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توضیحات

Composer is a PyTorch library that enables you to train neural networks faster, at lower cost, and to higher accuracy.

ویژگی	مقدار
سیستم عامل	-
نام فایل	composer-0.14.1
نام	composer
نسخه کتابخانه	0.14.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	MosaicML
ایمیل نویسنده	team@mosaicml.com
آدرس صفحه اصلی	https://github.com/mosaicml/composer
آدرس اینترنتی	https://pypi.org/project/composer/
مجوز	-

<br /> <p align="center"> <a href="https://github.com/mosaicml/composer#gh-light-mode-only" class="only-light"> <img src="https://storage.googleapis.com/docs.mosaicml.com/images/header_light.svg" width="50%"/> </a> </p> <h2><p align="center">A PyTorch Library for Efficient Neural Network Training</p></h2> <h3><p align="center">Train Faster, Reduce Cost, Get Better Models</p></h3> <h4><p align='center'> <a href="https://www.mosaicml.com">[Website]</a> - <a href="https://docs.mosaicml.com/projects/composer/en/stable/getting_started/installation.html">[Getting Started]</a> - <a href="https://docs.mosaicml.com/projects/composer/">[Docs]</a> - <a href="https://docs.mosaicml.com/projects/composer/en/stable/method_cards/methods_overview.html">[Methods]</a> - <a href="https://www.mosaicml.com/team">[We're Hiring!]</a> </p></h4> <p align="center"> <a href="https://pypi.org/project/mosaicml/"> <img alt="PyPi Version" src="https://img.shields.io/pypi/pyversions/mosaicml"> </a> <a href="https://pypi.org/project/mosaicml/"> <img alt="PyPi Package Version" src="https://img.shields.io/pypi/v/mosaicml"> </a> <a href="https://pepy.tech/project/mosaicml/"> <img alt="PyPi Downloads" src="https://static.pepy.tech/personalized-badge/mosaicml?period=month&units=international_system&left_color=grey&right_color=blue&left_text=Downloads/month"> </a> <a href="https://docs.mosaicml.com/projects/composer/en/stable/"> <img alt="Documentation" src="https://readthedocs.org/projects/composer/badge/?version=stable"> </a> <a href="https://join.slack.com/t/mosaicml-community/shared_invite/zt-w0tiddn9-WGTlRpfjcO9J5jyrMub1dg"> <img alt="Chat @ Slack" src="https://img.shields.io/badge/slack-chat-2eb67d.svg?logo=slack"> </a> <a href="https://github.com/mosaicml/composer/blob/dev/LICENSE"> <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-green.svg?logo=slack"> </a> </p> <br /> # 👋 Welcome Composer is a PyTorch library that enables you to <b>train neural networks faster, at lower cost, and to higher accuracy</b>. We've implemented more than two dozen speedup methods that can be applied to your training loop in just a few lines of code, or used with our built-in Trainer. We continually integrate the latest state-of-the-art in efficient neural network training. Composer features: - 20+ methods for speeding up training networks for computer vision and natural language. Don't waste hours trying to reproduce research papers when Composer has done the work for you. - An easy-to-use trainer that has been written to be as performant as possible and [integrates best practices](https://www.mosaicml.com/blog/5-best-practices-for-efficient-model-training) for efficient, multi-GPU training. - Functional forms of all of our speedup methods that allow you to integrate them into your existing training loop. - Strong, reproducible baselines to get you started as quickly as possible. ## Benefits  <p align="center"> <a href="https://storage.googleapis.com/docs.mosaicml.com/images/composer_graph_light_06212022.svg?ref=Fiey0Xei#gh-light-mode-only" class="only-light"> <img src="https://storage.googleapis.com/docs.mosaicml.com/images/composer_graph_light_06212022.svg?ref=Fiey0Xei" width="75%"/> </a>  </p>  With no additional tuning, you can apply our methods to:  - Train ResNet-50 on ImageNet to the standard 76.6% top-one accuracy for \$15 in 27 minutes (_with vanilla PyTorch:_ \$116 in 3.5 hours) on AWS. - Train GPT-2 125M to the standard perplexity of 24.11 for \$145 in 4.5 hours (_with vanilla PyTorch_: \$255 in 7.8 hours) on AWS. - Train DeepLab-v3 on ADE20k to the standard mean IOU of 45.7 for \$36 in 1.1 hours (_with vanilla PyTorch_: \$110 in 3.5 hours) on AWS.  # 🚀 Quickstart ## 💾 Installation Composer is available with Pip:  ```bash pip install mosaicml ``` Alternatively, install Composer with Conda:  ```bash conda install -c mosaicml mosaicml ``` --- ## 🚌 Usage You can use Composer's speedup methods in two ways: * Through a standalone **Functional API** (similar to `torch.nn.functional`) that allows you to integrate them into your existing training code. * Using Composer's built-in **Trainer**, which is designed to be performant and automatically takes care of the details of using speedup methods. ### Example: Functional API [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/functional_api.ipynb) Integrate our speedup methods into your training loop with just a few lines of code, and see the results. Here we easily apply [BlurPool](https://docs.mosaicml.com/projects/composer/en/stable/method_cards/blurpool.html) and [SqueezeExcite](https://docs.mosaicml.com/projects/composer/en/stable/method_cards/squeeze_excite.html):  ```python import composer.functional as cf from torchvision import models my_model = models.resnet18() # add blurpool and squeeze excite layers cf.apply_blurpool(my_model) cf.apply_squeeze_excite(my_model) # your own training code starts here ```  For more examples, see the [Composer Functional API Colab notebook](https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/functional_api.ipynb) and [Functional API guide](https://docs.mosaicml.com/projects/composer/en/latest/functional_api.html). ### Example: Trainer [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/getting_started.ipynb) For the best experience and the most efficient possible training, we recommend using Composer's built-in trainer, which automatically takes care of the details of using speedup methods and provides useful abstractions that facilitate rapid experimentation.     ```python import torch # adaptive_avg_pool2d_backward_cuda in mnist_classifier is not deterministic torch.use_deterministic_algorithms(False) ``` -->  ```python from torch.utils.data import DataLoader from torchvision import datasets, transforms from composer import Trainer from composer.algorithms import ChannelsLast, CutMix, LabelSmoothing from composer.models import mnist_model transform = transforms.Compose([transforms.ToTensor()]) train_dataset = datasets.MNIST("data", download=True, train=True, transform=transform) eval_dataset = datasets.MNIST("data", download=True, train=False, transform=transform) train_dataloader = DataLoader(train_dataset, batch_size=128) eval_dataloader = DataLoader(eval_dataset, batch_size=128) trainer = Trainer( model=mnist_model(), train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, max_duration="1ep", algorithms=[ ChannelsLast(), CutMix(alpha=1.0), LabelSmoothing(smoothing=0.1), ] ) trainer.fit() ```  Composer's built-in [trainer](https://docs.mosaicml.com/projects/composer/en/stable/trainer/using_the_trainer.html) makes it easy to **add multiple speedup methods in a single line of code!** Trying out new methods or combinations of methods is as easy as changing a single list. Here are some examples of methods available in Composer ([_see here for the full list_](https://docs.mosaicml.com/projects/composer/en/latest/trainer/algorithms.html)): Name|Attribution|tl;dr|Example Benchmark|Speed Up*| ----|-----------|-----|---------|---------| [Alibi](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/alibi)|[Press et al, 2021](https://arxiv.org/abs/2108.12409)|Replace attention with AliBi.|GPT-2|1.5x [BlurPool](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/blurpool)|[Zhang, 2019](https://arxiv.org/abs/1904.11486)|Applies an anti-aliasing filter before every downsampling operation.|ResNet-101|1.2x [ChannelsLast](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/channels_last)|[PyTorch](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html)|Uses channels last memory format (NHWC).|ResNet-101|1.5x [CutOut](https://docs.mosaicml.com/projects/composer/en/latest/method_cards/cutout.html)|[DeVries et al, 2017](https://arxiv.org/abs/1708.04552)|Randomly erases rectangular blocks from the image.|ResNet-101|1.2x [LabelSmoothing](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/label_smoothing)|[Szegedy et al, 2015](https://arxiv.org/abs/1512.00567)|Smooths the labels with a uniform prior|ResNet-101|1.5x [MixUp](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/mixup)|[Zhang et al, 2017](https://arxiv.org/abs/1710.09412)|Blends pairs of examples and labels.|ResNet-101|1.5x [RandAugment](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/randaugment)|[Cubuk et al, 2020](https://openaccess.thecvf.com/content_CVPRW_2020/html/w40/Cubuk_Randaugment_Practical_Automated_Data_Augmentation_With_a_Reduced_Search_Space_CVPRW_2020_paper.html)|Applies a series of random augmentations to each image.|ResNet-101|1.3x [SAM](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/sam)|[Foret et al, 2021](https://arxiv.org/abs/2010.01412)|An optimization strategy that seeks flatter minima.|ResNet-101|1.4x [SeqLengthWarmup](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/seq_length_warmup)|[Li et al, 2021](https://arxiv.org/abs/2108.06084)|Progressively increase sequence length.|GPT-2|1.2x [Stochastic Depth](https://docs.mosaicml.com/projects/composer/en/latest/method_cards/stochastic_depth.html)|[Huang et al, 2016](https://arxiv.org/abs/1603.09382)|Replaces a specified layer with a stochastic version that randomly drops the layer or samples during training|ResNet-101|1.1x <p align="right">* = time-to-train to the same quality as the baseline.</p> ## 🛠 Building Speedup Recipes Given two methods that speed up training by 1.5x each, do they combine to provide a 2.25x (1.5x * 1.5x) speedup? Not necessarily. They may optimize the [same part of the training process](https://en.wikipedia.org/wiki/Amdahl's_law) and lead to diminishing returns, or they may even interact in ways that prove detrimental. Determining which methods to compose together isn't as simple as assembling a set of methods that perform best individually. **We have come up with compositions of methods that work especially well together** through rigorous exploration of the design space of recipes and research on the science behind composition. <p align="center"> <img src="https://storage.googleapis.com/docs.mosaicml.com/images/methods/explorer.png"/> </p> As an example, here are two performant recipes, one for ResNet-101 on ImageNet, and the other for GPT-2 on OpenWebText, on 8xA100s: ### ResNet-101 Name|Functional|tl;dr|Benchmark|Speed Up ----|----------|-----|---------|-------- [Blur Pool](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/blurpool)|`cf.apply_blurpool`|[Applies an anti-aliasing filter before every downsampling operation.](https://arxiv.org/abs/1904.11486)|ResNet-101|1.2x [Channels Last](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/channels_last)|`cf.apply_`<br>`channels_last`|[Uses channels last memory format (NHWC).](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html)|ResNet-101|1.5x [Label Smoothing](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/label_smoothing)|`cf.smooth_labels`|[Smooths the labels with a uniform prior.](https://arxiv.org/abs/1512.00567)|ResNet-101|1.5x [MixUp](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/mixup)|`CF.mixup_batch`|[Blends pairs of examples and labels.](https://arxiv.org/abs/1710.09412)|ResNet-101|1.5x [Progressive Resizing](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/progressive_resizing)|`cf.resize_batch`|[Increases the input image size during training.](https://github.com/fastai/fastbook/blob/780b76bef3127ce5b64f8230fce60e915a7e0735/07_sizing_and_tta.ipynb)|ResNet-101|1.3x [SAM](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/sam)|`N/A`|[SAM optimizer measures sharpness of optimization space.](https://arxiv.org/abs/2010.01412)|ResNet-101|1.5x **Composition** | `N/A` | **Cheapest: \$49 @ 78.1% Acc** | **ResNet-101** | **3.5x** ### GPT-2 Name|Functional|tl;dr|Benchmark|Speed Up ----|----------|-----|---------|-------- [Alibi](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/alibi)|`cf.apply_alibi`|[Replace attention with AliBi.](https://arxiv.org/abs/2108.12409)|GPT-2|1.6x [Seq Length Warmup](https://github.com/mosaicml/composer/tree/dev/composer/algorithms/seq_length_warmup)|`cf.set_batch_`<br>`sequence_length`|[Progressively increase sequence length.](https://arxiv.org/abs/2108.06084)|GPT-2|1.5x **Composition** | `N/A` | **Cheapest: \$145 @ 24.11 PPL** | **GPT-2** | **1.7x** # ⚙️ What benchmarks does Composer support? We'll use the word _benchmark_ to denote a specific model trained on a specific dataset, with model quality assessed using a specific metric. Composer features computer vision and natural language processing benchmarks including (but not limited to): <div class="center"> <table> <thead> <tr> <th>Model</th> <th>Dataset</th> <th>Loss</th> <th>Task</th> <th>Evaluation Metrics</th> </tr> </thead> <tbody> <tr> <td colspan="5" align="center"><b>Computer Vision</b></td> </tr> <tr> <td>ResNet Family</td> <td>CIFAR-10</td> <td>Cross Entropy</td> <td>Image Classification</td> <td>Classification Accuracy</td> </tr> <tr> <td>ResNet Family</td> <td>ImageNet</td> <td>Cross Entropy</td> <td>Image Classification</td> <td>Classification Accuracy</td> </tr> <tr> <td>EfficientNet Family</td> <td>ImageNet</td> <td>Cross Entropy</td> <td>Image Classification</td> <td>Classification Accuracy</td> </tr> <tr> <td>UNet</td> <td>BraTS</td> <td>Dice Loss</td> <td>Image Segmentation</td> <td>Dice Coefficient</td> </tr> <tr> <td>DeepLab v3</td> <td>ADE20K</td> <td>Cross Entropy</td> <td>Image Segmentation</td> <td>mIoU</td> </tr> <tr> <td align="center" colspan="5"><b>Natural Language Processing</b></td> </tr> <tr> <td>BERT Family</td> <td>{Wikipedia & BooksCorpus, C4}</td> <td>Cross Entropy</td> <td>Masked Language Modeling</td> <td>GLUE </td> </tr> <tr> <td>GPT Family</td> <td>{OpenWebText, C4}</td> <td>Cross Entropy</td> <td>Language Modeling<br></td> <td>Perplexity</td> </tr> </tbody> </table> </div> # 🤔 Why should I use Composer? ### Speed The compute required to train a state-of-the-art machine learning model is [doubling every 6 months](https://arxiv.org/abs/2202.05924), putting such models further and further out of reach for most researchers and practitioners with each passing day. Composer addresses this challenge by focusing on training efficiency: it contains cutting-edge speedup methods that modify the training algorithm to reduce the time and cost necessary to train deep learning models. **When you use Composer, you can rest assured that you are training efficiently.** We have combed the literature, done the science, and built industrial-grade implementations to ensure this is the case. ### Flexibility Even after these speedup methods are implemented, assembling them together into recipes is nontrivial. We designed Composer with the **right abstractions for composing (and creating new) speedup methods.** Specifically, Composer uses two-way callbacks ([Howard et al, 2020](https://arxiv.org/abs/2002.04688)) to modify the **entire training state** at particular events in the training loop to effect speedups. We handle collisions between methods, proper method ordering, and more. Through this, methods can modify: - data inputs for batches (data augmentations, sequence length warmup, skipping examples, etc.) - neural network architecture (pruning, model surgery, etc.) - loss function (label smoothing, MixUp, CutMix, etc.) - optimizer (Sharpness Aware Minimization) - training dynamics (layer freezing, selective backprop, etc.) You can easily [add your own methods](https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/custom_speedup_methods.ipynb) or callbacks to try out your ideas or modify any part of the training loop. ### Support Composer is an active and ongoing project. We will respond quickly to issues filed in this repository. # 🧐 Why shouldn’t I use Composer? * Composer is mostly optimized for computer vision and natural language processing. If you work on, e.g., reinforcement learning, you might encounter rough edges when using Composer. * Composer currently only supports NVIDIA GPUs, although we're working on adding alternatives. * Since Composer is still in alpha, our API may not be stable. We recommend pegging your work to a Composer version. # 📚 Learn More Here are some resources actively maintained by the Composer community to help you get started: <table> <thead> <tr> <th><b>Resource</b></th> <th><b>Details</b></th> </tr> </thead> <tbody> <tr> <td><a href="https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/getting_started.ipynb" target="_blank" rel="noopener noreferrer">Getting started with our Trainer</a></td> <td>A Colab Notebook showing how to use our Trainer</td> </tr> <tr> <td><a href="https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/functional_api.ipynb" target="_blank" rel="noopener noreferrer">Getting started with our Functional API</a></td> <td>A Colab Notebook showing how to use our Functional API</td> </tr> <tr> <td><a href="https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/custom_speedup_methods.ipynb" target="_blank" rel="noopener noreferrer">Building Speedup Methods</a></td> <td>A Colab Notebook showing how to build new training modifications on top of Composer</td> </tr> <tr> <td><a href="https://colab.research.google.com/github/mosaicml/composer/blob/dev/examples/finetune_huggingface.ipynb" target="_blank" rel="noopener noreferrer">Training BERTs with Composer and 🤗 </a></td> <td>A Colab Notebook showing how to train BERT models with Composer and 🤗!</td> </tr> </tbody> </table> If you have any questions, please feel free to reach out to us on [Twitter](https://twitter.com/mosaicml), [email](mailto:community@mosaicml.com), or our [Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-1dc6mo5wg-arlv6Oo9JjEn_g4d5s7PXQ)! # 💫 Contributors Composer is part of the broader Machine Learning community, and we welcome any contributions, pull requests, or issues! To start contributing, see our [Contributing](https://github.com/mosaicml/composer/blob/dev/CONTRIBUTING.md) page. P.S.: [We're hiring](https://mosaicml.com/jobs)! # ✍️ Citation ``` @misc{mosaicml2022composer, author = {The Mosaic ML Team}, title = {composer}, year = {2021}, howpublished = {\url{https://github.com/mosaicml/composer/}}, } ```

نیازمندی

مقدار	نام
<7,>=6.0	pyyaml
<5,>=4.62.3	tqdm
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<0.4,>=0.3.0	torch-optimizer
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<2.1,>=1.10.0	torch
<3,>=2.26.0	requests
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<6,>=5.8.0	psutil
<3,>=1.1.0	coolname
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<10,>=8.0.0	py-cpuinfo
<23,>=21.3.0	packaging
<7,>=5.0.0	importlib-metadata
<1.2,>=0.9.1	monai
==0.17.1	docutils
==0.0.17	sphinx-markdown-tables
==2.3	pandoc
<4,>=3.19.5	slack-sdk
==7.2.5	coverage[toml]
<3,>=2.0.4	pycocotools
==7.3.1	pytest
==0.4.2	testbook
<3.21	protobuf
<=59.5.0	setuptools
==0.16.1	myst-parser
==3.0.0	junitparser
==1.31.0	yamllint
<0.16,>=0.13.2	wandb
==0.7.1	recommonmark
<3,>=2.18.1	pre-commit
==0.9.1	mock-ssh-server
<2,>=1.12.0	onnx
==0.9.4	sphinxcontrib-images
==1.0.0	jupyter
==38.0.4	cryptography
==0.8.3	deepspeed
<10,>=8.0.0	py-cpuinfo
==0.16.1	pytest-codeblocks
==0.9.1	nbsphinx
<2,>=1.12.1	onnxruntime
<0.4,>=0.3.6	mosaicml-cli
==2.4.1	custom-inherit
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<3,>=2.11.0	paramiko
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<3.0.0,>=2.9.1	tensorboard
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<2,>=1.0.1	scikit-learn
<3,>=2.4	datasets
<4,>=3.3.1	apache-libcloud
==1.11	pypandoc
<0.6,>=0.5.4	timm
==6.22.0	ipykernel
==0.35.8	vit-pytorch
<4.0.0,>=3.31.12	comet-ml
==0.5.2	sphinx-copybutton
==0.6.0	sphinx-panels
==0.10.2	toml
<4.29,>=4.11	transformers
==0.18	fasteners
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<3.0,>=2.0.1	mlflow
<3.0.0,>=2.88.2	oci
==0.1.98	sentencepiece
<12,>=11.5.0	pynvml
==4.4.0	sphinx
==3.1.31	GitPython
<3,>=2.0.4	pycocotools
<4.0.0,>=3.31.12	comet-ml
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==3.0.0	junitparser
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==0.18	fasteners
==7.3.1	pytest
==0.10.2	toml
==8.11.0	ipython
==6.22.0	ipykernel
==1.0.0	jupyter
==1.31.0	yamllint
==0.7.1	recommonmark
==4.4.0	sphinx
<3,>=2.18.1	pre-commit
==0.17.1	docutils
==0.0.17	sphinx-markdown-tables
==0.4.0	sphinx-argparse
==0.9.4	sphinxcontrib.katex
==0.8.2	sphinxext.opengraph
==0.2.0	sphinxemoji
==2022.9.29	furo
==0.5.2	sphinx-copybutton
==0.4.2	testbook
==0.16.1	myst-parser
==0.6.0	sphinx-panels
==0.9.4	sphinxcontrib-images
==0.16.1	pytest-codeblocks
==5.9.0	traitlets
==0.9.1	nbsphinx
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==1.11	pypandoc
==3.1.31	GitPython
<5,>=4.0.1	moto[s3]
==0.9.1	mock-ssh-server
==38.0.4	cryptography
<1.1,>=1.0.4	pytest-httpserver
<=59.5.0	setuptools
<12,>=11.5.0	pynvml
<4,>=3.3.1	apache-libcloud
<0.4,>=0.3.6	mosaicml-cli
<3.0,>=2.0.1	mlflow
<10,>=8.0.0	py-cpuinfo
<4.29,>=4.11	transformers
<3,>=2.4	datasets
<3.0.0,>=2.88.2	oci
<2,>=1.12.0	onnx
<2,>=1.12.1	onnxruntime
<3.21	protobuf
==0.1.98	sentencepiece
<4,>=3.19.5	slack-sdk
<1.0	mosaicml-streaming
<2,>=1.21.45	boto3
<3,>=2.11.0	paramiko
<3.0.0,>=2.9.1	tensorboard
<0.6,>=0.5.4	timm
<1.2,>=0.9.1	monai
<2,>=1.0.1	scikit-learn
==0.35.8	vit-pytorch
<0.16,>=0.13.2	wandb

زبان مورد نیاز

مقدار	نام
>=3.8	Python

نحوه نصب

نصب پکیج whl composer-0.14.1:

pip install composer-0.14.1.whl

نصب پکیج tar.gz composer-0.14.1:

pip install composer-0.14.1.tar.gz