From bcf330e629d261995378d049a2995dc898dac601 Mon Sep 17 00:00:00 2001
From: Suraj Kota <surakota@amazon.com>
Date: Fri, 19 Aug 2022 01:28:59 +0000
Subject: [PATCH] formatting

---
 .../pytorch_mnist/pytorch_mnist.ipynb         | 53 ++++++++++---------
 1 file changed, 28 insertions(+), 25 deletions(-)

diff --git a/sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb b/sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb
index 8adb1993b1..39cbc5eda9 100644
--- a/sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb
+++ b/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()"
    ]
@@ -111,14 +111,16 @@
     "from torchvision.datasets import MNIST\n",
     "from torchvision import transforms\n",
     "\n",
-    "MNIST.mirrors = [\"https://sagemaker-sample-files.s3.amazonaws.com/datasets/image/MNIST/\"]\n",
+    "MNIST.mirrors = [\n",
+    "    \"https://sagemaker-sample-files.s3.amazonaws.com/datasets/image/MNIST/\"\n",
+    "]\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 +146,8 @@
     }
    ],
    "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 +204,15 @@
    "source": [
     "from sagemaker.pytorch import PyTorch\n",
     "\n",
-    "estimator = PyTorch(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={\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 +533,7 @@
     }
    ],
    "source": [
-    "estimator.fit({'training': inputs})"
+    "estimator.fit({\"training\": inputs})"
    ]
   },
   {
@@ -562,7 +563,7 @@
     }
    ],
    "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 +601,20 @@
    "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",
+    "    images = (\n",
+    "        np.frombuffer(f.read(), np.uint8, offset=16)\n",
+    "        .reshape(-1, 28, 28)\n",
+    "        .astype(np.float32)\n",
+    "    )\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 +715,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sagemaker_session.delete_endpoint(\n",
-    "    endpoint_name = predictor.endpoint_name\n",
-    ")"
+    "sagemaker_session.delete_endpoint(endpoint_name=predictor.endpoint_name)"
    ]
   }
  ],