Lightning-AI/pytorch-lightning

Documentation: writing custom samplers compatible with multi GPU training

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#19,964 opened on Jun 10, 2024

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Description

📚 Documentation

Hi,

I'm trying to run distributed training with a custom sampler for the first time. The idea is rather simple (fixed budget for each class) and works fine in single GPU. When moving to multi GPU, unsurprisingly I get an error message, which tells me that I should subclass BatchSampler.

TypeError:  Lightning can't inject a (distributed) sampler into your batch sampler, because it doesn't subclass PyTorch's `BatchSampler`. To mitigate this, either follow the API of `BatchSampler` or set `Trainer(use_distributed_sampler=False)`. If you choose the latter, you will be responsible for handling the distributed sampling within your batch sampler.

It is my understanding that torch's BatchSampler takes one (single-sample) Sampler and samples from that repeatedly to fill up the batch size. Are there any guidelines for how samplers should be built to be compatible with the sampler injection? I can't seem to find it in the docs.

cc @borda

Contributor guide

Research direction
Examine the PyTorch Lightning documentation for custom samplers, focusing on subclassing BatchSampler. Look at the source code for DistributedSampler and related classes. Implement a simple custom batch sampler that inherits from BatchSampler and test with multi GPU using Trainer(use distributed sampler=True).
Tech stack
python
Domain
machine learning
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
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1-3 hours
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Active
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Clear
Prerequisites
PyTorchLightning
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75