Weights & Biases
Track and visualize your machine learning experiments and monitor models with Weights & Biases (W&B). Integrate seamlessly with major ML frameworks to log metrics, hyperparameters, and artifacts for better insights and reproducibility.
6 Steps
- 1
Install Weights & Biases: Install the `wandb` Python library in your development environment.
- 2
Log in to W&B: Authenticate your local environment with your Weights & Biases account. This will open a browser window for login or prompt you for an API key.
- 3
Initialize a W&B Run: In your Python script, import `wandb` and start a new experiment run using `wandb.init()`. You can pass hyperparameters or other configuration as a dictionary to `config`.
- 4
Log Metrics During Training: Use `wandb.log()` to record metrics (like loss or accuracy) during your training loop. Call it with a dictionary of key-value pairs.
- 5
Finish the Run: Call `wandb.finish()` at the end of your script to ensure all data is synced and the run is properly terminated.
- 6
View Your Dashboard: After your script completes, click the link provided in the console output (e.g., `View run at https://wandb.ai/your-user/my-ml-project/runs/...`) to see your experiment results, charts, and configurations on the W&B dashboard.
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