0.55.2 update

stack-showcase/pipelines/inference.py

@@ -21,7 +21,7 @@
     inference_preprocessor,
 )
 
-from zenml import get_pipeline_context, pipeline, ExternalArtifact
+from zenml import get_pipeline_context, pipeline
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -41,10 +41,12 @@ def inference(random_state: str, target: str):
         target: Name of target column in dataset.
     """
     # Get the production model artifact
-    model = ExternalArtifact(name="sklearn_classifier")
+    model = get_pipeline_context().model.get_artifact("sklearn_classifier")
 
     # Get the preprocess pipeline artifact associated with this version
-    preprocess_pipeline = ExternalArtifact(name="preprocess_pipeline")
+    preprocess_pipeline = get_pipeline_context().model.get_artifact(
+        "preprocess_pipeline"
+    )
 
     # Link all the steps together by calling them and passing the output
     #  of one step as the input of the next step.

stack-showcase/requirements.txt

@@ -1,4 +1,4 @@
-zenml[server]>=0.50.0
+zenml[server]>=0.55.2
 notebook
 scikit-learn<1.3
 s3fs>2022.3.0,<=2023.4.0

stack-showcase/run.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# pre-demo recommendations\n",
     "! zenml connect --url https://1cf18d95-zenml.cloudinfra.zenml.io \n",
-    "! zenml delete model breast_cancer_classifier -y"
+    "! zenml model delete breast_cancer_classifier -y"
    ]
   },
   {
@@ -549,8 +549,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from zenml import ExternalArtifact\n",
-    "\n",
     "@pipeline\n",
     "def training(\n",
     "    train_dataset_id: Optional[UUID] = None,\n",
@@ -565,8 +563,8 @@
     "        dataset_trn, dataset_tst = feature_engineering()\n",
     "    else:\n",
     "        # Load the datasets from an older pipeline\n",
-    "        dataset_trn = ExternalArtifact(id=train_dataset_id)\n",
-    "        dataset_tst = ExternalArtifact(id=test_dataset_id)\n",
+    "        dataset_trn = client.get_artifact_version(name_id_or_prefix=train_dataset_id)\n",
+    "        dataset_tst = client.get_artifact_version(name_id_or_prefix=test_dataset_id)\n",
     "\n",
     "    trained_model = model_trainer(\n",
     "        dataset_trn=dataset_trn,\n",
@@ -919,7 +917,7 @@
     "    df_inference = inference_preprocessor(\n",
     "        dataset_inf=df_inference,\n",
     "        # We use the preprocess pipeline from the feature engineering pipeline\n",
-    "        preprocess_pipeline=ExternalArtifact(id=preprocess_pipeline_id),\n",
+    "        preprocess_pipeline=client.get_artifact_version(name_id_or_prefix=preprocess_pipeline_id),\n",
     "        target=target,\n",
     "    )\n",
     "    inference_predict(\n",
@@ -1213,6 +1211,36 @@
     "print(f'SGD version: {sgd_model_version.run_metadata[\"wandb_url\"].value}')\n",
     "print(f'RF version: {rf_model_version.run_metadata[\"wandb_url\"].value}')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With Model Control Plane we can also easily track lineage of artifacts and pipeline runs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for artifact_name, versions in sgd_model_version.data_artifacts.items():\n",
+    "    if versions:\n",
+    "        print(f\"Existing version of `{artifact_name}`:\")\n",
+    "        for version_name, artifact_ in  versions.items():\n",
+    "            print(version_name, artifact_.data_type.attribute)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for run_name, run_ in sgd_model_version.pipeline_runs.items():\n",
+    "    print(run_name, run_.id)"
+   ]
   }
  ],
  "metadata": {

stack-showcase/steps/model_evaluator.py

@@ -21,10 +21,11 @@
 from sklearn.base import ClassifierMixin
 from sklearn.metrics import confusion_matrix
 
-from zenml import log_artifact_metadata, step, log_model_metadata
+from zenml import log_artifact_metadata, step, log_model_metadata, get_step_context
 from zenml.logger import get_logger
 import wandb
 from zenml.client import Client
+from zenml.exceptions import StepContextError
 
 
 logger = get_logger(__name__)
@@ -109,7 +110,13 @@ def model_evaluator(
         .ravel()
         .tolist(),
     }
-    log_model_metadata(metadata={"wandb_url": wandb.run.url})
+    try:
+        if get_step_context().model:
+            log_model_metadata(metadata={"wandb_url": wandb.run.url})
+    except StepContextError:
+        # if model not configured not able to log metadata
+        pass
+
     log_artifact_metadata(
         metadata=metadata,
         artifact_name="sklearn_classifier",