Comments (10)
Sounds great. can you send me a pm on twitter? we can coordinate through there _willfalcon
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Seems like on twitter both the sides have to follow each other in order to share PMs. Any other platform?
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good point haha. didn't know that about twitter. I've added you anyhow, but let's just chat here.
First step to helping is for you to do the following things and document your experience such as what was hard, what was not intuitive, what documentation would be helpful, etc.
- Build and run the demo.
- Build a simple classifier on your own using this.
From there we can see where the gaps are in usability and documentation. Then we can make a dev plan to get those things fixed in the library.
How does that sound? What's a good ETA to get that done?
Thanks!
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Yes. I will do these after this week only. (I am too busy this week)
Anyways, the things that majorly concern me is the organisation of the package. You can have a look at my other library (Not related to DL) EinsteinPy, but you can have a look at how extensively its tested etc.
From there we can see where the gaps are in usability and documentation. Then we can make a dev plan to get those things fixed in the library.
For this plan, I will get back to you this Tuesday?
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Sounds great. Yeah, this is a research library, so I didn't write tests for it because I've been moving quickly through research for publication. Tests would be great! I have however tested it myself when using/coding it, just didn't write an explicit test out.
Writing code for research has a less rigorous approach than code for a production system haha.
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I have however tested it myself when using/coding it, just didn't write an explicit test out.
No issues! I would love to help. I will make some organisational changes in a PR, for making docs and CI easy! Is that fine?
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perfect. Looking forward!
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One question. Why is the package name pytorch_lightning
somewhere and pytorch-lightning
somewhere else?
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Oh! I am sorry, the tuesday thing got slipped off my mind. Will do this shortly :)
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Hi! lighting has been updated to include: tensorboard visualization, multi-gpu training, multi-node training, thoroughly documented and updated clean syntax.
Would be auper helpful if you tried version 0.21 and provided feedback!
Thanks!
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Related Issues (20)
- Construct objects from yaml by classmethod
- FSDP Strategy checkpoint loading
- Current FSDPPrecision does not support custom scaler for 16-mixed precision
- Differentiate testing multiple sets/models when logging
- Issue in Manual optimisation, during self.manual_backward call HOT 1
- Existing metric keys not moved to device after LearningRateFinder
- Checkpoint every_n_steps reruns epoch on restore HOT 3
- Metrics logged by self.log and metric.compute() are different HOT 1
- Multi-node Training with DDP stuck at "Initialize distributed..." on SLURM cluster HOT 3
- Full validation after first microbatch when training after LearningRateFinder
- Add a warning when some of the modules are in eval mode before the training stage
- why pytorch-lightning doc say "Model-parallel training (FSDP and DeepSpeed)". I think there is something wrong. HOT 1
- AWS Trainium fails number of device validation when using more than 1 accelerator on the instances
- OnExceptionCheckpoint: training resumes if ckpt found, even if no ckpt_path provided
- TensorBoardLogger has the wrong epoch numbers much more than the fact HOT 1
- How to incorporate vLLM in Lightning for LLM inference?
- WandbLogger `save_dir` and `dir` parameters do not work as expected.
- Loading large models with fabric, FSDP and empty_init=True does not work
- Unable to extract confusion matrix as a metric from trainer HOT 1
- Torchmetrics Accuracy issue when dont shuffle test data. HOT 1
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