fairscale
PyTorch extensions for high performance and large scale training. (by facebookresearch)
pytorch-lightning
Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning] (by PyTorchLightning)
fairscale | pytorch-lightning | |
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6 | 19 | |
2,907 | 19,188 | |
2.4% | - | |
4.5 | 9.9 | |
5 days ago | almost 2 years ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fairscale
Posts with mentions or reviews of fairscale.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-27.
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[R] TorchScale: Transformers at Scale - Microsoft 2022 Shuming Ma et al - Improves modeling generality and capability, as well as training stability and efficiency.
I skimmed through the README and paper. What does this library have that that hasn't been included in xformers or fairscale?
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[D] DeepSpeed vs PyTorch native API
Things are slowly moving into PyTorch upstream such as the ZeRO redundancy optimizer but from my experience the team behind DeepSpeed just move faster. There is also fairscale from the FAIR team which seems to be a staging ground for experimental optimizations before they move into PyTorch. If you use Lightning, it's easy enough to try out these various libraries (docs here)
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How to Train Large Models on Many GPUs?
DeepSpeed [1] is amazing tool to enable the different kind of parallelisms and optimizations on your model. I would definitely not recommend reimplementing everything yourself.
Probably FairScale [2] too, but never tried it myself.
[1]: https://github.com/microsoft/DeepSpeed
[2]: https://github.com/facebookresearch/fairscale
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[P] PyTorch Lightning Multi-GPU Training Visualization using minGPT, from 250 Million to 4+ Billion Parameters
It was helpful for me to see how DeepSpeed/FairScale stack up compared to vanilla PyTorch Distributed Training specifically when trying to reach larger parameter sizes, visualizing the trade off with throughput. A lot of the learnings ended up in the Lightning Documentation under the advanced GPU docs!
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[D] Training 10x Larger Models and Accelerating Training with ZeRO-Offloading
I created a feature request on the FairScale project so that we can track the progress on the integration: Support ZeRO-Offload · Issue #337 · facebookresearch/fairscale (github.com)
pytorch-lightning
Posts with mentions or reviews of pytorch-lightning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-16.
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Problem with pytorch lightning and optuna with multiple callbacks
def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None: # Trainer calls `on_validation_end` for sanity check. Therefore, it is necessary to avoid # calling `trial.report` multiple times at epoch 0. For more details, see # https://github.com/PyTorchLightning/pytorch-lightning/issues/1391. if trainer.sanity_checking: return
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Please comment on my planned research project structure
Under the hood, the ModelWrapper object will create a ML model based on the config (so far, an XGBoost model and a PyTorch Lightning model). Each of those will have a wrapper that conducts training and evaluation (since from my understanding of Lightning, Trainers are required to be outside of the class). In lack of a better name, I call these wrappers Fitters. For uniformity, I thought about adding a common interface IFitter, which is inherited by all model wrappers as outlined below.
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Watch out for the (PyTorch) Lightning
Join their Slack to ask the community questions and check out the GitHub here.
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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.
- PyTorch Lightening
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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