Advanced Features
The guides in this section go beyond core Weights & Biases experiment-tracking features like logging data and media, building rich dashboards, and seamlessly integrating with popular frameworks and tools to cover advanced use cases and power-user features.
Looking for the gory details on how the wandb library, CLI, and UI tools work? You want the Reference documentation.
Need to track large-scale ML experiments distributed across multiple GPUs and multiple nodes? Then check out our guide to Distributed Training. For some approaches to distributed training and cross-validation, you also need to combine multiple runs together into a single experiment, as described in our guide on how to Group Runs.
At Weights & Biases, we're all about preventing you from losing any of your work. If you're using pre-emptible compute or your machine crashes, we'll help you Resume Runs where you left off. If you're in danger of losing valuable data, wandb can even Save & Restore Files.
Tired of wondering whether training has finished or, worse, crashed? Set up Alerts to Slack or your e-mail, with configurable triggers right in your Python code.
The behavior of the tool is controllable from the command line, as described in our guide to Environment Variables.
Last modified 6mo ago
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