Python Research Framework

Related tags

Machine Learning pyfra
Overview

pyfra

The Python Research Framework.

Design Philosophy

Research code has some of the fastest shifting requirements of any type of code. It's nearly impossible to plan ahead of time the proper abstractions, because it is exceedingly likely that in the course of the project what you originally thought was your main focus suddenly no longer is. Further, research code (especially in ML) often involves big and complicated pipelines, typically involving many different machines, which are either run by hand or using shell scripts that are far more complicated than any shell script ever should be.

Therefore, the objective of pyfra is to make it as fast and low-friction as possible to write research code involving complex pipelines over many machines. This entails making it as easy as possible to implement a research idea in reality, at the cost of fine-grained control and the long-term maintainability of the system. In other words, pyfra expects that code will either be rapidly obsoleted by newer code, or rewritten using some other framework once it is no longer a research project and requirements have settled down.

Pyfra is in its very early stages of development. The interface may change rapidly and without warning.

Features:

  • Spin up an internal webserver complete with a permissions system using only a few lines of code
  • Extremely elegant shell integration—run commands on any server seamlessly. All the best parts of bash and python combined
  • Automated remote environment setup, so you never have to worry about provisioning machines by hand again
  • (WIP) Tools for painless functional programming in python
  • (Coming soon) High level API for experiment management/scheduling and resource provisioning
  • (Coming soon) Idempotent resumable data pipelines with no cognitive overhead

Example code

from pyfra import *

loc = Remote()
rem = Remote("[email protected]")
nas = Remote("[email protected]")

@page("Run experiment", dropdowns={'server': ['local', 'remote']})
def run_experiment(server: str, config_file: str, some_numerical_value: int, some_checkbox: bool):
    r = loc if server == 'local' else rem

    r.sh("git clone https://github.com/EleutherAI/gpt-neox")
    
    # rsync as a function can do local-local, local-remote, and remote-remote
    rsync(config_file, r.file("gpt-neox/configs/my-config.yml"))
    rsync(nas.file('some_data_file'), r.file('gpt-neox/data/whatever'))
    
    return r.sh('cd gpt-neox; python3 main.py')

@page("Write example file and copy")
def example():
    rem.fwrite("testing.txt", "hello world")
    
    # tlocal files can be specified as just a string
    rsync(rem.file('testing123.txt'), 'test1.txt')
    rsync(rem.file('testing123.txt'), loc.file('test2.txt'))

    loc.sh('cat test1.txt')
    
    assert fread('test1.txt') == fread('test2.txt')
    
    # fread, fwrite, etc can take a `rem.file` instead of a string filename.
    # you can also use all *read and *write functions directly on the remote too.
    assert fread('test1.txt') == fread(rem.file('testing123.txt'))
    assert fread('test1.txt') == rem.fread('testing123.txt')

    # ls as a function returns a list of files (with absolute paths) on the selected remote.
    # the returned value is displayed on the webpage.
    return '\n'.join(rem.ls('/'))

@page("List files in some directory")
def list_files(directory):
    return sh(f"ls -la {directory | quote}")


# start internal webserver
webserver()

Installation

pip3 install git+https://github.com/EleutherAI/pyfra/

The version of PyPI is not up to date, do not use it.

Webserver screenshots

image image

Comments
  • Try to install sudo in _install

    Try to install sudo in _install

    Sudo is installed in setup.apt(), which is not run when python_version=None is set for an env. This PR tries to install the sudo package on _install which solves this issue.

    opened by kurumuz 1
  • Styling updates 2

    Styling updates 2

    This should fix some issues that were noticed recently.

    • increases the width of the content in the middle
    • all button icons are now the same (until we figure out better solution)
    • content that is overflowing should now be scrollable
    opened by jprester 0
  • Update styling

    Update styling

    I made some updates to styling for the admin dashboard pages.

    Stuff I did:

    • changed the styling to look like design mockup
    • moved ids to classes in css. Ids should be used for javascript selector
    • added some svg icons
    • made the UI somewhat responsive
    opened by jprester 0
  • docs: docs are empty

    docs: docs are empty

    Screenshot from the RTD page:

    image

    I recommend checking the raw output of the build on the RTD dashboard.

    Probably some library installation issue when running setup.

    opened by TomFrederik 0
  • Type annotations

    Type annotations

    Type annotations are a must-have for public facing library exports, as they allow users to infer a lot of information about calls/return values independent of documentation, as well as help with code completions.

    opened by hugbubby 0
Releases(v0.3.0)

  • v0.3.0(Dec 9, 2021)

    What's new

    • Envs now resume where they left off (and Remotes have an option for turning this behaviour on)
    • @stage caching added

    Breaking Changes

    • delegation promoted to full submodule and experiment removed
    • pyfra.functional removed
    • pyfra.web deprecated and moved to contrib
    • contrib revamp

    Full Changelog: https://github.com/EleutherAI/pyfra/compare/8e775df36ca8f2ae39b0b7add9c30eab446207b1...9616e835578f8ad04a6d9c3b405777fc4b7e0853

    Source code(tar.gz)
    Source code(zip)

  • v0.3.0rc6(Sep 1, 2021)

Owner
EleutherAI
EleutherAI
GitHub
easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.

easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.

Neuron AI 5 Jun 18, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
Simple structured learning framework for python

PyStruct PyStruct aims at being an easy-to-use structured learning and prediction library. Currently it implements only max-margin methods and a perce

pystruct 666 Jan 3, 2023
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 3, 2023
A Python implementation of GRAIL, a generic framework to learn compact time series representations.

GRAIL A Python implementation of GRAIL, a generic framework to learn compact time series representations. Requirements Python 3.6+ numpy scipy tslearn

null 3 Nov 24, 2021
Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

Christoph Mark 129 Dec 24, 2022
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Light Gradient Boosting Machine LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed a

Microsoft 14.5k Jan 7, 2023
A unified framework for machine learning with time series

Welcome to sktime A unified framework for machine learning with time series We provide specialized time series algorithms and scikit-learn compatible

The Alan Turing Institute 6k Jan 6, 2023
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear

null 23.3k Dec 31, 2022
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

Horovod Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make dis

Horovod 12.9k Jan 7, 2023
BigDL: Distributed Deep Learning Framework for Apache Spark

BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can w

null 4.1k Jan 9, 2023
A high performance and generic framework for distributed DNN training

BytePS BytePS is a high performance and general distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on eith

Bytedance Inc. 3.3k Dec 28, 2022
LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading

LiuAlgoTrader is a scalable, multi-process ML-ready framework for effective algorithmic trading. The framework simplify development, testing, deployment, analysis and training algo trading strategies. The framework automatically analyzes trading sessions, and the analysis may be used to train predictive models.

Amichay Oren 458 Dec 24, 2022
A Lucid Framework for Transparent and Interpretable Machine Learning Models.

Currently a Beta-Version lucidmode is an open-source, low-code and lightweight Python framework for transparent and interpretable machine learning mod

lucidmode 15 Aug 12, 2022
Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

Facebook Research 4.1k Dec 29, 2022
Automated modeling and machine learning framework FEDOT

This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks.

National Center for Cognitive Research of ITMO University 148 Jul 5, 2021
XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

null 92 Dec 14, 2022
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

Zalando Research 120 Dec 24, 2022
Xeasy-ml is a packaged machine learning framework.

xeasy-ml 1. What is xeasy-ml Xeasy-ml is a packaged machine learning framework. It allows a beginner to quickly build a machine learning model and use

null 9 Mar 14, 2022