Tensors and neural networks in Haskell

Related tags

Deep Learninghasktorch
Overview

Hasktorch

Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch.

This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch slack space for questions and discussions. Contributions/PR are encouraged.

Currently we are developing the second major release of Hasktorch (0.2). Note the 1st release, Hasktorch 0.1, on hackage is outdated and should not be used.

Documentation

The documentation is divided into several sections:

Introductory Videos

Getting Started

The following steps will get you started. They assume the hasktorch repository has just been cloned. After setup is done, read the online tutorials and API documents.

linux+cabal+cpu

Starting from the top-level directory of the project, run:

$ pushd deps       # Change to the deps directory and save the current directory.
$ ./get-deps.sh    # Run the shell script to retrieve the libtorch dependencies.
$ popd             # Go back to the root directory of the project.
$ source setenv    # Set the shell environment to reference the shared library locations.
$ ./setup-cabal.sh # Create a cabal project file

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

linux+cabal+cuda11

Starting from the top-level directory of the project, run:

$ pushd deps              # Change to the deps directory and save the current directory.
$ ./get-deps.sh -a cu111  # Run the shell script to retrieve the libtorch dependencies.
$ popd                    # Go back to the root directory of the project.
$ source setenv           # Set the shell environment to reference the shared library locations.
$ ./setup-cabal.sh        # Create a cabal project file

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE="cuda:0"        # Set device to CUDA for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

macos+cabal+cpu

Starting from the top-level directory of the project, run:

$ pushd deps       # Change to the deps directory and save the current directory.
$ ./get-deps.sh    # Run the shell script to retrieve the libtorch dependencies.
$ popd             # Go back to the root directory of the project.
$ source setenv    # Set the shell environment to reference the shared library locations.
$ ./setup-cabal.sh # Create a cabal project file

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

linux+stack+cpu

Install the Haskell Tool Stack if you haven't already, following instructions here

Starting from the top-level directory of the project, run:

$ pushd deps     # Change to the deps directory and save the current directory.
$ ./get-deps.sh  # Run the shell script to retrieve the libtorch dependencies.
$ popd           # Go back to the root directory of the project.
$ source setenv  # Set the shell environment to reference the shared library locations.

To build and test the Hasktorch library, run:

$ stack build hasktorch  # Build the Hasktorch library.
$ stack test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ stack build examples  # Build the Hasktorch examples.
$ stack test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ stack run static-mnist-cnn     # Run the MNIST CNN example.

macos+stack+cpu

Install the Haskell Tool Stack if you haven't already, following instructions here

Starting from the top-level directory of the project, run:

$ pushd deps     # Change to the deps directory and save the current directory.
$ ./get-deps.sh  # Run the shell script to retrieve the libtorch dependencies.
$ popd           # Go back to the root directory of the project.
$ source setenv  # Set the shell environment to reference the shared library locations.

To build and test the Hasktorch library, run:

$ stack build hasktorch  # Build the Hasktorch library.
$ stack test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ stack build examples  # Build the Hasktorch examples.
$ stack test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ stack run static-mnist-cnn     # Run the MNIST CNN example.

nixos+cabal+cpu

(Optional) Install and set up Cachix:

$ nix-env -iA cachix -f https://cachix.org/api/v1/install  # (Optional) Install Cachix.
$ cachix use iohk                                          # (Optional) Use IOHK's cache.
$ cachix use hasktorch                                     # (Optional) Use hasktorch's cache.

Starting from the top-level directory of the project, run:

$ nix-shell  # Enter the nix shell environment for Hasktorch.

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE=cpu             # Set device to CPU for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

nixos+cabal+cuda11

(Optional) Install and set up Cachix:

$ nix-env -iA cachix -f https://cachix.org/api/v1/install  # (Optional) Install Cachix.
$ cachix use iohk                                          # (Optional) Use IOHK's cache.
$ cachix use hasktorch                                     # (Optional) Use hasktorch's cache.

Starting from the top-level directory of the project, run:

$ nix-shell --arg cudaSupport true --argstr cudaMajorVersion 11  # Enter the nix shell environment for Hasktorch.

To build and test the Hasktorch library, run:

$ cabal build hasktorch  # Build the Hasktorch library.
$ cabal test hasktorch   # Build and run the Hasktorch library test suite.

To build and test the example executables shipped with hasktorch, run:

$ cabal build examples  # Build the Hasktorch examples.
$ cabal test examples   # Build and run the Hasktorch example test suites.

To run the MNIST CNN example, run:

$ cd examples                   # Change to the examples directory.
$ ./datasets/download-mnist.sh  # Download the MNIST dataset.
$ mv mnist data                 # Move the MNIST dataset to the data directory.
$ export DEVICE="cuda:0"        # Set device to CUDA for the MNIST CNN example.
$ cabal run static-mnist-cnn    # Run the MNIST CNN example.

docker+jupyterlab+cuda11

This dockerhub repository provides the docker-image of jupyterlab. It supports cuda11, cuda10 and cpu only. When you use jupyterlab with hasktorch, type following command, then click a url in a console.

$ docker run --gpus all -it --rm -p 8888:8888 htorch/hasktorch-jupyter
or
$ docker run --gpus all -it --rm -p 8888:8888 htorch/hasktorch-jupyter:latest-cu11

Known Issues

Tensors Cannot Be Moved to CUDA

In rare cases, you may see errors like

cannot move tensor to "CUDA:0"

although you have CUDA capable hardware in your machine and have followed the getting-started instructions for CUDA support.

If that happens, check if /run/opengl-driver/lib exists. If not, make sure your CUDA drivers are installed correctly.

Weird Behaviour When Switching from CPU-Only to CUDA-Enabled Nix Shell

If you have run cabal in a CPU-only Hasktorch Nix shell before, you may need to:

  • Clean the dist-newstyle folder using cabal clean.
  • Delete the .ghc.environment* file in the Hasktorch root folder.

Otherwise, at best, you will not be able to move tensors to CUDA, and, at worst, you will see weird linker errors like

gcc: error: hasktorch/dist-newstyle/build/x86_64-linux/ghc-8.8.3/libtorch-ffi-1.5.0.0/build/Torch/Internal/Unmanaged/Autograd.dyn_o: No such file or directory
`cc' failed in phase `Linker'. (Exit code: 1)

Contributing

We welcome new contributors.

Contact us for access to the hasktorch slack channel. You can send an email to [email protected] or on twitter as @austinvhuang, @SamStites, @tscholak, or @junjihashimoto3.

Notes for library developers

See the wiki for developer notes.

Project Folder Structure

Basic functionality:

  • deps/ - submodules and downloads for build dependencies (libtorch, mklml, pytorch) -- you can ignore this if you are on Nix
  • examples/ - high level example models (xor mlp, typed cnn, etc.)
  • experimental/ - experimental projects or tips
  • hasktorch/ - higher level user-facing library, calls into ffi/, used by examples/

Internals (for contributing developers):

  • codegen/ - code generation, parses Declarations.yaml spec from pytorch and produces ffi/ contents
  • inline-c/ - submodule to inline-cpp fork used for C++ FFI
  • libtorch-ffi/- low level FFI bindings to libtorch
  • spec/ - specification files used for codegen/
Owner
hasktorch
hasktorch
GitHub Repository https://hasktorch.github.io/
Studying Python release adoptions by looking at PyPI downloads

Analysis of version adoptions on PyPI We get PyPI download statistics via Google's BigQuery using the pypinfo tool. Usage First you need to get an acc

Julien Palard 9 Nov 04, 2022
MAT: Mask-Aware Transformer for Large Hole Image Inpainting

MAT: Mask-Aware Transformer for Large Hole Image Inpainting (CVPR2022, Oral) Wenbo Li, Zhe Lin, Kun Zhou, Lu Qi, Yi Wang, Jiaya Jia [Paper] News This

254 Dec 29, 2022
Unofficial TensorFlow implementation of the Keyword Spotting Transformer model

Keyword Spotting Transformer This is the unofficial TensorFlow implementation of the Keyword Spotting Transformer model. This model is used to train o

Intelligent Machines Limited 8 May 11, 2022
Social Network Ads Prediction

Social network advertising, also social media targeting, is a group of terms that are used to describe forms of online advertising that focus on social networking services.

Khazar 2 Jan 28, 2022
TransVTSpotter: End-to-end Video Text Spotter with Transformer

TransVTSpotter: End-to-end Video Text Spotter with Transformer Introduction A Multilingual, Open World Video Text Dataset and End-to-end Video Text Sp

weijiawu 66 Dec 26, 2022
Iran Open Source Hackathon

Iran Open Source Hackathon is an open-source hackathon (duh) with the aim of encouraging participation in open-source contribution amongst Iranian dev

OSS Hackathon 121 Dec 25, 2022
An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for

12 Dec 26, 2022
Research on Event Accumulator Settings for Event-Based SLAM

Research on Event Accumulator Settings for Event-Based SLAM This is the source code for paper "Research on Event Accumulator Settings for Event-Based

Robin Shaun 26 Dec 21, 2022
This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm.

This repository is the official implementation of the Hybrid Self-Attention NEAT algorithm. It contains the code to reproduce the results presented in the original paper: https://arxiv.org/abs/2112.0

Saman Khamesian 6 Dec 13, 2022
Image-retrieval-baseline - MUGE Multimodal Retrieval Baseline

MUGE Multimodal Retrieval Baseline This repo is implemented based on the open_cl

47 Dec 16, 2022
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning

MaCan 4.2k Dec 29, 2022
A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions

AlphaFold Analyser This program produces high quality visualisations of predicted structures produced by AlphaFold. These visualisations allow the use

Oliver Powell 3 Nov 13, 2022
Code for Paper "Evidential Softmax for Sparse MultimodalDistributions in Deep Generative Models"

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
Neural network for stock price prediction

neural_network_for_stock_price_prediction Neural networks for stock price predic

2 Feb 04, 2022
MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python

29 Oct 19, 2022
Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective

Does-MAML-Only-Work-via-Feature-Re-use-A-Data-Set-Centric-Perspective Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective Installin

2 Nov 07, 2022
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

TIA Toolbox Computational Pathology Toolbox developed at the TIA Centre Getting Started All Users This package is for those interested in digital path

Tissue Image Analytics (TIA) Centre 156 Jan 08, 2023
Learning 3D Part Assembly from a Single Image

Learning 3D Part Assembly from a Single Image This repository contains a PyTorch implementation of the paper: Learning 3D Part Assembly from A Single

18 Dec 21, 2022
PyTorch Implementation of Temporal Output Discrepancy for Active Learning, ICCV 2021

Temporal Output Discrepancy for Active Learning PyTorch implementation of Semi-Supervised Active Learning with Temporal Output Discrepancy, ICCV 2021.

Siyu Huang 33 Dec 06, 2022
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022