Dane konfiguracji serwera MLflow registry

IUM_08.MLFlow.ipynb

@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -117,7 +117,8 @@
     "logging.basicConfig(level=logging.WARN)\n",
     "logger = logging.getLogger(__name__)\n",
     "\n",
-    "#mlflow.set_tracking_uri(\"http://localhost:5001\")\n",
+    "mlflow.set_tracking_uri(\"http://localhost:5000\")\n",
+    "mlflow.set_experiment(\"s123456\")\n",
     "\n",
     "def eval_metrics(actual, pred):\n",
     "    rmse = np.sqrt(mean_squared_error(actual, pred))\n",
@@ -156,7 +157,10 @@
     "    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5\n",
     "    #l1_ratio = 0.5\n",
     "\n",
-    "    with mlflow.start_run():\n",
+    "    with mlflow.start_run() as run:\n",
+    "        print(\"MLflow run experiment_id: {0}\".format(run.info.experiment_id))\n",
+    "        print(\"MLflow run artifact_uri: {0}\".format(run.info.artifact_uri))\n",
+    "\n",
     "        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n",
     "        lr.fit(train_x, train_y)\n",
     "\n",
@@ -187,14 +191,14 @@
     "            # There are other ways to use the Model Registry, which depends on the use case,\n",
     "            # please refer to the doc for more information:\n",
     "            # https://mlflow.org/docs/latest/model-registry.html#api-workflow\n",
-    "            mlflow.sklearn.log_model(lr, \"model\", registered_model_name=\"ElasticnetWineModel\", signature=signature)\n",
+    "            mlflow.sklearn.log_model(lr, \"wines-model\", registered_model_name=\"ElasticnetWineModel\", signature=signature)\n",
     "        else:\n",
     "            mlflow.sklearn.log_model(lr, \"model\", signature=signature)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -205,14 +209,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Elasticnet model (alpha=0.500000, l1_ratio=0.500000):\r\n",
-      "  RMSE: 0.7931640229276851\r\n",
-      "  MAE: 0.6271946374319586\r\n",
-      "  R2: 0.10862644997792614\r\n"
+      "total 4\n",
+      "drwxrwxr-x 3 tomek tomek 4096 maj 19 21:31 1\n",
+      "INFO: 's123456' does not exist. Creating a new experiment\n",
+      "MLflow run experiment_id: 2\n",
+      "MLflow run artifact_uri: /tmp/mlruns/2/c15feb5df335490ba990ddd4dd977c1b/artifacts\n",
+      "Elasticnet model (alpha=0.500000, l1_ratio=0.500000):\n",
+      "  RMSE: 0.7931640229276851\n",
+      "  MAE: 0.6271946374319586\n",
+      "  R2: 0.10862644997792614\n",
+      "Registered model 'ElasticnetWineModel' already exists. Creating a new version of this model...\n",
+      "2021/05/19 22:34:48 INFO mlflow.tracking._model_registry.client: Waiting up to 300 seconds for model version to finish creation.                     Model name: ElasticnetWineModel, version 2\n",
+      "Created version '2' of model 'ElasticnetWineModel'.\n"
      ]
     }
    ],
    "source": [
+    "! ls -l /tmp/mlruns\n",
     "### Wtyrenujmy model z domyślnymi wartościami parametrów\n",
     "! cd ./IUM_08/examples/; python sklearn_elasticnet_wine/train.py"
    ]
@@ -1374,6 +1387,29 @@
     "\n",
     "4. [6 pkt] Stwórz na Jenkinsie projekt `s123456-predict-s654321-from-registry`, który zrealizuje to samo zadanie co `s123456-predict-s654321`, ale tym razem pobierze model z rejestru MLflow zamiast z artefaktów Jenkinsa"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Dane do konfiguracji MLflow registry\n",
+    "\n",
+    "- Podgląd w przeglądarce: http://tzietkiewicz.vm.wmi.amu.edu.pl/#/\n",
+    "   - user: `student`\n",
+    "   - hasło: Podane na MS Teams\n",
+    "\n",
+    "- Tracking URI:\n",
+    "   - Python: `mlflow.set_tracking_uri(\"http://172.17.0.1:5000\")`\n",
+    "   - CLI: `export MLFLOW_TRACKING_URI=http://172.17.0.1:5000`\n",
+    "   \n",
+    "- Żeby klient MLflow działający w kontenerze docker mógł zapisywać i pdczytywać artefakty, muszą Państwo podmonotwać katalog /tmp/mlrunsMożna to zrobić za pomocą flagi `-v`, którą można przekazać tak, jak pokazano tutaj: https://www.jenkins.io/doc/book/pipeline/docker/#caching-data-for-containers\n",
+    "- Proszę ustawić nazwę eksperymentu na numer indeksu, dzięki temu każdy z Państwa będzie widział swoje eksperymenty oddzielnie:\n",
+    "`mlflow.set_experiment(\"s123456\")`"
+   ]
   }
  ],
  "metadata": {