update samples from Release-92 as a part of SDK release

Add synapse sample notebooks

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

@@ -94,6 +94,17 @@ 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
+    datasets.MNIST.resources = [
+        ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+         "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
+        ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+         "d53e105ee54ea40749a09fcbcd1e9432"),
+        ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+         "9fb629c4189551a2d022fa330f9573f3"),
+        ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+         "ec29112dd5afa0611ce80d1b7f02629c")
+    ]
     train_loader = torch.utils.data.DataLoader(
         datasets.MNIST('data', train=True, download=True,
                        transform=transforms.Compose([transforms.ToTensor(),

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml

@@ -3,7 +3,11 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - interpret-community[visualization]
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
   - matplotlib
   - azureml-dataset-runtime
   - ipywidgets

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml

@@ -3,6 +3,10 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - interpret-community[visualization]
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
   - matplotlib
   - ipywidgets

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml

@@ -3,6 +3,10 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - interpret-community[visualization]
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
   - matplotlib
   - ipywidgets

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml

@@ -3,7 +3,11 @@ dependencies:
 - pip:
   - azureml-sdk
   - azureml-interpret
-  - interpret-community[visualization]
+  - flask
+  - flask-cors
+  - gevent>=1.3.6
+  - jinja2
+  - ipython
   - matplotlib
   - azureml-dataset-runtime
   - azureml-core

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

@@ -51,6 +51,17 @@ if args.cuda:
 
 
 kwargs = {}
+# Use Azure Open Datasets for MNIST dataset
+datasets.MNIST.resources = [
+    ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+     "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
+    ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+     "d53e105ee54ea40749a09fcbcd1e9432"),
+    ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+     "9fb629c4189551a2d022fa330f9573f3"),
+    ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+     "ec29112dd5afa0611ce80d1b7f02629c")
+]
 train_dataset = \
     datasets.MNIST('data-%d' % hvd.rank(), train=True, download=True,
                    transform=transforms.Compose([

how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/scripts/train.py

@@ -102,6 +102,17 @@ torch.manual_seed(args.seed)
 device = torch.device("cuda" if use_cuda else "cpu")
 
 kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+# Use Azure Open Datasets for MNIST dataset
+datasets.MNIST.resources = [
+    ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+     "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
+    ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+     "d53e105ee54ea40749a09fcbcd1e9432"),
+    ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+     "9fb629c4189551a2d022fa330f9573f3"),
+    ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+     "ec29112dd5afa0611ce80d1b7f02629c")
+]
 train_loader = torch.utils.data.DataLoader(
     datasets.MNIST('../data', train=True, download=True,
                    transform=transforms.Compose([

how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-pytorch/train-and-deploy-pytorch.ipynb

@@ -332,6 +332,18 @@
         "import random\n",
         "import numpy as np\n",
         "\n",
+        "# Use Azure Open Datasets for MNIST dataset\n",
+        "datasets.MNIST.resources = [\n",
+        "    (\"https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz\",\n",
+        "     \"f68b3c2dcbeaaa9fbdd348bbdeb94873\"),\n",
+        "    (\"https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz\",\n",
+        "     \"d53e105ee54ea40749a09fcbcd1e9432\"),\n",
+        "    (\"https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz\",\n",
+        "     \"9fb629c4189551a2d022fa330f9573f3\"),\n",
+        "    (\"https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz\",\n",
+        "     \"ec29112dd5afa0611ce80d1b7f02629c\")\n",
+        "]\n",
+        "\n",
         "test_data = datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
         "                       transforms.ToTensor(),\n",
         "                       transforms.Normalize((0.1307,), (0.3081,))]))\n",