wids 0.1.11

High performance storage and I/O for deep learning and data processing.

%matplotlib inline
import matplotlib.pyplot as plt
import torch.utils.data
import torch.nn
from random import randrange
import os
os.environ["WDS_VERBOSE_CACHE"] = "1"
os.environ["GOPEN_VERBOSE"] = "0"

The WebDataset Format

WebDataset format files are tar files, with two conventions:

• within each tar file, files that belong together and make up a training sample share the same basename when stripped of all filename extensions
• the shards of a tar file are numbered like something-000000.tar to something-012345.tar, usually specified using brace notation something-{000000..012345}.tar

You can find a longer, more detailed specification of the WebDataset format in the WebDataset Format Specification

WebDataset can read files from local disk or from any pipe, which allows it to access files using common cloud object stores. WebDataset can also read concatenated MsgPack and CBORs sources.

The WebDataset representation allows writing purely sequential I/O pipelines for large scale deep learning. This is important for achieving high I/O rates from local storage (3x-10x for local drives compared to random access) and for using object stores and cloud storage for training.

The WebDataset format represents images, movies, audio, etc. in their native file formats, making the creation of WebDataset format data as easy as just creating a tar archive. Because of the way data is aligned, WebDataset works well with block deduplication as well and aligns data on predictable boundaries.

Standard tools can be used for accessing and processing WebDataset-format files.

bucket = "https://storage.googleapis.com/webdataset/testdata/"
dataset = "publaynet-train-{000000..000009}.tar"

url = bucket + dataset
!curl -s {bucket}publaynet-train-000000.tar | dd count=5000 2> /dev/null | tar tf - 2> /dev/null | sed 10q

PMC4991227_00003.json
PMC4991227_00003.png


PMC4537884_00002.json


PMC4537884_00002.png


PMC4323233_00003.json
PMC4323233_00003.png


PMC5429906_00004.json
PMC5429906_00004.png


PMC5592712_00002.json
PMC5592712_00002.png

Note that in these .tar files, we have pairs of .json and .png files; each such pair makes up a training sample.

WebDataset Libraries

There are several libraries supporting the WebDataset format:

• webdataset for Python3 (includes the wids library), this repository
• Webdataset.jl a Julia implementation
• tarp, a Golang implementation and command line tool
• Ray Data sources and sinks

The webdataset library can be used with PyTorch, Tensorflow, and Jax.

The wids Library for Indexed WebDatasets

Installing the webdataset library installs a second library called wids. This library provides fully indexed/random access to the same datasets that webdataset accesses using iterators/streaming.

Like the webdataset library, wids is high scalable and provides efficient access to very large datasets. Being indexed, it is easily backwards compatible with existing data pipelines based on indexed dataset, including precise epochs for multinode training. The library comes with its own ChunkedSampler and DistributedChunkedSampler classes, which provided shuffling accross nodes while still preserving enough locality of reference for efficient training.

Internally, the library uses a mmap-based tar file reader implementation; this allows very fast access without precomputed indexes, and it also means that shard and the equivalet of "shuffle buffers" are shared in memory between workers on the same machine.

This additional power comes at some cost: the library requires a small metadata file that lists all the shards in a dataset and the number of samples contained in each, the library requires local storage for as many shards as there are I/O workers on a node, it uses shared memory and mmap, and the availability of indexing makes it easy to accidentally use inefficient access patterns.

Generally, the recommendation is to use webdataset for all data generation, data transformation, and training code, and to use wids only if you need fully random access to datasets (e.g., for browing or sparse sampling), need an indexed-based sampler, or are converting tricky legacy code.

import wids

train_url = "https://storage.googleapis.com/webdataset/fake-imagenet/imagenet-train.json"

dataset = wids.ShardListDataset(train_url)

sample = dataset[1900]

print(sample.keys())
print(sample[".txt"])
plt.imshow(sample[".jpg"])

dict_keys(['.cls', '.jpg', '.txt', '__key__', '__dataset__', '__index__', '__shard__', '__shardindex__'])
a high quality color photograph of a dog


https://storage.googleapis.com/webdataset/fake-ima base: https://storage.googleapis.com/webdataset/fake-imagenet name: imagenet-train nfiles: 1282 nbytes: 31242280960 samples: 128200 cache: /tmp/_wids_cache
/home/tmb/proj/wids/src/wids/wids.py:316: UserWarning: String specifications for transformations are deprecated. Use functions instead.
  warnings.warn("String specifications for transformations are deprecated. Use functions instead.")





<matplotlib.image.AxesImage at 0x7187ffd119d0>

There are several examples of how to use wids in the examples directory.

• train-resnet50-wids shows how to train a ResNet-50 model on ImageNet using wids
• train-resnet50-multiray-wids shows how to train a ResNet-50 model on ImageNet using multiple nodes

Note that the APIs between webdataset and wids are not fully consistent:

• wids keeps the extension's "." in the keys, while webdataset removes it (".txt" vs "txt")
• wids doesn't have a fully fluid interface, and add_transformation just adds to a list of transformations
• webdataset currently can't read the wids JSON specifications

Installation and Documentation

$ pip install wids

For the Github version:

$ pip install git+https://github.com/tmbdev/wids.git

Here are some videos talking about WebDataset and large scale deep learning:

• Introduction to Large Scale Deep Learning
• Loading Training Data with WebDataset
• Creating Datasets in WebDataset Format
• Tools for Working with Large Datasets

Dependencies

The wids library only requires PyTorch, NumPy, and a small library called braceexpand.

The wids library loads a few additional libraries dynamically only when they are actually needed and only in the decoder:

• PIL/Pillow for image decoding
• torchvision, torchvideo, torchaudio for image/video/audio decoding
• msgpack for MessagePack decoding
• the curl command line tool for accessing HTTP servers
• the Google/Amazon/Azure command line tools for accessing cloud storage buckets

Loading of one of these libraries is triggered by configuring a decoder that attempts to decode content in the given format and encountering a file in that format during decoding. (Eventually, the torch... dependencies will be refactored into those libraries.)