📝 reformat

Signed-off-by: Christoph Görn <[email protected]>

Author: goern

notebooks/neural-magic-torch-mnist/download_dataset.ipynb

@@ -102,7 +102,6 @@
     ")\n",
     "\n",
     "if use_ceph:\n",
-    "\n",
     "    s3_endpoint_url = os.environ[\"OBJECT_STORAGE_ENDPOINT_URL\"]\n",
     "    s3_access_key = os.environ[\"AWS_ACCESS_KEY_ID\"]\n",
     "    s3_secret_key = os.environ[\"AWS_SECRET_ACCESS_KEY\"]\n",
@@ -118,7 +117,6 @@
     "\n",
     "    # Store MNIST data for next step.\n",
     "    for path in destination_path.iterdir():\n",
-    "\n",
     "        file_downloaded_path = destination_path.joinpath(path.name)\n",
     "\n",
     "        key = f\"{project_name}/data/raw/{data_name}\"\n",

notebooks/neural-magic-torch-mnist/sparsify.ipynb

@@ -248,15 +248,13 @@
    "outputs": [],
    "source": [
     "def train_model(model, loaders, criterion, optimizer, num_epochs=1):\n",
-    "\n",
     "    model.train()\n",
     "\n",
     "    # Train the model\n",
     "    total_step = len(loaders[\"train\"])\n",
     "\n",
     "    for epoch in range(num_epochs):\n",
     "        for i, (images, labels) in enumerate(loaders[\"train\"]):\n",
-    "\n",
     "            # gives batch data, normalize x when iterate train_loader\n",
     "            b_x = Variable(images)  # batch x\n",
     "            b_y = Variable(labels)  # batch y\n",
@@ -412,7 +410,7 @@
     "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
     "\n",
     "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(cnn):\n",
+    "for name, layer in get_prunable_layers(cnn):\n",
     "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
    ]
   },

notebooks/neural-magic-torch-mnist/training.ipynb

@@ -391,15 +391,13 @@
     "\n",
     "\n",
     "def train(num_epochs, cnn, loaders, loss_func, optimizer):\n",
-    "\n",
     "    cnn.train()\n",
     "\n",
     "    # Train the model\n",
     "    total_step = len(loaders[\"train\"])\n",
     "\n",
     "    for epoch in range(num_epochs):\n",
     "        for i, (images, labels) in enumerate(loaders[\"train\"]):\n",
-    "\n",
     "            # gives batch data, normalize x when iterate train_loader\n",
     "            b_x = Variable(images)  # batch x\n",
     "            b_y = Variable(labels)  # batch y\n",

notebooks/tf-mnist/download_dataset.ipynb

@@ -181,15 +181,13 @@
     "    destination_path = Path(str(os.environ.get(\"DATASET\", \"data/raw\")))\n",
     "\n",
     "else:\n",
-    "\n",
     "    # Set path where to store\n",
     "    directory_path = Path.cwd().parents[0]\n",
     "    destination_path = directory_path.joinpath(\n",
     "        str(os.environ.get(\"DATASET\", \"data/raw\"))\n",
     "    )\n",
     "\n",
     "    if use_ceph:\n",
-    "\n",
     "        s3_endpoint_url = os.environ[\"OBJECT_STORAGE_ENDPOINT_URL\"]\n",
     "        s3_access_key = os.environ[\"AWS_ACCESS_KEY_ID\"]\n",
     "        s3_secret_key = os.environ[\"AWS_SECRET_ACCESS_KEY\"]\n",
@@ -206,7 +204,6 @@
     "\n",
     "# Store MNIST data for next step.\n",
     "for data_name, data_file in dataset.items():\n",
-    "\n",
     "    if not os.path.exists(destination_path):\n",
     "        destination_path.mkdir(parents=True, exist_ok=True)\n",
     "\n",
@@ -218,7 +215,6 @@
     "        s3.upload_file(Bucket=s3_bucket, Key=key, Filename=str(file_downloaded_path))\n",
     "\n",
     "    else:\n",
-    "\n",
     "        if not _is_file_downloaded(file_downloaded_path):\n",
     "            output = open(file_downloaded_path, \"wb\")\n",
     "            pickle.dump(data_file, output)\n",

notebooks/tf-mnist/training.ipynb

@@ -117,20 +117,17 @@
     "project_name = os.environ.get(\"PROJECT_NAME\", \"elyra-aidevsecops-tutorial\")\n",
     "\n",
     "if automation:\n",
-    "\n",
     "    # Get file path relative to notebook\n",
     "    raw_dataset_path = str(os.environ.get(\"DATASET\", \"../data/raw\"))\n",
     "\n",
     "else:\n",
-    "\n",
     "    # Download and store in project's data directory\n",
     "    directory_path = Path.cwd().parents[0]\n",
     "    raw_dataset_path = directory_path.joinpath(\n",
     "        str(os.environ.get(\"DATASET\", \"../data/raw/mnist_datasets_tf/\"))\n",
     "    )\n",
     "\n",
     "if use_ceph:\n",
-    "\n",
     "    # Download files from S3\n",
     "    s3_endpoint_url = os.environ[\"OBJECT_STORAGE_ENDPOINT_URL\"]\n",
     "    s3_access_key = os.environ[\"AWS_ACCESS_KEY_ID\"]\n",