Integrating with Weights & Biases (W&B)

Weights & Biases, which provides insight tools for machine learning, seamlessly integrates with Spell. This integration allows for much deeper insights into training metrics and examples of a model. It enables Spell runs to send metrics directly to your Weights & Biases account.

The steps in this guide demonstrate how to enable the W&B integration for your account. Every run, whether it is within a Developer or Teams account, may utilize this integration.

Adding the Weights & Biases Integration

Step 1. Navigate to your integration settings page.

Click the "Install" button in the Weights & Biases section.

Step 2. Navigate to the W&B user settings page. Under API Tokens use an existing token or create a new token. Copy your token.

Step 3. Paste your token. Click the "Confirm" button.

Step 4. View the integration listed in your integrations page. You may remove it from the list at any time.

Running A Run

Create scripts that integrate the wandb library without running wandb login before your script. We suggest that you follow a simple PyTorch W&B example. This can be done directly by running the following command:

spell run --github-url https://github.com/wandb/examples.git --machine-type k80 'python pytorch-cnn-mnist/main.py'

Once W&B integration is active, you do not need to add wandb as a pip package or run wandb login. This process is now automated by Spell. The run starts as follows:

💫 Casting spell #1…
✨ Stop viewing logs with ^C
✨ Building… done
✨ Machine_Requested… done
✨ Run is running
wandb: Started W&B process version 0.8.10 with PID 17
wandb: Local directory: wandb/run-20190909_212858-46pckj1w
wandb: Syncing run silvery-snow-21: https://app.wandb.ai/user123/uncategorized/runs/46pckj1w
wandb: Run `wandb off` to turn off syncing.

Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
Processing...
Done!
Train Epoch: 1 [0/60000 (0%)]   Loss: 2.329947
Train Epoch: 1 [640/60000 (1%)] Loss: 2.305957
...

Navigate to your Runs page to view Run Details. The W&B link is listed towards the bottom of the Run Details section.

Click on "Open" to view the corresponding run metrics in W&B: