> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# How can I use W&B with multiprocessing, for example distributed training?

This article explains how to use W\&B in training programs that span multiple processes, such as distributed training jobs, so that runs are logged correctly without conflicts.

If a training program uses multiple processes, structure the program to avoid making W\&B method calls from processes without `wandb.init()`.

Choose one of the following approaches to manage multiprocess training:

* Call `wandb.init()` in all processes and use the [group](/models/runs/grouping) keyword argument to create a shared group. Each process has its own W\&B run, and the UI groups the training processes together.
* Call `wandb.init()` from only one process and pass data to log through [multiprocessing queues](https://docs.python.org/3/library/multiprocessing.html#exchanging-objects-between-processes).

<Note>
  Refer to [Log distributed training experiments](/models/track/log/distributed-training) for detailed explanations of these approaches, including code examples with Torch DDP.
</Note>

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