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Fixed the priority of parameters defined in register curl cmd vs mode…
…l-config.yaml (#2858) * fmt * format java * update default value --------- Co-authored-by: Ankith Gunapal <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,142 @@ | ||
| import shutil | ||
| from pathlib import Path | ||
| from unittest.mock import patch | ||
|
|
||
| import pytest | ||
| import test_utils | ||
| from model_archiver import ModelArchiverConfig | ||
|
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| CURR_FILE_PATH = Path(__file__).parent | ||
| REPO_ROOT_DIR = CURR_FILE_PATH.parent.parent | ||
|
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| MODEL_PY = """ | ||
| import torch | ||
| import torch.nn as nn | ||
| class Foo(nn.Module): | ||
| def __init__(self): | ||
| super().__init__() | ||
| def forward(self, x): | ||
| return x | ||
| """ | ||
|
|
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| HANDLER_PY = """ | ||
| import os | ||
| import torch | ||
| from ts.torch_handler.base_handler import BaseHandler | ||
| class FooHandler(BaseHandler): | ||
| def initialize(self, ctx): | ||
| super().initialize(ctx) | ||
| def preprocess(self, data): | ||
| return torch.as_tensor(int(data[0].get('body').decode('utf-8')), device=self.device) | ||
| def postprocess(self, x): | ||
| return [x.item()] | ||
| """ | ||
|
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| MODEL_CONFIG_YAML = f""" | ||
| #frontend settings | ||
| # TorchServe frontend parameters | ||
| minWorkers: 1 | ||
| maxWorkers: 4 | ||
| maxBatchDelay: 100 | ||
| batchSize: 4 | ||
| """ | ||
|
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| @pytest.fixture(scope="module") | ||
| def model_name(): | ||
| yield "foo" | ||
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| @pytest.fixture(scope="module") | ||
| def work_dir(tmp_path_factory, model_name): | ||
| return Path(tmp_path_factory.mktemp(model_name)) | ||
|
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|
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| @pytest.fixture(scope="module", name="mar_file_path") | ||
| def create_mar_file(work_dir, model_archiver, model_name): | ||
| mar_file_path = work_dir.joinpath(model_name + ".mar") | ||
|
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| model_config_yaml_file = work_dir / "model_config.yaml" | ||
| model_config_yaml_file.write_text(MODEL_CONFIG_YAML) | ||
|
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| model_py_file = work_dir / "model.py" | ||
| model_py_file.write_text(MODEL_PY) | ||
|
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| handler_py_file = work_dir / "handler.py" | ||
| handler_py_file.write_text(HANDLER_PY) | ||
|
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| config = ModelArchiverConfig( | ||
| model_name=model_name, | ||
| version="1.0", | ||
| serialized_file=None, | ||
| model_file=model_py_file.as_posix(), | ||
| handler=handler_py_file.as_posix(), | ||
| extra_files=None, | ||
| export_path=work_dir, | ||
| requirements_file=None, | ||
| runtime="python", | ||
| force=False, | ||
| archive_format="default", | ||
| config_file=model_config_yaml_file.as_posix(), | ||
| ) | ||
|
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| with patch("archiver.ArgParser.export_model_args_parser", return_value=config): | ||
| model_archiver.generate_model_archive() | ||
|
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| assert mar_file_path.exists() | ||
|
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| yield mar_file_path.as_posix() | ||
|
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| # Clean up files | ||
| mar_file_path.unlink(missing_ok=True) | ||
|
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|
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| def register_model(mar_file_path, model_store, params, torchserve): | ||
| shutil.copy(mar_file_path, model_store) | ||
|
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| file_name = Path(mar_file_path).name | ||
|
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| model_name = Path(file_name).stem | ||
|
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| params = params + ( | ||
| ("model_name", model_name), | ||
| ("url", file_name), | ||
| ) | ||
|
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| test_utils.reg_resp = test_utils.register_model_with_params(params) | ||
| return model_name | ||
|
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| def test_register_model_with_batch_size(mar_file_path, model_store, torchserve): | ||
| params = ( | ||
| ("initial_workers", "2"), | ||
| ("synchronous", "true"), | ||
| ("batch_size", "2"), | ||
| ) | ||
|
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| model_name = register_model(mar_file_path, model_store, params, torchserve) | ||
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| describe_resp = test_utils.describe_model(model_name, "1.0") | ||
|
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| assert describe_resp[0]["batchSize"] == 2 | ||
|
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| test_utils.unregister_model(model_name) | ||
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| def test_register_model_without_batch_size(mar_file_path, model_store, torchserve): | ||
| params = ( | ||
| ("initial_workers", "2"), | ||
| ("synchronous", "true"), | ||
| ) | ||
| model_name = register_model(mar_file_path, model_store, params, torchserve) | ||
|
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| describe_resp = test_utils.describe_model(model_name, "1.0") | ||
|
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| assert describe_resp[0]["batchSize"] == 4 | ||
|
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| test_utils.unregister_model(model_name) |
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