update pytorch DLC version to 1.11 in pytorch mnist sample (#3574)

* update pytorch DLC version to 1.11

The notebook fails with current 1.8 pytorch. I think its a problem with the torchvision installed in the container.

```
AlgorithmError: ExecuteUserScriptError: Command "/opt/conda/bin/python3.6 mnist.py --backend gloo --epochs 1" INFO:__main__:Initialized the distributed environment: 'gloo' backend on 2 nodes. Current host rank is 0. Number of gpus: 0 INFO:__main__:Get train data loader Traceback (most recent call last): File "mnist.py", line 257, in <module> train(parser.parse_args()) File "mnist.py", line 114, in train train_loader = _get_train_data_loader(args.batch_size, args.data_dir, is_distributed, **kwargs) File "mnist.py", line 48, in _get_train_data_loader [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] File "/opt/conda/lib/python3.6/site-packages/torchvision/datasets/mnist.py", line 83, in __init__ ' You can use download=True to download it') RuntimeError: Dataset not found. You can use download=True to download it, exit code: 1
```

* formatting

* l = 100

sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb

@@ -69,7 +69,7 @@
     "sagemaker_session = sagemaker.Session()\n",
     "\n",
     "bucket = sagemaker_session.default_bucket()\n",
-    "prefix = 'sagemaker/DEMO-pytorch-mnist'\n",
+    "prefix = \"sagemaker/DEMO-pytorch-mnist\"\n",
     "\n",
     "role = sagemaker.get_execution_role()"
    ]
@@ -114,11 +114,11 @@
     "MNIST.mirrors = [\"https://sagemaker-sample-files.s3.amazonaws.com/datasets/image/MNIST/\"]\n",
     "\n",
     "MNIST(\n",
-    "    'data',\n",
+    "    \"data\",\n",
     "    download=True,\n",
     "    transform=transforms.Compose(\n",
     "        [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]\n",
-    "    )\n",
+    "    ),\n",
     ")"
    ]
   },
@@ -144,8 +144,8 @@
     }
    ],
    "source": [
-    "inputs = sagemaker_session.upload_data(path='data', bucket=bucket, key_prefix=prefix)\n",
-    "print('input spec (in this case, just an S3 path): {}'.format(inputs))"
+    "inputs = sagemaker_session.upload_data(path=\"data\", bucket=bucket, key_prefix=prefix)\n",
+    "print(\"input spec (in this case, just an S3 path): {}\".format(inputs))"
    ]
   },
   {
@@ -202,16 +202,15 @@
    "source": [
     "from sagemaker.pytorch import PyTorch\n",
     "\n",
-    "estimator = PyTorch(entry_point='mnist.py',\n",
-    "                    role=role,\n",
-    "                    py_version='py3',\n",
-    "                    framework_version='1.8.0',\n",
-    "                    instance_count=2,\n",
-    "                    instance_type='ml.c5.2xlarge',\n",
-    "                    hyperparameters={\n",
-    "                        'epochs': 1,\n",
-    "                        'backend': 'gloo'\n",
-    "                    })"
+    "estimator = PyTorch(\n",
+    "    entry_point=\"mnist.py\",\n",
+    "    role=role,\n",
+    "    py_version=\"py38\",\n",
+    "    framework_version=\"1.11.0\",\n",
+    "    instance_count=2,\n",
+    "    instance_type=\"ml.c5.2xlarge\",\n",
+    "    hyperparameters={\"epochs\": 1, \"backend\": \"gloo\"},\n",
+    ")"
    ]
   },
   {
@@ -532,7 +531,7 @@
     }
    ],
    "source": [
-    "estimator.fit({'training': inputs})"
+    "estimator.fit({\"training\": inputs})"
    ]
   },
   {
@@ -562,7 +561,7 @@
     }
    ],
    "source": [
-    "predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')"
+    "predictor = estimator.deploy(initial_instance_count=1, instance_type=\"ml.m4.xlarge\")"
    ]
   },
   {
@@ -600,16 +599,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import gzip \n",
+    "import gzip\n",
     "import numpy as np\n",
     "import random\n",
     "import os\n",
     "\n",
-    "data_dir = 'data/MNIST/raw'\n",
+    "data_dir = \"data/MNIST/raw\"\n",
     "with gzip.open(os.path.join(data_dir, \"t10k-images-idx3-ubyte.gz\"), \"rb\") as f:\n",
     "    images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28).astype(np.float32)\n",
     "\n",
-    "mask = random.sample(range(len(images)), 16) # randomly select some of the test images\n",
+    "mask = random.sample(range(len(images)), 16)  # randomly select some of the test images\n",
     "mask = np.array(mask, dtype=np.int)\n",
     "data = images[mask]"
    ]
@@ -710,9 +709,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sagemaker_session.delete_endpoint(\n",
-    "    endpoint_name = predictor.endpoint_name\n",
-    ")"
+    "sagemaker_session.delete_endpoint(endpoint_name=predictor.endpoint_name)"
    ]
   }
  ],