Can I still use custom training loops while using the PyTorch Lightning Trainer?
Sometimes I need to do something very non-standard, like GAN training or meta-learning, where the standard fit() method might be too restrictive. Does PyTorch Lightning allow for manual optimization where I control the backward() and step() calls myself? I love the logging and hardware management of Lightning, but I don't want to be "boxed in" by the Trainer's automation.
2025-09-18 in AI and Deep Learning by Donna King
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All answers to this question.
Yes, you can definitely do this! You just need to set 'self.automatic_optimization = False' in your LightningModule's init. Then, you manually call 'self.manual_backward(loss)' and 'opt.step()' inside your training_step. This gives you total control over the optimization process while still letting the Trainer handle things like multi-GPU logic and progress bars. I used this recently for a research paper on reinforcement learning, and it worked perfectly without any issues. It’s the best of both worlds.
Answered 2025-09-19 by Betty Nelson
When using manual optimization, do we still get the benefits of the built-in loggers, or do we have to manually log everything to TensorBoard?
Answered 2025-09-20 by Paul Rodriguez
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You still get to use self.log()! The only thing that changes is that the Trainer doesn't handle the optimizer steps. All the logging, checkpointing, and profiling features remain fully functional, which is why it's so powerful.
Commented 2025-09-21 by Donna King
This feature is why I chose Lightning over other high-level wrappers. It doesn't treat you like a beginner; it trusts you.
Answered 2025-09-22 by Brian Green
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Spot on, Brian. The flexibility to opt-out of the automation is what makes it a professional tool rather than just a toy.
Commented 2025-09-23 by Betty Nelson
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