chore: first debug test

.github/workflows/eval_retrain.yml

@@ -12,30 +12,19 @@ on:
 
 jobs:
   eval_check:
-    runs-on: ubuntu-latest
+    runs-on: self-hosted
     outputs:  # Define outputs for downstream jobs
       drift_detected: ${{ steps.eval_check.outputs.degraded }}
     steps:
       - name: ⏬ Checkout repository
         uses: actions/checkout@v4
 
-      - name: 🐍 Setup python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.10'
-
-      - name: 📦 Install dependencies
-        run: |
-          pip install pipenv
-          cd eval
-          pipenv sync
-
       - name: ⚙️ Run Evaluation
         env:
           WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
         id: eval_check
         run: |
-          cd drift
+          cd eval
           output=$(pipenv run python eval.py)
           echo "$output" >> $GITHUB_STEP_SUMMARY
 

drift/check_drift.py

@@ -10,12 +10,13 @@
 ) as run:
 
     # Grab the latest training and production dataframes
-    train_artifact = run.use_artifact("jdoc-org/wandb-registry-dataset/training:latest")
-    run.config["train_data"] = train_artifact.source_name
+    registered_training_dataset = "jdoc-org/wandb-registry-dataset/training:latest"
+    train_artifact = run.use_artifact(registered_training_dataset)
+    run.config["train_data"] = train_artifact.name
     train_data = train_artifact.get("training_data").get_dataframe()
 
     prod_artifact = run.use_artifact("production_data:latest")
-    run.config["prod_data"] = prod_artifact.source_name
+    run.config["prod_data"] = prod_artifact.name
     prod_data = prod_artifact.get("production_data").get_dataframe()
 
     feature_list = ["active_power", "temp", "humidity", "pressure"]
@@ -72,19 +73,19 @@
         artifact.description = prod_artifact.description
         artifact = run.log_artifact(artifact).wait()
         # Open a github issue asking for manual review
-        issue_title = f"Data drift detected on {train_artifact.source_name}"
+        issue_title = f"Data drift detected on {train_artifact.name}"
         issue_body = (
             f"Data drift has been detected when comparing the registered training dataset with recent production data.\n\n"
-            f"Please review the [candidate artifact](https://wandb.ai/{run.entity}/{run.project}/artifacts/{artifact.type}/{artifact.source_name}) "
+            f"Please review the [candidate artifact](https://wandb.ai/{run.entity}/{run.project}/artifacts/{artifact.type}/{artifact.name}) "
             f"and the [drift report]({report_url}) to determine if the registered training data should be updated.\n\n"
             f"To approve the new candidate after review, link it to [the training Dataset Registry](https://wandb.ai/registry/dataset?selectionPath=jdoc-org%2Fwandb-registry-dataset%2Ftraining&view=versions) at "
-            f"(`jdoc-org/wandb-registry-dataset/training`), otherwise close this issue."
+            f"(`{registered_training_dataset}`), otherwise close this issue."
         )
         issue_url = open_github_issue(issue_title, issue_body, labels=["drift", "data"])
         print(
             f"Production batch `{prod_artifact.source_name}` has been logged "
-            f"as candidate to replace training data `{artifact.source_name}`. "
-            f"An [issue]({issue_url}) was created for manual review:\n"
+            f"as candidate `{artifact.name}` to replace training data. "
+            f"An [issue]({issue_url}) was also created for manual review:\n"
         )
         print(f"- [Data Drift Issue]({issue_url})")
     else: