A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format

Last update: Jan 06, 2023

Overview

TorchArrow (Warning: Unstable Prototype)

This is a prototype library currently under heavy development. It does not currently have stable releases, and as such will likely be modified significantly in backwards compatibility breaking ways until beta release (targeting early 2022). If you have suggestions on the API or use cases you would like to be covered, please open a GitHub issue. We would love to hear thoughts and feedback.

TorchArrow is a torch.Tensor-like Python DataFrame library for data preprocessing in deep learning. It supports multiple execution runtimes and Arrow as a common format.

It plans to provide:

Python Dataframe library implementing streaming-friendly Pandas subset
Seamless handoff with PyTorch or other model authoring, such as Tensor collation and easily plugging into PyTorch DataLoader and DataPipes
Zero copy for external readers via Arrow in-memory columnar format
High-performance CPU backend via Velox
GPU backend via libcudf
High-performance C++ UDF support with vectorization

Installation

Binaries

Coming soon!

From Source

If you are installing from source, you will need Python 3.8 or later and a C++17 compiler. Also, we highly recommend installing an Miniconda environment.

Get the TorchArrow Source

git clone --recursive https://github.com/facebookresearch/torcharrow
cd torcharrow
# if you are updating an existing checkout
git submodule sync --recursive
git submodule update --init --recursive

Install Dependencies

On MacOS

HomeBrew is required to install development tools on MacOS.

# Install dependencies from Brew
brew install --formula ninja cmake ccache protobuf icu4c boost gflags glog libevent lz4 lzo snappy xz zstd

# Build and install other dependencies
scripts/build_mac_dep.sh ranges_v3 googletest fmt double_conversion folly re2

On Ubuntu (20.04 or later)

# Install dependencies from APT
apt install -y g++ cmake ccache ninja-build checkinstall \
    libssl-dev libboost-all-dev libdouble-conversion-dev libgoogle-glog-dev \
    libbz2-dev libgflags-dev libgtest-dev libgmock-dev libevent-dev libfmt-dev \
    libprotobuf-dev liblz4-dev libzstd-dev libre2-dev libsnappy-dev liblzo2-dev \
    protobuf-compiler
# Build and install Folly
scripts/install_ubuntu_folly.sh

Install TorchArrow

For local development, you can build with debug mode:

DEBUG=1 python setup.py develop

And run unit tests with

python -m unittest -v

To install TorchArrow with release mode (WARNING: may take very long to build):

python setup.py install

Documentation

This 10 minutes tutorial provides a short introduction to TorchArrow. More documents on advanced topics are coming soon!

Future Plans

We hope to sufficiently expand the library, harden APIs, and gather feedback to enable a beta release at the time of the PyTorch 1.11 release (early 2022).

License

TorchArrow is BSD licensed, as found in the LICENSE file.

Comments

Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/95b09c7bad6baa93d8f6add4562dfe0cdc8c26cd

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 80
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/0cb50b9fdfccbb277e62e1c2541ae084b29d6080

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 79
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/e432b1df0be62f65e0ba00b4fb966605bcb1443e

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 58
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/7673b382d909add4738240ae0157f2d5cafcf546

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 56
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/5e37e22c974fcd9caceb3dd97a0e84386d188474

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 55
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/08be6833961213b6679a7a7707ca53d486ff84df

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 46
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/41971b30c1cdd9f984018d6a496bc3b99afc7b45

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 36
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/4a36551237993f519dbf5bbae70a4ac9a660bf0d

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 35
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/0e4d3a5efece59b7d6a6a8f23ed7e4668d078e2e

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 33
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/fb7b62bede0beb66cf87fe888d71a9c366fe5ed6

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 33
Automated submodule update: velox

This is an automated pull request to update the first-party submodule for facebookincubator/velox.

New submodule commit: https://github.com/facebookincubator/velox/commit/b32878fb54eefb01c0c577439d0d6d61644dcff9

Test Plan: Ensure that CI jobs succeed on GitHub before landing.
CLA Signed

opened by facebook-github-bot 33
Stable Release Roadmap

Hello, I see that the development of the library has slowed down a bit, hence I would like to ask if there exists a roadmap for the first stable release or if there's any other plan for TorchArrow. Thank you very much for your work!

opened by mbignotti 0
`from_arrow` with `List` columns
Summary: Adds some basic functionality to allow Arrow tables/arrays with List[primitive_type] columns to be converted to a ta.Dataframe.

Implemented by converting the list column to a pylist and wrapping _from_pysequence. Not super efficient, but provides some functionality to unblock these columns.

Tests: Modified previous test case that checked for unsupported type. python -m unittest -v

---------------------------------------------------------------------- Ran 196 tests in 1.108s OK
CLA Signed
opened by myzha0 0
Generalize Dispatcher class

Summary: Generalizing it for reusing it in different contexts. Also changing to global-instance-as-singleton pattern, so that we can instantiate instances for different use cases without worrying about mis-sharing the class variable calls

Differential Revision: D40188963
CLA Signed fb-exported

opened by OswinC 1

Support for arrays in torcharrow.from_arrow

Hi guys! When trying to use ParquetDataFrameLoader I ran across a problem when trying to load parquet file if it has an array field. It looks like it comes down to torcharrow.from_arrow not supporting array columns. But it seems that torcharrow already has support for array columns. Are there any plans to implement it when loading from parquet files or are there any problems which stop this from being implemented?

The error basically looks like this:

NotImplementedError                       Traceback (most recent call last)
Input In [25], in <cell line: 1>()
----> 1 next(iter(datapipe))

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/datapipes/_typing.py:514, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
    512         response = gen.send(None)
    513 else:
--> 514     response = gen.send(None)
    516 while True:
    517     request = yield response

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/datapipes/iter/combinatorics.py:127, in ShufflerIterDataPipe.__iter__(self)
    125 self._rng.seed(self._seed)
    126 self._seed = None
--> 127 for x in self.datapipe:
    128     if len(self._buffer) == self.buffer_size:
    129         idx = self._rng.randint(0, len(self._buffer) - 1)

File /opt/conda/lib/python3.8/site-packages/torch/utils/data/datapipes/_typing.py:514, in hook_iterator.<locals>.wrap_generator(*args, **kwargs)
    512         response = gen.send(None)
    513 else:
--> 514     response = gen.send(None)
    516 while True:
    517     request = yield response

File /opt/conda/lib/python3.8/site-packages/torchdata/datapipes/iter/util/dataframemaker.py:138, in ParquetDFLoaderIterDataPipe.__iter__(self)
    135 for i in range(num_row_groups):
    136     # TODO: More fine-grain control over the number of rows or row group per DataFrame
    137     row_group = parquet_file.read_row_group(i, columns=self.columns, use_threads=self.use_threads)
--> 138     yield torcharrow.from_arrow(row_group, dtype=self.dtype)

File /opt/conda/lib/python3.8/site-packages/torcharrow/interop.py:32, in from_arrow(data, dtype, device)
     30     return _from_arrow_array(data, dtype, device=device)
     31 elif isinstance(data, pa.Table):
---> 32     return _from_arrow_table(data, dtype, device=device)
     33 else:
     34     raise ValueError

File /opt/conda/lib/python3.8/site-packages/torcharrow/interop_arrow.py:86, in _from_arrow_table(table, dtype, device)
     83     field = table.schema.field(i)
     85     assert len(table[i].chunks) == 1
---> 86     df_data[field.name] = _from_arrow_array(
     87         table[i].chunk(0),
     88         dtype=(
     89             # pyre-fixme[16]: `DType` has no attribute `get`.
     90             dtype.get(field.name)
     91             if dtype is not None
     92             else _arrowtype_to_dtype(field.type, field.nullable)
     93         ),
     94         device=device,
     95     )
     97 return dataframe(df_data, device=device)

File /opt/conda/lib/python3.8/site-packages/torcharrow/interop_arrow.py:37, in _from_arrow_array(array, dtype, device)
     28 assert isinstance(array, pa.Array)
     30 # Using the most narrow type we can, we (i) don't restrict in any
     31 # way where it can be used (since we can pass a narrower typed
     32 # non-null column to a function expecting a nullable type, but not
   (...)
     35 # increase the amount of places we can use the from_arrow result
     36 # pyre-fixme[16]: `Array` has no attribute `type`.
---> 37 dtype_from_arrowtype = _arrowtype_to_dtype(array.type, array.null_count > 0)
     38 if dtype and (
     39     dt.get_underlying_dtype(dtype) != dt.get_underlying_dtype(dtype_from_arrowtype)
     40 ):
     41     raise NotImplementedError("Type casting is not supported")

File /opt/conda/lib/python3.8/site-packages/torcharrow/_interop.py:205, in _arrowtype_to_dtype(t, nullable)
    199 if pa.types.is_struct(t):
    200     return dt.Struct(
    201         # pyre-fixme[16]: `DataType` has no attribute `__iter__`.
    202         [dt.Field(f.name, _arrowtype_to_dtype(f.type, f.nullable)) for f in t],
    203         nullable,
    204     )
--> 205 raise NotImplementedError(f"Unsupported Arrow type: {str(t)}")

NotImplementedError: Unsupported Arrow type: list<element: float>
This exception is thrown by __iter__ of ParquetDFLoaderIterDataPipe()

opened by grapefroot 2

Releases(v0.1.0)

v0.1.0(Jul 13, 2022)
We are excited to release the very first Beta version of TorchArrow! TorchArrow is a machine learning preprocessing library over batch data, providing performant and Pandas-style easy-to-use API for model development.

Highlights

TorchArrow provides a Python DataFrame that allows extensible UDFs with Velox, with the following features:

Seamless handoff with PyTorch or other model authoring, such as Tensor collation and easily plugging into PyTorch DataLoader and DataPipes

Zero copy for external readers via Arrow in-memory columnar format

Multiple execution runtimes support:

High-performance CPU backend via Velox

(Future Work) GPU backend via libcudf

High-performance C++ UDF support with vectorization

Installation

In this release we are supporting install via PYPI: pip install torcharrow.

Documentation

You can find the API documentation here.

This 10 minutes tutorial provides a short introduction to TorchArrow, and you can also try it in this Colab.

Examples

You can find the example about integrating a TorchRec based training loop utilizing TorchArrow's on-the-fly preprocessing here. More examples are coming soon!

Future Plans

We hope to continue to expand the library, harden API, and gather feedback to enable future releases. Stay tuned!

Beta Usage Note

TorchArrow is currently in the Beta stage and does not have a stable release. The API may change based on user feedback or performance. We are committed to bring this library to stable release, but future changes may not be completely backward compatible. If you have suggestions on the API or use cases you'd like to be covered, please open a GitHub issue. We'd love to hear thoughts and feedback.
Source code(tar.gz)
Source code(zip)

Owner

Facebook Research

GitHub Repository

Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)

EPSR (Enhanced Perceptual Super-resolution Network) paper This repo provides the test code, pretrained models, and results on benchmark datasets of ou

78 Nov 19, 2022

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

33 Dec 07, 2022

Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild Akash Sengupta, Ignas Budvytis, Robert

149 Dec 14, 2022

CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

Diverse Structure Inpainting ArXiv | Papar | Supplementary Material | BibTex This repository is for the CVPR 2021 paper, "Generating Diverse Structure

152 Nov 04, 2022

A symbolic-model-guided fuzzer for TLS

tlspuffin TLS Protocol Under FuzzINg A symbolic-model-guided fuzzer for TLS Master Thesis | Thesis Presentation | Documentation Disclaimer: The term "

69 Dec 20, 2022

Code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation

PiecewiseLinearTimeSeriesApproximation code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation, SIAM Data Mining 20

21 Oct 27, 2022

Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22) Paper Link | Project Page Abstract : Manual an

152 Dec 23, 2022

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Secti

64 Dec 11, 2022

Ratatoskr: Worcester Tech's conference scheduling system

Ratatoskr: Worcester Tech's conference scheduling system In Norse mythology, Ratatoskr is a squirrel who runs up and down the world tree Yggdrasil to

4 Dec 22, 2022

Efficient Training of Visual Transformers with Small Datasets

Official codes for "Efficient Training of Visual Transformers with Small Datasets", NerIPS 2021.

112 Dec 25, 2022

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021)

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021) Alexey Nekrasov*, Jonas Schult*, Or Litany, Bastian Leibe, Francis Engelmann Mix3D is

189 Dec 26, 2022

Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.

Image Quality Evaluation Metrics Implementation of some common full reference image quality metrics. The repo is built based on full reference image q

10 Jan 01, 2023

A torch.Tensor-like DataFrame library supporting multiple execution runtimes and Arrow as a common memory format

Related tags

Overview

TorchArrow (Warning: Unstable Prototype)

Installation

Binaries

From Source

Get the TorchArrow Source

Install Dependencies

Install TorchArrow

Documentation

Future Plans

License

Comments

Releases(v0.1.0)

v0.1.0(Jul 13, 2022)

Highlights

Installation

Documentation

Examples

Future Plans

Beta Usage Note

Owner

Facebook Research

Project page of the paper 'Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network' (ECCVW 2018)

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

Code repository for the paper: Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild (ICCV 2021)

CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

A symbolic-model-guided fuzzer for TLS

Code from Daniel Lemire, A Better Alternative to Piecewise Linear Time Series Segmentation

Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?”

Ratatoskr: Worcester Tech's conference scheduling system

Efficient Training of Visual Transformers with Small Datasets

Mix3D: Out-of-Context Data Augmentation for 3D Scenes (3DV 2021)

Yolov3 pytorch implementation

People log into different sites every day to get information and browse through these sites one by one

Image-Scaling Attacks and Defenses

Neural Scene Graphs for Dynamic Scene (CVPR 2021)

Training DALL-E with volunteers from all over the Internet using hivemind and dalle-pytorch (NeurIPS 2021 demo)

Who calls the shots? Rethinking Few-Shot Learning for Audio (WASPAA 2021)

This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637

AI virtual gym is an AI program which can be used to exercise and can be used to see if we are doing the exercises