Updated curated environments in sample notebooks (#1958)

* Updated curated environments in sample notebooks

* Fixed continuous retraining notebook

how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py

@@ -36,7 +36,10 @@ except Exception:
     last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
 
 train_ds = Dataset.get_by_name(ws, args.ds_name)
-dataset_changed_time = train_ds.data_changed_time
+dataset_changed_time = train_ds.data_changed_time.replace(tzinfo=pytz.UTC)
+
+print("dataset_changed_time=" + str(dataset_changed_time))
+print("last_train_time=" + str(last_train_time))
 
 if not dataset_changed_time > last_train_time:
     print("Cancelling run since there is no new data.")

how-to-use-azureml/deployment/onnx/mnist.py

@@ -94,15 +94,15 @@ def main():
     os.makedirs(output_dir, exist_ok=True)
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
-    # Use Azure Open Datasets for MNIST dataset
+    # MNIST dataset
     datasets.MNIST.resources = [
-        ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+        ("train-images-idx3-ubyte.gz",
          "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-        ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+        ("train-labels-idx1-ubyte.gz",
          "d53e105ee54ea40749a09fcbcd1e9432"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+        ("t10k-images-idx3-ubyte.gz",
          "9fb629c4189551a2d022fa330f9573f3"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+        ("t10k-labels-idx1-ubyte.gz",
          "ec29112dd5afa0611ce80d1b7f02629c")
     ]
     train_loader = torch.utils.data.DataLoader(

how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb

@@ -200,18 +200,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-        "from azureml.train.dnn import PyTorch\n",
+    "from azureml.core import Environment, ScriptRunConfig\n",
     "\n",
-        "estimator = PyTorch(source_directory=project_folder, \n",
-        "                    script_params={'--output-dir': './outputs'},\n",
+    "pytorch_env = Environment.get(ws, name='AzureML-acpt-pytorch-1.13-cuda11.7')\n",
+    "\n",
+    "estimator = ScriptRunConfig(source_directory=project_folder,\n",
     "                            compute_target=compute_target,\n",
-        "                    entry_script='mnist.py',\n",
-        "                    use_gpu=True)\n",
-        "\n",
-        "# upgrade to PyTorch 1.0 Preview, which has better support for ONNX\n",
-        "estimator.conda_dependencies.remove_conda_package('pytorch=0.4.0')\n",
-        "estimator.conda_dependencies.add_conda_package('pytorch-nightly')\n",
-        "estimator.conda_dependencies.add_channel('pytorch')"
+    "                            script='mnist.py',\n",
+    "                            environment=pytorch_env)"
    ]
   },
   {
@@ -578,7 +574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-      "version": "3.6.6"
+   "version": "3.9.18"
   },
   "star_tag": [],
   "tags": [
@@ -659,5 +655,5 @@
   }
  },
  "nbformat": 4,
-  "nbformat_minor": 2
+ "nbformat_minor": 4
 }

how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb

@@ -245,7 +245,7 @@
    "source": [
     "from azureml.core import Environment\n",
     "\n",
-        "pytorch_env = Environment.get(ws, name='AzureML-PyTorch-1.6-GPU')"
+    "pytorch_env = Environment.get(ws, name='AzureML-acpt-pytorch-1.13-cuda11.7')"
    ]
   },
   {
@@ -366,7 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+   "version": "3.9.18"
   },
   "tags": [
    "None"
@@ -374,5 +374,5 @@
   "task": "Train a model using the distributed training via Horovod"
  },
  "nbformat": 4,
-  "nbformat_minor": 2
+ "nbformat_minor": 4
 }

how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py

@@ -51,15 +51,15 @@ if args.cuda:
 
 
 kwargs = {}
-# Use Azure Open Datasets for MNIST dataset
+# MNIST dataset
 datasets.MNIST.resources = [
-    ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+    ("train-images-idx3-ubyte.gz",
      "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-    ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+    ("train-labels-idx1-ubyte.gz",
      "d53e105ee54ea40749a09fcbcd1e9432"),
-    ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+    ("t10k-images-idx3-ubyte.gz",
      "9fb629c4189551a2d022fa330f9573f3"),
-    ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+    ("t10k-labels-idx1-ubyte.gz",
      "ec29112dd5afa0611ce80d1b7f02629c")
 ]
 train_dataset = \

tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb

@@ -280,8 +280,7 @@
     "# get a curated environment\n",
     "env = Environment.get(\n",
     "    workspace=ws, \n",
-        "    name=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu\",\n",
-        "    version=1\n",
+    "    name=\"AzureML-sklearn-1.0\"\n",
     ")\n",
     "env.inferencing_stack_version='latest'\n",
     "\n",
@@ -490,7 +489,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-      "version": "3.6.9"
+   "version": "3.9.18"
   },
   "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
   "nteract": {