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 Note the 1st release, Hasktorch 0.1, on hackage is outdated and should not be used.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/
⚡ Fast • 🪶 Lightweight • 0️⃣ Dependency • 🔌 Pluggable • 😈 TLS interception • 🔒 DNS-over-HTTPS • 🔥 Poor Man's VPN • ⏪ Reverse & ⏩ Forward • 👮🏿 "Proxy Server" framework • 🌐 "Web Server" framework • ➵ ➶ ➷ ➠ "PubSub" framework • 👷 "Work" acceptor & executor framework

Table of Contents Features Install Using PIP Stable version Development version Using Docker Stable version Development version Using HomeBrew Stable

Abhinav Singh 2.2k Jan 08, 2023
CLIP (Contrastive Language–Image Pre-training) for Italian

Italian CLIP CLIP (Radford et al., 2021) is a multimodal model that can learn to represent images and text jointly in the same space. In this project,

Italian CLIP 114 Dec 29, 2022
Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation".

PixelTransformer Code release for the ICML 2021 paper "PixelTransformer: Sample Conditioned Signal Generation". Project Page Installation Please insta

Shubham Tulsiani 24 Dec 17, 2022
GPU-accelerated Image Processing library using OpenCL

pyclesperanto pyclesperanto is a python package for clEsperanto - a multi-language framework for GPU-accelerated image processing. clEsperanto uses Op

17 Dec 25, 2022
An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

0 May 06, 2022
Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, L

3 Dec 02, 2022
PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.

D2C: Diffuison-Decoding Models for Few-shot Conditional Generation Project | Paper PyTorch implementation of D2C: Diffuison-Decoding Models for Few-sh

Jiaming Song 90 Dec 27, 2022
Deep Learning for Human Part Discovery in Images - Chainer implementation

Deep Learning for Human Part Discovery in Images - Chainer implementation NOTE: This is not official implementation. Original paper is Deep Learning f

Shintaro Shiba 63 Sep 25, 2022
Gans-in-action - Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

GANs in Action by Jakub Langr and Vladimir Bok List of available code: Chapter 2: Colab, Notebook Chapter 3: Notebook Chapter 4: Notebook Chapter 6: C

GANs in Action 914 Dec 21, 2022
Deep Learning for Computer Vision final project

Deep Learning for Computer Vision final project

grassking100 1 Nov 30, 2021
Rank1 Conversation Emotion Detection Task

Rank1-Conversation_Emotion_Detection_Task accuracy macro-f1 recall 0.826 0.7544 0.719 基于预训练模型和时序预测模型的对话情感探测任务 1 摘要 针对对话情感探测任务，本文将其分为文本分类和时间序列预测两个子任务，分

Yuchen Han 2 Nov 28, 2021
PyTorch implementation of Constrained Policy Optimization

PyTorch implementation of Constrained Policy Optimization (CPO) This repository has a simple to understand and use implementation of CPO in PyTorch. A

Sapana Chaudhary 25 Dec 08, 2022
Motion and Shape Capture from Sparse Markers

MoSh++ This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface Sh

Nima Ghorbani 135 Dec 23, 2022
A New Approach to Overgenerating and Scoring Abstractive Summaries

We provide the source code for the paper "A New Approach to Overgenerating and Scoring Abstractive Summaries" accepted at NAACL'21. If you find the code useful, please cite the following paper.

Kaiqiang Song 4 Apr 03, 2022
(EI 2022) Controllable Confidence-Based Image Denoising

Image Denoising with Control over Deep Network Hallucination Paper and arXiv preprint -- Our frequency-domain insights derive from SFM and the concept

Images and Visual Representation Laboratory (IVRL) at EPFL 5 Dec 18, 2022
Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

FedLearn-algo Installation Development Environment Checklist python3 (3.6 or 3.7) is required. To configure and check the development environment is c

89 Nov 14, 2022
Awesome-google-colab - Google Colaboratory Notebooks and Repositories

Unofficial Google Colaboratory Notebook and Repository Gallery Please contact me to take over and revamp this repo (it gets around 30k views and 200k

Derek Snow 1.2k Jan 03, 2023
Code Repository for The Kaggle Book, Published by Packt Publishing

The Kaggle Book Data analysis and machine learning for competitive data science Code Repository for The Kaggle Book, Published by Packt Publishing "Lu

Packt 1.6k Jan 07, 2023
Code To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment.

COLIEE 2021 - task 2: Legal Case Entailment This repository contains the code to reproduce NeuralMind's submissions to COLIEE 2021 presented in the pa

NeuralMind 13 Dec 16, 2022
Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

longlongman 170 Dec 01, 2022