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 Note the 1st release, Hasktorch 0.1, on hackage is outdated and should not be used.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/
A python module for scientific analysis of 3D objects based on VTK and Numpy

A lightweight and powerful python module for scientific analysis and visualization of 3d objects.

Marco Musy 1.5k Jan 06, 2023
Fight Recognition from Still Images in the Wild @ WACVW2022, Real-world Surveillance Workshop

Fight Detection from Still Images in the Wild Detecting fights from still images is an important task required to limit the distribution of social med

Şeymanur Aktı 10 Nov 09, 2022
Implementation of the Swin Transformer in PyTorch.

Swin Transformer - PyTorch Implementation of the Swin Transformer architecture. This paper presents a new vision Transformer, called Swin Transformer,

597 Jan 03, 2023
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia

25.7k Jan 09, 2023
Repository for publicly available deep learning models developed in Rosetta community

trRosetta2 This package contains deep learning models and related scripts used by Baker group in CASP14. Installation Linux/Mac clone the package git

81 Dec 29, 2022
Informal Persian Universal Dependency Treebank

Informal Persian Universal Dependency Treebank (iPerUDT) Informal Persian Universal Dependency Treebank, consisting of 3000 sentences and 54,904 token

Roya Kabiri 0 Jan 05, 2022
🐦 Opytimizer is a Python library consisting of meta-heuristic optimization techniques.

Opytimizer: A Nature-Inspired Python Optimizer Welcome to Opytimizer. Did you ever reach a bottleneck in your computational experiments? Are you tired

Gustavo Rosa 546 Dec 31, 2022
This repository accompanies the ACM TOIS paper "What can I cook with these ingredients?" - Understanding cooking-related information needs in conversational search

In this repository you find data that has been gathered when conducting in-situ experiments in a conversational cooking setting. These data include tr

6 Sep 22, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
Code for "LASR: Learning Articulated Shape Reconstruction from a Monocular Video". CVPR 2021.

LASR Installation Build with conda conda env create -f lasr.yml conda activate lasr # install softras cd third_party/softras; python setup.py install;

Google 157 Dec 26, 2022
Python library for tracking human heads with FLAME (a 3D morphable head model)

Video Head Tracker 3D tracking library for human heads based on FLAME (a 3D morphable head model). The tracking algorithm is inspired by face2face. It

61 Dec 25, 2022
OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Documentation: https://mmsegmentation.readthedocs.io/ English | 简体中文 Introduction MMSegmentation is an open source semantic segmentation toolbox based

OpenMMLab 5k Dec 31, 2022
Least Square Calibration for Peer Reviews

Least Square Calibration for Peer Reviews Requirements gurobipy - for solving convex programs GPy - for Bayesian baseline numpy pandas To generate p

Sigma <a href=[email protected]"> 1 Nov 01, 2021
AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

AMTML-KD: Adaptive Multi-teacher Multi-level Knowledge Distillation

Frank Liu 26 Oct 13, 2022
Rename Images with Auto Generated Neural Image Captions

Recaption Images with Generated Neural Image Caption Example Usage: Commandline: Recaption all images from folder /home/feng/Downloads/images to folde

feng wang 3 May 01, 2022
[ACM MM2021] MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification

Introduction This project is developed based on FastReID, which is an ongoing ReID project. Projects BUC In projects/BUC, we implement AAAI 2019 paper

WuYiming 7 Apr 13, 2022
PyTorch implementation for 3D human pose estimation

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach This repository is the PyTorch implementation for the network presented in:

Xingyi Zhou 579 Dec 22, 2022
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
A novel Engagement Detection with Multi-Task Training (ED-MTT) system

A novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes MSE and triplet loss together to determine the engagement level of students in an e-learning environment.

Onur Çopur 12 Nov 11, 2022
An implementation of the efficient attention module.

Efficient Attention An implementation of the efficient attention module. Description Efficient attention is an attention mechanism that substantially

Shen Zhuoran 194 Dec 15, 2022