resolving Anish's comments

wandb · Dec 6, 2023 · 63155e2 · 63155e2
1 parent e04329e
commit 63155e2
Showing 1 changed file with 78 additions and 26 deletions.
diff --git a/colabs/wandb-model-registry/New_Model_Logging_in_W&B.ipynb b/colabs/wandb-model-registry/New_Model_Logging_in_W&B.ipynb
@@ -5,7 +5,7 @@
     "colab": {
       "provenance": [],
       "gpuType": "T4",
-      "authorship_tag": "ABX9TyMqvDDIo1WDFe0kuxoSgmq6",
+      "authorship_tag": "ABX9TyOlvkZjseUluPYEdqGH1TjK",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -43,6 +43,15 @@
         "id": "saeQIrcF155i"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Imports"
+      ],
+      "metadata": {
+        "id": "_ewwM5T_OWTr"
+      }
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -51,13 +60,53 @@
       },
       "outputs": [],
       "source": [
-        "!pip install wandb einops"
+        "!pip install -qqq wandb einops"
       ]
     },
     {
       "cell_type": "code",
       "source": [
-        "!wandb login"
+        "import torch\n",
+        "from torch import nn\n",
+        "from einops import rearrange, repeat\n",
+        "from einops.layers.torch import Rearrange\n",
+        "import torch\n",
+        "from torch.utils.data import DataLoader, Dataset\n",
+        "from torchvision import transforms\n",
+        "import torch\n",
+        "from torch import nn\n",
+        "from torch.optim import Adam\n",
+        "from torch.utils.data import DataLoader\n",
+        "from torchvision import datasets, transforms\n",
+        "\n",
+        "import wandb"
+      ],
+      "metadata": {
+        "id": "6OsMa64TOacf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Log in to W&B\n",
+        "- You can explicitly login using `wandb login` or `wandb.login()` (See below)\n",
+        "- Alternatively you can set environment variables. There are several env variables which you can set to change the behavior of W&B logging. The most important are:\n",
+        "    - `WANDB_API_KEY` - find this in your \"Settings\" section under your profile\n",
+        "    - `WANDB_BASE_URL` - this is the url of the W&B server\n",
+        "- Find your API Token in \"Profile\" -> \"Setttings\" in the W&B App\n",
+        "\n",
+        "![api_token](https://drive.google.com/uc?export=view&id=1Xn7hnn0rfPu_EW0A_-32oCXqDmpA0-kx)"
+      ],
+      "metadata": {
+        "id": "m3Q_QG14Ovvz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wandb.login()"
       ],
       "metadata": {
         "id": "5-r3O3gFwfcr"
@@ -69,24 +118,41 @@
       "cell_type": "markdown",
       "source": [
         "# Define the Model and Dataset\n",
+        "This is a simple implementation of a Vision Transformer (ViT) and utilizes a random dataset for training.\n",
         "- Credit to https://github.com/lucidrains/vit-pytorch"
       ],
       "metadata": {
         "id": "ZuL3yZ17qIGE"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Define some config for the model and dataset"
+      ],
+      "metadata": {
+        "id": "WE4j_CI7Tt9O"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define the number of samples, classes, and image size\n",
+        "num_samples = 100\n",
+        "num_classes = 10\n",
+        "image_size = 256\n",
+        "batch_size = 32"
+      ],
+      "metadata": {
+        "id": "D7sVekyVTlxN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "source": [
-        "import torch\n",
-        "from torch import nn\n",
-        "from einops import rearrange, repeat\n",
-        "from einops.layers.torch import Rearrange\n",
-        "import torch\n",
-        "from torch.utils.data import DataLoader, Dataset\n",
-        "\n",
         "# helpers\n",
-        "\n",
         "def pair(t):\n",
         "    return t if isinstance(t, tuple) else (t, t)\n",
         "\n",
@@ -224,16 +290,12 @@
         "        label = torch.randint(0, self.num_classes, (1,)).item()\n",
         "        return image, label\n",
         "\n",
-        "# Define the number of samples, classes, and image size\n",
-        "num_samples = 100\n",
-        "num_classes = 10\n",
-        "image_size = 256\n",
+        "\n",
         "\n",
         "# Create the dataset\n",
         "dataset = RandomImageDataset(num_samples=num_samples, num_classes=num_classes, image_size=image_size)\n",
         "\n",
         "# Create a DataLoader\n",
-        "batch_size = 32\n",
         "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)"
       ],
       "metadata": {
@@ -262,17 +324,7 @@
     {
       "cell_type": "code",
       "source": [
-        "from torchvision import transforms\n",
-        "import torch\n",
-        "from torch import nn\n",
-        "from torch.optim import Adam\n",
-        "from torch.utils.data import DataLoader\n",
-        "from torchvision import datasets, transforms\n",
-        "import wandb\n",
-        "\n",
-        "\n",
         "run = wandb.init(project=\"new_model_logging\",\n",
-        "                 entity=\"wandb\",\n",
         "                 job_type=\"training\")\n",
         "\n",
         "v = ViT(\n",