=model_b_prompt_parameters,
+ response_column=response_column_b,
+ human_preference_column=human_preference_column,
+ id_columns=id_columns,
+ ).set_display_name('Preprocess Model B Inputs')
+
+ is_model_a_inference = function_based.get_usage_metric(
+ metadata=prediction_inputs_a.outputs['metadata'],
+ key='is_model_inference',
+ ).set_display_name('Read is_model_a_inference')
+
+ is_model_b_inference = function_based.get_usage_metric(
+ metadata=prediction_inputs_b.outputs['metadata'],
+ key='is_model_inference',
+ ).set_display_name('Read is_model_b_inference')
+
+ inferrer_a = _get_predictions(
+ name='A',
+ project=project,
+ location=location,
+ model=model_a,
+ model_parameters=model_a_parameters,
+ prediction_inputs=prediction_inputs_a.outputs['prediction_inputs'],
+ is_model_inference=is_model_a_inference.output,
+ ).set_display_name('Model A Responses')
+
+ inferrer_b = _get_predictions(
+ name='B',
+ project=project,
+ location=location,
+ model=model_b,
+ model_parameters=model_b_parameters,
+ prediction_inputs=prediction_inputs_b.outputs['prediction_inputs'],
+ is_model_inference=is_model_b_inference.output,
+ ).set_display_name('Model B Responses')
+
+ arbiter_input_preprocess = arbiter_preprocess.arbiter_preprocess(
+ autorater_prompt_parameters=autorater_prompt_parameters,
+ evaluation_dataset=evaluation_dataset,
+ id_columns=id_columns,
+ prediction_uris_b=inferrer_b.output,
+ prediction_uris_a=inferrer_a.output,
+ model_a_prompt_parameters=model_a_prompt_parameters,
+ model_b_prompt_parameters=model_b_prompt_parameters,
+ task=task,
+ response_column_a=response_column_a,
+ response_column_b=response_column_b,
+ human_preference_column=human_preference_column,
+ is_bp_output_a=is_model_a_inference.output,
+ is_bp_output_b=is_model_b_inference.output,
+ ).set_display_name('Preprocess Predictions')
+
+ autosxs_arbiter_task = autosxs_arbiter.autosxs_arbiter(
+ inference_output_uri=arbiter_input_preprocess.outputs[
+ 'preprocessed_evaluation_dataset_uri'
+ ],
+ id_columns=id_columns,
+ human_preference_column=human_preference_column,
+ task=task,
+ judgments_format=judgments_format,
+ bigquery_destination_prefix=bigquery_destination_prefix,
+ experimental_args=experimental_args,
+ ).set_display_name('AutoSxS Arbiter')
+
+ has_human_preference = function_based.get_usage_metric(
+ metadata=prediction_inputs_a.outputs['metadata'],
+ key='has_human_preference_column',
+ ).set_display_name('Read has_human_preference_column')
+
+ autosxs_metrics_computer.autosxs_metrics_computer(
+ judgments_dir=autosxs_arbiter_task.outputs['judgments_uri'],
+ has_human_preference=has_human_preference.output,
+ ).set_display_name('AutoSxS Metrics')
diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/__init__.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/__init__.py
index 6dbcd85caf..d56ec1b4a2 100644
--- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/__init__.py
+++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/__init__.py
@@ -15,7 +15,15 @@
"""GA AutoML forecasting components."""
from google_cloud_pipeline_components.v1.automl.forecasting.prophet_trainer import prophet_trainer as ProphetTrainerOp
+from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_bqml_arima_predict_pipeline_and_parameters
+from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_bqml_arima_train_pipeline_and_parameters
+from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_prophet_prediction_pipeline_and_parameters
+from google_cloud_pipeline_components.v1.automl.forecasting.utils import get_prophet_train_pipeline_and_parameters
__all__ = [
'ProphetTrainerOp',
+ 'get_bqml_arima_predict_pipeline_and_parameters',
+ 'get_bqml_arima_train_pipeline_and_parameters',
+ 'get_prophet_prediction_pipeline_and_parameters',
+ 'get_prophet_train_pipeline_and_parameters',
]
diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/utils.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/utils.py
index b69d5430a5..31610deb9b 100644
--- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/utils.py
+++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/forecasting/utils.py
@@ -31,54 +31,37 @@ def get_bqml_arima_train_pipeline_and_parameters(
max_order: int = 5,
run_evaluation: bool = True,
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the BQML ARIMA_PLUS training pipeline.
Args:
project: The GCP project that runs the pipeline components.
location: The GCP region for Vertex AI.
root_dir: The Cloud Storage location to store the output.
- time_column: Name of the column that identifies time order in the time
- series.
- time_series_identifier_column: Name of the column that identifies the time
- series.
+ time_column: Name of the column that identifies time order in the time series.
+ time_series_identifier_column: Name of the column that identifies the time series.
target_column: Name of the column that the model is to predict values for.
- forecast_horizon: The number of time periods into the future for which
- forecasts will be created. Future periods start after the latest timestamp
- for each time series.
- data_granularity_unit: The data granularity unit. Accepted values are:
- minute, hour, day, week, month, year.
+ forecast_horizon: The number of time periods into the future for which forecasts will be created. Future periods start after the latest timestamp for each time series.
+ data_granularity_unit: The data granularity unit. Accepted values are: minute, hour, day, week, month, year.
predefined_split_key: The predefined_split column name.
timestamp_split_key: The timestamp_split column name.
training_fraction: The training fraction.
validation_fraction: The validation fraction.
test_fraction: float = The test fraction.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
- window_column: Name of the column that should be used to filter input rows.
- The column should contain either booleans or string booleans; if the value
- of the row is True, generate a sliding window from that row.
- window_stride_length: Step length used to generate input examples. Every
- window_stride_length rows will be used to generate a sliding window.
- window_max_count: Number of rows that should be used to generate input
- examples. If the total row count is larger than this number, the input
- data will be randomly sampled to hit the count.
- bigquery_destination_uri: URI of the desired destination dataset. If not
- specified, resources will be created under a new dataset in the project.
- Unlike in Vertex Forecasting, all resources will be given hardcoded names
- under this dataset, and the model artifact will also be exported here.
- override_destination: Whether to overwrite the metrics and evaluated
- examples tables if they already exist. If this is False and the tables
- exist, this pipeline will fail.
- max_order: Integer between 1 and 5 representing the size of the parameter
- search space for ARIMA_PLUS. 5 would result in the highest accuracy model,
- but also the longest training runtime.
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format: `bq://bq_project.bq_dataset.bq_table`.
+ window_column: Name of the column that should be used to filter input rows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window from that row.
+ window_stride_length: Step length used to generate input examples. Every window_stride_length rows will be used to generate a sliding window.
+ window_max_count: Number of rows that should be used to generate input examples. If the total row count is larger than this number, the input data will be randomly sampled to hit the count.
+ bigquery_destination_uri: URI of the desired destination dataset. If not specified, resources will be created under a new dataset in the project. Unlike in Vertex Forecasting, all resources will be given hardcoded names under this dataset, and the model artifact will also be exported here.
+ override_destination: Whether to overwrite the metrics and evaluated examples tables if they already exist. If this is False and the tables exist, this pipeline will fail.
+ max_order: Integer between 1 and 5 representing the size of the parameter search space for ARIMA_PLUS. 5 would result in the highest accuracy model, but also the longest training runtime.
run_evaluation: Whether to run evaluation steps during training.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = {
'project': project,
'location': location,
@@ -118,25 +101,22 @@ def get_bqml_arima_predict_pipeline_and_parameters(
bigquery_destination_uri: str = '',
generate_explanation: bool = False,
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the BQML ARIMA_PLUS prediction pipeline.
Args:
project: The GCP project that runs the pipeline components.
location: The GCP region for Vertex AI.
model_name: ARIMA_PLUS BQML model URI.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
- bigquery_destination_uri: URI of the desired destination dataset. If not
- specified, a resource will be created under a new dataset in the project.
- generate_explanation: Generate explanation along with the batch prediction
- results. This will cause the batch prediction output to include
- explanations.
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format: `bq://bq_project.bq_dataset.bq_table`.
+ bigquery_destination_uri: URI of the desired destination dataset. If not specified, a resource will be created under a new dataset in the project.
+ generate_explanation: Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = {
'project': project,
'location': location,
@@ -184,62 +164,45 @@ def get_prophet_train_pipeline_and_parameters(
dataflow_use_public_ips: bool = True,
run_evaluation: bool = True,
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Returns Prophet train pipeline and formatted parameters.
Args:
project: The GCP project that runs the pipeline components.
location: The GCP region for Vertex AI.
root_dir: The Cloud Storage location to store the output.
- time_column: Name of the column that identifies time order in the time
- series.
- time_series_identifier_column: Name of the column that identifies the time
- series.
+ time_column: Name of the column that identifies time order in the time series.
+ time_series_identifier_column: Name of the column that identifies the time series.
target_column: Name of the column that the model is to predict values for.
- forecast_horizon: The number of time periods into the future for which
- forecasts will be created. Future periods start after the latest timestamp
- for each time series.
+ forecast_horizon: The number of time periods into the future for which forecasts will be created. Future periods start after the latest timestamp for each time series.
optimization_objective: Optimization objective for the model.
- data_granularity_unit: String representing the units of time for the time
- column.
+ data_granularity_unit: String representing the units of time for the time column.
predefined_split_key: The predefined_split column name.
timestamp_split_key: The timestamp_split column name.
training_fraction: The training fraction.
validation_fraction: The validation fraction.
test_fraction: float = The test fraction.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
- window_column: Name of the column that should be used to filter input rows.
- The column should contain either booleans or string booleans; if the value
- of the row is True, generate a sliding window from that row.
- window_stride_length: Step length used to generate input examples. Every
- window_stride_length rows will be used to generate a sliding window.
- window_max_count: Number of rows that should be used to generate input
- examples. If the total row count is larger than this number, the input
- data will be randomly sampled to hit the count.
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format: `bq://bq_project.bq_dataset.bq_table`.
+ window_column: Name of the column that should be used to filter input rows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window from that row.
+ window_stride_length: Step length used to generate input examples. Every window_stride_length rows will be used to generate a sliding window.
+ window_max_count: Number of rows that should be used to generate input examples. If the total row count is larger than this number, the input data will be randomly sampled to hit the count.
max_num_trials: Maximum number of tuning trials to perform per time series.
trainer_dataflow_machine_type: The dataflow machine type used for training.
- trainer_dataflow_max_num_workers: The max number of Dataflow workers used
- for training.
- trainer_dataflow_disk_size_gb: Dataflow worker's disk size in GB during
- training.
- evaluation_dataflow_machine_type: The dataflow machine type used for
- evaluation.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers used
- for evaluation.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB during
- evaluation.
+ trainer_dataflow_max_num_workers: The max number of Dataflow workers used for training.
+ trainer_dataflow_disk_size_gb: Dataflow worker's disk size in GB during training.
+ evaluation_dataflow_machine_type: The dataflow machine type used for evaluation.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers used for evaluation.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB during evaluation.
dataflow_service_account: Custom service account to run dataflow jobs.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used.
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used.
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
run_evaluation: Whether to run evaluation steps during training.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = {
'project': project,
'location': location,
@@ -293,6 +256,7 @@ def get_prophet_prediction_pipeline_and_parameters(
machine_type: str = 'n1-standard-2',
max_num_workers: int = 10,
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Returns Prophet prediction pipeline and formatted parameters.
Unlike the prediction server for Vertex Forecasting, the Prophet prediction
@@ -303,25 +267,20 @@ def get_prophet_prediction_pipeline_and_parameters(
Args:
project: The GCP project that runs the pipeline components.
location: The GCP region for Vertex AI.
- model_name: The name of the Model resource, in a form of
- projects/{project}/locations/{location}/models/{model}.
- time_column: Name of the column that identifies time order in the time
- series.
- time_series_identifier_column: Name of the column that identifies the time
- series.
+ model_name: The name of the Model resource, in a form of `projects/{project}/locations/{location}/models/{model}`.
+ time_column: Name of the column that identifies time order in the time series.
+ time_series_identifier_column: Name of the column that identifies the time series.
target_column: Name of the column that the model is to predict values for.
- data_source_csv_filenames: A string that represents a list of comma
- separated CSV filenames.
- data_source_bigquery_table_path: The BigQuery table path of format
- bq://bq_project.bq_dataset.bq_table
- bigquery_destination_uri: URI of the desired destination dataset. If not
- specified, resources will be created under a new dataset in the project.
+ data_source_csv_filenames: A string that represents a list of comma separated CSV filenames.
+ data_source_bigquery_table_path: The BigQuery table path of format: `bq://bq_project.bq_dataset.bq_table`.
+ bigquery_destination_uri: URI of the desired destination dataset. If not specified, resources will be created under a new dataset in the project.
machine_type: The machine type used for batch prediction.
max_num_workers: The max number of workers used for batch prediction.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = {
'project': project,
'location': location,
diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/__init__.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/__init__.py
index 840cd055f5..b765bbc0b0 100644
--- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/__init__.py
+++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/__init__.py
@@ -25,18 +25,20 @@
from google_cloud_pipeline_components.v1.automl.tabular.stats_and_example_gen import tabular_stats_and_example_gen as StatsAndExampleGenOp
from google_cloud_pipeline_components.v1.automl.tabular.training_configurator_and_validator import training_configurator_and_validator as TrainingConfiguratorAndValidatorOp
from google_cloud_pipeline_components.v1.automl.tabular.transform import automl_tabular_transform as TransformOp
+from google_cloud_pipeline_components.v1.automl.tabular.utils import get_automl_tabular_pipeline_and_parameters
from kfp import components
__all__ = [
'CvTrainerOp',
- 'InfraValidatorOp',
- 'Stage1TunerOp',
'EnsembleOp',
- 'StatsAndExampleGenOp',
- 'TransformOp',
'FinalizerOp',
+ 'InfraValidatorOp',
'SplitMaterializedDataOp',
+ 'Stage1TunerOp',
+ 'StatsAndExampleGenOp',
'TrainingConfiguratorAndValidatorOp',
+ 'TransformOp',
+ 'get_automl_tabular_pipeline_and_parameters',
]
automl_tabular_pipeline = components.load_component_from_file(
diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/utils.py b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/utils.py
index 6889db79ae..7fd944425e 100644
--- a/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/utils.py
+++ b/components/google-cloud/google_cloud_pipeline_components/v1/automl/tabular/utils.py
@@ -464,6 +464,7 @@ def get_automl_tabular_pipeline_and_parameters(
model_display_name: str = '',
model_description: str = '',
) -> Tuple[str, Dict[str, Any]]:
+ # fmt: off
"""Get the AutoML Tabular v1 default training pipeline.
Args:
@@ -471,18 +472,10 @@ def get_automl_tabular_pipeline_and_parameters(
location: The GCP region that runs the pipeline components.
root_dir: The root GCS directory for the pipeline components.
target_column: The target column name.
- prediction_type: The type of prediction the model is to produce.
- "classification" or "regression".
- optimization_objective: For binary classification, "maximize-au-roc",
- "minimize-log-loss", "maximize-au-prc", "maximize-precision-at-recall", or
- "maximize-recall-at-precision". For multi class classification,
- "minimize-log-loss". For regression, "minimize-rmse", "minimize-mae", or
- "minimize-rmsle".
- transformations: The path to a GCS file containing the transformations to
- apply.
- train_budget_milli_node_hours: The train budget of creating this model,
- expressed in milli node hours i.e. 1,000 value in this field means 1 node
- hour.
+ prediction_type: The type of prediction the model is to produce. "classification" or "regression".
+ optimization_objective: For binary classification, "maximize-au-roc", "minimize-log-loss", "maximize-au-prc", "maximize-precision-at-recall", or "maximize-recall-at-precision". For multi class classification, "minimize-log-loss". For regression, "minimize-rmse", "minimize-mae", or "minimize-rmsle".
+ transformations: The path to a GCS file containing the transformations to apply.
+ train_budget_milli_node_hours: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
stage_1_num_parallel_trials: Number of parallel trails for stage 1.
stage_2_num_parallel_trials: Number of parallel trails for stage 2.
stage_2_num_selected_trials: Number of selected trials for stage 2.
@@ -495,89 +488,49 @@ def get_automl_tabular_pipeline_and_parameters(
validation_fraction: The validation fraction.
test_fraction: float = The test fraction.
weight_column: The weight column name.
- study_spec_parameters_override: The list for overriding study spec. The list
- should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/study.proto#L181.
- optimization_objective_recall_value: Required when optimization_objective is
- "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
- optimization_objective_precision_value: Required when optimization_objective
- is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
- stage_1_tuner_worker_pool_specs_override: The dictionary for overriding.
- stage 1 tuner worker pool spec. The dictionary should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
- cv_trainer_worker_pool_specs_override: The dictionary for overriding stage
- cv trainer worker pool spec. The dictionary should be of format
- https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
- export_additional_model_without_custom_ops: Whether to export additional
- model without custom TensorFlow operators.
- stats_and_example_gen_dataflow_machine_type: The dataflow machine type for
- stats_and_example_gen component.
- stats_and_example_gen_dataflow_max_num_workers: The max number of Dataflow
- workers for stats_and_example_gen component.
- stats_and_example_gen_dataflow_disk_size_gb: Dataflow worker's disk size in
- GB for stats_and_example_gen component.
- transform_dataflow_machine_type: The dataflow machine type for transform
- component.
- transform_dataflow_max_num_workers: The max number of Dataflow workers for
- transform component.
- transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- transform component.
- dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty
- the default subnetwork will be used. Example:
- https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
- dataflow_use_public_ips: Specifies whether Dataflow workers use public IP
- addresses.
+ study_spec_parameters_override: The list for overriding study spec. The list should be of format: https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/study.proto#L181.
+ optimization_objective_recall_value: Required when optimization_objective is "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
+ optimization_objective_precision_value: Required when optimization_objective is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
+ stage_1_tuner_worker_pool_specs_override: The dictionary for overriding. stage 1 tuner worker pool spec. The dictionary should be of format: https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
+ cv_trainer_worker_pool_specs_override: The dictionary for overriding stage cv trainer worker pool spec. The dictionary should be of format: https://github.com/googleapis/googleapis/blob/4e836c7c257e3e20b1de14d470993a2b1f4736a8/google/cloud/aiplatform/v1beta1/custom_job.proto#L172.
+ export_additional_model_without_custom_ops: Whether to export additional model without custom TensorFlow operators.
+ stats_and_example_gen_dataflow_machine_type: The dataflow machine type for stats_and_example_gen component.
+ stats_and_example_gen_dataflow_max_num_workers: The max number of Dataflow workers for stats_and_example_gen component.
+ stats_and_example_gen_dataflow_disk_size_gb: Dataflow worker's disk size in GB for stats_and_example_gen component.
+ transform_dataflow_machine_type: The dataflow machine type for transform component.
+ transform_dataflow_max_num_workers: The max number of Dataflow workers for transform component.
+ transform_dataflow_disk_size_gb: Dataflow worker's disk size in GB for transform component.
+ dataflow_subnetwork: Dataflow's fully qualified subnetwork name, when empty the default subnetwork will be used. Example: https://cloud.google.com/dataflow/docs/guides/specifying-networks#example_network_and_subnetwork_specifications
+ dataflow_use_public_ips: Specifies whether Dataflow workers use public IP addresses.
encryption_spec_key_name: The KMS key name.
additional_experiments: Use this field to config private preview features.
dataflow_service_account: Custom service account to run dataflow jobs.
run_evaluation: Whether to run evaluation in the training pipeline.
- evaluation_batch_predict_machine_type: The prediction server machine type
- for batch predict components during evaluation.
- evaluation_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict components during evaluation.
- evaluation_batch_predict_max_replica_count: The max number of prediction
- server for batch predict components during evaluation.
- evaluation_batch_explain_machine_type: The prediction server machine type
- for batch explain components during evaluation.
- evaluation_batch_explain_starting_replica_count: The initial number of
- prediction server for batch explain components during evaluation.
- evaluation_batch_explain_max_replica_count: The max number of prediction
- server for batch explain components during evaluation.
- evaluation_dataflow_machine_type: The dataflow machine type for evaluation
- components.
- evaluation_dataflow_starting_num_workers: The initial number of Dataflow
- workers for evaluation components.
- evaluation_dataflow_max_num_workers: The max number of Dataflow workers for
- evaluation components.
- evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for
- evaluation components.
+ evaluation_batch_predict_machine_type: The prediction server machine type for batch predict components during evaluation.
+ evaluation_batch_predict_starting_replica_count: The initial number of prediction server for batch predict components during evaluation.
+ evaluation_batch_predict_max_replica_count: The max number of prediction server for batch predict components during evaluation.
+ evaluation_batch_explain_machine_type: The prediction server machine type for batch explain components during evaluation.
+ evaluation_batch_explain_starting_replica_count: The initial number of prediction server for batch explain components during evaluation.
+ evaluation_batch_explain_max_replica_count: The max number of prediction server for batch explain components during evaluation.
+ evaluation_dataflow_machine_type: The dataflow machine type for evaluation components.
+ evaluation_dataflow_starting_num_workers: The initial number of Dataflow workers for evaluation components.
+ evaluation_dataflow_max_num_workers: The max number of Dataflow workers for evaluation components.
+ evaluation_dataflow_disk_size_gb: Dataflow worker's disk size in GB for evaluation components.
run_distillation: Whether to run distill in the training pipeline.
- distill_batch_predict_machine_type: The prediction server machine type for
- batch predict component in the model distillation.
- distill_batch_predict_starting_replica_count: The initial number of
- prediction server for batch predict component in the model distillation.
- distill_batch_predict_max_replica_count: The max number of prediction server
- for batch predict component in the model distillation.
- stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS
- URI.
- quantiles: Quantiles to use for probabilistic inference. Up to 5 quantiles
- are allowed of values between 0 and 1, exclusive. Represents the quantiles
- to use for that objective. Quantiles must be unique.
- enable_probabilistic_inference: If probabilistic inference is enabled, the
- model will fit a distribution that captures the uncertainty of a
- prediction. At inference time, the predictive distribution is used to make
- a point prediction that minimizes the optimization objective. For example,
- the mean of a predictive distribution is the point prediction that
- minimizes RMSE loss. If quantiles are specified, then the quantiles of the
- distribution are also returned.
- num_selected_features: Number of selected features for feature selection,
- defaults to None, in which case all features are used.
+ distill_batch_predict_machine_type: The prediction server machine type for batch predict component in the model distillation.
+ distill_batch_predict_starting_replica_count: The initial number of prediction server for batch predict component in the model distillation.
+ distill_batch_predict_max_replica_count: The max number of prediction server for batch predict component in the model distillation.
+ stage_1_tuning_result_artifact_uri: The stage 1 tuning result artifact GCS URI.
+ quantiles: Quantiles to use for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Represents the quantiles to use for that objective. Quantiles must be unique.
+ enable_probabilistic_inference: If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. At inference time, the predictive distribution is used to make a point prediction that minimizes the optimization objective. For example, the mean of a predictive distribution is the point prediction that minimizes RMSE loss. If quantiles are specified, then the quantiles of the distribution are also returned.
+ num_selected_features: Number of selected features for feature selection, defaults to None, in which case all features are used.
model_display_name: The display name of the uploaded Vertex model.
model_description: The description for the uploaded model.
Returns:
Tuple of pipeline_definition_path and parameter_values.
"""
+ # fmt: on
parameter_values = _get_default_pipeline_params(
project=project,
location=location,
diff --git a/components/google-cloud/google_cloud_pipeline_components/v1/bigquery/query_job/component.py b/components/google-cloud/google_cloud_pipeline_components/v1/bigquery/query_job/component.py
index 625330844d..0c1b37b684 100644
--- a/components/google-cloud/google_cloud_pipeline_components/v1/bigquery/query_job/component.py
+++ b/components/google-cloud/google_cloud_pipeline_components/v1/bigquery/query_job/component.py
@@ -39,6 +39,8 @@ def bigquery_query_job(
# fmt: off
"""Launch a BigQuery query job and waits for it to finish.
+ Note: The total input commands/args to the component can be at most 50KB. This means the BigQuery query must be less than 50KB, since the input commands/args contain other non-query characters, including all parameter names, parameter values, and various JSON characters.
+
Args:
location: Location for creating the BigQuery job. If not set, default to `US` multi-region. For more details, see https://cloud.google.com/bigquery/docs/locations#specifying_your_location
query: SQL query text to execute. Only standard SQL is supported. If query are both specified in here and in job_configuration_query, the value in here will override the other one.
diff --git a/components/google-cloud/google_cloud_pipeline_components/version.py b/components/google-cloud/google_cloud_pipeline_components/version.py
index 32cb00b22b..9cea42fd12 100644
--- a/components/google-cloud/google_cloud_pipeline_components/version.py
+++ b/components/google-cloud/google_cloud_pipeline_components/version.py
@@ -13,4 +13,4 @@
# limitations under the License.
"""Google Cloud Pipeline Components version."""
-__version__ = "2.6.0"
+__version__ = "2.8.0"
diff --git a/components/google-cloud/setup.py b/components/google-cloud/setup.py
index 9745961def..af54486684 100644
--- a/components/google-cloud/setup.py
+++ b/components/google-cloud/setup.py
@@ -72,7 +72,7 @@
"sphinx==5.0.2",
"sphinx-immaterial==0.9.0",
"sphinx-rtd-theme==1.0.0",
- "m2r2==0.3.2",
+ "m2r2==0.3.3",
"sphinx-notfound-page==0.8.3",
],
},
diff --git a/docs/conf.py b/docs/conf.py
index 3892551834..cf6d7398dc 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -132,12 +132,26 @@
True,
'version_info': [
# need to use the sdk- prefix to avoid conflict with the BE's GitHub release tags
+ {
+ 'version':
+ 'https://kubeflow-pipelines.readthedocs.io/en/sdk-2.6.0/',
+ 'title':
+ '2.6.0',
+ 'aliases': ['stable'],
+ },
+ {
+ 'version':
+ 'https://kubeflow-pipelines.readthedocs.io/en/sdk-2.5.0/',
+ 'title':
+ '2.5.0',
+ 'aliases': [],
+ },
{
'version':
'https://kubeflow-pipelines.readthedocs.io/en/sdk-2.4.0/',
'title':
'2.4.0',
- 'aliases': ['stable'],
+ 'aliases': [],
},
{
'version':
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 02afab9963..55e215d0eb 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -1,14 +1,49965 @@
{
"name": "pipelines-frontend",
"version": "0.1.0",
- "lockfileVersion": 1,
+ "lockfileVersion": 2,
"requires": true,
+ "packages": {
+ "": {
+ "name": "pipelines-frontend",
+ "version": "0.1.0",
+ "hasInstallScript": true,
+ "license": "Apache-2.0",
+ "dependencies": {
+ "@craco/craco": "^6.2.0",
+ "@material-ui/core": "^3.9.1",
+ "@material-ui/icons": "^3.0.1",
+ "@types/lodash.groupby": "^4.6.6",
+ "@types/pako": "^1.0.3",
+ "brace": "^0.11.1",
+ "d3": "^5.7.0",
+ "d3-dsv": "^1.0.10",
+ "dagre": "^0.8.2",
+ "google-protobuf": "^3.11.2",
+ "grpc-web": "^1.2.1",
+ "http-proxy-middleware": "^0.19.0",
+ "immer": "^9.0.6",
+ "js-yaml": "^3.14.1",
+ "lodash": "^4.17.21",
+ "lodash.debounce": "^4.0.8",
+ "lodash.flatten": "^4.4.0",
+ "lodash.groupby": "^4.6.0",
+ "lodash.isfunction": "^3.0.9",
+ "markdown-to-jsx": "^6.11.4",
+ "pako": "^2.0.4",
+ "portable-fetch": "^3.0.0",
+ "proto3-json-serializer": "^0.1.6",
+ "protobufjs": "~6.11.2",
+ "re-resizable": "^4.9.0",
+ "react": "^16.12.0",
+ "react-ace": "^7.0.2",
+ "react-dom": "^16.12.0",
+ "react-dropzone": "^5.1.0",
+ "react-flow-renderer": "^9.6.3",
+ "react-query": "^3.16.0",
+ "react-router-dom": "^4.3.1",
+ "react-svg-line-chart": "^2.0.2",
+ "react-textarea-autosize": "^8.3.3",
+ "react-virtualized": "^9.20.1",
+ "react-vis": "^1.11.2",
+ "request": "^2.88.2",
+ "runtypes": "^6.3.0",
+ "ts-proto": "^1.95.0",
+ "typestyle": "^2.0.4"
+ },
+ "devDependencies": {
+ "@google-cloud/storage": "^4.1.3",
+ "@storybook/addon-actions": "^6.3.6",
+ "@storybook/addon-essentials": "^6.3.6",
+ "@storybook/addon-links": "^6.3.6",
+ "@storybook/node-logger": "^6.3.6",
+ "@storybook/preset-create-react-app": "^3.2.0",
+ "@storybook/react": "^6.3.6",
+ "@testing-library/dom": "^8.6.0",
+ "@testing-library/react": "^11.2.6",
+ "@testing-library/user-event": "^13.2.1",
+ "@types/d3": "^5.0.0",
+ "@types/d3-dsv": "^1.0.33",
+ "@types/dagre": "^0.7.40",
+ "@types/enzyme": "^3.10.3",
+ "@types/enzyme-adapter-react-16": "^1.0.5",
+ "@types/express": "^4.16.0",
+ "@types/google-protobuf": "^3.7.2",
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"version": "4.0.3",
"resolved": "https://registry.npmjs.org/y18n/-/y18n-4.0.3.tgz",
- "integrity": "sha512-JKhqTOwSrqNA1NY5lSztJ1GrBiUodLMmIZuLiDaMRJ+itFd+ABVE8XBjOvIWL+rSqNDC74LCSFmlb/U4UZ4hJQ==",
- "dev": true
+ "integrity": "sha512-JKhqTOwSrqNA1NY5lSztJ1GrBiUodLMmIZuLiDaMRJ+itFd+ABVE8XBjOvIWL+rSqNDC74LCSFmlb/U4UZ4hJQ=="
},
"yallist": {
"version": "4.0.0",
@@ -40045,7 +87648,6 @@
"version": "16.2.0",
"resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
"integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
- "dev": true,
"requires": {
"cliui": "^7.0.2",
"escalade": "^3.1.1",
@@ -40059,16 +87661,14 @@
"y18n": {
"version": "5.0.8",
"resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
- "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
- "dev": true
+ "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA=="
}
}
},
"yargs-parser": {
"version": "20.2.9",
"resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz",
- "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==",
- "dev": true
+ "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w=="
},
"yn": {
"version": "3.1.1",
@@ -40079,8 +87679,7 @@
"yocto-queue": {
"version": "0.1.0",
"resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
- "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
- "dev": true
+ "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q=="
},
"zwitch": {
"version": "1.0.5",
diff --git a/frontend/package.json b/frontend/package.json
index 47d812102f..b6b47e9875 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -23,7 +23,7 @@
"lodash.flatten": "^4.4.0",
"lodash.groupby": "^4.6.0",
"lodash.isfunction": "^3.0.9",
- "markdown-to-jsx": "^6.10.3",
+ "markdown-to-jsx": "^6.11.4",
"pako": "^2.0.4",
"portable-fetch": "^3.0.0",
"proto3-json-serializer": "^0.1.6",
diff --git a/frontend/src/components/Description.tsx b/frontend/src/components/Description.tsx
index 21f6a3b0cc..b51e99e7f2 100644
--- a/frontend/src/components/Description.tsx
+++ b/frontend/src/components/Description.tsx
@@ -28,6 +28,7 @@ const renderExternalLink = (props: {}) => (
const options = {
overrides: { a: { component: renderExternalLink } },
+ disableParsingRawHTML: true,
};
const optionsForceInline = {
diff --git a/frontend/src/components/viewers/MarkdownViewer.tsx b/frontend/src/components/viewers/MarkdownViewer.tsx
index 79ea8c23ba..f375250015 100644
--- a/frontend/src/components/viewers/MarkdownViewer.tsx
+++ b/frontend/src/components/viewers/MarkdownViewer.tsx
@@ -19,6 +19,7 @@ import Viewer, { ViewerConfig } from './Viewer';
import { cssRaw } from 'typestyle';
import Markdown from 'markdown-to-jsx';
import Banner from '../Banner';
+import { ExternalLink } from 'src/atoms/ExternalLink';
cssRaw(`
.markdown-viewer h1,
@@ -93,6 +94,17 @@ interface MarkdownAdvancedProps {
content: string;
}
+function preventEventBubbling(e: React.MouseEvent): void {
+ e.stopPropagation();
+}
+const renderExternalLink = (props: {}) => (
+
+);
+const markdownOptions = {
+ overrides: { a: { component: renderExternalLink } },
+ disableParsingRawHTML: true,
+};
+
const MarkdownAdvanced = ({
maxMarkdownStrLength = MAX_MARKDOWN_STR_LENGTH,
content,
@@ -109,7 +121,7 @@ const MarkdownAdvanced = ({
{content.length > maxMarkdownStrLength && (
)}
- {truncatedContent}
+ {truncatedContent}
);
};
diff --git a/frontend/src/components/viewers/__snapshots__/MarkdownViewer.test.tsx.snap b/frontend/src/components/viewers/__snapshots__/MarkdownViewer.test.tsx.snap
index cb33b34667..54e4f801dd 100644
--- a/frontend/src/components/viewers/__snapshots__/MarkdownViewer.test.tsx.snap
+++ b/frontend/src/components/viewers/__snapshots__/MarkdownViewer.test.tsx.snap
@@ -14,7 +14,10 @@ exports[`MarkdownViewer renders some basic markdown 1`] = `
some link here
diff --git a/frontend/src/pages/GettingStarted.tsx b/frontend/src/pages/GettingStarted.tsx
index 6a9d0550fa..6e6db7eba0 100644
--- a/frontend/src/pages/GettingStarted.tsx
+++ b/frontend/src/pages/GettingStarted.tsx
@@ -79,6 +79,7 @@ cssRaw(`
const OPTIONS = {
overrides: { a: { component: AutoLink } },
+ disableParsingRawHTML: true,
};
export class GettingStarted extends Page<{}, { links: string[] }> {
diff --git a/frontend/src/pages/__snapshots__/GettingStarted.test.tsx.snap b/frontend/src/pages/__snapshots__/GettingStarted.test.tsx.snap
index 43442f3956..ed8790ca6c 100644
--- a/frontend/src/pages/__snapshots__/GettingStarted.test.tsx.snap
+++ b/frontend/src/pages/__snapshots__/GettingStarted.test.tsx.snap
@@ -6,7 +6,9 @@ exports[`GettingStarted page initially renders documentation 1`] = `
class="page kfp-start-page"
>
-
+
+ <br/>
+
@@ -25,7 +27,9 @@ exports[`GettingStarted page initially renders documentation 1`] = `
-
+
+ <br/>
+
diff --git a/frontend/src/pages/__snapshots__/PipelineVersionList.test.tsx.snap b/frontend/src/pages/__snapshots__/PipelineVersionList.test.tsx.snap
index 173fceda0c..0358d51202 100644
--- a/frontend/src/pages/__snapshots__/PipelineVersionList.test.tsx.snap
+++ b/frontend/src/pages/__snapshots__/PipelineVersionList.test.tsx.snap
@@ -4303,6 +4303,7 @@ exports[`PipelineVersionList calls Apis to list pipeline versions, sorted by cre
kfp_kubernetes.SecretAsVolume
2, // 1: kfp_kubernetes.KubernetesExecutorConfig.secret_as_env:type_name -> kfp_kubernetes.SecretAsEnv
4, // 2: kfp_kubernetes.KubernetesExecutorConfig.pvc_mount:type_name -> kfp_kubernetes.PvcMount
7, // 3: kfp_kubernetes.KubernetesExecutorConfig.node_selector:type_name -> kfp_kubernetes.NodeSelector
- 8, // 4: kfp_kubernetes.SecretAsEnv.key_to_env:type_name -> kfp_kubernetes.SecretAsEnv.SecretKeyToEnvMap
- 3, // 5: kfp_kubernetes.PvcMount.task_output_parameter:type_name -> kfp_kubernetes.TaskOutputParameterSpec
- 10, // 6: kfp_kubernetes.CreatePvc.annotations:type_name -> google.protobuf.Struct
- 3, // 7: kfp_kubernetes.DeletePvc.task_output_parameter:type_name -> kfp_kubernetes.TaskOutputParameterSpec
- 9, // 8: kfp_kubernetes.NodeSelector.labels:type_name -> kfp_kubernetes.NodeSelector.LabelsEntry
- 9, // [9:9] is the sub-list for method output_type
- 9, // [9:9] is the sub-list for method input_type
- 9, // [9:9] is the sub-list for extension type_name
- 9, // [9:9] is the sub-list for extension extendee
- 0, // [0:9] is the sub-list for field type_name
+ 8, // 4: kfp_kubernetes.KubernetesExecutorConfig.pod_metadata:type_name -> kfp_kubernetes.PodMetadata
+ 9, // 5: kfp_kubernetes.SecretAsEnv.key_to_env:type_name -> kfp_kubernetes.SecretAsEnv.SecretKeyToEnvMap
+ 3, // 6: kfp_kubernetes.PvcMount.task_output_parameter:type_name -> kfp_kubernetes.TaskOutputParameterSpec
+ 13, // 7: kfp_kubernetes.CreatePvc.annotations:type_name -> google.protobuf.Struct
+ 3, // 8: kfp_kubernetes.DeletePvc.task_output_parameter:type_name -> kfp_kubernetes.TaskOutputParameterSpec
+ 10, // 9: kfp_kubernetes.NodeSelector.labels:type_name -> kfp_kubernetes.NodeSelector.LabelsEntry
+ 11, // 10: kfp_kubernetes.PodMetadata.labels:type_name -> kfp_kubernetes.PodMetadata.LabelsEntry
+ 12, // 11: kfp_kubernetes.PodMetadata.annotations:type_name -> kfp_kubernetes.PodMetadata.AnnotationsEntry
+ 12, // [12:12] is the sub-list for method output_type
+ 12, // [12:12] is the sub-list for method input_type
+ 12, // [12:12] is the sub-list for extension type_name
+ 12, // [12:12] is the sub-list for extension extendee
+ 0, // [0:12] is the sub-list for field type_name
}
func init() { file_kubernetes_executor_config_proto_init() }
@@ -995,6 +1088,18 @@ func file_kubernetes_executor_config_proto_init() {
}
}
file_kubernetes_executor_config_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} {
+ switch v := v.(*PodMetadata); i {
+ case 0:
+ return &v.state
+ case 1:
+ return &v.sizeCache
+ case 2:
+ return &v.unknownFields
+ default:
+ return nil
+ }
+ }
+ file_kubernetes_executor_config_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*SecretAsEnv_SecretKeyToEnvMap); i {
case 0:
return &v.state
@@ -1027,7 +1132,7 @@ func file_kubernetes_executor_config_proto_init() {
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_kubernetes_executor_config_proto_rawDesc,
NumEnums: 0,
- NumMessages: 10,
+ NumMessages: 13,
NumExtensions: 0,
NumServices: 0,
},
diff --git a/kubernetes_platform/proto/kubernetes_executor_config.proto b/kubernetes_platform/proto/kubernetes_executor_config.proto
index 8bba56463c..8b215c8ae1 100644
--- a/kubernetes_platform/proto/kubernetes_executor_config.proto
+++ b/kubernetes_platform/proto/kubernetes_executor_config.proto
@@ -25,6 +25,7 @@ message KubernetesExecutorConfig {
repeated SecretAsEnv secret_as_env = 2;
repeated PvcMount pvc_mount = 3;
NodeSelector node_selector = 4;
+ PodMetadata pod_metadata = 5;
}
message SecretAsVolume {
@@ -114,3 +115,10 @@ message NodeSelector {
// corresponds to Pod.spec.nodeSelector field https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#scheduling
map labels = 1;
}
+
+message PodMetadata {
+ // values of metadata spec such as labels and annotations for the pod object
+ // corresponds to Pod.metadata field https://kubernetes.io/docs/reference/kubernetes-api/workload-resources/pod-v1/#Pod
+ map labels = 1;
+ map annotations = 2;
+}
diff --git a/kubernetes_platform/python/setup.py b/kubernetes_platform/python/setup.py
index 1a4984c0bb..c4a929f12f 100644
--- a/kubernetes_platform/python/setup.py
+++ b/kubernetes_platform/python/setup.py
@@ -19,9 +19,9 @@
NAME = 'kfp-kubernetes'
REQUIREMENTS = [
- 'protobuf>=3.13.0,<4',
- # bump version when platform-specific compilation is released and tests no longer install kfp from source
- 'kfp>=2.0.0-beta.13',
+ 'protobuf>=4.21.1,<5',
+ # TODO: bump to 2.6.0 after next KFP SDK release with protobuf 4
+ 'kfp>=2.5.0',
]
DEV_REQUIREMENTS = [
'docformatter==1.4',
diff --git a/manifests/kustomize/base/installs/generic/pipeline-install-config.yaml b/manifests/kustomize/base/installs/generic/pipeline-install-config.yaml
index 30dd486cbb..5b41da33a0 100644
--- a/manifests/kustomize/base/installs/generic/pipeline-install-config.yaml
+++ b/manifests/kustomize/base/installs/generic/pipeline-install-config.yaml
@@ -93,3 +93,4 @@ data:
## If this value doesn't include a unit abbreviation, the units will be assumed
## to be nanoseconds.
ConMaxLifeTime: "120s"
+ LOG_LEVEL: "info"
diff --git a/manifests/kustomize/base/pipeline/ml-pipeline-apiserver-deployment.yaml b/manifests/kustomize/base/pipeline/ml-pipeline-apiserver-deployment.yaml
index 754adf9057..27f823c68a 100644
--- a/manifests/kustomize/base/pipeline/ml-pipeline-apiserver-deployment.yaml
+++ b/manifests/kustomize/base/pipeline/ml-pipeline-apiserver-deployment.yaml
@@ -17,6 +17,8 @@ spec:
spec:
containers:
- env:
+ - name: LOG_LEVEL
+ value: "info"
- name: AUTO_UPDATE_PIPELINE_DEFAULT_VERSION
valueFrom:
configMapKeyRef:
diff --git a/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml b/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml
index 7e979be070..611560a653 100644
--- a/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml
+++ b/manifests/kustomize/base/pipeline/ml-pipeline-persistenceagent-deployment.yaml
@@ -31,6 +31,8 @@ spec:
value: ""
- name: EXECUTIONTYPE
value: Workflow
+ - name: LOG_LEVEL
+ value: "info"
image: gcr.io/ml-pipeline/persistenceagent:dummy
imagePullPolicy: IfNotPresent
name: ml-pipeline-persistenceagent
diff --git a/manifests/kustomize/base/pipeline/ml-pipeline-scheduledworkflow-deployment.yaml b/manifests/kustomize/base/pipeline/ml-pipeline-scheduledworkflow-deployment.yaml
index 75562d1ff4..0c68a5a58c 100644
--- a/manifests/kustomize/base/pipeline/ml-pipeline-scheduledworkflow-deployment.yaml
+++ b/manifests/kustomize/base/pipeline/ml-pipeline-scheduledworkflow-deployment.yaml
@@ -20,6 +20,8 @@ spec:
imagePullPolicy: IfNotPresent
name: ml-pipeline-scheduledworkflow
env:
+ - name: LOG_LEVEL
+ value: "info"
- name: NAMESPACE
valueFrom:
fieldRef:
diff --git a/samples/core/loop_output/loop_output.py b/samples/core/loop_output/loop_output.py
index b6e6da4863..60148a991c 100644
--- a/samples/core/loop_output/loop_output.py
+++ b/samples/core/loop_output/loop_output.py
@@ -21,7 +21,7 @@ def args_generator_op() -> str:
# TODO(Bobgy): how can we make this component with type float?
-# got error: kfp.components.types.type_utils.InconsistentTypeException:
+# got error: kfp.dsl.types.type_utils.InconsistentTypeException:
# Incompatible argument passed to the input "s" of component "Print op": Argument
# type "STRING" is incompatible with the input type "NUMBER_DOUBLE"
@dsl.component
diff --git a/sdk/RELEASE.md b/sdk/RELEASE.md
index bca5ffabd3..d408178691 100644
--- a/sdk/RELEASE.md
+++ b/sdk/RELEASE.md
@@ -1,7 +1,33 @@
# Current Version (in development)
+## Features
+
+## Breaking changes
+
+## Deprecations
+
+## Bug fixes and other changes
+
+## Documentation updates
+
+# 2.6.0
+
+## Features
+
+## Breaking changes
+* Soft breaking change for [Protobuf 3 EOL](https://protobuf.dev/support/version-support/#python). Migrate to `protobuf==4`. Drop support for `protobuf==3`. [\#10307](https://github.com/kubeflow/pipelines/pull/10307)
+
+## Deprecations
+
+## Bug fixes and other changes
+
+## Documentation updates
+
+# 2.5.0
+
## Features
* Add support for `dsl.PIPELINE_TASK_EXECUTOR_OUTPUT_PATH_PLACEHOLDER` and `dsl.PIPELINE_TASK_EXECUTOR_INPUT_PLACEHOLDER` [\#10240](https://github.com/kubeflow/pipelines/pull/10240)
+* Add support for local component execution using `local.init()`, `DockerRunner`, and `SubprocessRunner`
## Breaking changes
diff --git a/sdk/python/kfp/__init__.py b/sdk/python/kfp/__init__.py
index 72f210e1ea..c696ab3e5a 100644
--- a/sdk/python/kfp/__init__.py
+++ b/sdk/python/kfp/__init__.py
@@ -16,7 +16,7 @@
# https://packaging.python.org/guides/packaging-namespace-packages/#pkgutil-style-namespace-packages
__path__ = __import__('pkgutil').extend_path(__path__, __name__)
-__version__ = '2.4.0'
+__version__ = '2.6.0'
import sys
import warnings
diff --git a/sdk/python/kfp/deprecated/dsl/_component_bridge.py b/sdk/python/kfp/deprecated/dsl/_component_bridge.py
index 47775efa21..0f10afecbd 100644
--- a/sdk/python/kfp/deprecated/dsl/_component_bridge.py
+++ b/sdk/python/kfp/deprecated/dsl/_component_bridge.py
@@ -29,8 +29,8 @@
from kfp.deprecated.dsl import component_spec as dsl_component_spec
from kfp.deprecated.dsl import dsl_utils
from kfp.deprecated.dsl import types
+from kfp.deprecated.dsl.types import type_utils
from kfp.pipeline_spec import pipeline_spec_pb2
-from kfp.components.types import type_utils
# Placeholder to represent the output directory hosting all the generated URIs.
# Its actual value will be specified during pipeline compilation.
diff --git a/sdk/python/kfp/deprecated/dsl/type_utils.py b/sdk/python/kfp/deprecated/dsl/type_utils.py
index 0464733b63..c0ed2238df 100644
--- a/sdk/python/kfp/deprecated/dsl/type_utils.py
+++ b/sdk/python/kfp/deprecated/dsl/type_utils.py
@@ -11,23 +11,22 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
-"""Deprecated. See kfp.components.types.type_utils instead.
+"""Deprecated. See kfp.dsl.types.type_utils instead.
This module will be removed in KFP v2.0.
"""
import warnings
-from kfp.components.types import type_utils
+from kfp.dsl.types import type_utils, artifact_types
from kfp.pipeline_spec import pipeline_spec_pb2
import inspect
from typing import Union, Type
import re
from typing import List, Optional
from kfp.deprecated.components import _structures
-from kfp.components.types import artifact_types
warnings.warn(
'Module kfp.dsl.type_utils is deprecated and will be removed'
- ' in KFP v2.0. Please use from kfp.components.types.type_utils instead.',
+ ' in KFP v2.0. Please use from kfp.dsl.types.type_utils instead.',
category=FutureWarning)
is_parameter_type = type_utils.is_parameter_type
diff --git a/sdk/python/kfp/deprecated/dsl/types.py b/sdk/python/kfp/deprecated/dsl/types.py
index 2965a22c1d..8c793759ae 100644
--- a/sdk/python/kfp/deprecated/dsl/types.py
+++ b/sdk/python/kfp/deprecated/dsl/types.py
@@ -19,7 +19,7 @@
from typing import Dict, Union
import warnings
-from kfp.components.types import type_utils
+from kfp.deprecated.dsl import type_utils
class BaseType:
diff --git a/sdk/python/kfp/dsl/base_component.py b/sdk/python/kfp/dsl/base_component.py
index 25a10f84df..86e291018a 100644
--- a/sdk/python/kfp/dsl/base_component.py
+++ b/sdk/python/kfp/dsl/base_component.py
@@ -16,6 +16,7 @@
import abc
from typing import List
+from kfp.dsl import pipeline_context
from kfp.dsl import pipeline_task
from kfp.dsl import structures
from kfp.dsl.types import type_utils
@@ -100,6 +101,8 @@ def __call__(self, *args, **kwargs) -> pipeline_task.PipelineTask:
return pipeline_task.PipelineTask(
component_spec=self.component_spec,
args=task_inputs,
+ execute_locally=pipeline_context.Pipeline.get_default_pipeline() is
+ None,
)
@property
diff --git a/sdk/python/kfp/dsl/base_component_test.py b/sdk/python/kfp/dsl/base_component_test.py
index 48ed1d6065..cfee0d79ce 100644
--- a/sdk/python/kfp/dsl/base_component_test.py
+++ b/sdk/python/kfp/dsl/base_component_test.py
@@ -14,10 +14,8 @@
"""Tests for kfp.dsl.base_component."""
import unittest
-from unittest.mock import patch
from kfp import dsl
-from kfp.dsl import pipeline_task
from kfp.dsl import placeholders
from kfp.dsl import python_component
from kfp.dsl import structures
@@ -59,34 +57,6 @@
class BaseComponentTest(unittest.TestCase):
- @patch.object(pipeline_task, 'PipelineTask', autospec=True)
- def test_instantiate_component_with_keyword_arguments(
- self, mock_PipelineTask):
-
- component_op(input1='hello', input2=100, input3=1.23, input4=3.21)
-
- mock_PipelineTask.assert_called_once_with(
- component_spec=component_op.component_spec,
- args={
- 'input1': 'hello',
- 'input2': 100,
- 'input3': 1.23,
- 'input4': 3.21,
- })
-
- @patch.object(pipeline_task, 'PipelineTask', autospec=True)
- def test_instantiate_component_omitting_arguments_with_default(
- self, mock_PipelineTask):
-
- component_op(input1='hello', input2=100)
-
- mock_PipelineTask.assert_called_once_with(
- component_spec=component_op.component_spec,
- args={
- 'input1': 'hello',
- 'input2': 100,
- })
-
def test_instantiate_component_with_positional_arugment(self):
with self.assertRaisesRegex(
TypeError,
diff --git a/sdk/python/kfp/dsl/component_factory.py b/sdk/python/kfp/dsl/component_factory.py
index 2881b92ef2..311c6ef3c9 100644
--- a/sdk/python/kfp/dsl/component_factory.py
+++ b/sdk/python/kfp/dsl/component_factory.py
@@ -448,6 +448,14 @@ def parse_docstring_with_return_as_args(
)
+EXECUTOR_MODULE = 'kfp.dsl.executor_main'
+CONTAINERIZED_PYTHON_COMPONENT_COMMAND = [
+ 'python3',
+ '-m',
+ EXECUTOR_MODULE,
+]
+
+
def _get_command_and_args_for_lightweight_component(
func: Callable) -> Tuple[List[str], List[str]]:
imports_source = [
@@ -466,11 +474,11 @@ def _get_command_and_args_for_lightweight_component(
command = [
'sh',
'-ec',
- textwrap.dedent('''\
+ textwrap.dedent(f'''\
program_path=$(mktemp -d)
printf "%s" "$0" > "$program_path/ephemeral_component.py"
- _KFP_RUNTIME=true python3 -m kfp.dsl.executor_main \
+ _KFP_RUNTIME=true python3 -m {EXECUTOR_MODULE} \
--component_module_path \
"$program_path/ephemeral_component.py" \
"$@"
@@ -490,11 +498,6 @@ def _get_command_and_args_for_lightweight_component(
def _get_command_and_args_for_containerized_component(
function_name: str) -> Tuple[List[str], List[str]]:
- command = [
- 'python3',
- '-m',
- 'kfp.dsl.executor_main',
- ]
args = [
'--executor_input',
@@ -502,7 +505,7 @@ def _get_command_and_args_for_containerized_component(
'--function_to_execute',
function_name,
]
- return command, args
+ return CONTAINERIZED_PYTHON_COMPONENT_COMMAND, args
def create_component_from_func(
diff --git a/sdk/python/kfp/dsl/executor.py b/sdk/python/kfp/dsl/executor.py
index 87d20e43c8..272c3c1f61 100644
--- a/sdk/python/kfp/dsl/executor.py
+++ b/sdk/python/kfp/dsl/executor.py
@@ -116,7 +116,7 @@ def make_artifact(
else:
artifact_cls = annotation
return create_artifact_instance(
- runtime_artifact, artifact_cls=artifact_cls)
+ runtime_artifact, fallback_artifact_cls=artifact_cls)
def get_input_artifact(self, name: str) -> Optional[dsl.Artifact]:
return self.input_artifacts.get(name)
@@ -364,14 +364,13 @@ def execute(self) -> Optional[str]:
def create_artifact_instance(
runtime_artifact: Dict,
- artifact_cls=dsl.Artifact,
+ fallback_artifact_cls=dsl.Artifact,
) -> type:
"""Creates an artifact class instances from a runtime artifact
dictionary."""
schema_title = runtime_artifact.get('type', {}).get('schemaTitle', '')
-
artifact_cls = artifact_types._SCHEMA_TITLE_TO_TYPE.get(
- schema_title, artifact_cls)
+ schema_title, fallback_artifact_cls)
return artifact_cls._from_executor_fields(
uri=runtime_artifact.get('uri', ''),
name=runtime_artifact.get('name', ''),
diff --git a/sdk/python/kfp/dsl/executor_test.py b/sdk/python/kfp/dsl/executor_test.py
index 8b799d2c5a..b5082dd9a3 100644
--- a/sdk/python/kfp/dsl/executor_test.py
+++ b/sdk/python/kfp/dsl/executor_test.py
@@ -1720,7 +1720,8 @@ def test_dict_to_artifact_kfp_artifact(
# with artifact_cls
self.assertIsInstance(
executor.create_artifact_instance(
- runtime_artifact, artifact_cls=artifact_cls), expected_type)
+ runtime_artifact, fallback_artifact_cls=artifact_cls),
+ expected_type)
# without artifact_cls
self.assertIsInstance(
diff --git a/sdk/python/kfp/dsl/pipeline_task.py b/sdk/python/kfp/dsl/pipeline_task.py
index ecf1640a88..b3d168330a 100644
--- a/sdk/python/kfp/dsl/pipeline_task.py
+++ b/sdk/python/kfp/dsl/pipeline_task.py
@@ -14,6 +14,8 @@
"""Pipeline task class and operations."""
import copy
+import enum
+import functools
import inspect
import itertools
import re
@@ -32,6 +34,34 @@
task.component_spec.name)
+class TaskState(enum.Enum):
+ FUTURE = 'FUTURE'
+ FINAL = 'FINAL'
+
+
+def block_if_final(custom_message: Optional[str] = None):
+
+ def actual_decorator(method):
+ method_name = method.__name__
+
+ @functools.wraps(method)
+ def wrapper(self: 'PipelineTask', *args, **kwargs):
+ if self.state == TaskState.FINAL:
+ raise Exception(
+ custom_message or
+ f"Task configuration methods are not supported for local execution. Got call to '.{method_name}()'."
+ )
+ elif self.state == TaskState.FUTURE:
+ return method(self, *args, **kwargs)
+ else:
+ raise ValueError(
+ f'Got unknown {TaskState.__name__}: {self.state}.')
+
+ return wrapper
+
+ return actual_decorator
+
+
class PipelineTask:
"""Represents a pipeline task (instantiated component).
@@ -65,12 +95,13 @@ def my_pipeline():
def __init__(
self,
component_spec: structures.ComponentSpec,
- args: Mapping[str, Any],
- ):
+ args: Dict[str, Any],
+ execute_locally: bool = False,
+ ) -> None:
"""Initilizes a PipelineTask instance."""
# import within __init__ to avoid circular import
from kfp.dsl.tasks_group import TasksGroup
-
+ self.state = TaskState.FUTURE
self.parent_task_group: Union[None, TasksGroup] = None
args = args or {}
@@ -148,7 +179,29 @@ def validate_placeholder_types(
if not isinstance(value, pipeline_channel.PipelineChannel)
])
+ if execute_locally:
+ self._execute_locally(args=args)
+
+ def _execute_locally(self, args: Dict[str, Any]) -> None:
+ """Execute the pipeline task locally.
+
+ Set the task state to FINAL and update the outputs.
+ """
+ from kfp.local import task_dispatcher
+
+ if self.pipeline_spec is not None:
+ raise NotImplementedError(
+ 'Local pipeline execution is not currently supported.')
+
+ self._outputs = task_dispatcher.run_single_component(
+ pipeline_spec=self.component_spec.to_pipeline_spec(),
+ arguments=args,
+ )
+ self.state = TaskState.FINAL
+
@property
+ @block_if_final(
+ 'Platform-specific features are not supported for local execution.')
def platform_spec(self) -> pipeline_spec_pb2.PlatformSpec:
"""PlatformSpec for all tasks in the pipeline as task.
@@ -173,9 +226,9 @@ def name(self) -> str:
@property
def inputs(
self
- ) -> List[Union[type_utils.PARAMETER_TYPES,
- pipeline_channel.PipelineChannel]]:
- """The list of actual inputs passed to the task."""
+ ) -> Dict[str, Union[type_utils.PARAMETER_TYPES,
+ pipeline_channel.PipelineChannel]]:
+ """The inputs passed to the task."""
return self._inputs
@property
@@ -208,6 +261,8 @@ def outputs(self) -> Mapping[str, pipeline_channel.PipelineChannel]:
return self._outputs
@property
+ @block_if_final(
+ 'Task has no dependent tasks since it is executed independently.')
def dependent_tasks(self) -> List[str]:
"""A list of the dependent task names."""
return self._task_spec.dependent_tasks
@@ -236,6 +291,7 @@ def _extract_container_spec_and_convert_placeholders(
]
return container_spec
+ @block_if_final()
def set_caching_options(self, enable_caching: bool) -> 'PipelineTask':
"""Sets caching options for the task.
@@ -280,6 +336,7 @@ def _validate_cpu_request_limit(self, cpu: str) -> float:
return float(cpu[:-1]) / 1000 if cpu.endswith('m') else float(cpu)
+ @block_if_final()
def set_cpu_request(self, cpu: str) -> 'PipelineTask':
"""Sets CPU request (minimum) for the task.
@@ -304,6 +361,7 @@ def set_cpu_request(self, cpu: str) -> 'PipelineTask':
return self
+ @block_if_final()
def set_cpu_limit(self, cpu: str) -> 'PipelineTask':
"""Sets CPU limit (maximum) for the task.
@@ -328,6 +386,7 @@ def set_cpu_limit(self, cpu: str) -> 'PipelineTask':
return self
+ @block_if_final()
def set_accelerator_limit(self, limit: int) -> 'PipelineTask':
"""Sets accelerator limit (maximum) for the task. Only applies if
accelerator type is also set via .set_accelerator_type().
@@ -353,6 +412,7 @@ def set_accelerator_limit(self, limit: int) -> 'PipelineTask':
return self
+ @block_if_final()
def set_gpu_limit(self, gpu: str) -> 'PipelineTask':
"""Sets GPU limit (maximum) for the task. Only applies if accelerator
type is also set via .add_accelerator_type().
@@ -422,6 +482,7 @@ def _validate_memory_request_limit(self, memory: str) -> float:
return memory
+ @block_if_final()
def set_memory_request(self, memory: str) -> 'PipelineTask':
"""Sets memory request (minimum) for the task.
@@ -445,6 +506,7 @@ def set_memory_request(self, memory: str) -> 'PipelineTask':
return self
+ @block_if_final()
def set_memory_limit(self, memory: str) -> 'PipelineTask':
"""Sets memory limit (maximum) for the task.
@@ -468,6 +530,7 @@ def set_memory_limit(self, memory: str) -> 'PipelineTask':
return self
+ @block_if_final()
def set_retry(self,
num_retries: int,
backoff_duration: Optional[str] = None,
@@ -492,6 +555,7 @@ def set_retry(self,
)
return self
+ @block_if_final()
def add_node_selector_constraint(self, accelerator: str) -> 'PipelineTask':
"""Sets accelerator type to use when executing this task.
@@ -506,6 +570,7 @@ def add_node_selector_constraint(self, accelerator: str) -> 'PipelineTask':
category=DeprecationWarning)
return self.set_accelerator_type(accelerator)
+ @block_if_final()
def set_accelerator_type(self, accelerator: str) -> 'PipelineTask':
"""Sets accelerator type to use when executing this task.
@@ -527,6 +592,7 @@ def set_accelerator_type(self, accelerator: str) -> 'PipelineTask':
return self
+ @block_if_final()
def set_display_name(self, name: str) -> 'PipelineTask':
"""Sets display name for the task.
@@ -539,6 +605,7 @@ def set_display_name(self, name: str) -> 'PipelineTask':
self._task_spec.display_name = name
return self
+ @block_if_final()
def set_env_variable(self, name: str, value: str) -> 'PipelineTask':
"""Sets environment variable for the task.
@@ -557,6 +624,7 @@ def set_env_variable(self, name: str, value: str) -> 'PipelineTask':
self.container_spec.env = {name: value}
return self
+ @block_if_final()
def after(self, *tasks) -> 'PipelineTask':
"""Specifies an explicit dependency on other tasks by requiring this
task be executed after other tasks finish completion.
@@ -580,6 +648,7 @@ def my_pipeline():
self._task_spec.dependent_tasks.append(task.name)
return self
+ @block_if_final()
def ignore_upstream_failure(self) -> 'PipelineTask':
"""If called, the pipeline task will run when any specified upstream
tasks complete, even if unsuccessful.
diff --git a/sdk/python/kfp/dsl/pipeline_task_test.py b/sdk/python/kfp/dsl/pipeline_task_test.py
index 6e7443fc1a..c5bedc3811 100644
--- a/sdk/python/kfp/dsl/pipeline_task_test.py
+++ b/sdk/python/kfp/dsl/pipeline_task_test.py
@@ -377,5 +377,134 @@ def my_pipeline():
t.platform_spec
+class TestTaskInFinalState(unittest.TestCase):
+ """Tests PipelineTask in the state FINAL.
+
+ Many properties and methods will be blocked.
+
+ Also tests that the .output and .outputs behavior behaves as expected when the outputs are values, not placeholders, as will be the case when PipelineTask is in the state FINAL.
+ """
+
+ def test_output_property(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ task._outputs = {'Output': 1}
+ self.assertEqual(task.output, 1)
+ self.assertEqual(task.outputs['Output'], 1)
+
+ def test_outputs_property(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ task._outputs = {
+ 'int_output':
+ 1,
+ 'str_output':
+ 'foo',
+ 'dataset_output':
+ dsl.Dataset(
+ name='dataset_output',
+ uri='foo/bar/dataset_output',
+ metadata={'key': 'value'})
+ }
+ self.assertEqual(task.outputs['int_output'], 1)
+ self.assertEqual(task.outputs['str_output'], 'foo')
+ assert_artifacts_equal(
+ self,
+ task.outputs['dataset_output'],
+ dsl.Dataset(
+ name='dataset_output',
+ uri='foo/bar/dataset_output',
+ metadata={'key': 'value'}),
+ )
+
+ def test_platform_spec_property(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ with self.assertRaisesRegex(
+ Exception,
+ r'Platform-specific features are not supported for local execution\.'
+ ):
+ task.platform_spec
+
+ def test_name_property(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ self.assertEqual(task.name, 'component1')
+
+ def test_inputs_property(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ self.assertEqual(task.inputs, {'input1': 'value'})
+
+ def test_dependent_tasks_property(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ with self.assertRaisesRegex(
+ Exception,
+ r'Task has no dependent tasks since it is executed independently\.'
+ ):
+ task.dependent_tasks
+
+ def test_sampling_of_task_configuration_methods(self):
+ task = pipeline_task.PipelineTask(
+ component_spec=structures.ComponentSpec.from_yaml_documents(
+ V2_YAML),
+ args={'input1': 'value'},
+ )
+ task.state = pipeline_task.TaskState.FINAL
+ with self.assertRaisesRegex(
+ Exception,
+ r"Task configuration methods are not supported for local execution\. Got call to '\.set_caching_options\(\)'\."
+ ):
+ task.set_caching_options(enable_caching=True)
+ with self.assertRaisesRegex(
+ Exception,
+ r"Task configuration methods are not supported for local execution\. Got call to '\.set_env_variable\(\)'\."
+ ):
+ task.set_env_variable(name='foo', value='BAR')
+ with self.assertRaisesRegex(
+ Exception,
+ r"Task configuration methods are not supported for local execution\. Got call to '\.ignore_upstream_failure\(\)'\."
+ ):
+ task.ignore_upstream_failure()
+
+
+def assert_artifacts_equal(
+ test_class: unittest.TestCase,
+ a1: dsl.Artifact,
+ a2: dsl.Artifact,
+) -> None:
+ test_class.assertEqual(a1.name, a2.name)
+ test_class.assertEqual(a1.uri, a2.uri)
+ test_class.assertEqual(a1.metadata, a2.metadata)
+ test_class.assertEqual(a1.schema_title, a2.schema_title)
+ test_class.assertEqual(a1.schema_version, a2.schema_version)
+ test_class.assertIsInstance(a1, type(a2))
+
+
if __name__ == '__main__':
unittest.main()
diff --git a/sdk/python/kfp/dsl/types/artifact_types.py b/sdk/python/kfp/dsl/types/artifact_types.py
index f7a676573d..271de58c29 100644
--- a/sdk/python/kfp/dsl/types/artifact_types.py
+++ b/sdk/python/kfp/dsl/types/artifact_types.py
@@ -89,7 +89,8 @@ def _get_path(self) -> Optional[str]:
return _MINIO_LOCAL_MOUNT_PREFIX + self.uri[len('minio://'):]
elif self.uri.startswith('s3://'):
return _S3_LOCAL_MOUNT_PREFIX + self.uri[len('s3://'):]
- return None
+ # uri == path for local execution
+ return self.uri
def _set_path(self, path: str) -> None:
self.uri = convert_local_path_to_remote_path(path)
diff --git a/sdk/python/kfp/dsl/types/type_utils.py b/sdk/python/kfp/dsl/types/type_utils.py
index cd84a37041..666ee7247b 100644
--- a/sdk/python/kfp/dsl/types/type_utils.py
+++ b/sdk/python/kfp/dsl/types/type_utils.py
@@ -227,6 +227,10 @@ def _get_type_string_from_component_argument(
if argument_type in _TYPE_TO_TYPE_NAME:
return _TYPE_TO_TYPE_NAME[argument_type]
+ if isinstance(argument_value, artifact_types.Artifact):
+ raise ValueError(
+ f'Input artifacts are not supported. Got input artifact of type {argument_value.__class__.__name__!r}.'
+ )
raise ValueError(
f'Constant argument inputs must be one of type {list(_TYPE_TO_TYPE_NAME.values())} Got: {argument_value!r} of type {type(argument_value)!r}.'
)
diff --git a/sdk/python/kfp/dsl/utils.py b/sdk/python/kfp/dsl/utils.py
index 781ddd0de5..4400bca894 100644
--- a/sdk/python/kfp/dsl/utils.py
+++ b/sdk/python/kfp/dsl/utils.py
@@ -20,7 +20,7 @@
import types
from typing import List
-_COMPONENT_NAME_PREFIX = 'comp-'
+COMPONENT_NAME_PREFIX = 'comp-'
_EXECUTOR_LABEL_PREFIX = 'exec-'
@@ -69,7 +69,7 @@ def sanitize_input_name(name: str) -> str:
def sanitize_component_name(name: str) -> str:
"""Sanitizes component name."""
- return _COMPONENT_NAME_PREFIX + maybe_rename_for_k8s(name)
+ return COMPONENT_NAME_PREFIX + maybe_rename_for_k8s(name)
def sanitize_task_name(name: str) -> str:
diff --git a/sdk/python/kfp/local/__init__.py b/sdk/python/kfp/local/__init__.py
old mode 100644
new mode 100755
index 6a6e17e025..5428cdca4d
--- a/sdk/python/kfp/local/__init__.py
+++ b/sdk/python/kfp/local/__init__.py
@@ -13,11 +13,12 @@
# limitations under the License.
"""The KFP local runner."""
+from kfp.local.config import DockerRunner
from kfp.local.config import init
from kfp.local.config import SubprocessRunner
-# TODO: uncomment when local execution is publicly available
-# __all__ = [
-# 'SubprocessRunner',
-# 'init',
-# ]
+__all__ = [
+ 'init',
+ 'SubprocessRunner',
+ 'DockerRunner',
+]
diff --git a/sdk/python/kfp/local/config.py b/sdk/python/kfp/local/config.py
old mode 100644
new mode 100755
index 043c733b4f..aba0488b5b
--- a/sdk/python/kfp/local/config.py
+++ b/sdk/python/kfp/local/config.py
@@ -12,7 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Objects for configuring local execution."""
+import abc
import dataclasses
+import os
+from typing import Union
+
+
+class LocalRunnerType(abc.ABC):
+ """The ABC for user-facing Runner configurations.
+
+ Subclasses should be a dataclass.
+
+ They should implement a .validate() method.
+ """
+
+ @abc.abstractmethod
+ def validate(self) -> None:
+ """Validates that the configuration arguments provided by the user are
+ valid."""
+ raise NotImplementedError
@dataclasses.dataclass
@@ -25,6 +43,20 @@ class SubprocessRunner:
use_venv: bool = True
+@dataclasses.dataclass
+class DockerRunner:
+ """Runner that indicates that local tasks should be run as a Docker
+ container."""
+
+ def __post_init__(self):
+ try:
+ import docker # noqa
+ except ImportError as e:
+ raise ImportError(
+ f"Package 'docker' must be installed to use {DockerRunner.__name__!r}. Install it using 'pip install docker'."
+ ) from e
+
+
class LocalExecutionConfig:
instance = None
@@ -32,7 +64,6 @@ def __new__(
cls,
runner: SubprocessRunner,
pipeline_root: str,
- cleanup: bool,
raise_on_error: bool,
) -> 'LocalExecutionConfig':
# singleton pattern
@@ -43,20 +74,22 @@ def __init__(
self,
runner: SubprocessRunner,
pipeline_root: str,
- cleanup: bool,
raise_on_error: bool,
) -> None:
+ permitted_runners = (SubprocessRunner, DockerRunner)
+ if not isinstance(runner, permitted_runners):
+ raise ValueError(
+ f'Got unknown runner {runner} of type {runner.__class__.__name__}. Runner should be one of the following types: {". ".join(prunner.__name__ for prunner in permitted_runners)}.'
+ )
self.runner = runner
self.pipeline_root = pipeline_root
- self.cleanup = cleanup
self.raise_on_error = raise_on_error
def init(
- # more runner types will eventually be supported
- runner: SubprocessRunner,
+ # annotate with subclasses, not parent class, for more helpful ref docs
+ runner: Union[SubprocessRunner, DockerRunner],
pipeline_root: str = './local_outputs',
- cleanup: bool = False,
raise_on_error: bool = True,
) -> None:
"""Initializes a local execution session.
@@ -64,15 +97,14 @@ def init(
Once called, components can be invoked locally outside of a pipeline definition.
Args:
- runner: The runner to use. Currently only SubprocessRunner is supported.
+ runner: The runner to use. Supported runners: kfp.local.SubprocessRunner and kfp.local.DockerRunner.
pipeline_root: Destination for task outputs.
- cleanup: Whether to ensure outputs are cleaned up after execution. If True, the task will be run in a temporary directory, rather than pipeline_root.
- raise_on_error: If True, raises an exception when a local task execution fails. If Falls, fails gracefully and does not terminal the current program.
+ raise_on_error: If True, raises an exception when a local task execution fails. If False, fails gracefully and does not terminate the current program.
"""
# updates a global config
+ pipeline_root = os.path.abspath(pipeline_root)
LocalExecutionConfig(
runner=runner,
pipeline_root=pipeline_root,
- cleanup=cleanup,
raise_on_error=raise_on_error,
)
diff --git a/sdk/python/kfp/local/config_test.py b/sdk/python/kfp/local/config_test.py
old mode 100644
new mode 100755
index 23f7d3ea6e..60943f0a44
--- a/sdk/python/kfp/local/config_test.py
+++ b/sdk/python/kfp/local/config_test.py
@@ -12,7 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for config.py."""
+import os
import unittest
+from unittest import mock
from kfp import local
from kfp.local import config
@@ -28,7 +30,6 @@ def test_local_runner_config_init(self):
config.LocalExecutionConfig(
pipeline_root='my/local/root',
runner=local.SubprocessRunner(use_venv=True),
- cleanup=True,
raise_on_error=True,
)
@@ -36,7 +37,6 @@ def test_local_runner_config_init(self):
self.assertEqual(instance.pipeline_root, 'my/local/root')
self.assertEqual(instance.runner, local.SubprocessRunner(use_venv=True))
- self.assertIs(instance.cleanup, True)
self.assertIs(instance.raise_on_error, True)
def test_local_runner_config_is_singleton(self):
@@ -44,13 +44,11 @@ def test_local_runner_config_is_singleton(self):
config.LocalExecutionConfig(
pipeline_root='my/local/root',
runner=local.SubprocessRunner(),
- cleanup=True,
raise_on_error=True,
)
config.LocalExecutionConfig(
pipeline_root='other/local/root',
runner=local.SubprocessRunner(use_venv=False),
- cleanup=False,
raise_on_error=False,
)
@@ -59,7 +57,6 @@ def test_local_runner_config_is_singleton(self):
self.assertEqual(instance.pipeline_root, 'other/local/root')
self.assertEqual(instance.runner,
local.SubprocessRunner(use_venv=False))
- self.assertFalse(instance.cleanup, False)
self.assertFalse(instance.raise_on_error, False)
@@ -73,37 +70,52 @@ def test_init_more_than_once(self):
local.init(
pipeline_root='my/local/root',
runner=local.SubprocessRunner(use_venv=True),
- cleanup=True,
)
instance = config.LocalExecutionConfig.instance
self.assertEqual(instance.pipeline_root, 'my/local/root')
self.assertEqual(instance.runner, local.SubprocessRunner(use_venv=True))
- self.assertTrue(instance.cleanup, True)
def test_init_more_than_once(self):
"""Test config instance attributes with multiple init() calls."""
local.init(
pipeline_root='my/local/root',
runner=local.SubprocessRunner(),
- cleanup=True,
)
local.init(
pipeline_root='other/local/root',
runner=local.SubprocessRunner(use_venv=False),
- cleanup=False,
raise_on_error=False,
)
instance = config.LocalExecutionConfig.instance
- self.assertEqual(instance.pipeline_root, 'other/local/root')
+ self.assertEqual(instance.pipeline_root,
+ os.path.abspath('other/local/root'))
self.assertEqual(instance.runner,
local.SubprocessRunner(use_venv=False))
- self.assertFalse(instance.cleanup, False)
self.assertFalse(instance.raise_on_error, False)
+ def test_runner_validation(self):
+ """Test config instance attributes with multiple init() calls."""
+ with self.assertRaisesRegex(
+ ValueError,
+ r'Got unknown runner foo of type str\. Runner should be one of the following types: SubprocessRunner\.'
+ ):
+ local.init(runner='foo')
+
+
+class TestDockerRunner(unittest.TestCase):
+
+ def test_import_error(self):
+ with mock.patch.dict('sys.modules', {'docker': None}):
+ with self.assertRaisesRegex(
+ ImportError,
+ r"Package 'docker' must be installed to use 'DockerRunner'\. Install it using 'pip install docker'\."
+ ):
+ local.DockerRunner()
+
if __name__ == '__main__':
unittest.main()
diff --git a/sdk/python/kfp/local/docker_task_handler.py b/sdk/python/kfp/local/docker_task_handler.py
new file mode 100755
index 0000000000..08d48b35b7
--- /dev/null
+++ b/sdk/python/kfp/local/docker_task_handler.py
@@ -0,0 +1,101 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from typing import Any, Dict, List
+
+from kfp.local import config
+from kfp.local import status
+from kfp.local import task_handler_interface
+
+
+class DockerTaskHandler(task_handler_interface.ITaskHandler):
+ """The task handler corresponding to DockerRunner."""
+
+ def __init__(
+ self,
+ image: str,
+ full_command: List[str],
+ pipeline_root: str,
+ runner: config.DockerRunner,
+ ) -> None:
+ self.image = image
+ self.full_command = full_command
+ self.pipeline_root = pipeline_root
+ self.runner = runner
+
+ def get_volumes_to_mount(self) -> Dict[str, Any]:
+ """Gets the volume configuration to mount the pipeline root to the
+ container so that outputs can be obtained outside of the container."""
+ if not os.path.isabs(self.pipeline_root):
+ # defensive check. this is enforced by upstream code.
+ # users should not hit this,
+ raise ValueError(
+ "'pipeline_root' should be an absolute path to correctly construct the volume mount specification."
+ )
+ return {self.pipeline_root: {'bind': self.pipeline_root, 'mode': 'rw'}}
+
+ def run(self) -> status.Status:
+ """Runs the Docker container and returns the status."""
+ # nest docker import in case not available in user env so that
+ # this module is runnable, even if not using DockerRunner
+ import docker
+ client = docker.from_env()
+ try:
+ volumes = self.get_volumes_to_mount()
+ return_code = run_docker_container(
+ client=client,
+ image=self.image,
+ command=self.full_command,
+ volumes=volumes,
+ )
+ finally:
+ client.close()
+ return status.Status.SUCCESS if return_code == 0 else status.Status.FAILURE
+
+
+def add_latest_tag_if_not_present(image: str) -> str:
+ if ':' not in image:
+ image = f'{image}:latest'
+ return image
+
+
+def run_docker_container(
+ client: 'docker.DockerClient',
+ image: str,
+ command: List[str],
+ volumes: Dict[str, Any],
+) -> int:
+ image = add_latest_tag_if_not_present(image=image)
+ image_exists = any(
+ image in existing_image.tags for existing_image in client.images.list())
+ if image_exists:
+ print(f'Found image {image!r}\n')
+ else:
+ print(f'Pulling image {image!r}')
+ repository, tag = image.split(':')
+ client.images.pull(repository=repository, tag=tag)
+ print('Image pull complete\n')
+ container = client.containers.run(
+ image=image,
+ command=command,
+ detach=True,
+ stdout=True,
+ stderr=True,
+ volumes=volumes,
+ )
+ for line in container.logs(stream=True):
+ # the inner logs should already have trailing \n
+ # we do not need to add another
+ print(line.decode(), end='')
+ return container.wait()['StatusCode']
diff --git a/sdk/python/kfp/local/docker_task_handler_test.py b/sdk/python/kfp/local/docker_task_handler_test.py
new file mode 100755
index 0000000000..06e3e8a18c
--- /dev/null
+++ b/sdk/python/kfp/local/docker_task_handler_test.py
@@ -0,0 +1,531 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from typing import Optional
+import unittest
+from unittest import mock
+
+import docker
+from kfp import dsl
+from kfp import local
+from kfp.dsl import Artifact
+from kfp.dsl import Output
+from kfp.local import docker_task_handler
+from kfp.local import testing_utilities
+
+
+class DockerMockTestCase(unittest.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self.docker_mock = mock.Mock()
+ patcher = mock.patch('docker.from_env')
+ self.mocked_docker_client = patcher.start().return_value
+
+ mock_container = mock.Mock()
+ self.mocked_docker_client.containers.run.return_value = mock_container
+ # mock successful run
+ mock_container.logs.return_value = [
+ 'fake'.encode('utf-8'),
+ 'container'.encode('utf-8'),
+ 'logs'.encode('utf-8'),
+ ]
+ mock_container.wait.return_value = {'StatusCode': 0}
+
+ def teardown(self):
+ super().tearDown()
+ self.docker_mock.reset_mock()
+
+
+class TestRunDockerContainer(DockerMockTestCase):
+
+ def test_no_volumes(self):
+ docker_task_handler.run_docker_container(
+ docker.from_env(),
+ image='alpine',
+ command=['echo', 'foo'],
+ volumes={},
+ )
+
+ self.mocked_docker_client.containers.run.assert_called_once_with(
+ image='alpine:latest',
+ command=['echo', 'foo'],
+ detach=True,
+ stdout=True,
+ stderr=True,
+ volumes={},
+ )
+
+ def test_cwd_volume(self):
+ current_test_dir = os.path.dirname(os.path.abspath(__file__))
+ docker_task_handler.run_docker_container(
+ client=docker.from_env(),
+ image='alpine',
+ command=['cat', '/localdir/docker_task_handler_test.py'],
+ volumes={current_test_dir: {
+ 'bind': '/localdir',
+ 'mode': 'ro'
+ }},
+ )
+ self.mocked_docker_client.containers.run.assert_called_once_with(
+ image='alpine:latest',
+ command=['cat', '/localdir/docker_task_handler_test.py'],
+ detach=True,
+ stdout=True,
+ stderr=True,
+ volumes={current_test_dir: {
+ 'bind': '/localdir',
+ 'mode': 'ro'
+ }})
+
+
+class TestDockerTaskHandler(DockerMockTestCase):
+
+ def test_get_volumes_to_mount(self):
+ handler = docker_task_handler.DockerTaskHandler(
+ image='alpine',
+ full_command=['echo', 'foo'],
+ pipeline_root=os.path.abspath('my_root'),
+ runner=local.DockerRunner(),
+ )
+ volumes = handler.get_volumes_to_mount()
+ self.assertEqual(
+ volumes, {
+ os.path.abspath('my_root'): {
+ 'bind': os.path.abspath('my_root'),
+ 'mode': 'rw'
+ }
+ })
+
+ def test_run(self):
+ handler = docker_task_handler.DockerTaskHandler(
+ image='alpine',
+ full_command=['echo', 'foo'],
+ pipeline_root=os.path.abspath('my_root'),
+ runner=local.DockerRunner(),
+ )
+
+ handler.run()
+ self.mocked_docker_client.containers.run.assert_called_once_with(
+ image='alpine:latest',
+ command=['echo', 'foo'],
+ detach=True,
+ stdout=True,
+ stderr=True,
+ volumes={
+ os.path.abspath('my_root'): {
+ 'bind': os.path.abspath('my_root'),
+ 'mode': 'rw'
+ }
+ },
+ )
+
+ def test_pipeline_root_relpath(self):
+ with self.assertRaisesRegex(
+ ValueError,
+ r"'pipeline_root' should be an absolute path to correctly construct the volume mount specification\."
+ ):
+ docker_task_handler.DockerTaskHandler(
+ image='alpine',
+ full_command=['echo', 'foo'],
+ pipeline_root='my_relpath',
+ runner=local.DockerRunner(),
+ ).run()
+
+
+class TestAddLatestTagIfNotPresent(unittest.TestCase):
+
+ def test_no_tag(self):
+ actual = docker_task_handler.add_latest_tag_if_not_present(
+ image='alpine')
+ expected = 'alpine:latest'
+ self.assertEqual(actual, expected)
+
+ def test_latest_tag(self):
+ actual = docker_task_handler.add_latest_tag_if_not_present(
+ image='alpine:latest')
+ expected = 'alpine:latest'
+ self.assertEqual(actual, expected)
+
+ def test_no_tag(self):
+ actual = docker_task_handler.add_latest_tag_if_not_present(
+ image='alpine:3.19.0')
+ expected = 'alpine:3.19.0'
+ self.assertEqual(actual, expected)
+
+
+class TestE2E(DockerMockTestCase,
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def setUp(self):
+ super().setUp()
+ local.init(runner=local.DockerRunner())
+
+ def test_python(self):
+
+ @dsl.component
+ def artifact_maker(x: str, a: Output[Artifact]):
+ with open(a.path, 'w') as f:
+ f.write(x)
+
+ try:
+ artifact_maker(x='foo')
+ # cannot get outputs if they aren't created due to mock
+ except FileNotFoundError:
+ pass
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'python:3.7',
+ )
+ self.assertTrue(
+ any('def artifact_maker' in c for c in kwargs['command']))
+ self.assertTrue(kwargs['detach'])
+ self.assertTrue(kwargs['stdout'])
+ self.assertTrue(kwargs['stderr'])
+ root_vol_key = [
+ key for key in kwargs['volumes'].keys() if 'local_outputs' in key
+ ][0]
+ self.assertEqual(kwargs['volumes'][root_vol_key]['bind'], root_vol_key)
+ self.assertEqual(kwargs['volumes'][root_vol_key]['mode'], 'rw')
+
+ def test_empty_container_component(self):
+
+ @dsl.container_component
+ def comp():
+ return dsl.ContainerSpec(image='alpine')
+
+ try:
+ comp()
+ # cannot get outputs if they aren't created due to mock
+ except FileNotFoundError:
+ pass
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], [])
+
+ def test_container_component(self):
+
+ @dsl.container_component
+ def artifact_maker(x: str,):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=['sh', '-c', f'echo prefix-{x}'],
+ )
+
+ try:
+ artifact_maker(x='foo')
+ # cannot get outputs if they aren't created due to mock
+ except FileNotFoundError:
+ pass
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], [
+ 'sh',
+ '-c',
+ 'echo prefix-foo',
+ ])
+ self.assertTrue(kwargs['detach'])
+ self.assertTrue(kwargs['stdout'])
+ self.assertTrue(kwargs['stderr'])
+ root_vol_key = [
+ key for key in kwargs['volumes'].keys() if 'local_outputs' in key
+ ][0]
+ self.assertEqual(kwargs['volumes'][root_vol_key]['bind'], root_vol_key)
+ self.assertEqual(kwargs['volumes'][root_vol_key]['mode'], 'rw')
+
+ def test_if_present_with_string_omitted(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine:3.19.0',
+ command=[
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then=['echo', x],
+ else_=['echo', 'No input provided!'])
+ ])
+
+ comp()
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:3.19.0',
+ )
+ self.assertEqual(kwargs['command'], [
+ 'echo',
+ 'No input provided!',
+ ])
+
+ def test_if_present_with_string_provided(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine:3.19.0',
+ command=[
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then=['echo', x],
+ else_=['echo', 'No artifact provided!'])
+ ])
+
+ comp(x='foo')
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:3.19.0',
+ )
+ self.assertEqual(kwargs['command'], [
+ 'echo',
+ 'foo',
+ ])
+
+ def test_if_present_single_element_with_string_omitted(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine:3.19.0',
+ command=[
+ 'echo',
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then=x,
+ else_='No artifact provided!',
+ )
+ ])
+
+ comp()
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:3.19.0',
+ )
+ self.assertEqual(kwargs['command'], [
+ 'echo',
+ 'No artifact provided!',
+ ])
+
+ def test_if_present_single_element_with_string_provided(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine:3.19.0',
+ command=[
+ 'echo',
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then=x,
+ else_='No artifact provided!',
+ )
+ ])
+
+ comp(x='foo')
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:3.19.0',
+ )
+ self.assertEqual(kwargs['command'], [
+ 'echo',
+ 'foo',
+ ])
+
+ def test_concat_placeholder(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=[dsl.ConcatPlaceholder(['prefix-', x, '-suffix'])])
+
+ comp()
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], ['prefix-null-suffix'])
+
+ def test_nested_concat_placeholder(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=[
+ 'echo',
+ dsl.ConcatPlaceholder(
+ ['a', dsl.ConcatPlaceholder(['b', x, 'd'])])
+ ])
+
+ comp(x='c')
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], ['echo', 'abcd'])
+
+ def test_ifpresent_in_concat_provided(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=[
+ 'echo',
+ dsl.ConcatPlaceholder([
+ 'there ',
+ dsl.ConcatPlaceholder([
+ 'is ',
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then='one thing',
+ else_='another thing')
+ ])
+ ])
+ ])
+
+ comp(x='c')
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], ['echo', 'there is one thing'])
+
+ def test_ifpresent_in_concat_omitted(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=[
+ 'echo',
+ dsl.ConcatPlaceholder([
+ 'there ',
+ dsl.ConcatPlaceholder([
+ 'is ',
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then='one thing',
+ else_='another thing')
+ ])
+ ])
+ ])
+
+ comp()
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], ['echo', 'there is another thing'])
+
+ def test_concat_in_ifpresent_provided(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=[
+ 'echo',
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then=dsl.ConcatPlaceholder([x]),
+ else_=dsl.ConcatPlaceholder(['something', ' ', 'else']))
+ ])
+
+ comp(x='something')
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], ['echo', 'something'])
+
+ def test_concat_in_ifpresent_omitted(self):
+
+ @dsl.container_component
+ def comp(x: Optional[str] = None):
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=[
+ 'echo',
+ dsl.IfPresentPlaceholder(
+ input_name='x',
+ then=dsl.ConcatPlaceholder([x]),
+ else_=dsl.ConcatPlaceholder(['another', ' ', 'thing']))
+ ])
+
+ comp()
+
+ run_mock = self.mocked_docker_client.containers.run
+ run_mock.assert_called_once()
+ kwargs = run_mock.call_args[1]
+ self.assertEqual(
+ kwargs['image'],
+ 'alpine:latest',
+ )
+ self.assertEqual(kwargs['command'], ['echo', 'another thing'])
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/executor_input_utils.py b/sdk/python/kfp/local/executor_input_utils.py
new file mode 100644
index 0000000000..3ba5b6fdda
--- /dev/null
+++ b/sdk/python/kfp/local/executor_input_utils.py
@@ -0,0 +1,160 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities for constructing the ExecutorInput message."""
+import datetime
+import os
+from typing import Any, Dict
+
+from google.protobuf import json_format
+from kfp.compiler import pipeline_spec_builder
+from kfp.dsl import utils
+from kfp.pipeline_spec import pipeline_spec_pb2
+
+_EXECUTOR_OUTPUT_FILE = 'executor_output.json'
+
+
+def construct_executor_input(
+ component_spec: pipeline_spec_pb2.ComponentSpec,
+ arguments: Dict[str, Any],
+ task_root: str,
+) -> pipeline_spec_pb2.ExecutorInput:
+ """Constructs the executor input message for a task execution."""
+ input_parameter_keys = list(
+ component_spec.input_definitions.parameters.keys())
+ input_artifact_keys = list(
+ component_spec.input_definitions.artifacts.keys())
+ if input_artifact_keys:
+ raise ValueError(
+ 'Input artifacts are not yet supported for local execution.')
+
+ output_parameter_keys = list(
+ component_spec.output_definitions.parameters.keys())
+ output_artifact_specs_dict = component_spec.output_definitions.artifacts
+
+ inputs = pipeline_spec_pb2.ExecutorInput.Inputs(
+ parameter_values={
+ param_name:
+ pipeline_spec_builder.to_protobuf_value(arguments[param_name])
+ if param_name in arguments else component_spec.input_definitions
+ .parameters[param_name].default_value
+ for param_name in input_parameter_keys
+ },
+ # input artifact constants are not supported yet
+ artifacts={},
+ )
+ outputs = pipeline_spec_pb2.ExecutorInput.Outputs(
+ parameters={
+ param_name: pipeline_spec_pb2.ExecutorInput.OutputParameter(
+ output_file=os.path.join(task_root, param_name))
+ for param_name in output_parameter_keys
+ },
+ artifacts={
+ artifact_name: make_artifact_list(
+ name=artifact_name,
+ artifact_type=artifact_spec.artifact_type,
+ task_root=task_root,
+ ) for artifact_name, artifact_spec in
+ output_artifact_specs_dict.items()
+ },
+ output_file=os.path.join(task_root, _EXECUTOR_OUTPUT_FILE),
+ )
+ return pipeline_spec_pb2.ExecutorInput(
+ inputs=inputs,
+ outputs=outputs,
+ )
+
+
+def get_local_pipeline_resource_name(pipeline_name: str) -> str:
+ """Gets the local pipeline resource name from the pipeline name in
+ PipelineSpec.
+
+ Args:
+ pipeline_name: The pipeline name provided by PipelineSpec.pipelineInfo.name.
+
+ Returns:
+ The local pipeline resource name. Includes timestamp.
+ """
+ timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S-%f')
+ return f'{pipeline_name}-{timestamp}'
+
+
+def get_local_task_resource_name(component_name: str) -> str:
+ """Gets the local task resource name from the component name in
+ PipelineSpec.
+
+ Args:
+ component_name: The component name provided as the key for the component's ComponentSpec
+ message. Takes the form comp-*.
+
+ Returns:
+ The local task resource name.
+ """
+ return component_name[len(utils.COMPONENT_NAME_PREFIX):]
+
+
+def construct_local_task_root(
+ pipeline_root: str,
+ pipeline_resource_name: str,
+ task_resource_name: str,
+) -> str:
+ """Constructs the local task root directory for a task."""
+ return os.path.join(
+ pipeline_root,
+ pipeline_resource_name,
+ task_resource_name,
+ )
+
+
+def make_artifact_list(
+ name: str,
+ artifact_type: pipeline_spec_pb2.ArtifactTypeSchema,
+ task_root: str,
+) -> pipeline_spec_pb2.ArtifactList:
+ """Constructs an ArtifactList instance for an artifact in ExecutorInput."""
+ return pipeline_spec_pb2.ArtifactList(artifacts=[
+ pipeline_spec_pb2.RuntimeArtifact(
+ name=name,
+ type=artifact_type,
+ uri=os.path.join(task_root, name),
+ # metadata always starts empty for output artifacts
+ metadata={},
+ )
+ ])
+
+
+def executor_input_to_dict(
+ executor_input: pipeline_spec_pb2.ExecutorInput,
+ component_spec: pipeline_spec_pb2.ComponentSpec,
+) -> Dict[str, Any]:
+ """Converts the executor input to a dictionary.
+
+ Since protobuf value represents ints and floats the same way, we
+ cast ints to their correct type. This should be called before
+ replacing placeholders with values.
+
+ This is consistent with the remote backend behavior.
+ """
+ executor_input_dict = json_format.MessageToDict(executor_input)
+ inputs_typed_int = [
+ in_param_name for in_param_name, parameter_spec in
+ component_spec.input_definitions.parameters.items()
+ if parameter_spec.parameter_type ==
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.NUMBER_INTEGER
+ ]
+ for param_name, param_value in executor_input_dict.get('inputs', {}).get(
+ 'parameterValues', {}).items():
+ if param_name in inputs_typed_int:
+ executor_input_dict['inputs']['parameterValues'][param_name] = int(
+ param_value)
+ return executor_input_dict
diff --git a/sdk/python/kfp/local/executor_input_utils_test.py b/sdk/python/kfp/local/executor_input_utils_test.py
new file mode 100644
index 0000000000..29fe312619
--- /dev/null
+++ b/sdk/python/kfp/local/executor_input_utils_test.py
@@ -0,0 +1,275 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License")
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for executor_input_utils.py."""
+
+import unittest
+
+from google.protobuf import json_format
+from kfp.local import executor_input_utils
+from kfp.local import testing_utilities
+from kfp.pipeline_spec import pipeline_spec_pb2
+
+
+class GetLocalPipelineResourceName(testing_utilities.MockedDatetimeTestCase):
+
+ def test(self):
+ actual = executor_input_utils.get_local_pipeline_resource_name(
+ 'my-pipeline')
+ expected = 'my-pipeline-2023-10-10-13-32-59-420710'
+ self.assertEqual(actual, expected)
+
+
+class GetLocalTaskResourceName(unittest.TestCase):
+
+ def test(self):
+ actual = executor_input_utils.get_local_task_resource_name(
+ 'comp-my-comp')
+ expected = 'my-comp'
+ self.assertEqual(actual, expected)
+
+
+class TestConstructLocalTaskRoot(testing_utilities.MockedDatetimeTestCase):
+
+ def test(self):
+
+ task_root = executor_input_utils.construct_local_task_root(
+ pipeline_root='/foo/bar',
+ pipeline_resource_name='my-pipeline-2023-10-10-13-32-59-420710',
+ task_resource_name='my-comp',
+ )
+ self.assertEqual(
+ task_root,
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/my-comp',
+ )
+
+
+class TestConstructExecutorInput(unittest.TestCase):
+
+ def test_no_inputs(self):
+ component_spec = pipeline_spec_pb2.ComponentSpec()
+ json_format.ParseDict(
+ {
+ 'outputDefinitions': {
+ 'parameters': {
+ 'Output': {
+ 'parameterType': 'STRING'
+ }
+ }
+ },
+ 'executorLabel': 'exec-comp'
+ }, component_spec)
+ arguments = {}
+ task_root = '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp'
+
+ actual = executor_input_utils.construct_executor_input(
+ component_spec=component_spec,
+ arguments=arguments,
+ task_root=task_root,
+ )
+ expected = pipeline_spec_pb2.ExecutorInput()
+ json_format.ParseDict(
+ {
+ 'inputs': {},
+ 'outputs': {
+ 'parameters': {
+ 'Output': {
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output'
+ }
+ },
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json'
+ }
+ }, expected)
+ self.assertEqual(actual, expected)
+
+ def test_various_io_types(self):
+ component_spec = pipeline_spec_pb2.ComponentSpec()
+ json_format.ParseDict(
+ {
+ 'inputDefinitions': {
+ 'parameters': {
+ 'boolean': {
+ 'parameterType': 'BOOLEAN'
+ }
+ }
+ },
+ 'outputDefinitions': {
+ 'artifacts': {
+ 'out_a': {
+ 'artifactType': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ }
+ }
+ },
+ 'parameters': {
+ 'Output': {
+ 'parameterType': 'NUMBER_INTEGER'
+ }
+ }
+ },
+ 'executorLabel': 'exec-comp'
+ }, component_spec)
+ arguments = {'boolean': False}
+ task_root = '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp'
+
+ actual = executor_input_utils.construct_executor_input(
+ component_spec=component_spec,
+ arguments=arguments,
+ task_root=task_root,
+ )
+ expected = pipeline_spec_pb2.ExecutorInput()
+ json_format.ParseDict(
+ {
+ 'inputs': {
+ 'parameterValues': {
+ 'boolean': False
+ }
+ },
+ 'outputs': {
+ 'parameters': {
+ 'Output': {
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output'
+ }
+ },
+ 'artifacts': {
+ 'out_a': {
+ 'artifacts': [{
+ 'name':
+ 'out_a',
+ 'type': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ },
+ 'uri':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/out_a',
+ 'metadata': {}
+ }]
+ }
+ },
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json'
+ }
+ }, expected)
+ self.assertEqual(actual, expected)
+
+ def test_input_artifacts_not_yet_supported(self):
+ component_spec = pipeline_spec_pb2.ComponentSpec()
+ json_format.ParseDict(
+ {
+ 'inputDefinitions': {
+ 'artifacts': {
+ 'in_artifact': {
+ 'artifactType': {
+ 'schemaTitle': 'system.Artifact',
+ 'schemaVersion': '0.0.1'
+ }
+ }
+ }
+ },
+ 'executorLabel': 'exec-comp'
+ }, component_spec)
+ arguments = {}
+ task_root = '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp'
+ with self.assertRaisesRegex(
+ ValueError,
+ 'Input artifacts are not yet supported for local execution.'):
+ executor_input_utils.construct_executor_input(
+ component_spec=component_spec,
+ arguments=arguments,
+ task_root=task_root,
+ )
+
+
+class TestExecutorInputToDict(unittest.TestCase):
+
+ def test_with_ints_and_floats(self):
+ component_spec = pipeline_spec_pb2.ComponentSpec()
+ json_format.ParseDict(
+ {
+ 'inputDefinitions': {
+ 'parameters': {
+ 'x': {
+ 'parameterType': 'NUMBER_INTEGER'
+ },
+ 'y': {
+ 'parameterType': 'NUMBER_DOUBLE'
+ }
+ }
+ },
+ 'outputDefinitions': {
+ 'parameters': {
+ 'Output': {
+ 'parameterType': 'STRING'
+ }
+ }
+ },
+ 'executorLabel': 'exec-comp'
+ }, component_spec)
+
+ executor_input = pipeline_spec_pb2.ExecutorInput()
+ json_format.ParseDict(
+ {
+ 'inputs': {
+ 'parameterValues': {
+ 'x': 1.0,
+ 'y': 2.0
+ }
+ },
+ 'outputs': {
+ 'parameters': {
+ 'Output': {
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output'
+ }
+ },
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json'
+ }
+ }, executor_input)
+
+ executor_input_dict = executor_input_utils.executor_input_to_dict(
+ executor_input=executor_input,
+ component_spec=component_spec,
+ )
+ expected = {
+ 'inputs': {
+ 'parameterValues': {
+ 'x': 1,
+ 'y': 2.0
+ }
+ },
+ 'outputs': {
+ 'parameters': {
+ 'Output': {
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output'
+ }
+ },
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json'
+ }
+ }
+ # assert types since 1.0 == 1
+ self.assertIsInstance(
+ executor_input_dict['inputs']['parameterValues']['x'], int)
+ self.assertIsInstance(
+ executor_input_dict['inputs']['parameterValues']['y'], float)
+ self.assertEqual(executor_input_dict, expected)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/executor_output_utils.py b/sdk/python/kfp/local/executor_output_utils.py
new file mode 100644
index 0000000000..716eb405e6
--- /dev/null
+++ b/sdk/python/kfp/local/executor_output_utils.py
@@ -0,0 +1,235 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities for reading and processing the ExecutorOutput message."""
+import json
+import os
+from typing import Any, Dict, List, Union
+
+from google.protobuf import json_format
+from google.protobuf import struct_pb2
+from kfp import dsl
+from kfp.compiler import pipeline_spec_builder
+from kfp.dsl import executor
+from kfp.pipeline_spec import pipeline_spec_pb2
+
+
+def load_executor_output(
+ executor_output_path: str) -> pipeline_spec_pb2.ExecutorOutput:
+ """Loads the ExecutorOutput message from a path."""
+ executor_output = pipeline_spec_pb2.ExecutorOutput()
+
+ if not os.path.isfile(executor_output_path):
+ return executor_output
+
+ with open(executor_output_path) as f:
+ json_format.Parse(f.read(), executor_output)
+ return executor_output
+
+
+def cast_protobuf_numbers(
+ output_parameters: Dict[str, Any],
+ output_parameter_types: Dict[
+ str, pipeline_spec_pb2.ComponentOutputsSpec.ParameterSpec],
+) -> Dict[str, Any]:
+ """Casts output fields that are typed as NUMBER_INTEGER to a Python int.
+
+ This is required, since google.protobuf.struct_pb2.Value uses
+ number_value to represent both floats and ints. When converting
+ struct_pb2.Value to a dict/json, int will be upcast to float, even
+ if the component output specifies int.
+ """
+ int_output_keys = [
+ output_param_name
+ for output_param_name, parameter_spec in output_parameter_types.items()
+ if parameter_spec.parameter_type ==
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.NUMBER_INTEGER
+ ]
+ for int_output_key in int_output_keys:
+ # avoid KeyError when the user never writes to the dsl.OutputPath
+ if int_output_key in output_parameters:
+ output_parameters[int_output_key] = int(
+ output_parameters[int_output_key])
+ return output_parameters
+
+
+def get_outputs_from_executor_output(
+ executor_output: pipeline_spec_pb2.ExecutorOutput,
+ executor_input: pipeline_spec_pb2.ExecutorInput,
+ component_spec: pipeline_spec_pb2.ComponentSpec,
+) -> Dict[str, Any]:
+ """Obtains a dictionary of output key to output value from several messages
+ corresponding to the executed a component/task."""
+ executor_output = add_type_to_executor_output(
+ executor_input=executor_input,
+ executor_output=executor_output,
+ )
+
+ # merge any parameter outputs written using dsl.OutputPath with the rest of ExecutorOutput
+ executor_output = merge_dsl_output_file_parameters_to_executor_output(
+ executor_input=executor_input,
+ executor_output=executor_output,
+ component_spec=component_spec,
+ )
+
+ # collect parameter outputs from executor output
+ output_parameters = {
+ param_name: pb2_value_to_python(value)
+ for param_name, value in executor_output.parameter_values.items()
+ }
+ # process the special case of protobuf ints
+ output_parameters = cast_protobuf_numbers(
+ output_parameters,
+ component_spec.output_definitions.parameters,
+ )
+
+ # collect artifact outputs from executor output
+ output_artifact_definitions = component_spec.output_definitions.artifacts
+ output_artifacts = {
+ artifact_name: artifact_list_to_dsl_artifact(
+ artifact_list,
+ is_artifact_list=output_artifact_definitions[artifact_name]
+ .is_artifact_list,
+ ) for artifact_name, artifact_list in executor_output.artifacts.items()
+ }
+ return {**output_parameters, **output_artifacts}
+
+
+def special_dsl_outputpath_read(
+ parameter_name: str,
+ output_file: str,
+ dtype: pipeline_spec_pb2.ParameterType.ParameterTypeEnum,
+) -> Any:
+ """Reads the text in dsl.OutputPath files in the same way as the remote
+ backend.
+
+ In brief: read strings as strings and JSON load everything else.
+ """
+ try:
+ with open(output_file) as f:
+ value = f.read()
+
+ if dtype == pipeline_spec_pb2.ParameterType.ParameterTypeEnum.STRING:
+ value = value
+ elif dtype == pipeline_spec_pb2.ParameterType.ParameterTypeEnum.BOOLEAN:
+ # permit true/True and false/False, consistent with remote BE
+ value = json.loads(value.lower())
+ else:
+ value = json.loads(value)
+ return value
+ except Exception as e:
+ raise ValueError(
+ f'Could not deserialize output {parameter_name!r} from path {output_file}'
+ ) from e
+
+
+def merge_dsl_output_file_parameters_to_executor_output(
+ executor_input: pipeline_spec_pb2.ExecutorInput,
+ executor_output: pipeline_spec_pb2.ExecutorOutput,
+ component_spec: pipeline_spec_pb2.ComponentSpec,
+) -> pipeline_spec_pb2.ExecutorOutput:
+ """Merges and output parameters specified via dsl.OutputPath with the rest
+ of the ExecutorOutput message."""
+ for parameter_key, output_parameter in executor_input.outputs.parameters.items(
+ ):
+ if os.path.exists(output_parameter.output_file):
+ parameter_value = special_dsl_outputpath_read(
+ parameter_name=parameter_key,
+ output_file=output_parameter.output_file,
+ dtype=component_spec.output_definitions
+ .parameters[parameter_key].parameter_type,
+ )
+ executor_output.parameter_values[parameter_key].CopyFrom(
+ pipeline_spec_builder.to_protobuf_value(parameter_value))
+
+ return executor_output
+
+
+def pb2_value_to_python(value: struct_pb2.Value) -> Any:
+ """Converts protobuf Value to the corresponding Python type."""
+ if value.HasField('null_value'):
+ return None
+ elif value.HasField('number_value'):
+ return value.number_value
+ elif value.HasField('string_value'):
+ return value.string_value
+ elif value.HasField('bool_value'):
+ return value.bool_value
+ elif value.HasField('struct_value'):
+ return pb2_struct_to_python(value.struct_value)
+ elif value.HasField('list_value'):
+ return [pb2_value_to_python(v) for v in value.list_value.values]
+ else:
+ raise ValueError(f'Unknown value type: {value}')
+
+
+def pb2_struct_to_python(struct: struct_pb2.Struct) -> Dict[str, Any]:
+ """Converts protobuf Struct to a dict."""
+ return {k: pb2_value_to_python(v) for k, v in struct.fields.items()}
+
+
+def runtime_artifact_to_dsl_artifact(
+ runtime_artifact: pipeline_spec_pb2.RuntimeArtifact) -> dsl.Artifact:
+ """Converts a single RuntimeArtifact instance to the corresponding
+ dsl.Artifact instance."""
+ return executor.create_artifact_instance(
+ json_format.MessageToDict(runtime_artifact))
+
+
+def artifact_list_to_dsl_artifact(
+ artifact_list: pipeline_spec_pb2.ArtifactList,
+ is_artifact_list: bool,
+) -> Union[dsl.Artifact, List[dsl.Artifact]]:
+ """Converts an ArtifactList instance to a single dsl.Artifact or a list of
+ dsl.Artifacts, depending on whether the ArtifactList is a true list or
+ simply a container for single artifact element."""
+ dsl_artifacts = [
+ runtime_artifact_to_dsl_artifact(artifact_spec)
+ for artifact_spec in artifact_list.artifacts
+ ]
+ return dsl_artifacts if is_artifact_list else dsl_artifacts[0]
+
+
+def add_type_to_executor_output(
+ executor_input: pipeline_spec_pb2.ExecutorInput,
+ executor_output: pipeline_spec_pb2.ExecutorOutput,
+) -> pipeline_spec_pb2.ExecutorOutput:
+ """Adds artifact type information (ArtifactTypeSchema) from the
+ ExecutorInput message to the ExecutorOutput message.
+
+ This information is not present in the ExecutorOutput message
+ written by a task, though it is useful to have it for constructing
+ local outputs. We don't want to change the executor logic and the
+ serialized outputs of all tasks for this case, so we add this extra
+ info to ExecutorOutput in memory.
+ """
+ for key, artifact_list in executor_output.artifacts.items():
+ for artifact in artifact_list.artifacts:
+ artifact.type.CopyFrom(
+ executor_input.outputs.artifacts[key].artifacts[0].type)
+ return executor_output
+
+
+def get_outputs_for_task(
+ executor_input: pipeline_spec_pb2.ExecutorInput,
+ component_spec: pipeline_spec_pb2.ComponentSpec,
+) -> Dict[str, Any]:
+ """Gets outputs from a recently executed task, if available, using the
+ ExecutorInput and ComponentSpec of the task."""
+ executor_output = load_executor_output(
+ executor_output_path=executor_input.outputs.output_file)
+ return get_outputs_from_executor_output(
+ executor_output=executor_output,
+ executor_input=executor_input,
+ component_spec=component_spec,
+ )
diff --git a/sdk/python/kfp/local/executor_output_utils_test.py b/sdk/python/kfp/local/executor_output_utils_test.py
new file mode 100644
index 0000000000..f539c8dc0f
--- /dev/null
+++ b/sdk/python/kfp/local/executor_output_utils_test.py
@@ -0,0 +1,663 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for executor_output_utils.py."""
+
+import json
+import os
+import tempfile
+from typing import List
+import unittest
+
+from absl.testing import parameterized
+from google.protobuf import json_format
+from google.protobuf import struct_pb2
+from kfp import dsl
+from kfp.local import executor_output_utils
+from kfp.local import testing_utilities
+from kfp.pipeline_spec import pipeline_spec_pb2
+
+
+class TestGetOutputsFromMessages(
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def test(self):
+ executor_input = pipeline_spec_pb2.ExecutorInput()
+ json_format.ParseDict(
+ {
+ 'inputs': {
+ 'parameterValues': {
+ 'string_in': 'foo'
+ }
+ },
+ 'outputs': {
+ 'parameters': {
+ 'int_out': {
+ 'outputFile':
+ 'foo/multiple-io-2023-11-09-12-12-05-528112/multiple-io/int_out'
+ },
+ 'str_out': {
+ 'outputFile':
+ 'foo/multiple-io-2023-11-09-12-12-05-528112/multiple-io/str_out'
+ }
+ },
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'type': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ },
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-12-05-528112/multiple-io/dataset_out',
+ 'metadata': {}
+ }]
+ }
+ },
+ 'outputFile':
+ 'foo/multiple-io-2023-11-09-12-12-05-528112/multiple-io/executor_output.json'
+ }
+ }, executor_input)
+ component_spec = pipeline_spec_pb2.ComponentSpec()
+ json_format.ParseDict(
+ {
+ 'inputDefinitions': {
+ 'parameters': {
+ 'string_in': {
+ 'parameterType': 'STRING'
+ }
+ }
+ },
+ 'outputDefinitions': {
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifactType': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ }
+ }
+ },
+ 'parameters': {
+ 'int_out': {
+ 'parameterType': 'NUMBER_INTEGER'
+ },
+ 'str_out': {
+ 'parameterType': 'STRING'
+ }
+ }
+ },
+ 'executorLabel': 'exec-multiple-io'
+ }, component_spec)
+ executor_output = pipeline_spec_pb2.ExecutorOutput()
+ json_format.ParseDict(
+ {
+ 'parameterValues': {
+ 'int_out': 1,
+ 'str_out': 'foo'
+ },
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-12-05-528112/multiple-io/dataset_out',
+ 'metadata': {
+ 'foo': 'bar'
+ }
+ }]
+ }
+ }
+ }, executor_output)
+
+ os.makedirs(os.path.dirname(executor_input.outputs.output_file))
+ with open(executor_input.outputs.output_file, 'w') as f:
+ f.write(json_format.MessageToJson(executor_output))
+ outputs = executor_output_utils.get_outputs_for_task(
+ executor_input=executor_input,
+ component_spec=component_spec,
+ )
+
+ self.assertEqual(outputs['int_out'], 1)
+ self.assertEqual(outputs['str_out'], 'foo')
+ assert_artifacts_equal(
+ self,
+ outputs['dataset_out'],
+ dsl.Dataset(
+ name='dataset_out',
+ uri='foo/multiple-io-2023-11-09-12-12-05-528112/multiple-io/dataset_out',
+ metadata={'foo': 'bar'}),
+ )
+
+
+class TestLoadExecutorOutput(unittest.TestCase):
+
+ def test_exists(self):
+ with tempfile.TemporaryDirectory() as tempdir:
+ executor_output = pipeline_spec_pb2.ExecutorOutput(
+ parameter_values={
+ 'foo': struct_pb2.Value(string_value='foo_value')
+ })
+ path = os.path.join(tempdir, 'executor_output.json')
+ testing_utilities.write_proto_to_json_file(executor_output, path)
+
+ actual = executor_output_utils.load_executor_output(path)
+ expected = pipeline_spec_pb2.ExecutorOutput()
+ expected.parameter_values['foo'].CopyFrom(
+ struct_pb2.Value(string_value='foo_value'))
+ self.assertEqual(
+ actual.SerializeToString(deterministic=True),
+ expected.SerializeToString(deterministic=True),
+ )
+
+ def test_not_exists(self):
+ non_existent_path = 'non_existent_path.json'
+ actual = executor_output_utils.load_executor_output(non_existent_path)
+ expected = pipeline_spec_pb2.ExecutorOutput()
+ self.assertEqual(
+ actual.SerializeToString(deterministic=True),
+ expected.SerializeToString(deterministic=True),
+ )
+
+
+class TestGetOutputsFromExecutorOutput(unittest.TestCase):
+
+ def test_param_and_artifact_outputs(self):
+ # include the special case of an output int for more complete testing of behavior
+ executor_output = pipeline_spec_pb2.ExecutorOutput()
+ json_format.ParseDict(
+ {
+ 'parameterValues': {
+ 'int_out': 1,
+ 'str_out': 'foo'
+ },
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'uri':
+ 'foo/multiple-io-2023-11-09-11-31-31-064429/multiple-io/dataset_out',
+ 'metadata': {
+ 'foo': 'bar'
+ }
+ }]
+ }
+ }
+ }, executor_output)
+ executor_input = pipeline_spec_pb2.ExecutorInput()
+ json_format.ParseDict(
+ {
+ 'inputs': {
+ 'parameterValues': {
+ 'string_in': 'foo'
+ }
+ },
+ 'outputs': {
+ 'parameters': {
+ 'int_out': {
+ 'outputFile':
+ 'foo/temp_root/multiple-io-2023-11-09-11-31-31-064429/multiple-io/int_out'
+ },
+ 'str_out': {
+ 'outputFile':
+ 'foo/multiple-io-2023-11-09-11-31-31-064429/multiple-io/str_out'
+ }
+ },
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'type': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ },
+ 'uri':
+ 'foo/multiple-io-2023-11-09-11-31-31-064429/multiple-io/dataset_out',
+ 'metadata': {}
+ }]
+ }
+ },
+ 'outputFile':
+ 'foo/multiple-io-2023-11-09-11-31-31-064429/multiple-io/executor_output.json'
+ }
+ }, executor_input)
+ component_spec = pipeline_spec_pb2.ComponentSpec()
+ json_format.ParseDict(
+ {
+ 'inputDefinitions': {
+ 'parameters': {
+ 'string_in': {
+ 'parameterType': 'STRING'
+ }
+ }
+ },
+ 'outputDefinitions': {
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifactType': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ }
+ }
+ },
+ 'parameters': {
+ 'int_out': {
+ 'parameterType': 'NUMBER_INTEGER'
+ },
+ 'str_out': {
+ 'parameterType': 'STRING'
+ }
+ }
+ },
+ 'executorLabel': 'exec-multiple-io'
+ }, component_spec)
+
+ outputs = executor_output_utils.get_outputs_from_executor_output(
+ executor_output=executor_output,
+ executor_input=executor_input,
+ component_spec=component_spec,
+ )
+ self.assertIsInstance(outputs, dict)
+ self.assertIsInstance(outputs['dataset_out'], dsl.Dataset)
+ self.assertEqual(outputs['dataset_out'].name, 'dataset_out')
+ self.assertEqual(
+ outputs['dataset_out'].uri,
+ 'foo/multiple-io-2023-11-09-11-31-31-064429/multiple-io/dataset_out'
+ )
+ self.assertEqual(outputs['dataset_out'].metadata, {'foo': 'bar'})
+ self.assertEqual(outputs['int_out'], 1)
+ self.assertEqual(outputs['str_out'], 'foo')
+
+
+class TestPb2ValueToPython(unittest.TestCase):
+
+ def test_null(self):
+ inp = struct_pb2.Value(null_value=struct_pb2.NullValue.NULL_VALUE)
+ actual = executor_output_utils.pb2_value_to_python(inp)
+ expected = None
+ self.assertEqual(actual, expected)
+
+ def test_string(self):
+ inp = struct_pb2.Value(string_value='foo_value')
+ actual = executor_output_utils.pb2_value_to_python(inp)
+ expected = 'foo_value'
+ self.assertEqual(actual, expected)
+
+ def test_number_int(self):
+ inp = struct_pb2.Value(number_value=1)
+ actual = executor_output_utils.pb2_value_to_python(inp)
+ expected = 1.0
+ self.assertEqual(actual, expected)
+
+ def test_number_float(self):
+ inp = struct_pb2.Value(number_value=1.0)
+ actual = executor_output_utils.pb2_value_to_python(inp)
+ expected = 1.0
+ self.assertEqual(actual, expected)
+
+ def test_bool(self):
+ inp = struct_pb2.Value(bool_value=True)
+ actual = executor_output_utils.pb2_value_to_python(inp)
+ expected = True
+ self.assertIs(actual, expected)
+
+ def test_dict(self):
+ struct_value = struct_pb2.Struct()
+ struct_value.fields['my_key'].string_value = 'my_value'
+ struct_value.fields['other_key'].bool_value = True
+ inp = struct_pb2.Value(struct_value=struct_value)
+ actual = executor_output_utils.pb2_value_to_python(inp)
+ expected = {'my_key': 'my_value', 'other_key': True}
+ self.assertEqual(actual, expected)
+
+
+class TestRuntimeArtifactToDslArtifact(unittest.TestCase):
+
+ def test_artifact(self):
+ metadata = struct_pb2.Struct()
+ metadata.fields['foo'].string_value = 'bar'
+ type_ = pipeline_spec_pb2.ArtifactTypeSchema(
+ schema_title='system.Artifact',
+ schema_version='0.0.1',
+ )
+ runtime_artifact = pipeline_spec_pb2.RuntimeArtifact(
+ name='a',
+ uri='gs://bucket/foo',
+ metadata=metadata,
+ type=type_,
+ )
+ actual = executor_output_utils.runtime_artifact_to_dsl_artifact(
+ runtime_artifact)
+ expected = dsl.Artifact(
+ name='a',
+ uri='gs://bucket/foo',
+ metadata={'foo': 'bar'},
+ )
+ assert_artifacts_equal(self, actual, expected)
+
+ def test_dataset(self):
+ metadata = struct_pb2.Struct()
+ metadata.fields['baz'].string_value = 'bat'
+ type_ = pipeline_spec_pb2.ArtifactTypeSchema(
+ schema_title='system.Dataset',
+ schema_version='0.0.1',
+ )
+ runtime_artifact = pipeline_spec_pb2.RuntimeArtifact(
+ name='d',
+ uri='gs://bucket/foo',
+ metadata=metadata,
+ type=type_,
+ )
+ actual = executor_output_utils.runtime_artifact_to_dsl_artifact(
+ runtime_artifact)
+ expected = dsl.Dataset(
+ name='d',
+ uri='gs://bucket/foo',
+ metadata={'baz': 'bat'},
+ )
+ assert_artifacts_equal(self, actual, expected)
+
+
+class TestArtifactListToDslArtifact(unittest.TestCase):
+
+ def test_not_list(self):
+ metadata = struct_pb2.Struct()
+ metadata.fields['foo'].string_value = 'bar'
+ type_ = pipeline_spec_pb2.ArtifactTypeSchema(
+ schema_title='system.Artifact',
+ schema_version='0.0.1',
+ )
+ runtime_artifact = pipeline_spec_pb2.RuntimeArtifact(
+ name='a',
+ uri='gs://bucket/foo',
+ metadata=metadata,
+ type=type_,
+ )
+ artifact_list = pipeline_spec_pb2.ArtifactList(
+ artifacts=[runtime_artifact])
+
+ actual = executor_output_utils.artifact_list_to_dsl_artifact(
+ artifact_list,
+ is_artifact_list=False,
+ )
+ expected = dsl.Artifact(
+ name='a',
+ uri='gs://bucket/foo',
+ metadata={'foo': 'bar'},
+ )
+ assert_artifacts_equal(self, actual, expected)
+
+ def test_single_entry_list(self):
+ metadata = struct_pb2.Struct()
+ metadata.fields['foo'].string_value = 'bar'
+ type_ = pipeline_spec_pb2.ArtifactTypeSchema(
+ schema_title='system.Dataset',
+ schema_version='0.0.1',
+ )
+ runtime_artifact = pipeline_spec_pb2.RuntimeArtifact(
+ name='a',
+ uri='gs://bucket/foo',
+ metadata=metadata,
+ type=type_,
+ )
+ artifact_list = pipeline_spec_pb2.ArtifactList(
+ artifacts=[runtime_artifact])
+
+ actual = executor_output_utils.artifact_list_to_dsl_artifact(
+ artifact_list,
+ is_artifact_list=True,
+ )
+ expected = [
+ dsl.Dataset(
+ name='a',
+ uri='gs://bucket/foo',
+ metadata={'foo': 'bar'},
+ )
+ ]
+ assert_artifact_lists_equal(self, actual, expected)
+
+ def test_multi_entry_list(self):
+ metadata = struct_pb2.Struct()
+ metadata.fields['foo'].string_value = 'bar'
+ type_ = pipeline_spec_pb2.ArtifactTypeSchema(
+ schema_title='system.Dataset',
+ schema_version='0.0.1',
+ )
+ runtime_artifact1 = pipeline_spec_pb2.RuntimeArtifact(
+ name='a',
+ uri='gs://bucket/foo/a',
+ metadata=metadata,
+ type=type_,
+ )
+ runtime_artifact2 = pipeline_spec_pb2.RuntimeArtifact(
+ name='b',
+ uri='gs://bucket/foo/b',
+ type=type_,
+ )
+ artifact_list = pipeline_spec_pb2.ArtifactList(
+ artifacts=[runtime_artifact1, runtime_artifact2])
+
+ actual = executor_output_utils.artifact_list_to_dsl_artifact(
+ artifact_list,
+ is_artifact_list=True,
+ )
+ expected = [
+ dsl.Dataset(
+ name='a',
+ uri='gs://bucket/foo/a',
+ metadata={'foo': 'bar'},
+ ),
+ dsl.Dataset(
+ name='b',
+ uri='gs://bucket/foo/b',
+ )
+ ]
+
+ assert_artifact_lists_equal(self, actual, expected)
+
+
+class AddTypeToExecutorOutput(unittest.TestCase):
+
+ def test(self):
+ executor_input = pipeline_spec_pb2.ExecutorInput()
+ json_format.ParseDict(
+ {
+ 'inputs': {},
+ 'outputs': {
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'type': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ },
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/dataset_out',
+ 'metadata': {}
+ }]
+ },
+ 'model_out': {
+ 'artifacts': [{
+ 'name':
+ 'model_out',
+ 'type': {
+ 'schemaTitle': 'system.Model',
+ 'schemaVersion': '0.0.1'
+ },
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/model_out',
+ 'metadata': {}
+ }]
+ }
+ },
+ 'outputFile':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/executor_output.json'
+ }
+ }, executor_input)
+ executor_output = pipeline_spec_pb2.ExecutorOutput()
+ json_format.ParseDict(
+ {
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/dataset_out',
+ 'metadata': {
+ 'foo': 'bar'
+ }
+ }]
+ },
+ 'model_out': {
+ 'artifacts': [{
+ 'name':
+ 'model_out',
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/model_out',
+ 'metadata': {
+ 'baz': 'bat'
+ }
+ }]
+ }
+ }
+ }, executor_output)
+
+ expected = pipeline_spec_pb2.ExecutorOutput()
+ json_format.ParseDict(
+ {
+ 'artifacts': {
+ 'dataset_out': {
+ 'artifacts': [{
+ 'name':
+ 'dataset_out',
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/dataset_out',
+ 'metadata': {
+ 'foo': 'bar'
+ },
+ 'type': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ },
+ }]
+ },
+ 'model_out': {
+ 'artifacts': [{
+ 'name':
+ 'model_out',
+ 'uri':
+ 'foo/multiple-io-2023-11-09-12-04-18-616263/multiple-io/model_out',
+ 'metadata': {
+ 'baz': 'bat'
+ },
+ 'type': {
+ 'schemaTitle': 'system.Model',
+ 'schemaVersion': '0.0.1'
+ },
+ }]
+ }
+ }
+ }, expected)
+
+ actual = executor_output_utils.add_type_to_executor_output(
+ executor_input=executor_input,
+ executor_output=executor_output,
+ )
+ self.assertEqual(actual, expected)
+
+
+class TestSpecialDslOutputPathRead(parameterized.TestCase):
+
+ @parameterized.parameters([
+ ('foo', 'foo',
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.STRING),
+ ('foo', 'foo',
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.STRING),
+ ('true', True,
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.BOOLEAN),
+ ('True', True,
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.BOOLEAN),
+ ('false', False,
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.BOOLEAN),
+ ('False', False,
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.BOOLEAN),
+ (json.dumps({'x': 'y'}), {
+ 'x': 'y'
+ }, pipeline_spec_pb2.ParameterType.ParameterTypeEnum.STRUCT),
+ ('3.14', 3.14,
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.NUMBER_DOUBLE),
+ ('100', 100,
+ pipeline_spec_pb2.ParameterType.ParameterTypeEnum.NUMBER_INTEGER),
+ ])
+ def test(self, written, expected, dtype):
+ with tempfile.TemporaryDirectory() as tempdir:
+ output_file = os.path.join(tempdir, 'Output')
+ with open(output_file, 'w') as f:
+ f.write(written)
+
+ actual = executor_output_utils.special_dsl_outputpath_read(
+ parameter_name='name',
+ output_file=output_file,
+ dtype=dtype,
+ )
+
+ self.assertEqual(actual, expected)
+
+ def test_exception(self):
+ with tempfile.TemporaryDirectory() as tempdir:
+ output_file = os.path.join(tempdir, 'Output')
+ with open(output_file, 'w') as f:
+ f.write(str({'x': 'y'}))
+ with self.assertRaisesRegex(
+ ValueError,
+ r"Could not deserialize output 'name' from path"):
+ executor_output_utils.special_dsl_outputpath_read(
+ parameter_name='name',
+ output_file=output_file,
+ dtype=pipeline_spec_pb2.ParameterType.ParameterTypeEnum
+ .STRUCT,
+ )
+
+
+def assert_artifacts_equal(
+ test_class: unittest.TestCase,
+ a1: dsl.Artifact,
+ a2: dsl.Artifact,
+) -> None:
+ test_class.assertEqual(a1.name, a2.name)
+ test_class.assertEqual(a1.uri, a2.uri)
+ test_class.assertEqual(a1.metadata, a2.metadata)
+ test_class.assertEqual(a1.schema_title, a2.schema_title)
+ test_class.assertEqual(a1.schema_version, a2.schema_version)
+ test_class.assertIsInstance(a1, type(a2))
+
+
+def assert_artifact_lists_equal(
+ test_class: unittest.TestCase,
+ l1: List[dsl.Artifact],
+ l2: List[dsl.Artifact],
+) -> None:
+ test_class.assertEqual(len(l1), len(l2))
+ for a1, a2 in zip(l1, l2):
+ assert_artifacts_equal(test_class, a1, a2)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/logging_utils.py b/sdk/python/kfp/local/logging_utils.py
new file mode 100644
index 0000000000..54f559604c
--- /dev/null
+++ b/sdk/python/kfp/local/logging_utils.py
@@ -0,0 +1,141 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilitites for formatting, coloring, and controlling the output of logs."""
+import builtins
+import contextlib
+import datetime
+import logging
+import sys
+from typing import Any, Dict, Generator, List
+
+from kfp import dsl
+
+
+class Color:
+ CYAN = '\033[96m'
+ GREEN = '\033[92m'
+ RED = '\033[91m'
+ RESET = '\033[0m'
+
+
+class MillisecondFormatter(logging.Formatter):
+
+ def formatTime(
+ self,
+ record: logging.LogRecord,
+ datefmt: str = None,
+ ) -> str:
+ created = datetime.datetime.fromtimestamp(record.created)
+ s = created.strftime(datefmt)
+ # truncate microseconds to milliseconds
+ return s[:-3]
+
+
+@contextlib.contextmanager
+def local_logger_context() -> Generator[None, None, None]:
+ """Context manager for creating and reseting the local execution logger."""
+
+ logger = logging.getLogger()
+ original_level = logger.level
+ original_handlers = logger.handlers[:]
+ formatter = MillisecondFormatter(
+ fmt='%(asctime)s - %(levelname)s - %(message)s',
+ datefmt='%H:%M:%S.%f',
+ )
+ # use sys.stdout so that both inner process and outer process logs
+ # go to stdout
+ # this is needed for logs to present sequentially in a colab notebook,
+ # since stderr will print above stdout
+ handler = logging.StreamHandler(sys.stdout)
+ handler.setFormatter(formatter)
+ logger.handlers.clear()
+ logger.addHandler(handler)
+ logger.setLevel(logging.INFO)
+
+ try:
+ yield
+ finally:
+ logger.setLevel(original_level)
+ logger.handlers.clear()
+ for handler in original_handlers:
+ logger.addHandler(handler)
+
+
+@contextlib.contextmanager
+def indented_print(num_spaces: int = 4) -> Generator[None, None, None]:
+ """Context manager to indent all print statements in its scope by
+ num_prints.
+
+ Useful for visually separating a subprocess logs from the outer
+ process logs.
+ """
+ original_print = builtins.print
+
+ def indented_print_function(*args, **kwargs):
+ original_print(' ' * num_spaces, end='')
+ return original_print(*args, **kwargs)
+
+ builtins.print = indented_print_function
+ try:
+ yield
+ finally:
+ builtins.print = original_print
+
+
+def color_text(text: str, color: Color) -> str:
+ return f'{color}{text}{Color.RESET}'
+
+
+def make_log_lines_for_artifact(artifact: dsl.Artifact,) -> List[str]:
+ """Returns a list of log lines that represent a single artifact output."""
+ artifact_class_name_and_paren = f'{artifact.__class__.__name__}( '
+ # name
+ artifact_lines = [f"{artifact_class_name_and_paren}name='{artifact.name}',"]
+ newline_spaces = len(artifact_class_name_and_paren) * ' '
+ # uri
+ artifact_lines.append(f"{newline_spaces}uri='{artifact.uri}',")
+ # metadata
+ artifact_lines.append(f'{newline_spaces}metadata={artifact.metadata} )')
+ return artifact_lines
+
+
+def make_log_lines_for_outputs(outputs: Dict[str, Any]) -> List[str]:
+ """Returns a list of log lines to repesent the outputs of a task."""
+ INDENT = ' ' * 4
+ SEPARATOR = ': '
+ output_lines = []
+ for key, value in outputs.items():
+ key_chars = INDENT + key + SEPARATOR
+
+ # present artifacts
+ if isinstance(value, dsl.Artifact):
+ artifact_lines = make_log_lines_for_artifact(value)
+
+ first_artifact_line = artifact_lines[0]
+ output_lines.append(f'{key_chars}{first_artifact_line}')
+
+ remaining_artifact_lines = artifact_lines[1:]
+ # indent to align with first char in artifact
+ # to visually separate output keys
+ remaining_artifact_lines = [
+ len(key_chars) * ' ' + l for l in remaining_artifact_lines
+ ]
+ output_lines.extend(remaining_artifact_lines)
+
+ # present params
+ else:
+ value = f"'{value}'" if isinstance(value, str) else value
+ output_lines.append(f'{key_chars}{value}')
+
+ return output_lines
diff --git a/sdk/python/kfp/local/logging_utils_test.py b/sdk/python/kfp/local/logging_utils_test.py
new file mode 100644
index 0000000000..53a03a8913
--- /dev/null
+++ b/sdk/python/kfp/local/logging_utils_test.py
@@ -0,0 +1,206 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for logging_utils.py."""
+
+import io
+import unittest
+from unittest import mock
+
+from kfp import dsl
+from kfp.local import logging_utils
+
+
+class TestIndentedPrint(unittest.TestCase):
+
+ @mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test(self, mocked_stdout):
+ with logging_utils.indented_print(num_spaces=6):
+ print('foo should be indented')
+ expected = ' foo should be indented\n'
+ actual = mocked_stdout.getvalue()
+ self.assertEqual(
+ actual,
+ expected,
+ )
+
+
+class TestColorText(unittest.TestCase):
+
+ def test_cyan(self):
+
+ actual = logging_utils.color_text(
+ 'text to color',
+ logging_utils.Color.CYAN,
+ )
+ expected = '\x1b[91mtext to color\x1b[0m'
+ self.assertEqual(actual, expected)
+
+ def test_cyan(self):
+
+ actual = logging_utils.color_text(
+ 'text to color',
+ logging_utils.Color.RED,
+ )
+ expected = '\x1b[91mtext to color\x1b[0m'
+ self.assertEqual(actual, expected)
+
+
+class TestRenderArtifact(unittest.TestCase):
+
+ def test_empty(self):
+ actual = logging_utils.make_log_lines_for_artifact(dsl.Artifact())
+ expected = [
+ "Artifact( name='',",
+ " uri='',",
+ ' metadata={} )',
+ ]
+ self.assertListEqual(actual, expected)
+
+ def test_contains_value(self):
+ actual = logging_utils.make_log_lines_for_artifact(
+ dsl.Model(
+ name='my_artifact',
+ uri='/local/foo/bar',
+ metadata={
+ 'dict_field': {
+ 'baz': 'bat'
+ },
+ 'float_field': 3.14
+ }))
+ expected = [
+ "Model( name='my_artifact',",
+ " uri='/local/foo/bar',",
+ " metadata={'dict_field': {'baz': 'bat'}, 'float_field': 3.14} )",
+ ]
+ self.assertListEqual(actual, expected)
+
+
+class TestMakeLogLinesForOutputs(unittest.TestCase):
+
+ def test_empty(self):
+ actual = logging_utils.make_log_lines_for_outputs(dict())
+ expected = []
+ self.assertListEqual(actual, expected)
+
+ def test_only_params(self):
+ actual = logging_utils.make_log_lines_for_outputs({
+ 'foo': 'bar',
+ 'baz': 100,
+ 'bat': 1.0,
+ 'brap': True,
+ 'my_list': [1, 2, 3],
+ 'my_dict': {
+ 'foo': 'bar'
+ }
+ })
+ expected = [
+ " foo: 'bar'",
+ ' baz: 100',
+ ' bat: 1.0',
+ ' brap: True',
+ ' my_list: [1, 2, 3]',
+ " my_dict: {'foo': 'bar'}",
+ ]
+ self.assertListEqual(actual, expected)
+
+ def test_only_artifacts(self):
+ actual = logging_utils.make_log_lines_for_outputs({
+ 'my_artifact':
+ dsl.Artifact(name=''),
+ 'my_model':
+ dsl.Model(
+ name='my_artifact',
+ uri='/local/foo/bar/1234567890/1234567890/1234567890/1234567890/1234567890',
+ metadata={
+ 'dict_field': {
+ 'baz': 'bat'
+ },
+ 'float_field': 3.14
+ }),
+ 'my_dataset':
+ dsl.Dataset(
+ name='my_dataset',
+ uri='/local/foo/baz',
+ metadata={},
+ ),
+ })
+ expected = [
+ " my_artifact: Artifact( name='',",
+ " uri='',",
+ ' metadata={} )',
+ " my_model: Model( name='my_artifact',",
+ " uri='/local/foo/bar/1234567890/1234567890/1234567890/1234567890/1234567890',",
+ " metadata={'dict_field': {'baz': 'bat'}, 'float_field': 3.14} )",
+ " my_dataset: Dataset( name='my_dataset',",
+ " uri='/local/foo/baz',",
+ ' metadata={} )',
+ ]
+ self.assertListEqual(actual, expected)
+
+ def test_mix_params_and_artifacts(self):
+ actual = logging_utils.make_log_lines_for_outputs({
+ 'foo':
+ 'bar',
+ 'baz':
+ 100,
+ 'bat':
+ 1.0,
+ 'brap':
+ True,
+ 'my_list': [1, 2, 3],
+ 'my_dict': {
+ 'foo': 'bar'
+ },
+ 'my_artifact':
+ dsl.Artifact(name=''),
+ 'my_model':
+ dsl.Model(
+ name='my_artifact',
+ uri='/local/foo/bar/1234567890/1234567890/1234567890/1234567890/1234567890',
+ metadata={
+ 'dict_field': {
+ 'baz': 'bat'
+ },
+ 'float_field': 3.14
+ }),
+ 'my_dataset':
+ dsl.Dataset(
+ name='my_dataset',
+ uri='/local/foo/baz',
+ metadata={},
+ ),
+ })
+ expected = [
+ " foo: 'bar'",
+ ' baz: 100',
+ ' bat: 1.0',
+ ' brap: True',
+ ' my_list: [1, 2, 3]',
+ " my_dict: {'foo': 'bar'}",
+ " my_artifact: Artifact( name='',",
+ " uri='',",
+ ' metadata={} )',
+ " my_model: Model( name='my_artifact',",
+ " uri='/local/foo/bar/1234567890/1234567890/1234567890/1234567890/1234567890',",
+ " metadata={'dict_field': {'baz': 'bat'}, 'float_field': 3.14} )",
+ " my_dataset: Dataset( name='my_dataset',",
+ " uri='/local/foo/baz',",
+ ' metadata={} )',
+ ]
+
+ self.assertListEqual(actual, expected)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/placeholder_utils.py b/sdk/python/kfp/local/placeholder_utils.py
new file mode 100644
index 0000000000..3333fff6e5
--- /dev/null
+++ b/sdk/python/kfp/local/placeholder_utils.py
@@ -0,0 +1,254 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities for working with placeholders."""
+import json
+import random
+import re
+from typing import Any, Dict, List, Optional, Union
+
+from kfp import dsl
+
+
+def make_random_id():
+ """Makes a random 8 digit integer."""
+ return str(random.randint(0, 99999999))
+
+
+def replace_placeholders(
+ full_command: List[str],
+ executor_input_dict: Dict[str, Any],
+ pipeline_resource_name: str,
+ task_resource_name: str,
+ pipeline_root: str,
+) -> List[str]:
+ """Iterates over each element in the command and replaces placeholders."""
+ unique_pipeline_id = make_random_id()
+ unique_task_id = make_random_id()
+ provided_inputs = get_provided_inputs(executor_input_dict)
+ full_command = [
+ resolve_struct_placeholders(
+ placeholder,
+ provided_inputs,
+ ) for placeholder in full_command
+ ]
+ full_command = flatten_list(full_command)
+ resolved_command = []
+ for el in full_command:
+ resolved_el = resolve_individual_placeholder(
+ element=el,
+ executor_input_dict=executor_input_dict,
+ pipeline_resource_name=pipeline_resource_name,
+ task_resource_name=task_resource_name,
+ pipeline_root=pipeline_root,
+ pipeline_job_id=unique_pipeline_id,
+ pipeline_task_id=unique_task_id,
+ )
+ if resolved_el is None:
+ continue
+ elif isinstance(resolved_el, str):
+ resolved_command.append(resolved_el)
+ elif isinstance(resolved_el, list):
+ resolved_command.extend(resolved_el)
+ else:
+ raise ValueError(
+ f'Got unknown command element {resolved_el} of type {type(resolved_el)}.'
+ )
+ return resolved_command
+
+
+def flatten_list(l: List[Union[str, list, None]]) -> List[str]:
+ """Iteratively flattens arbitrarily deeply nested lists, filtering out
+ elements that are None."""
+ result = []
+ stack = l.copy()
+ while stack:
+ element = stack.pop(0)
+ if isinstance(element, list):
+ stack = element + stack
+ elif element is not None:
+ result.append(element)
+ return result
+
+
+def get_provided_inputs(executor_input_dict: Dict[str, Any]) -> Dict[str, Any]:
+ params = executor_input_dict.get('inputs', {}).get('parameterValues', {})
+ pkeys = [k for k, v in params.items() if v is not None]
+ artifacts = executor_input_dict.get('inputs', {}).get('artifacts', {})
+ akeys = [k for k, v in artifacts.items() if v is not None]
+ return pkeys + akeys
+
+
+def get_value_using_path(
+ dictionary: Dict[str, Any],
+ path: List[str],
+) -> Optional[Any]:
+ list_or_dict = dictionary
+ if not path:
+ raise ValueError('path cannot be empty.')
+ try:
+ for p in path:
+ list_or_dict = list_or_dict[p]
+ return list_or_dict
+ except KeyError:
+ return None
+
+
+def convert_placeholder_parts_to_path(parts: List[str]) -> List[str]:
+ # if inputs, parameters --> parameterValues
+ if parts[0] == 'inputs' and parts[1] == 'parameters':
+ parts[1] = 'parameterValues'
+
+ # if outputs, parameter output_file --> outputFile
+ if parts[0] == 'outputs' and parts[1] == 'parameters' and parts[
+ 3] == 'output_file':
+ parts[3] = 'outputFile'
+
+ # if artifacts...
+ if parts[1] == 'artifacts':
+
+ # ...need to get nested artifact object...
+ parts.insert(3, 'artifacts')
+ # ...and first entry in list with index 0
+ parts.insert(4, 0)
+
+ # for local, path is the uri
+ if parts[5] == 'path':
+ parts[5] = 'uri'
+
+ return parts
+
+
+def resolve_io_placeholders(
+ executor_input: Dict[str, Any],
+ command: str,
+) -> str:
+ placeholders = re.findall(r'\{\{\$\.(.*?)\}\}', command)
+
+ # e.g., placeholder = "inputs.parameters[''text'']"
+ for placeholder in placeholders:
+ if 'json_escape' in placeholder:
+ raise ValueError('JSON escape placeholders are not supported.')
+
+ # e.g., parts = ['inputs', 'parameters', '', 'text', '', '']
+ parts = re.split(r'\.|\[|\]|\'\'|\'', placeholder)
+
+ # e.g., nonempty_parts = ['inputs', 'parameters', 'text']
+ nonempty_parts = [part for part in parts if part]
+
+ # e.g., path = ['inputs', 'parameterValues', 'text']
+ path = convert_placeholder_parts_to_path(nonempty_parts)
+
+ # e.g., path = ['inputs', 'parameterValues', 'text']
+ value = get_value_using_path(executor_input, path)
+ if not isinstance(value, str):
+ # even if value is None, should json.dumps to null
+ # and still resolve placeholder
+ value = json.dumps(value)
+ command = command.replace('{{$.' + placeholder + '}}', value)
+
+ return command
+
+
+def resolve_struct_placeholders(
+ placeholder: str,
+ provided_inputs: List[str],
+) -> List[Any]:
+ """Resolves IfPresent and Concat placeholders to an arbitrarily deeply
+ nested list of strings, which may contain None."""
+
+ # throughout, filter out None for the case where IfPresent False and no else
+ def filter_none(l: List[Any]) -> List[Any]:
+ return [e for e in l if e is not None]
+
+ def recursively_resolve_struct(placeholder: Dict[str, Any]) -> str:
+ if isinstance(placeholder, str):
+ return placeholder
+ elif isinstance(placeholder, list):
+ raise ValueError(
+ f"You have an incorrectly nested {dsl.IfPresentPlaceholder!r} with a list provided for 'then' or 'else'."
+ )
+
+ first_key = list(placeholder.keys())[0]
+ if first_key == 'Concat':
+ concat = [
+ recursively_resolve_struct(p) for p in placeholder['Concat']
+ ]
+ return ''.join(filter_none(concat))
+ elif first_key == 'IfPresent':
+ inner_struct = placeholder['IfPresent']
+ if inner_struct['InputName'] in provided_inputs:
+ then = inner_struct['Then']
+ if isinstance(then, str):
+ return then
+ elif isinstance(then, list):
+ return filter_none(
+ [recursively_resolve_struct(p) for p in then])
+ elif isinstance(then, dict):
+ return recursively_resolve_struct(then)
+ else:
+ else_ = inner_struct.get('Else')
+ if else_ is None:
+ return else_
+ if isinstance(else_, str):
+ return else_
+ elif isinstance(else_, list):
+ return filter_none(
+ [recursively_resolve_struct(p) for p in else_])
+ elif isinstance(else_, dict):
+ return recursively_resolve_struct(else_)
+ else:
+ raise ValueError
+
+ if placeholder.startswith('{"Concat": ') or placeholder.startswith(
+ '{"IfPresent": '):
+ des_placeholder = json.loads(placeholder)
+ return recursively_resolve_struct(des_placeholder)
+ else:
+ return placeholder
+
+
+def resolve_individual_placeholder(
+ element: str,
+ executor_input_dict: Dict[str, Any],
+ pipeline_resource_name: str,
+ task_resource_name: str,
+ pipeline_root: str,
+ pipeline_job_id: str,
+ pipeline_task_id: str,
+) -> str:
+ """Replaces placeholders for a single element."""
+ # match on literal for constant placeholders
+ PLACEHOLDERS = {
+ r'{{$.outputs.output_file}}':
+ executor_input_dict['outputs']['outputFile'],
+ r'{{$.outputMetadataUri}}':
+ executor_input_dict['outputs']['outputFile'],
+ r'{{$}}':
+ json.dumps(executor_input_dict),
+ dsl.PIPELINE_JOB_NAME_PLACEHOLDER:
+ pipeline_resource_name,
+ dsl.PIPELINE_JOB_ID_PLACEHOLDER:
+ pipeline_job_id,
+ dsl.PIPELINE_TASK_NAME_PLACEHOLDER:
+ task_resource_name,
+ dsl.PIPELINE_TASK_ID_PLACEHOLDER:
+ pipeline_task_id,
+ dsl.PIPELINE_ROOT_PLACEHOLDER:
+ pipeline_root,
+ }
+ for placeholder, value in PLACEHOLDERS.items():
+ element = element.replace(placeholder, value)
+
+ # match non-constant placeholders (i.e., have key(s))
+ return resolve_io_placeholders(executor_input_dict, element)
diff --git a/sdk/python/kfp/local/placeholder_utils_test.py b/sdk/python/kfp/local/placeholder_utils_test.py
new file mode 100644
index 0000000000..7ecd71dfa0
--- /dev/null
+++ b/sdk/python/kfp/local/placeholder_utils_test.py
@@ -0,0 +1,378 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for placeholder_utils.py."""
+
+import json
+from typing import List, Optional
+import unittest
+
+from absl.testing import parameterized
+from google.protobuf import json_format
+from kfp.local import placeholder_utils
+from kfp.pipeline_spec import pipeline_spec_pb2
+
+executor_input = pipeline_spec_pb2.ExecutorInput()
+json_format.ParseDict(
+ {
+ 'inputs': {
+ 'parameterValues': {
+ 'boolean': False,
+ 'dictionary': {
+ 'foo': 'bar'
+ },
+ }
+ },
+ 'outputs': {
+ 'parameters': {
+ 'Output': {
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output'
+ }
+ },
+ 'artifacts': {
+ 'out_a': {
+ 'artifacts': [{
+ 'name':
+ 'out_a',
+ 'type': {
+ 'schemaTitle': 'system.Dataset',
+ 'schemaVersion': '0.0.1'
+ },
+ 'uri':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/out_a',
+ 'metadata': {
+ 'foo': {
+ 'bar': 'baz'
+ }
+ }
+ }]
+ }
+ },
+ 'outputFile':
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json'
+ }
+ }, executor_input)
+
+EXECUTOR_INPUT_DICT = json_format.MessageToDict(executor_input)
+
+
+class TestReplacePlaceholders(unittest.TestCase):
+ # most of the logic is tested in TestReplacePlaceholderForElement, so this is just a basic test to invoke the code and make sure the placeholder resolution is applied correctly to every element in the list
+
+ def test(self):
+ full_command = [
+ 'echo',
+ 'something before the placeholder {{$}}',
+ 'something else',
+ '{{$.outputs.output_file}}',
+ ]
+ actual = placeholder_utils.replace_placeholders(
+ full_command=full_command,
+ executor_input_dict=EXECUTOR_INPUT_DICT,
+ pipeline_resource_name='my-pipeline-2023-10-10-13-32-59-420710',
+ task_resource_name='comp',
+ pipeline_root='/foo/bar/my-pipeline-2023-10-10-13-32-59-420710',
+ )
+ expected = [
+ 'echo',
+ f'something before the placeholder {json.dumps(EXECUTOR_INPUT_DICT)}',
+ 'something else',
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json',
+ ]
+ self.assertEqual(actual, expected)
+
+
+class TestResolveIndividualPlaceholder(parameterized.TestCase):
+
+ # TODO: consider supporting JSON escape
+ # TODO: update when input artifact constants supported
+ # TODO: update when output lists of artifacts are supported
+ @parameterized.parameters([
+ (
+ '{{$}}',
+ json.dumps(EXECUTOR_INPUT_DICT),
+ ),
+ (
+ '{{$.outputs.output_file}}',
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json',
+ ),
+ (
+ '{{$.outputMetadataUri}}',
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/executor_output.json',
+ ),
+ (
+ '{{$.pipeline_job_name}}',
+ 'my-pipeline-2023-10-10-13-32-59-420710',
+ ),
+ (
+ '{{$.pipeline_job_uuid}}',
+ '123456789',
+ ),
+ (
+ '{{$.pipeline_task_name}}',
+ 'comp',
+ ),
+ (
+ '{{$.pipeline_task_uuid}}',
+ '987654321',
+ ),
+ (
+ '{{$.pipeline_root}}',
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710',
+ ),
+ ])
+ def test_constant_placeholders(self, element: str, expected: str):
+ actual = placeholder_utils.resolve_individual_placeholder(
+ element=element,
+ executor_input_dict=EXECUTOR_INPUT_DICT,
+ pipeline_resource_name='my-pipeline-2023-10-10-13-32-59-420710',
+ task_resource_name='comp',
+ pipeline_root='/foo/bar/my-pipeline-2023-10-10-13-32-59-420710',
+ pipeline_job_id='123456789',
+ pipeline_task_id='987654321',
+ )
+ self.assertEqual(actual, expected)
+
+ @parameterized.parameters([
+ (
+ '{{$}}invalidjson',
+ json.dumps(EXECUTOR_INPUT_DICT) + 'invalidjson',
+ ),
+ (
+ '{{$.pipeline_job_name}}/{{$.pipeline_task_name}}',
+ 'my-pipeline-2023-10-10-13-32-59-420710/comp',
+ ),
+ (
+ '{{$.pipeline_root}}/foo/bar',
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/foo/bar',
+ ),
+ ])
+ def test_concatenated_placeholders_resolve(self, element: str,
+ expected: str):
+ actual = placeholder_utils.resolve_individual_placeholder(
+ element=element,
+ executor_input_dict=EXECUTOR_INPUT_DICT,
+ pipeline_resource_name='my-pipeline-2023-10-10-13-32-59-420710',
+ task_resource_name='comp',
+ pipeline_root='/foo/bar/my-pipeline-2023-10-10-13-32-59-420710',
+ pipeline_job_id='123456789',
+ pipeline_task_id='987654321',
+ )
+ self.assertEqual(actual, expected)
+
+ @parameterized.parameters([
+ (
+ "{{$.inputs.parameters[''boolean'']}}",
+ json.dumps(False),
+ ),
+ (
+ "{{$.inputs.parameters[''not_present'']}}",
+ json.dumps(None),
+ ),
+ (
+ "{{$.outputs.artifacts[''out_a''].metadata}}",
+ json.dumps({'foo': {
+ 'bar': 'baz'
+ }}),
+ ),
+ (
+ "{{$.outputs.parameters[''Output''].output_file}}",
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output',
+ ),
+ (
+ "{{$.outputs.artifacts[''out_a''].uri}}",
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/out_a',
+ ),
+ (
+ "{{$.outputs.artifacts[''out_a''].path}}",
+ '/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/out_a',
+ ),
+ (
+ "{{$.outputs.artifacts[''out_a''].metadata[''foo'']}}",
+ json.dumps({'bar': 'baz'}),
+ ),
+ ])
+ def test_io_placeholders(self, element: str, expected: str):
+ actual = placeholder_utils.resolve_individual_placeholder(
+ element=element,
+ executor_input_dict=EXECUTOR_INPUT_DICT,
+ pipeline_resource_name='my-pipeline-2023-10-10-13-32-59-420710',
+ task_resource_name='comp',
+ pipeline_root='/foo/bar/my-pipeline-2023-10-10-13-32-59-420710',
+ pipeline_job_id='123456789',
+ pipeline_task_id='987654321',
+ )
+ self.assertEqual(actual, expected)
+
+ @parameterized.parameters([
+ (
+ "my-prefix-{{$.inputs.parameters[''boolean'']}}-suffix",
+ 'my-prefix-false-suffix',
+ ),
+ (
+ "--param={{$.inputs.parameters[''not_present'']}}",
+ '--param=null',
+ ),
+ (
+ "prefix{{$.outputs.parameters[''Output''].output_file}}/suffix",
+ 'prefix/foo/bar/my-pipeline-2023-10-10-13-32-59-420710/comp/Output/suffix',
+ ),
+ (
+ "prefix{{$.inputs.parameters[''dictionary'']}}suffix",
+ 'prefix{"foo": "bar"}suffix',
+ ),
+ ])
+ def test_io_placeholder_with_string_concat(self, element: str,
+ expected: str):
+ actual = placeholder_utils.resolve_individual_placeholder(
+ element=element,
+ executor_input_dict=EXECUTOR_INPUT_DICT,
+ pipeline_resource_name='my-pipeline-2023-10-10-13-32-59-420710',
+ task_resource_name='comp',
+ pipeline_root='/foo/bar/my-pipeline-2023-10-10-13-32-59-420710',
+ pipeline_job_id='123456789',
+ pipeline_task_id='987654321',
+ )
+ self.assertEqual(actual, expected)
+
+
+class TestGetValueUsingPath(unittest.TestCase):
+
+ def test_valid_path(self):
+ actual = placeholder_utils.get_value_using_path(
+ {'a': {
+ 'b': {
+ 'c': 10
+ }
+ }},
+ ['a', 'b', 'c'],
+ )
+ expected = 10
+ self.assertEqual(actual, expected)
+
+ def test_invalid_path(self):
+ actual = placeholder_utils.get_value_using_path(
+ {'a': {
+ 'b': {
+ 'c': 10
+ }
+ }},
+ ['a', 'x'],
+ )
+ self.assertIsNone(actual)
+
+ def test_empty_path(self):
+ with self.assertRaisesRegex(ValueError, r'path cannot be empty\.'):
+ placeholder_utils.get_value_using_path({'a': 20}, [])
+
+
+class TestResolveStructPlaceholders(parameterized.TestCase):
+
+ @parameterized.parameters([
+ (
+ """{"Concat": ["a", "b", "c"]}""",
+ [],
+ 'abc',
+ ),
+ (
+ """{"Concat": ["prefix", "-", "{{$.outputs.artifacts[''x''].uri}}"]}""",
+ [],
+ "prefix-{{$.outputs.artifacts[''x''].uri}}",
+ ),
+ (
+ """{"Concat": ["a", {"Concat": ["b", "c"]}]}""",
+ [],
+ 'abc',
+ ),
+ (
+ """{"IfPresent": {"InputName": "x", "Then": ["foo"], "Else": ["bar"]}}""",
+ [],
+ ['bar'],
+ ),
+ (
+ """{"IfPresent": {"InputName": "x", "Then": ["foo"], "Else": ["bar"]}}""",
+ ['x'],
+ ['foo'],
+ ),
+ (
+ """{"Concat": ["a", {"Concat": ["b", {"Concat": ["c", "{{$.inputs.parameters[''input2'']}}"]}]}]}""",
+ [],
+ "abc{{$.inputs.parameters[''input2'']}}",
+ ),
+ (
+ """{"Concat": ["a", {"Concat": ["b", {"IfPresent": {"InputName": "foo", "Then": "c", "Else": "d"}}]}]}""",
+ [],
+ 'abd',
+ ),
+ (
+ """{"Concat": ["--flag", {"Concat": ["=", {"IfPresent": {"InputName": "x", "Then": "thing", "Else": "otherwise"}}]}]}""",
+ ['x'],
+ '--flag=thing',
+ ),
+ (
+ """{"Concat": ["a", {"IfPresent": {"InputName": "foo", "Then": {"Concat": ["--", "flag", "{{$.inputs.artifacts['input2'].path}}"]}, "Else": "b"}}, "c"]}""",
+ [],
+ 'abc',
+ ),
+ (
+ """{"Concat": ["--flag", {"IfPresent": {"InputName": "foo", "Then": {"Concat": ["=", "{{$.inputs.artifacts['input2'].path}}"]}, "Else": "b"}}, "-suffix"]}""",
+ ['foo'],
+ "--flag={{$.inputs.artifacts['input2'].path}}-suffix",
+ ),
+ (
+ """{"Concat": ["a-", {"IfPresent": {"InputName": "foo", "Then": {"Concat": ["--", "flag"]}, "Else": "{{$.inputs.artifacts['input2'].path}}"}}, "-c"]}""",
+ [],
+ "a-{{$.inputs.artifacts['input2'].path}}-c",
+ ),
+ (
+ """{"Concat": ["--", {"IfPresent": {"InputName": "foo", "Then": {"Concat": ["flag"]}, "Else": "{{$.inputs.artifacts['input2'].path}}"}}, "=c"]}""",
+ ['foo'],
+ '--flag=c',
+ ),
+ (
+ """{"Concat": ["--", {"IfPresent": {"InputName": "foo", "Then": {"Concat": ["flag"]}}}, "=c"]}""",
+ ['foo'],
+ '--flag=c',
+ ),
+ (
+ """{"Concat": ["--flag", {"IfPresent": {"InputName": "foo", "Then": {"Concat": ["=", "other", "_val"]}}}, "=foo"]}""",
+ [],
+ '--flag=foo',
+ ),
+ (
+ """{"IfPresent": {"InputName": "foo", "Then": {"Concat": ["--", "flag"]}}}""",
+ ['foo'],
+ '--flag',
+ ),
+ (
+ """{"IfPresent": {"InputName": "foo", "Then": {"Concat": ["--", "flag"]}}}""",
+ [],
+ None,
+ ),
+ ])
+ def test(
+ self,
+ placeholder: str,
+ provided_inputs: List[str],
+ expected: Optional[None],
+ ):
+ actual = placeholder_utils.resolve_struct_placeholders(
+ placeholder,
+ provided_inputs,
+ )
+ self.assertEqual(actual, expected)
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/status.py b/sdk/python/kfp/local/status.py
new file mode 100644
index 0000000000..17eddb8338
--- /dev/null
+++ b/sdk/python/kfp/local/status.py
@@ -0,0 +1,21 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The status of a locally executed task."""
+import enum
+
+
+@enum.unique
+class Status(enum.Enum):
+ SUCCESS = 'SUCCESS'
+ FAILURE = 'FAILURE'
diff --git a/sdk/python/kfp/local/subprocess_task_handler.py b/sdk/python/kfp/local/subprocess_task_handler.py
new file mode 100644
index 0000000000..63e150e85e
--- /dev/null
+++ b/sdk/python/kfp/local/subprocess_task_handler.py
@@ -0,0 +1,148 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Implementation of the subprocess runner."""
+
+import contextlib
+import os
+import subprocess
+import sys
+import tempfile
+from typing import List
+import venv
+import warnings
+
+from kfp.dsl import component_factory
+from kfp.local import config
+from kfp.local import status
+from kfp.local import task_handler_interface
+
+
+class SubprocessTaskHandler(task_handler_interface.ITaskHandler):
+ """The task handler corresponding to kfp.local.SubprocessRunner."""
+
+ def __init__(
+ self,
+ image: str,
+ full_command: List[str],
+ pipeline_root: str,
+ runner: config.SubprocessRunner,
+ ) -> None:
+ self.validate_image(image)
+ self.validate_not_container_component(full_command)
+ self.validate_not_containerized_python_component(full_command)
+
+ self.image = image
+ self.full_command = full_command
+ self.pipeline_root = pipeline_root
+ self.runner = runner
+
+ def run(self) -> status.Status:
+ """Runs the local subprocess and returns the status.
+
+ Returns:
+ Status.
+ """
+ with environment(use_venv=self.runner.use_venv) as py_executable:
+ full_command = replace_python_executable(
+ self.full_command,
+ py_executable,
+ )
+ return_code = run_local_subprocess(full_command=full_command)
+ return status.Status.SUCCESS if return_code == 0 else status.Status.FAILURE
+
+ def validate_image(self, image: str) -> None:
+ if 'python' not in image:
+ warnings.warn(
+ f"You may be attemping to run a task that uses custom or non-Python base image '{image}' in a Python environment. This may result in incorrect dependencies and/or incorrect behavior. Consider using the 'DockerRunner' to run this task in a container.",
+ RuntimeWarning,
+ )
+
+ def validate_not_container_component(
+ self,
+ full_command: List[str],
+ ) -> None:
+ if not any(component_factory.EXECUTOR_MODULE in part
+ for part in full_command):
+ raise RuntimeError(
+ f'The {config.SubprocessRunner.__name__} only supports running Lightweight Python Components. You are attempting to run a Container Component.'
+ )
+
+ def validate_not_containerized_python_component(
+ self,
+ full_command: List[str],
+ ) -> None:
+ if full_command[:len(
+ component_factory.CONTAINERIZED_PYTHON_COMPONENT_COMMAND
+ )] == component_factory.CONTAINERIZED_PYTHON_COMPONENT_COMMAND:
+ raise RuntimeError(
+ f'The {config.SubprocessRunner.__name__} only supports running Lightweight Python Components. You are attempting to run a Containerized Python Component.'
+ )
+
+
+def run_local_subprocess(full_command: List[str]) -> int:
+ with subprocess.Popen(
+ full_command,
+ stdout=subprocess.PIPE,
+ # No change to behavior in terminal for user,
+ # but inner process logs redirected to stdout. This separates from
+ # the outer process logs which, per logging module default, go to
+ # stderr.
+ stderr=subprocess.STDOUT,
+ text=True,
+ # buffer line-by-line
+ bufsize=1,
+ ) as process:
+ if process.stdout:
+ for line in iter(process.stdout.readline, ''):
+ print(line, end='')
+
+ # help with visual separation to show termination of subprocess logs
+ print('\n')
+
+ return process.wait()
+
+
+def replace_python_executable(full_command: List[str],
+ new_executable: str) -> List[str]:
+ """Replaces the 'python3' string in each element of the full_command with
+ the new_executable.
+
+ Args:
+ full_command: Commands and args.
+ new_executable: The Python executable to use for local execution.
+
+ Returns:
+ The updated commands and args.
+ """
+ return [el.replace('python3', f'{new_executable}') for el in full_command]
+
+
+@contextlib.contextmanager
+def environment(use_venv: bool) -> str:
+ """Context manager that handles the environment used for the subprocess.
+
+ Args:
+ use_venv: Whether to use the virtual environment instead of current environment.
+
+ Returns:
+ The Python executable path to use.
+ """
+ if use_venv:
+ with tempfile.TemporaryDirectory() as tempdir:
+ # Create the virtual environment inside the temporary directory
+ venv.create(tempdir, with_pip=True)
+
+ yield os.path.join(tempdir, 'bin', 'python')
+ else:
+ yield sys.executable
diff --git a/sdk/python/kfp/local/subprocess_task_handler_test.py b/sdk/python/kfp/local/subprocess_task_handler_test.py
new file mode 100644
index 0000000000..4a1d6b662b
--- /dev/null
+++ b/sdk/python/kfp/local/subprocess_task_handler_test.py
@@ -0,0 +1,432 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for subprocess_local_task_handler.py."""
+import contextlib
+import io
+from typing import NamedTuple, Optional
+import unittest
+from unittest import mock
+
+from absl.testing import parameterized
+from kfp import dsl
+from kfp import local
+from kfp.dsl import Artifact
+from kfp.dsl import Dataset
+from kfp.dsl import Output
+from kfp.local import subprocess_task_handler
+from kfp.local import testing_utilities
+
+# NOTE: When testing SubprocessRunner, use_venv=True throughout to avoid
+# modifying current code under test.
+# If the dsl.component mocks are modified in a way that makes them not work,
+# the code may install kfp from PyPI rather from source. To mitigate the
+# impact of such an error we should not install into the main test process'
+# environment.
+
+
+class TestSubprocessRunner(testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ @mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_basic(self, mock_stdout):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def comp():
+ print('foobar!')
+
+ comp()
+
+ output = mock_stdout.getvalue().strip()
+
+ self.assertContainsSubsequence(output, 'foobar!')
+
+ def test_image_warning(self):
+ with self.assertWarnsRegex(
+ RuntimeWarning,
+ r"You may be attemping to run a task that uses custom or non-Python base image 'my_custom_image' in a Python environment\. This may result in incorrect dependencies and/or incorrect behavior\."
+ ):
+ subprocess_task_handler.SubprocessTaskHandler(
+ image='my_custom_image',
+ # avoid catching the Container Component and
+ # Containerized Python Component validation errors
+ full_command=['kfp.dsl.executor_main'],
+ pipeline_root='pipeline_root',
+ runner=local.SubprocessRunner(use_venv=True),
+ )
+
+ def test_cannot_run_container_component(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.container_component
+ def comp():
+ return dsl.ContainerSpec(
+ image='alpine',
+ command=['echo'],
+ args=['foo'],
+ )
+
+ with self.assertRaisesRegex(
+ RuntimeError,
+ r'The SubprocessRunner only supports running Lightweight Python Components\. You are attempting to run a Container Component\.',
+ ):
+ comp()
+
+ def test_cannot_run_containerized_python_component(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component(target_image='foo')
+ def comp():
+ pass
+
+ with self.assertRaisesRegex(
+ RuntimeError,
+ r'The SubprocessRunner only supports running Lightweight Python Components\. You are attempting to run a Containerized Python Component\.',
+ ):
+ comp()
+
+
+class TestRunLocalSubproces(unittest.TestCase):
+
+ def test_simple_program(self):
+ buffer = io.StringIO()
+
+ with contextlib.redirect_stdout(buffer):
+ subprocess_task_handler.run_local_subprocess([
+ 'echo',
+ 'foo!',
+ ])
+
+ output = buffer.getvalue().strip()
+
+ self.assertEqual(output, 'foo!')
+
+
+class TestUseCurrentPythonExecutable(
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def test(self):
+ full_command = ['python3 -c "from kfp import dsl"']
+ actual = subprocess_task_handler.replace_python_executable(
+ full_command=full_command,
+ new_executable='/foo/bar/python3',
+ )
+ expected = ['/foo/bar/python3 -c "from kfp import dsl"']
+ self.assertEqual(actual, expected)
+
+
+class TestUseVenv(testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ @parameterized.parameters([
+ ({
+ 'runner': local.SubprocessRunner(use_venv=True),
+ }),
+ ({
+ 'runner': local.SubprocessRunner(use_venv=True),
+ }),
+ ])
+ def test_use_venv_true(self, **kwargs):
+ local.init(**kwargs)
+
+ @dsl.component(packages_to_install=['cloudpickle'])
+ def installer_component():
+ import cloudpickle
+ print('Cloudpickle is installed:', cloudpickle)
+
+ installer_component()
+
+ # since the module was installed in the virtual environment, it should not exist in the current environment
+ with self.assertRaisesRegex(ModuleNotFoundError,
+ r"No module named 'cloudpickle'"):
+ import cloudpickle
+
+
+class TestLightweightPythonComponentLogic(
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def test_single_output_simple_case(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ actual = identity(x='hello').output
+ expected = 'hello'
+ self.assertIsInstance(actual, str)
+ self.assertEqual(actual, expected)
+
+ def test_many_primitives_in_and_out(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(
+ string: str,
+ integer: int,
+ decimal: float,
+ boolean: bool,
+ l: list,
+ d: dict,
+ ) -> NamedTuple(
+ 'Outputs',
+ string=str,
+ integer=int,
+ decimal=float,
+ boolean=bool,
+ l=list,
+ d=dict):
+ Outputs = NamedTuple(
+ 'Outputs',
+ string=str,
+ integer=int,
+ decimal=float,
+ boolean=bool,
+ l=list,
+ d=dict)
+ return Outputs(
+ string=string,
+ integer=integer,
+ decimal=decimal,
+ boolean=boolean,
+ l=l,
+ d=d,
+ )
+
+ task = identity(
+ string='foo',
+ integer=1,
+ decimal=3.14,
+ boolean=True,
+ l=['a', 'b'],
+ d={'x': 'y'})
+ self.assertIsInstance(task.outputs['string'], str)
+ self.assertEqual(task.outputs['string'], 'foo')
+
+ self.assertIsInstance(task.outputs['integer'], int)
+ self.assertEqual(task.outputs['integer'], 1)
+
+ self.assertIsInstance(task.outputs['decimal'], float)
+ self.assertEqual(task.outputs['decimal'], 3.14)
+
+ self.assertIsInstance(task.outputs['boolean'], bool)
+ self.assertTrue(task.outputs['boolean'])
+
+ self.assertIsInstance(task.outputs['l'], list)
+ self.assertEqual(task.outputs['l'], ['a', 'b'])
+
+ self.assertIsInstance(task.outputs['d'], dict)
+ self.assertEqual(task.outputs['d'], {'x': 'y'})
+
+ def test_single_output_not_available(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+ from typing import NamedTuple
+
+ @dsl.component
+ def return_twice(x: str) -> NamedTuple('Outputs', x=str, y=str):
+ Outputs = NamedTuple('Output', x=str, y=str)
+ return Outputs(x=x, y=x)
+
+ local_task = return_twice(x='foo')
+ with self.assertRaisesRegex(
+ AttributeError,
+ r'The task has multiple outputs\. Please reference the output by its name\.'
+ ):
+ local_task.output
+
+ def test_single_artifact_output_traditional(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def artifact_maker(x: str, a: Output[Artifact]):
+ with open(a.path, 'w') as f:
+ f.write(x)
+
+ a.metadata['foo'] = 'bar'
+
+ actual = artifact_maker(x='hello').output
+ self.assertIsInstance(actual, Artifact)
+ self.assertEqual(actual.name, 'a')
+ self.assertTrue(actual.uri.endswith('/a'))
+ self.assertEqual(actual.metadata, {'foo': 'bar'})
+ with open(actual.path) as f:
+ contents = f.read()
+ self.assertEqual(contents, 'hello')
+
+ def test_single_artifact_output_pythonic(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def artifact_maker(x: str) -> Artifact:
+ artifact = Artifact(
+ name='a', uri=dsl.get_uri('a'), metadata={'foo': 'bar'})
+ with open(artifact.path, 'w') as f:
+ f.write(x)
+
+ return artifact
+
+ actual = artifact_maker(x='hello').output
+ self.assertIsInstance(actual, Artifact)
+ self.assertEqual(actual.name, 'a')
+ self.assertTrue(actual.uri.endswith('/a'))
+ self.assertEqual(actual.metadata, {'foo': 'bar'})
+ with open(actual.path) as f:
+ contents = f.read()
+ self.assertEqual(contents, 'hello')
+
+ def test_multiple_artifact_outputs_traditional(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def double_artifact_maker(
+ x: str,
+ y: str,
+ a: Output[Artifact],
+ b: Output[Dataset],
+ ):
+ with open(a.path, 'w') as f:
+ f.write(x)
+
+ with open(b.path, 'w') as f:
+ f.write(y)
+
+ a.metadata['foo'] = 'bar'
+ b.metadata['baz'] = 'bat'
+
+ local_task = double_artifact_maker(x='hello', y='goodbye')
+
+ actual_a = local_task.outputs['a']
+ actual_b = local_task.outputs['b']
+
+ self.assertIsInstance(actual_a, Artifact)
+ self.assertEqual(actual_a.name, 'a')
+ self.assertTrue(actual_a.uri.endswith('/a'))
+ with open(actual_a.path) as f:
+ contents = f.read()
+ self.assertEqual(contents, 'hello')
+ self.assertEqual(actual_a.metadata, {'foo': 'bar'})
+
+ self.assertIsInstance(actual_b, Dataset)
+ self.assertEqual(actual_b.name, 'b')
+ self.assertTrue(actual_b.uri.endswith('/b'))
+ self.assertEqual(actual_b.metadata, {'baz': 'bat'})
+ with open(actual_b.path) as f:
+ contents = f.read()
+ self.assertEqual(contents, 'goodbye')
+
+ def test_multiple_artifact_outputs_pythonic(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def double_artifact_maker(
+ x: str,
+ y: str,
+ ) -> NamedTuple(
+ 'Outputs', a=Artifact, b=Dataset):
+ a = Artifact(
+ name='a', uri=dsl.get_uri('a'), metadata={'foo': 'bar'})
+ b = Dataset(name='b', uri=dsl.get_uri('b'), metadata={'baz': 'bat'})
+
+ with open(a.path, 'w') as f:
+ f.write(x)
+
+ with open(b.path, 'w') as f:
+ f.write(y)
+
+ Outputs = NamedTuple('Outputs', a=Artifact, b=Dataset)
+ return Outputs(a=a, b=b)
+
+ local_task = double_artifact_maker(x='hello', y='goodbye')
+
+ actual_a = local_task.outputs['a']
+ actual_b = local_task.outputs['b']
+
+ self.assertIsInstance(actual_a, Artifact)
+ self.assertEqual(actual_a.name, 'a')
+ self.assertTrue(actual_a.uri.endswith('/a'))
+ with open(actual_a.path) as f:
+ contents = f.read()
+ self.assertEqual(contents, 'hello')
+ self.assertEqual(actual_a.metadata, {'foo': 'bar'})
+
+ self.assertIsInstance(actual_b, Dataset)
+ self.assertEqual(actual_b.name, 'b')
+ self.assertTrue(actual_b.uri.endswith('/b'))
+ with open(actual_b.path) as f:
+ contents = f.read()
+ self.assertEqual(contents, 'goodbye')
+ self.assertEqual(actual_b.metadata, {'baz': 'bat'})
+
+ def test_str_input_uses_default(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str = 'hi') -> str:
+ return x
+
+ actual = identity().output
+ expected = 'hi'
+ self.assertIsInstance(actual, str)
+ self.assertEqual(actual, expected)
+
+ def test_placeholder_default_resolved(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str = dsl.PIPELINE_TASK_NAME_PLACEHOLDER) -> str:
+ return x
+
+ actual = identity().output
+ expected = 'identity'
+ self.assertIsInstance(actual, str)
+ self.assertEqual(actual, expected)
+
+ def test_outputpath(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def my_comp(out_param: dsl.OutputPath(str),) -> int:
+ with open(out_param, 'w') as f:
+ f.write('Hello' * 2)
+ return 1
+
+ task = my_comp()
+
+ self.assertEqual(task.outputs['out_param'], 'HelloHello')
+ self.assertEqual(task.outputs['Output'], 1)
+
+ def test_outputpath_result_not_written(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ # use dsl.OutputPath(int) for more thorough testing
+ # want to ensure that the code that converts protobuf number to
+ # Python int permits unwritten outputs
+ @dsl.component
+ def my_comp(out_param: dsl.OutputPath(int)):
+ pass
+
+ task = my_comp()
+ self.assertEmpty(task.outputs)
+
+ def test_optional_param(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def my_comp(string: Optional[str] = None) -> str:
+ return 'is none' if string is None else 'not none'
+
+ task = my_comp()
+ self.assertEqual(task.output, 'is none')
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/task_dispatcher.py b/sdk/python/kfp/local/task_dispatcher.py
new file mode 100755
index 0000000000..f051f64b9f
--- /dev/null
+++ b/sdk/python/kfp/local/task_dispatcher.py
@@ -0,0 +1,169 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Code for dispatching a local task execution."""
+import logging
+from typing import Any, Dict
+
+from kfp import local
+from kfp.local import config
+from kfp.local import docker_task_handler
+from kfp.local import executor_input_utils
+from kfp.local import executor_output_utils
+from kfp.local import logging_utils
+from kfp.local import placeholder_utils
+from kfp.local import status
+from kfp.local import subprocess_task_handler
+from kfp.local import task_handler_interface
+from kfp.pipeline_spec import pipeline_spec_pb2
+
+
+def run_single_component(
+ pipeline_spec: pipeline_spec_pb2.PipelineSpec,
+ arguments: Dict[str, Any],
+) -> Dict[str, Any]:
+ """Runs a single component from its compiled PipelineSpec.
+
+ Args:
+ pipeline_spec: The PipelineSpec of the component to run.
+ arguments: The runtime arguments.
+
+ Returns:
+ A LocalTask instance.
+ """
+ if config.LocalExecutionConfig.instance is None:
+ raise RuntimeError(
+ f"Local environment not initialized. Please run '{local.__name__}.{local.init.__name__}()' before executing tasks locally."
+ )
+ # all global state should be accessed here
+ # do not access local config state downstream
+ return _run_single_component_implementation(
+ pipeline_spec=pipeline_spec,
+ arguments=arguments,
+ pipeline_root=config.LocalExecutionConfig.instance.pipeline_root,
+ runner=config.LocalExecutionConfig.instance.runner,
+ raise_on_error=config.LocalExecutionConfig.instance.raise_on_error,
+ )
+
+
+def _run_single_component_implementation(
+ pipeline_spec: pipeline_spec_pb2.PipelineSpec,
+ arguments: Dict[str, Any],
+ pipeline_root: str,
+ runner: config.LocalRunnerType,
+ raise_on_error: bool,
+) -> Dict[str, Any]:
+ """The implementation of a single component runner."""
+
+ component_name, component_spec = list(pipeline_spec.components.items())[0]
+
+ pipeline_resource_name = executor_input_utils.get_local_pipeline_resource_name(
+ pipeline_spec.pipeline_info.name)
+ task_resource_name = executor_input_utils.get_local_task_resource_name(
+ component_name)
+ task_root = executor_input_utils.construct_local_task_root(
+ pipeline_root=pipeline_root,
+ pipeline_resource_name=pipeline_resource_name,
+ task_resource_name=task_resource_name,
+ )
+ executor_input = executor_input_utils.construct_executor_input(
+ component_spec=component_spec,
+ arguments=arguments,
+ task_root=task_root,
+ )
+
+ executor_spec = pipeline_spec.deployment_spec['executors'][
+ component_spec.executor_label]
+
+ container = executor_spec['container']
+ image = container['image']
+
+ command = list(container['command']) if 'command' in container else []
+ args = list(container['args']) if 'args' in container else []
+ full_command = command + args
+
+ executor_input_dict = executor_input_utils.executor_input_to_dict(
+ executor_input=executor_input,
+ component_spec=component_spec,
+ )
+ full_command = placeholder_utils.replace_placeholders(
+ full_command=full_command,
+ executor_input_dict=executor_input_dict,
+ pipeline_resource_name=pipeline_resource_name,
+ task_resource_name=task_resource_name,
+ pipeline_root=pipeline_root,
+ )
+
+ runner_type = type(runner)
+ task_handler_map: Dict[
+ local.LocalRunnerType, task_handler_interface.ITaskHandler] = {
+ local.SubprocessRunner:
+ subprocess_task_handler.SubprocessTaskHandler,
+ local.DockerRunner:
+ docker_task_handler.DockerTaskHandler,
+ }
+ TaskHandler = task_handler_map[runner_type]
+
+ with logging_utils.local_logger_context():
+ task_name_for_logs = logging_utils.color_text(
+ f'{task_resource_name!r}',
+ logging_utils.Color.CYAN,
+ )
+
+ logging.info(f'Executing task {task_name_for_logs}')
+ task_handler = TaskHandler(
+ image=image,
+ full_command=full_command,
+ pipeline_root=pipeline_root,
+ runner=runner,
+ )
+
+ # trailing newline helps visually separate subprocess logs
+ logging.info(f'Streamed logs:\n')
+
+ with logging_utils.indented_print():
+ # subprocess logs printed here
+ task_status = task_handler.run()
+
+ if task_status == status.Status.SUCCESS:
+ logging.info(
+ f'Task {task_name_for_logs} finished with status {logging_utils.color_text(task_status.value, logging_utils.Color.GREEN)}'
+ )
+
+ outputs = executor_output_utils.get_outputs_for_task(
+ executor_input=executor_input,
+ component_spec=component_spec,
+ )
+ if outputs:
+ output_string = [
+ f'Task {task_name_for_logs} outputs:',
+ *logging_utils.make_log_lines_for_outputs(outputs),
+ '\n',
+ ]
+ logging.info('\n'.join(output_string))
+ else:
+ logging.info(f'Task {task_name_for_logs} has no outputs')
+
+ elif task_status == status.Status.FAILURE:
+ msg = f'Task {task_name_for_logs} finished with status {logging_utils.color_text(task_status.value, logging_utils.Color.RED)}'
+ if raise_on_error:
+ raise RuntimeError(msg)
+ else:
+ logging.error(msg)
+ outputs = {}
+
+ else:
+ # for developers; user should never hit this
+ raise ValueError(f'Got unknown status: {task_status}')
+
+ return outputs
diff --git a/sdk/python/kfp/local/task_dispatcher_test.py b/sdk/python/kfp/local/task_dispatcher_test.py
new file mode 100755
index 0000000000..bd086e25ad
--- /dev/null
+++ b/sdk/python/kfp/local/task_dispatcher_test.py
@@ -0,0 +1,318 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for task_dispatcher.py.
+
+The difference between these tests and the E2E test are that E2E tests
+focus on how the runner should behave to be local execution conformant,
+whereas these tests focus on how the task dispatcher should behave,
+irrespective of the runner. While there will inevitably some overlap, we
+should seek to minimize it.
+"""
+import io
+import os
+import re
+import unittest
+from unittest import mock
+
+from absl.testing import parameterized
+from kfp import dsl
+from kfp import local
+from kfp.dsl import Artifact
+from kfp.dsl import Model
+from kfp.dsl import Output
+from kfp.local import testing_utilities
+
+# NOTE: uses SubprocessRunner throughout to test the taks dispatcher behavior
+# NOTE: When testing SubprocessRunner, use_venv=True throughout to avoid
+# modifying current code under test.
+# If the dsl.component mocks are modified in a way that makes them not work,
+# the code may install kfp from PyPI rather from source. To mitigate the
+# impact of such an error we should not install into the main test process'
+# environment.
+
+
+class TestLocalExecutionValidation(
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def test_env_not_initialized(self):
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ with self.assertRaisesRegex(
+ RuntimeError,
+ r"Local environment not initialized\. Please run 'kfp\.local\.init\(\)' before executing tasks locally\."
+ ):
+ identity(x='foo')
+
+
+class TestArgumentValidation(parameterized.TestCase):
+
+ def test_no_argument_no_default(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ with self.assertRaisesRegex(
+ TypeError, r'identity\(\) missing 1 required argument: x'):
+
+ identity()
+
+ def test_default_wrong_type(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ with self.assertRaisesRegex(
+ dsl.types.type_utils.InconsistentTypeException,
+ r"Incompatible argument passed to the input 'x' of component 'identity': Argument type 'NUMBER_INTEGER' is incompatible with the input type 'STRING'"
+ ):
+ identity(x=1)
+
+ def test_extra_argument(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ with self.assertRaisesRegex(
+ TypeError,
+ r'identity\(\) got an unexpected keyword argument "y"\.'):
+ identity(x='foo', y='bar')
+
+ def test_input_artifact_provided(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def artifact_identity(a: Artifact) -> Artifact:
+ return a
+
+ with self.assertRaisesRegex(
+ ValueError,
+ r"Input artifacts are not supported. Got input artifact of type 'Artifact'."
+ ):
+ artifact_identity(a=Artifact(name='a', uri='gs://bucket/foo'))
+
+
+class TestSupportOfComponentTypes(
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def test_local_pipeline_unsupported_two_tasks(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ @dsl.pipeline
+ def my_pipeline():
+ identity(x='foo')
+ identity(x='bar')
+
+ # compile and load into a YamlComponent to ensure the NotImplementedError isn't simply being thrown because this is a GraphComponent
+ my_pipeline = testing_utilities.compile_and_load_component(my_pipeline)
+ with self.assertRaisesRegex(
+ NotImplementedError,
+ r'Local pipeline execution is not currently supported\.',
+ ):
+ my_pipeline()
+
+ def test_local_pipeline_unsupported_one_task_different_interface(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ @dsl.pipeline
+ def my_pipeline():
+ identity(x='foo')
+
+ # compile and load into a YamlComponent to ensure the NotImplementedError isn't simply being thrown because this is a GraphComponent
+ my_pipeline = testing_utilities.compile_and_load_component(my_pipeline)
+ with self.assertRaisesRegex(
+ NotImplementedError,
+ r'Local pipeline execution is not currently supported\.',
+ ):
+ my_pipeline()
+
+ def test_local_pipeline_unsupported_if_is_graph_component(self):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ # even if there is one task with the same interface as the pipeline, the code should catch that the pipeline is a GraphComponent and throw the NotImplementedError
+ @dsl.pipeline
+ def my_pipeline(string: str) -> str:
+ return identity(x=string).output
+
+ with self.assertRaisesRegex(
+ NotImplementedError,
+ r'Local pipeline execution is not currently supported\.',
+ ):
+ my_pipeline(string='foo')
+
+ def test_can_run_loaded_component(self):
+ # use venv to avoid installing non-local KFP into test process
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ loaded_identity = testing_utilities.compile_and_load_component(identity)
+
+ actual = loaded_identity(x='hello').output
+ expected = 'hello'
+ # since == is overloaded for dsl.Condition, if local execution is not
+ # "hit", then actual will be a channel and actual == expected evaluates
+ # to ConditionOperation. Since ConditionOperation is truthy,
+ # this may result in a false negative test result. For this reason,
+ # we perform an isinstance check first.
+ self.assertIsInstance(actual, str)
+ self.assertEqual(actual, expected)
+
+
+class TestExceptionHandlingAndLogging(
+ testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ @mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_user_code_throws_exception_if_raise_on_error(self, mock_stdout):
+ local.init(
+ runner=local.SubprocessRunner(use_venv=True),
+ raise_on_error=True,
+ )
+
+ @dsl.component
+ def fail_comp():
+ raise Exception('String to match on')
+
+ with self.assertRaisesRegex(
+ RuntimeError,
+ r"Task \x1b\[96m'fail-comp'\x1b\[0m finished with status \x1b\[91mFAILURE\x1b\[0m",
+ ):
+ fail_comp()
+
+ self.assertIn(
+ 'Exception: String to match on',
+ mock_stdout.getvalue(),
+ )
+
+ @mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_user_code_no_exception_if_not_raise_on_error(self, mock_stdout):
+ local.init(
+ runner=local.SubprocessRunner(use_venv=True),
+ raise_on_error=False,
+ )
+
+ @dsl.component
+ def fail_comp():
+ raise Exception('String to match on')
+
+ task = fail_comp()
+ self.assertDictEqual(task.outputs, {})
+
+ self.assertRegex(
+ mock_stdout.getvalue(),
+ r"\d+:\d+:\d+\.\d+ - ERROR - Task \x1b\[96m'fail-comp'\x1b\[0m finished with status \x1b\[91mFAILURE\x1b\[0m",
+ )
+ self.assertIn(
+ 'Exception: String to match on',
+ mock_stdout.getvalue(),
+ )
+
+ @mock.patch('sys.stdout', new_callable=io.StringIO)
+ def test_all_logs(self, mock_stdout):
+ local.init(runner=local.SubprocessRunner(use_venv=True))
+
+ @dsl.component
+ def many_type_component(
+ num: int,
+ model: Output[Model],
+ ) -> str:
+ print('Inside of my component!')
+ model.metadata['foo'] = 'bar'
+ return 'hello' * num
+
+ many_type_component(num=2)
+
+ # inner process logs correctly nested inside outer process logs
+ outer_log_regex_sections = [
+ r"\d+:\d+:\d+\.\d+ - INFO - Executing task \x1b\[96m'many-type-component'\x1b\[0m\n",
+ r'\d+:\d+:\d+\.\d+ - INFO - Streamed logs:\n\n',
+ r'.*',
+ r'Looking for component ',
+ r'.*',
+ r'Loading KFP component ',
+ r'.*',
+ r'Got executor_input:',
+ r'.*',
+ r'Inside of my component!',
+ r'.*',
+ r'Wrote executor output file to',
+ r'.*',
+ r"\d+:\d+:\d+\.\d+ - INFO - Task \x1b\[96m'many-type-component'\x1b\[0m finished with status \x1b\[92mSUCCESS\x1b\[0m\n",
+ r"\d+:\d+:\d+\.\d+ - INFO - Task \x1b\[96m'many-type-component'\x1b\[0m outputs:\n Output: 'hellohello'\n model: Model\( name='model',\n uri='[a-zA-Z0-9/_\.-]+/local_outputs/many-type-component-\d+-\d+-\d+-\d+-\d+-\d+-\d+/many-type-component/model',\n metadata={'foo': 'bar'} \)\n\n",
+ ]
+
+ self.assertRegex(
+ mock_stdout.getvalue(),
+ # use dotall os that .* include newline characters
+ re.compile(''.join(outer_log_regex_sections), re.DOTALL),
+ )
+
+
+class TestPipelineRootPaths(testing_utilities.LocalRunnerEnvironmentTestCase):
+
+ def test_relpath(self):
+ local.init(
+ runner=local.SubprocessRunner(use_venv=True),
+ pipeline_root='relpath_root')
+
+ # define in test to force install from source
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ task = identity(x='foo')
+ self.assertIsInstance(task.output, str)
+ self.assertEqual(task.output, 'foo')
+
+ def test_abspath(self):
+ import tempfile
+ with tempfile.TemporaryDirectory() as tmpdir:
+ local.init(
+ runner=local.SubprocessRunner(use_venv=True),
+ pipeline_root=os.path.join(tmpdir, 'asbpath_root'))
+
+ # define in test to force install from source
+ @dsl.component
+ def identity(x: str) -> str:
+ return x
+
+ task = identity(x='foo')
+ self.assertIsInstance(task.output, str)
+ self.assertEqual(task.output, 'foo')
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/task_handler_interface.py b/sdk/python/kfp/local/task_handler_interface.py
new file mode 100644
index 0000000000..a81f5e3b88
--- /dev/null
+++ b/sdk/python/kfp/local/task_handler_interface.py
@@ -0,0 +1,41 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""An abstract base class that specifies the interface of a task handler."""
+import abc
+from typing import List
+
+from kfp.local import config
+from kfp.local import status
+
+
+class ITaskHandler(abc.ABC):
+ """Interface for a TaskHandler."""
+
+ def __init__(
+ self,
+ image: str,
+ full_command: List[str],
+ pipeline_root: str,
+ runner: config.LocalRunnerType,
+ ) -> None:
+ pass
+
+ @abc.abstractmethod
+ def run(self) -> status.Status:
+ """Runs the task and returns the status.
+
+ Returns:
+ Status.
+ """
+ pass
diff --git a/sdk/python/kfp/local/task_handler_interface_test.py b/sdk/python/kfp/local/task_handler_interface_test.py
new file mode 100644
index 0000000000..0f2ad69382
--- /dev/null
+++ b/sdk/python/kfp/local/task_handler_interface_test.py
@@ -0,0 +1,36 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for task_handler_interface_test.py."""
+import unittest
+
+from kfp.local import task_handler_interface
+
+
+class TestTaskHandler(unittest.TestCase):
+
+ def test_incomplete_concrete_class(self):
+
+ class IncompleteTaskHandler(
+ task_handler_interface.ITaskHandler,):
+
+ pass
+
+ with self.assertRaisesRegex(
+ TypeError,
+ r"Can't instantiate abstract class IncompleteTaskHandler"):
+ IncompleteTaskHandler()
+
+
+if __name__ == '__main__':
+ unittest.main()
diff --git a/sdk/python/kfp/local/testing_utilities.py b/sdk/python/kfp/local/testing_utilities.py
new file mode 100755
index 0000000000..7b4324ba75
--- /dev/null
+++ b/sdk/python/kfp/local/testing_utilities.py
@@ -0,0 +1,128 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities for testing local execution."""
+
+import datetime
+import functools
+import os
+import pathlib
+import tempfile
+from typing import Any, Callable, Dict
+import unittest
+from unittest import mock
+
+from absl.testing import parameterized
+from google.protobuf import json_format
+from google.protobuf import message
+from kfp import components
+from kfp import dsl
+from kfp.local import config as local_config
+from kfp.local import docker_task_handler
+
+_LOCAL_KFP_PACKAGE_PATH = os.path.join(
+ os.path.dirname(__file__),
+ os.path.pardir,
+ os.path.pardir,
+)
+
+
+def modify_volumes_decorator(
+ original_method: Callable[..., Any]) -> Callable[..., Any]:
+
+ def wrapper(self, *args, **kwargs) -> Dict[str, Any]:
+ original_volumes = original_method(self, *args, **kwargs)
+ LOCAL_KFP_VOLUME = {
+ _LOCAL_KFP_PACKAGE_PATH: {
+ 'bind': _LOCAL_KFP_PACKAGE_PATH,
+ 'mode': 'rw'
+ }
+ }
+ original_volumes.update(LOCAL_KFP_VOLUME)
+ return original_volumes
+
+ return wrapper
+
+
+class LocalRunnerEnvironmentTestCase(parameterized.TestCase):
+ """Test class that uses an isolated filesystem and updates the
+ dsl.component decorator to install from the local KFP source, rather than
+ the latest release."""
+
+ def setUp(self):
+ # ENTER: start each test case without an uninitialized environment
+ local_config.LocalExecutionConfig.instance = None
+
+ # ENTER: use tempdir for all tests
+ self.working_dir = pathlib.Path.cwd()
+ self.temp_dir = tempfile.TemporaryDirectory()
+ os.chdir(self.temp_dir.name)
+
+ # ENTER: mount KFP dir to enable install from source for docker runner
+ self.original_get_volumes_to_mount = docker_task_handler.DockerTaskHandler.get_volumes_to_mount
+ docker_task_handler.DockerTaskHandler.get_volumes_to_mount = modify_volumes_decorator(
+ docker_task_handler.DockerTaskHandler.get_volumes_to_mount)
+
+ def tearDown(self):
+ # EXIT: use tempdir for all tests
+ self.temp_dir.cleanup()
+ os.chdir(self.working_dir)
+
+ # EXIT: mount KFP dir to enable install from source for docker runner
+ docker_task_handler.DockerTaskHandler.get_volumes_to_mount = self.original_get_volumes_to_mount
+
+ @classmethod
+ def setUpClass(cls):
+ # ENTER: use local KFP package path for subprocess runner
+ cls.original_component, dsl.component = dsl.component, functools.partial(
+ dsl.component, kfp_package_path=_LOCAL_KFP_PACKAGE_PATH)
+
+ @classmethod
+ def tearDownClass(cls):
+ # EXIT: use local KFP package path for subprocess runner
+ dsl.component = cls.original_component
+
+
+class MockedDatetimeTestCase(unittest.TestCase):
+
+ def setUp(self):
+ # set up patch, cleanup, and start
+ patcher = mock.patch('kfp.local.executor_input_utils.datetime.datetime')
+ self.addCleanup(patcher.stop)
+ self.mock_datetime = patcher.start()
+
+ # set mock return values
+ mock_now = mock.MagicMock(
+ wraps=datetime.datetime(2023, 10, 10, 13, 32, 59, 420710))
+ self.mock_datetime.now.return_value = mock_now
+ mock_now.strftime.return_value = '2023-10-10-13-32-59-420710'
+
+
+def write_proto_to_json_file(
+ proto_message: message.Message,
+ file_path: str,
+) -> None:
+ """Writes proto_message to file_path as JSON."""
+ json_string = json_format.MessageToJson(proto_message)
+
+ with open(file_path, 'w') as json_file:
+ json_file.write(json_string)
+
+
+def compile_and_load_component(
+ base_component: dsl.base_component.BaseComponent,
+) -> dsl.yaml_component.YamlComponent:
+ """Compiles a component to PipelineSpec and reloads it as a
+ YamlComponent."""
+ return components.load_component_from_text(
+ json_format.MessageToJson(base_component.pipeline_spec))
diff --git a/sdk/python/requirements.in b/sdk/python/requirements.in
index 975568b8b7..b3428b7b25 100644
--- a/sdk/python/requirements.in
+++ b/sdk/python/requirements.in
@@ -11,14 +11,14 @@ google-auth>=1.6.1,<3
# https://github.com/googleapis/python-storage/blob/main/CHANGELOG.md#221-2022-03-15
google-cloud-storage>=2.2.1,<3
# pin kfp-pipeline-spec to an exact version, since this is the contract between a given KFP SDK version and the BE. we don't want old version of the SDK to write new fields and to have the BE reject the new unsupported field (even if the new field backward compatible from a proto perspective)
-kfp-pipeline-spec==0.2.2
+kfp-pipeline-spec==0.3.0
# Update the upper version whenever a new major version of the
# kfp-server-api package is released.
# Update the lower version when kfp sdk depends on new apis/fields in
# kfp-server-api.
kfp-server-api>=2.0.0,<2.1.0
kubernetes>=8.0.0,<27
-protobuf>=3.13.0,<4
+protobuf>=4.21.1,<5
PyYAML>=5.3,<7
requests-toolbelt>=0.8.0,<1
tabulate>=0.8.6,<1
diff --git a/sdk/python/requirements.txt b/sdk/python/requirements.txt
index 5f6347023e..0ba3e543a7 100644
--- a/sdk/python/requirements.txt
+++ b/sdk/python/requirements.txt
@@ -41,15 +41,15 @@ googleapis-common-protos==1.56.4
# via google-api-core
idna==3.3
# via requests
-kfp-pipeline-spec==0.2.2
+kfp-pipeline-spec==0.3.0
# via -r requirements.in
-kfp-server-api==2.0.0-rc.1
+kfp-server-api==2.0.5
# via -r requirements.in
kubernetes==23.6.0
# via -r requirements.in
oauthlib==3.2.0
# via requests-oauthlib
-protobuf==3.20.1
+protobuf==4.25.1
# via
# -r requirements.in
# google-api-core
diff --git a/test/kfp-kubernetes-execution-tests/requirements.txt b/test/kfp-kubernetes-execution-tests/requirements.txt
new file mode 100644
index 0000000000..bf44f12049
--- /dev/null
+++ b/test/kfp-kubernetes-execution-tests/requirements.txt
@@ -0,0 +1,3 @@
+sdk/python
+pytest==7.1.3
+pytest-asyncio-cooperative==0.28.0
diff --git a/test/kfp-kubernetes-execution-tests/sdk_execution_tests.py b/test/kfp-kubernetes-execution-tests/sdk_execution_tests.py
new file mode 100644
index 0000000000..fd9e2aa4a6
--- /dev/null
+++ b/test/kfp-kubernetes-execution-tests/sdk_execution_tests.py
@@ -0,0 +1,141 @@
+# Copyright 2023 The Kubeflow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import asyncio
+import dataclasses
+import functools
+import os
+import sys
+from typing import Any, Dict, List, Tuple
+
+from kfp import client
+from kfp import dsl
+import kfp_server_api
+import pytest
+import yaml
+
+KFP_ENDPOINT = os.environ['KFP_ENDPOINT']
+TIMEOUT_SECONDS = os.environ['TIMEOUT_SECONDS']
+CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
+PROJECT_ROOT = os.path.abspath(
+ os.path.join(CURRENT_DIR, *([os.path.pardir] * 2)))
+CONFIG_PATH = os.path.join(
+ PROJECT_ROOT,
+ 'kubernetes_platform',
+ 'python',
+ 'test',
+ 'snapshot',
+ 'test_data_config.yaml',
+)
+
+kfp_client = client.Client(host=KFP_ENDPOINT)
+
+
+@dataclasses.dataclass
+class TestCase:
+ name: str
+ module_path: str
+ yaml_path: str
+ function_name: str
+ arguments: Dict[str, Any]
+
+
+def create_test_case_parameters() -> List[TestCase]:
+ parameters: List[TestCase] = []
+ with open(CONFIG_PATH) as f:
+ config = yaml.safe_load(f)
+ test_data_dir = os.path.join(
+ PROJECT_ROOT,
+ 'kubernetes_platform',
+ 'python',
+ 'test',
+ 'snapshot',
+ 'data',
+ )
+
+ parameters.extend(
+ TestCase(
+ name=test_case['name'] + '-' + test_case['module'],
+ module_path=os.path.join(test_data_dir,
+ f'{test_case["module"]}.py'),
+ yaml_path=os.path.join(test_data_dir,
+ f'{test_case["module"]}.yaml'),
+ function_name=test_case['name'],
+ arguments=test_case.get('arguments'),
+ ) for test_case in config['test_cases'])
+
+ return parameters
+
+
+def wait(
+ run_result: client.client.RunPipelineResult
+) -> kfp_server_api.V2beta1Run:
+ return kfp_client.wait_for_run_completion(
+ run_id=run_result.run_id, timeout=int(TIMEOUT_SECONDS))
+
+
+def import_obj_from_file(python_path: str, obj_name: str) -> Any:
+ sys.path.insert(0, os.path.dirname(python_path))
+ module_name = os.path.splitext(os.path.split(python_path)[1])[0]
+ module = __import__(module_name, fromlist=[obj_name])
+ if not hasattr(module, obj_name):
+ raise ValueError(
+ f'Object "{obj_name}" not found in module {python_path}.')
+ return getattr(module, obj_name)
+
+
+def run(test_case: TestCase) -> Tuple[str, client.client.RunPipelineResult]:
+ full_path = os.path.join(PROJECT_ROOT, test_case.module_path)
+ pipeline_func = import_obj_from_file(full_path, test_case.function_name)
+ run_result = kfp_client.create_run_from_pipeline_func(
+ pipeline_func,
+ enable_caching=True,
+ arguments=test_case.arguments,
+ )
+ run_url = f'{KFP_ENDPOINT}/#/runs/details/{run_result.run_id}'
+ print(
+ f'- Created run {test_case.name}\n\tModule: {test_case.module_path}\n\tURL: {run_url}\n'
+ )
+ return run_url, run_result
+
+
+def get_kfp_package_path() -> str:
+ if os.environ.get('PULL_NUMBER') is not None:
+ path = f'git+https://github.com/kubeflow/pipelines.git@refs/pull/{os.environ.get("PULL_NUMBER")}/merge#subdirectory=sdk/python'
+ else:
+ path = 'git+https://github.com/kubeflow/pipelines.git@master#subdirectory=sdk/python'
+ print(f'Using the following KFP package path for tests: {path}')
+ return path
+
+
+dsl.component = functools.partial(
+ dsl.component, kfp_package_path=get_kfp_package_path())
+
+
+@pytest.mark.asyncio_cooperative
+@pytest.mark.parametrize('test_case', create_test_case_parameters())
+async def test(test_case: TestCase) -> None:
+ """Asynchronously runs all samples and test that they succeed."""
+ event_loop = asyncio.get_running_loop()
+ try:
+ run_url, run_result = run(test_case)
+ except Exception as e:
+ raise RuntimeError(
+ f'Error triggering pipeline {test_case.name}.') from e
+
+ api_run = await event_loop.run_in_executor(None, wait, run_result)
+ assert api_run.state == 'SUCCEEDED', f'Pipeline {test_case.name} ended with incorrect status: {api_run.state}. More info: {run_url}'
+
+
+if __name__ == '__main__':
+ pytest.main()
diff --git a/test/presubmit-component-yaml.sh b/test/presubmit-component-yaml.sh
index 62a4f403ed..3772e6c513 100755
--- a/test/presubmit-component-yaml.sh
+++ b/test/presubmit-component-yaml.sh
@@ -17,6 +17,11 @@ source_root=$(pwd)
python3 -m pip install --upgrade pip
python3 -m pip install sdk/python
+apt-get update && apt-get install -y protobuf-compiler
+pushd api
+make clean python
+popd
+python3 -m pip install api/v2alpha1/python
# Test loading all component.yaml definitions
"$source_root/components/test_load_all_components.sh"
diff --git a/test/presubmit-kfp-kubernetes-execution-tests.sh b/test/presubmit-kfp-kubernetes-execution-tests.sh
new file mode 100755
index 0000000000..0fa2327fbe
--- /dev/null
+++ b/test/presubmit-kfp-kubernetes-execution-tests.sh
@@ -0,0 +1,41 @@
+#!/bin/bash -ex
+# Copyright 2023 Kubeflow Pipelines contributors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+source_root=$(pwd)
+
+python3 -m pip install --upgrade pip
+python3 -m pip install $source_root/sdk/python
+apt-get update && apt-get install -y protobuf-compiler
+# install kfp-pipeline-spec from source
+pushd api
+make clean python
+popd
+python3 -m pip install api/v2alpha1/python
+
+# generate kfp-kubernetes proto files from source
+pushd "$source_root/kubernetes_platform"
+make clean python
+popd
+
+# install kfp-kubernetes from source
+# rust needed for transitive deps in dev extras on Python:3.12
+apt-get install rustc -y
+pip install -e $source_root/kubernetes_platform/python[dev]
+
+pip install -r $source_root/test/kfp-kubernetes-execution-tests/requirements.txt
+
+export KFP_ENDPOINT="https://$(curl https://raw.githubusercontent.com/kubeflow/testing/master/test-infra/kfp/endpoint)"
+export TIMEOUT_SECONDS=2700
+pytest $source_root/test/kfp-kubernetes-execution-tests/sdk_execution_tests.py --asyncio-task-timeout $TIMEOUT_SECONDS
diff --git a/test/presubmit-sdk-execution-tests.sh b/test/presubmit-sdk-execution-tests.sh
index e44a250922..4a710c17c4 100755
--- a/test/presubmit-sdk-execution-tests.sh
+++ b/test/presubmit-sdk-execution-tests.sh
@@ -17,6 +17,11 @@ source_root=$(pwd)
python3 -m pip install --upgrade pip
python3 -m pip install $source_root/sdk/python
+apt-get update && apt-get install -y protobuf-compiler
+pushd api
+make clean python
+popd
+python3 -m pip install api/v2alpha1/python
python3 -m pip install -r $source_root/test/sdk-execution-tests/requirements.txt
diff --git a/test/presubmit-test-kfp-kubernetes-library.sh b/test/presubmit-test-kfp-kubernetes-library.sh
index a8225de5f7..30a688872e 100755
--- a/test/presubmit-test-kfp-kubernetes-library.sh
+++ b/test/presubmit-test-kfp-kubernetes-library.sh
@@ -18,15 +18,20 @@ source_root=$(pwd)
pip install --upgrade pip
pip install wheel
-pip install 'kfp>=2.0.0,<3.0.0'
+pip install sdk/python
+apt-get update && apt-get install -y protobuf-compiler
+pushd api
+make clean python
+popd
+python3 -m pip install api/v2alpha1/python
+pip install api/v2alpha1/python
# generate Python proto code from source
-apt-get update -y
-apt-get install -y protobuf-compiler
-
pushd "$source_root/kubernetes_platform"
make clean python
popd
+# rust needed for transitive deps in dev extras on Python:3.12
+apt-get install rustc -y
pip install -e "$source_root/kubernetes_platform/python[dev]"
pytest "$source_root/kubernetes_platform/python/test" -n auto
diff --git a/test/presubmit-test-run-all-gcpc-modules.sh b/test/presubmit-test-run-all-gcpc-modules.sh
index 81389e128a..c47d92bc4c 100755
--- a/test/presubmit-test-run-all-gcpc-modules.sh
+++ b/test/presubmit-test-run-all-gcpc-modules.sh
@@ -17,6 +17,11 @@ source_root=$(pwd)
pip install --upgrade pip
pip install $source_root/sdk/python
+apt-get update && apt-get install -y protobuf-compiler
+pushd api
+make clean python
+popd
+python3 -m pip install api/v2alpha1/python
pip install components/google-cloud
pip install $(grep 'pytest==' sdk/python/requirements-dev.txt)