Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/libffcv/ffcv

FFCV: Fast Forward Computer Vision (and other ML workloads!)
https://github.com/libffcv/ffcv

data-science machine-learning pytorch

Last synced: 10 days ago
JSON representation

FFCV: Fast Forward Computer Vision (and other ML workloads!)

Awesome Lists containing this project

README 

        


Fast Forward Computer Vision: train models at a fraction of the cost with accelerated data loading!








[install]

[quickstart]

[features]

[docs]

[support slack]

[homepage]

[paper]




Maintainers:

Guillaume Leclerc,

Andrew Ilyas and

Logan Engstrom




`ffcv` is a drop-in data loading system that dramatically increases data throughput in model training:



- [Train an ImageNet model](#prepackaged-computer-vision-benchmarks)

on one GPU in 35 minutes (98¢/model on AWS)

- [Train a CIFAR-10 model](https://docs.ffcv.io/ffcv_examples/cifar10.html)

on one GPU in 36 seconds (2¢/model on AWS)

- Train a `$YOUR_DATASET` model `$REALLY_FAST` (for `$WAY_LESS`)



Keep your training algorithm the same, just replace the data loader! Look at these speedups:






`ffcv` also comes prepacked with [fast, simple code](https://github.com/libffcv/imagenet-example) for [standard vision benchmarks]((https://docs.ffcv.io/benchmarks.html)):






## Installation

### Linux

```

conda create -y -n ffcv python=3.9 cupy pkg-config libjpeg-turbo opencv pytorch torchvision cudatoolkit=11.3 numba -c pytorch -c conda-forge

conda activate ffcv

pip install ffcv

```

Troubleshooting note 1: if the above commands result in a package conflict error, try running ``conda config --env --set channel_priority flexible`` in the environment and rerunning the installation command.



Troubleshooting note 2: on some systems (but rarely), you'll need to add the ``compilers`` package to the first command above.



Troubleshooting note 3: courtesy of @kschuerholt, here is a [Dockerfile](https://github.com/kschuerholt/pytorch_cuda_opencv_ffcv_docker) that may help with conda-free installation



### Windows

* Install opencv4

  * Add `..../opencv/build/x64/vc15/bin` to PATH environment variable

* Install libjpeg-turbo, download libjpeg-turbo-x.x.x-vc64.exe, not gcc64

  * Add `..../libjpeg-turbo64/bin` to PATH environment variable

* Install pthread, download last release.zip

  * After unzip, rename Pre-build.2 folder to pthread

  * Open `pthread/include/pthread.h`, and add the code below to the top of the file.  

  ```cpp

  #define HAVE_STRUCT_TIMESPEC

  ```

  * Add `..../pthread/dll` to PATH environment variable

* Install cupy depending on your CUDA Toolkit version.

* `pip install ffcv`



## Citation

If you use FFCV, please cite it as:



```

@inproceedings{leclerc2023ffcv,

    author = {Guillaume Leclerc and Andrew Ilyas and Logan Engstrom and Sung Min Park and Hadi Salman and Aleksander Madry},

    title = {{FFCV}: Accelerating Training by Removing Data Bottlenecks},

    year = {2023},

    booktitle = {Computer Vision and Pattern Recognition (CVPR)},

    note = {\url{https://github.com/libffcv/ffcv/}. commit xxxxxxx}

}

```

(Make sure to replace xxxxxxx above with the hash of the commit used!)



## Quickstart

Accelerate *any* learning system with `ffcv`.

First,

convert your dataset into `ffcv` format (`ffcv` converts both indexed PyTorch datasets and

WebDatasets):

```python

from ffcv.writer import DatasetWriter

from ffcv.fields import RGBImageField, IntField



# Your dataset (`torch.utils.data.Dataset`) of (image, label) pairs

my_dataset = make_my_dataset()

write_path = '/output/path/for/converted/ds.beton'



# Pass a type for each data field

writer = DatasetWriter(write_path, {

    # Tune options to optimize dataset size, throughput at train-time

    'image': RGBImageField(max_resolution=256),

    'label': IntField()

})



# Write dataset

writer.from_indexed_dataset(my_dataset)

```

Then replace your old loader with the `ffcv` loader at train time (in PyTorch,

no other changes required!):

```python

from ffcv.loader import Loader, OrderOption

from ffcv.transforms import ToTensor, ToDevice, ToTorchImage, Cutout

from ffcv.fields.decoders import IntDecoder, RandomResizedCropRGBImageDecoder



# Random resized crop

decoder = RandomResizedCropRGBImageDecoder((224, 224))



# Data decoding and augmentation

image_pipeline = [decoder, Cutout(), ToTensor(), ToTorchImage(), ToDevice(0)]

label_pipeline = [IntDecoder(), ToTensor(), ToDevice(0)]



# Pipeline for each data field

pipelines = {

    'image': image_pipeline,

    'label': label_pipeline

}



# Replaces PyTorch data loader (`torch.utils.data.Dataloader`)

loader = Loader(write_path, batch_size=bs, num_workers=num_workers,

                order=OrderOption.RANDOM, pipelines=pipelines)



# rest of training / validation proceeds identically

for epoch in range(epochs):

    ...

```

[See here](https://docs.ffcv.io/basics.html) for a more detailed guide to deploying `ffcv` for your dataset.



## Prepackaged Computer Vision Benchmarks

From gridding to benchmarking to fast research iteration, there are many reasons

to want faster model training. Below we present premade codebases for training

on ImageNet and CIFAR, including both (a) extensible codebases and (b)

numerous premade training configurations.



### ImageNet

We provide a self-contained script for training ImageNet fast.

Above we plot the training time versus

accuracy frontier, and the dataloading speeds, for 1-GPU ResNet-18 and 8-GPU

ResNet-50 alongside a few baselines.



| Link to Config                                                                                                                         |   top_1 |   top_5 |   # Epochs |   Time (mins) | Architecture   | Setup    |

|:---------------------------------------------------------------------------------------------------------------------------------------|--------:|--------:|-----------:|--------------:|:---------------|:---------|

| Link | 0.784 | 0.941  |         88 |       77.2 | ResNet-50      | 8 x A100 |

| Link | 0.780 | 0.937 |         56 |       49.4 | ResNet-50      | 8 x A100 |

| Link | 0.772 | 0.932 |         40 |       35.6 | ResNet-50      | 8 x A100 |

| Link | 0.766 | 0.927 |         32 |       28.7 | ResNet-50      | 8 x A100 |

| Link | 0.756 | 0.921 |         24 |       21.7  | ResNet-50      | 8 x A100 |

| Link | 0.738 | 0.908 |         16 |       14.9 | ResNet-50      | 8 x A100 |

| Link | 0.724 | 0.903   |         88 |      187.3  | ResNet-18      | 1 x A100 |

| Link | 0.713  | 0.899 |         56 |      119.4   | ResNet-18      | 1 x A100 |

| Link | 0.706 | 0.894 |         40 |       85.5 | ResNet-18      | 1 x A100 |

| Link | 0.700 | 0.889 |         32 |       68.9   | ResNet-18      | 1 x A100 |

| Link | 0.688  | 0.881 |         24 |       51.6 | ResNet-18      | 1 x A100 |

| Link | 0.669 | 0.868 |         16 |       35.0 | ResNet-18      | 1 x A100 |



**Train your own ImageNet models!** You can use our training script and premade configurations to train any model seen on the above graphs.



### CIFAR-10

We also include premade code for efficient training on CIFAR-10 in the `examples/`

directory, obtaining 93\% top1 accuracy in 36 seconds on a single A100 GPU

(without optimizations such as MixUp, Ghost BatchNorm, etc. which have the

potential to raise the accuracy even further). You can find the training script

here.



## Features




Computer vision or not, FFCV can help make training faster in a variety of

resource-constrained settings!

Our performance guide

has a more detailed account of the ways in which FFCV can adapt to different

performance bottlenecks.



- **Plug-and-play with any existing training code**: Rather than changing

  aspects of model training itself, FFCV focuses on removing *data bottlenecks*,

  which turn out to be a problem everywhere from neural network training to

  linear regression. This means that:



    - FFCV can be introduced into any existing training code in just a few

      lines of code (e.g., just swapping out the data loader and optionally the

      augmentation pipeline);

    - You don't have to change the model itself to make it faster (e.g., feel

      free to analyze models *without* CutMix, Dropout, momentum scheduling, etc.);

    - FFCV can speed up a lot more beyond just neural network training---in

      fact, the more data-bottlenecked the application (e.g., linear regression,

      bulk inference, etc.), the faster FFCV will make it!



  See our [Getting started](https://docs.ffcv.io/basics.html) guide,

  [Example walkthroughs](https://docs.ffcv.io/examples.html), and

  [Code examples](https://github.com/libffcv/ffcv/tree/main/examples)

  to see how easy it is to get started!

- **Fast data processing without the pain**: FFCV automatically handles data

  reading, pre-fetching, caching, and transfer between devices in an extremely

  efficiently way, so that users don't have to think about it.

- **Automatically fused-and-compiled data processing**: By either using

  [pre-written](https://docs.ffcv.io/api/transforms.html) FFCV transformations

  or

  [easily writing custom ones](https://docs.ffcv.io/ffcv_examples/custom_transforms.html),

  users can

  take advantage of FFCV's compilation and pipelining abilities, which will

  automatically fuse and compile simple Python augmentations to machine code

  using [Numba](https://numba.pydata.org), and schedule them asynchronously to avoid

  loading delays.

- **Load data fast from RAM, SSD, or networked disk**: FFCV exposes

  user-friendly options that can be adjusted based on the resources

  available. For example, if a dataset fits into memory, FFCV can cache it

  at the OS level and ensure that multiple concurrent processes all get fast

  data access. Otherwise, FFCV can use fast process-level caching and will

  optimize data loading to minimize the underlying number of disk reads. See

  [The Bottleneck Doctor](https://docs.ffcv.io/bottleneck_doctor.html)

  guide for more information.

- **Training multiple models per GPU**: Thanks to fully asynchronous

  thread-based data loading, you can now interleave training multiple models on

  the same GPU efficiently, without any data-loading overhead. See

  [this guide](https://docs.ffcv.io/parameter_tuning.html) for more info.

- **Dedicated tools for image handling**: All the features above work are

  equally applicable to all sorts of machine learning models, but FFCV also

  offers some vision-specific features, such as fast JPEG encoding and decoding,

  storing datasets as mixtures of raw and compressed images to trade off I/O

  overhead and compute overhead, etc. See the

  [Working with images](https://docs.ffcv.io/working_with_images.html) guide for

  more information.



# Contributors



- [Guillaume Leclerc](https://github.com/GuillaumeLeclerc)

- [Logan Engstrom](http://loganengstrom.com/)

- [Andrew Ilyas](http://andrewilyas.com/)

- [Sam Park](http://sungminpark.com/)

- [Hadi Salman](http://hadisalman.com/)