ffcv
pytorch-lightning
ffcv | pytorch-lightning | |
---|---|---|
8 | 19 | |
2,742 | 19,188 | |
0.7% | - | |
3.5 | 9.9 | |
8 days ago | almost 2 years ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
ffcv
- Question: TIFF image dataset - size in RAM.
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[P] Composer: a new PyTorch library to train models ~2-4x faster with better algorithms
PyTorch Lightning is also very slow compared to Composer. You don't have to believe us: our friends who wrote the FFCV library benchmarked us against PTL (see the lower left plot in the first cluster of graphs) , and you can see the difference for yourself. For the same accuracy, the FFCV folks found that Composer is about 5x faster than PTL on ResNet-50 on ImageNet.
- FFCV: Fast Forward Computer Vision
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Does anyone know where I can find research papers for preprocessing large image datasets?
maybe something like this? https://github.com/libffcv/ffcv
- Ffcv: Train models at a fraction of the cost with accelerated data loading
- Show HN: FFCV – Accelerated machine learning via fast data loading
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[P] FFCV: Accelerated Model Training via Fast Data Loading
Hi! You can join the slack directly from the link on the homepage! (ffcv.io)
pytorch-lightning
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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
What are some alternatives?
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
composer - Supercharge Your Model Training
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
apex - A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
array_storage_benchmark - Compare some methods of array storage in Python (numpy)
fastai - The fastai deep learning library
ffcv-imagenet - Train ImageNet *fast* in 500 lines of code with FFCV
pillow-simd - The friendly PIL fork
sparktorch - Train and run Pytorch models on Apache Spark.