New SDK version

tutorials/01.train-models.ipynb

@@ -159,8 +159,8 @@
         "autoscale_enabled = os.environ.get(\"BATCHAI_CLUSTER_AUTOSCALE_ENABLED\", True)\n",
         "\n",
         "\n",
-        "if batchai_cluster_name in ws.compute_targets():\n",
-        "    compute_target = ws.compute_targets()[batchai_cluster_name]\n",
+        "if batchai_cluster_name in ws.compute_targets:\n",
+        "    compute_target = ws.compute_targets[batchai_cluster_name]\n",
         "    if compute_target and type(compute_target) is BatchAiCompute:\n",
         "        print('found compute target. just use it. ' + batchai_cluster_name)\n",
         "else:\n",

tutorials/02.deploy-models.ipynb

@@ -134,7 +134,7 @@
         "\n",
         "ws = Workspace.from_config()\n",
         "model=Model(ws, 'sklearn_mnist')\n",
-        "model.download(target_dir = '.')\n",
+        "model.download(target_dir='.', exists_ok=True)\n",
         "import os \n",
         "# verify the downloaded model file\n",
         "os.stat('./sklearn_mnist_model.pkl')"
@@ -210,7 +210,7 @@
         "\n",
         "conf_mx = confusion_matrix(y_test, y_hat)\n",
         "print(conf_mx)\n",
-        "print('Overall accuracy:', np.average(y_hat==y_test))"
+        "print('Overall accuracy:', np.average(y_hat == y_test))"
       ]
     },
     {
@@ -478,7 +478,7 @@
         "sample_indices = np.random.permutation(X_test.shape[0])[0:n]\n",
         "\n",
         "test_samples = json.dumps({\"data\": X_test[sample_indices].tolist()})\n",
-        "test_samples = bytes(test_samples, encoding = 'utf8')\n",
+        "test_samples = bytes(test_samples, encoding='utf8')\n",
         "\n",
         "# predict using the deployed model\n",
         "result = service.run(input_data=test_samples)\n",

tutorials/03.auto-train-models.ipynb

@@ -424,4 +424,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}