Show how to get experiment tracker and model deployer URLs

zenml-io · Feb 14, 2023 · 205f348 · 205f348
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diff --git a/.gitignore b/.gitignore
@@ -133,6 +133,7 @@ dmypy.json
 
 # for wandb
 mlruns
+wandb/
 
 # poetry
 *poetry.lock*
diff --git a/2-1_Experiment_Tracking.ipynb b/2-1_Experiment_Tracking.ipynb
@@ -281,7 +281,26 @@
     "\n",
     "Click on the `Parameters >` tab on top of the table to see *all* hyperparameters of your model. Now you can see at a glance which model performed best and which hyperparameters changed between different runs. In our case, we can see that the SVC model with `gamma=0.001` achieved the best test accuracy of `0.969`.\n",
     "\n",
-    "If we click on one of the links in the `Start Time` column, we can see additional details of the respective run. In particular, we can find a `model.pkl` file under the `Artifacts` tab, which we could now use to deploy our model in an inference/production environment. In the next lesson, `2-2_Local_Deployment.ipynb`, we will learn how to do this automatically as part of our pipelines with the [MLflow Models](https://mlflow.org/docs/latest/models.html)  component."
+    "If we click on one of the links in the `Start Time` column, we can see additional details of the respective run. In particular, we can find a `model.pkl` file under the `Artifacts` tab, which we could now use to deploy our model in an inference/production environment. In the next lesson, `2-2_Local_Deployment.ipynb`, we will learn how to do this automatically as part of our pipelines with the [MLflow Models](https://mlflow.org/docs/latest/models.html)  component.\n",
+    "\n",
+    "If you would like to inspect the MLflow logs of your runs manually, you can find\n",
+    "the logging location using the `experiment_tracker_url` metadata field of the \n",
+    "trainer step of your pipeline run, e.g.:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from zenml.post_execution import get_unlisted_runs\n",
+    "\n",
+    "pipeline_run = get_unlisted_runs()[-1]\n",
+    "step = pipeline_run.get_step(\"trainer\")\n",
+    "experiment_tracker_url = step.metadata[\"experiment_tracker_url\"].value\n",
+    "\n",
+    "print(experiment_tracker_url)"
    ]
   },
   {
@@ -390,7 +409,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, execute the cell below to run your pipeline with different gamma values. Follow the link to see your runs recorded in your Weights & Biases project:"
+    "Finally, execute the cell below to run your pipeline with different gamma values."
    ]
   },
   {
@@ -399,9 +418,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "svc_wandb_pipeline.run(unlisted=True)\n",
-    "\n",
-    "print(f\"https://wandb.ai/{WANDB_ENTITY}/{WANDB_PROJECT}/runs/\")"
+    "svc_wandb_pipeline.run(unlisted=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the cell below and follow the link to see the run in your Weights & Biases \n",
+    "project:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer_step = get_unlisted_runs()[-1].get_step(\"trainer\")\n",
+    "experiment_tracker_url = trainer_step.metadata[\"experiment_tracker_url\"].value\n",
+    "print(experiment_tracker_url)"
    ]
   },
   {
@@ -435,7 +472,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0 (default, Nov 29 2022, 17:00:25) [Clang 14.0.0 (clang-1400.0.29.202)]"
+   "version": "3.10.0"
   },
   "vscode": {
    "interpreter": {

diff --git a/2-2_Local_Deployment.ipynb b/2-2_Local_Deployment.ipynb
@@ -183,6 +183,30 @@
    "source": [
     "If you see a checkmark under status, the model was correctly deployed. Congrats!\n",
     "\n",
+    "To find the URL of a model deployed by a specific run, you can use the \n",
+    "`deployed_model_url` metadata field of the model deployer step of your pipeline \n",
+    "run, e.g.:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from zenml.post_execution import get_unlisted_runs\n",
+    "\n",
+    "last_run = get_unlisted_runs()[-1]\n",
+    "deployer_step = last_run.get_step(\"model_deployer\")\n",
+    "deployed_model_url = deployer_step.metadata[\"deployed_model_url\"].value\n",
+    "print(deployed_model_url)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "To interact with our deployed model in Python, we can use the `find_model_server()` method of ZenMLs model-deployer stack component:"
    ]
   },
@@ -220,8 +244,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from zenml.post_execution import get_unlisted_runs\n",
-    "\n",
     "last_run = get_unlisted_runs()[-1]\n",
     "X_test = last_run.steps[0].outputs[\"X_test\"].read()\n",
     "y_test = last_run.steps[0].outputs[\"y_test\"].read()"
@@ -258,7 +280,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.13 64-bit ('zenbytes-dev')",
+   "display_name": "zenml310",
    "language": "python",
    "name": "python3"
   },
@@ -272,11 +294,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.10.0"
   },
   "vscode": {
    "interpreter": {
-    "hash": "ec45946565c50b1d690aa5a9e3c974f5b62b9cc8d8934e441e52186140f79402"
+    "hash": "569b3361e3ec4d7692543ddda480ca8173a6c158bb706498f2e35ca1687a80ea"
    }
   }
  },