Torchrecipes provides a set of reproduci-able, re-usable, ready-to-run RECIPES for training different types of models, across multiple domains, on PyTorch Lightning.

Overview

License

torchrecipes

This library is currently under heavy development - if you have suggestions on the API or use-cases you'd like to be covered, please open an github issue or reach out. We'd love to hear about how you're using torchrecipes.

torchrecipes is a prototype is built on top of PyTORCH and provides a set of reproduci-able, re-usable, ready-to-run RECIPES for training different types of models, across multiple domains, on PyTorch Lightning.

It aims to provide reproduci-able "applications" built on top of PyTorch with good performance and easy reproduciability. Because this project builds on the pytorch ecosystem and requires significant investment, we'd love to hear from and work with early adopters to shape the design. Please reach out on the issue tracker if you're interested in using this for your project.

Why torchrecipes?

The primary goal of the torchrecipes is to 10x ML development by providing standard blueprints to easily train production-ready ML models across environemnts (from local development to cluster deployment).

Requirements

PyTorch Recipes (torchrecipes):

  • python3 (3.8+)
  • torch

Running

The easiest way to run torchrecipes is to use torchx. You can install it directly (if not already included as part of our requirements.txt) with:

pip install torchx

Then go to torchrecipes/launcher/ and create a file torchx_app.py:

specs.AppDef: return specs.AppDef( name="run", roles=[ specs.Role( name="run", image=image, entrypoint="python", args=[*image_classification_args, *job_args], env={ "CONFIG_MODULE": "torchrecipes.vision.image_classification.conf", "MODE": "prod", "HYDRA_FULL_ERROR": "1", } ) ], ) ">
# 'torchrecipes/launcher/torchx_app.py'

import torchx.specs as specs

image_classification_args = [
    "-m", "run",
    "--config-name",
    "train_app",
    "--config-path",
    "torchrecipes/vision/image_classification/conf",
]

def torchx_app(image: str = "run.py:latest", *job_args: str) -> specs.AppDef:
    return specs.AppDef(
        name="run",
        roles=[
            specs.Role(
                name="run",
                image=image,
                entrypoint="python",
                args=[*image_classification_args, *job_args],
                env={
                    "CONFIG_MODULE": "torchrecipes.vision.image_classification.conf",
                    "MODE": "prod",
                    "HYDRA_FULL_ERROR": "1",
                }
            )
        ],
    )

This app defines the entrypoint, args and image for launching.

Now that we have created a torchx app, we are (almost) ready for launching a job!

Firstly, create a symlink for launcher/run.py at the top level of the repo:

ln -s torchrecipes/launcher/run.py ./run.py

Then we are ready-to-go! Simply launch the image_classification recipe with the following command:

torchx run --scheduler local_cwd torchrecipes/launcher/torchx_app.py:torchx_app trainer.fast_dev_run=True trainer.checkpoint_callback=False +tb_save_dir=/tmp/

Release

# install torchrecipes
pip install torchrecipes

Contributing

We welcome PRs! See the CONTRIBUTING file.

License

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

Owner
Meta Research
Meta Research
NL. The natural language programming language.

NL A Natural-Language programming language. Built using Codex. A few examples are inside the nl_projects directory. How it works Write any code in pur

2 Jan 17, 2022
An open-source NLP research library, built on PyTorch.

An Apache 2.0 NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. Quic

AI2 11.4k Jan 01, 2023
Count the frequency of letters or words in a text file and show a graph.

Word Counter By EBUS Coding Club Count the frequency of letters or words in a text file and show a graph. Requirements Python 3.9 or higher matplotlib

EBUS Coding Club 0 Apr 09, 2022
Official implementation of MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis

MLP Singer Official implementation of MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis. Audio samples are available on our demo page.

Neosapience 103 Dec 23, 2022
Conditional probing: measuring usable information beyond a baseline

Conditional probing: measuring usable information beyond a baseline

John Hewitt 20 Dec 15, 2022
Minimal GUI for accessing the Watson Text to Speech service.

Description Minimal graphical application for accessing the Watson Text to Speech service. Requirements Python 3 plus all dependencies listed in requi

Moritz Maxeiner 1 Oct 22, 2021
source code for paper: WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach.

WhiteningBERT Source code and data for paper WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach. Preparation git clone https://github.com

49 Dec 17, 2022
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
Multilingual word vectors in 78 languages

Aligning the fastText vectors of 78 languages Facebook recently open-sourced word vectors in 89 languages. However these vectors are monolingual; mean

Babylon Health 1.2k Dec 17, 2022
ChatterBot is a machine learning, conversational dialog engine for creating chat bots

ChatterBot ChatterBot is a machine-learning based conversational dialog engine build in Python which makes it possible to generate responses based on

Gunther Cox 12.8k Jan 03, 2023
Twitter-Sentiment-Analysis - Analysis of twitter posts' positive and negative score.

Twitter-Sentiment-Analysis The hands-on project is in Python 3 Programming class offered by University of Michigan via Coursera. The task is to build

Eszter Pai 1 Jan 03, 2022
Speach Recognitions

easy_meeting Добро пожаловать в интерфейс сервиса автопротоколирования совещаний Easy Meeting. Website - http://cf5c-62-192-251-83.ngrok.io/ Принципиа

Maksim 3 Feb 18, 2022
Healthsea is a spaCy pipeline for analyzing user reviews of supplementary products for their effects on health.

Welcome to Healthsea ✨ Create better access to health with spaCy. Healthsea is a pipeline for analyzing user reviews to supplement products by extract

Explosion 75 Dec 19, 2022
simpleT5 is built on top of PyTorch-lightning⚡️ and Transformers🤗 that lets you quickly train your T5 models.

Quickly train T5 models in just 3 lines of code + ONNX support simpleT5 is built on top of PyTorch-lightning ⚡️ and Transformers 🤗 that lets you quic

Shivanand Roy 220 Dec 30, 2022
(ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.

BERT Convolutions Code for the paper Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models. Contains expe

mlpc-ucsd 21 Jul 18, 2022
Mesh TensorFlow: Model Parallelism Made Easier

Mesh TensorFlow - Model Parallelism Made Easier Introduction Mesh TensorFlow (mtf) is a language for distributed deep learning, capable of specifying

1.3k Dec 26, 2022
🐍 A hyper-fast Python module for reading/writing JSON data using Rust's serde-json.

A hyper-fast, safe Python module to read and write JSON data. Works as a drop-in replacement for Python's built-in json module. This is alpha software

Matthias 479 Jan 01, 2023
Code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".

This repository contains the code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".

Chenhe Dong 28 Nov 10, 2022
My implementation of Safaricom Machine Learning Codility test. The code has bugs, logical I guess I made errors and any correction will be appreciated.

Safaricom_Codility Machine Learning 2022 The test entails two questions. Question 1 was on Machine Learning. Question 2 was on SQL I ran out of time.

Lawrence M. 1 Mar 03, 2022
Code for paper: An Effective, Robust and Fairness-awareHate Speech Detection Framework

BiQQLSTM_HS Code and data for paper: Title: An Effective, Robust and Fairness-awareHate Speech Detection Framework. Authors: Guanyi Mou and Kyumin Lee

Guanyi Mou 2 Dec 27, 2022