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pytorch-lightning reviews and mentions
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[P] Composer: a new PyTorch library to train models ~2-4x faster with better algorithms
Pytorch lightning benchmarks against pytorch on every PR (benchmarks to make sure that it is mot slower.
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[D] What Repetitive Tasks Related to Machine Learning do You Hate Doing?
There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.
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[D] Are you using PyTorch or TensorFlow going into 2022?
Is the problem the sheer number of options, or the fact that they are all together in one place? Would it be better if they were organized into the different trainer entrypoints (fit, validate, ...)? If that is the case, there was an RFC proposing this which you might find interesting, feel free to drop by and comment on the issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/10444
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[D] Colab TPU low performance
I wanted to make a quick performance comparison between the GPU (Tesla K80) and TPU (v2-8) available in Google Colab with PyTorch. To do so quickly, I used an MNIST example from pytorch-lightning that trains a simple CNN.
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[D] How to avoid CPU bottlenecking in PyTorch - training slowed by augmentations and data loading?
We've noticed GPU 0 on our 3 GPU system is sometimes idle (which would explain performance differences). However its unclear to us why that may be. Similar to this issue
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[P] An introduction to PyKale https://github.com/pykale/pykale, a PyTorch library that provides a unified pipeline-based API for knowledge-aware multimodal learning and transfer learning on graphs, images, texts, and videos to accelerate interdisciplinary research. Welcome feedback/contribution!
If you want a good example for reference, take a look at Pytorch Lightning's readme (https://github.com/PyTorchLightning/pytorch-lightning) It answers the 3 questions of "what is this", "why should I care", and "how do i use it" almost instantly
- Pytorch Template
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[D] Advanced Takeaways from fast.ai book
Lower precision training can help and on pytorch lightning is just a simple flag you can set
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[D] How to be more productive while doing Deep Learning experiments?
First of all, use high-level ML frameworks (AllenNLP, PyTorch-Lightning). No need to write boilerplate code and implement standard ML approaches from scratch. Here are some suggestions (thought more NLP-focused) that I feel improved my research coding experience a lot.
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PyTorch Lightning Flash appears to be copying fastai (without any credit) [D]
According to the README it's patent pending, but I learned about that from this HN thread. Funny thing is I didn't even remember there was a snafu about patents, but looked it up because of some vague recollection of the PL founder getting into a tussle about some other trivial topic (apparently it was how well PyTorch works on TPUs).
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PyTorchLightning/pytorch-lightning is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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