{
    "info": {
        "author": "Evgeny Semyonov",
        "author_email": "lightsanweb@gmail.com",
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        "classifiers": [
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "\n<div align=\"center\">\n\n![MLComp logo](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/pics/MLcomp.png)\n\n**MLComp is a distributed DAG (Directed acyclic graph) framework for machine learning with UI**\n\n[![Pipi version](https://img.shields.io/pypi/v/mlcomp.svg)](https://pypi.org/project/mlcomp/)\n[![Docs](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fmlcomp%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://catalyst-team.github.io/mlcomp/index.html)\n[![PyPI Status](https://pepy.tech/badge/mlcomp)](https://pepy.tech/project/mlcomp)\n[![Github contributors](https://img.shields.io/github/contributors/catalyst-team/mlcomp.svg?logo=github&logoColor=white)](https://github.com/catalyst-team/mlcomp/graphs/contributors)\n[![License](https://img.shields.io/github/license/catalyst-team/mlcomp.svg)](LICENSE)\n\n[![Build Status](https://travis-ci.com/catalyst-team/mlcomp.svg?branch=master)](https://travis-ci.com/catalyst-team/mlcomp) \n[![Telegram](./docs/imgs/telegram.svg)](https://t.me/catalyst_team)\n[![Gitter](https://badges.gitter.im/catalyst-team/community.svg)](https://gitter.im/catalyst-team/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n[![Slack](./docs/imgs/slack.svg)](https://opendatascience.slack.com/messages/CGK4KQBHD)\n[![Donate](https://raw.githubusercontent.com/catalyst-team/catalyst-pics/master/third_party_pics/patreon.png)](https://www.patreon.com/catalyst_team)\n\n</div>\n\nThe goal of MLComp is to provide tools for training, inferencing, creating complex pipelines\n(especially for computer vision) in a rapid, well manageable way.\n\nMLComp is compatible with: Python 3.6+, Unix operation system.\n\n**Features**\n\n- Amazing UI\n- [Catalyst](https://github.com/catalyst-team/catalyst) support\n- Distributed training\n- Supervisor that controls computational resources\n- Synchronization of both code and data\n- Resource monitoring\n- Full functionality of the pause and continue on UI\n- Auto control of the requirements\n- Code dumping (with syntax highlight on UI)\n- [Kaggle](https://www.kaggle.com/) integration\n- Hierarchical logging\n- Grid search\n- Experiments comparison\n- Customizing layouts\n\n**Contents**\n\n- [Screenshots](#screenshots)\n\n- [Installation](#installation)\n\n- [UI](#ui)\n\n- [Usage](#usage)\n\n- [Docs and examples](#docs-and-examples)\n\n- [Environment variables](#environment-variables)\n\n# Screenshots\n\nDags\n\n![dags](docs/imgs/dags.png)\n\nComputers\n\n![computers](docs/imgs/computers.png)\n\nReports\n\n![reports](docs/imgs/reports.png)\n\nCode\n\n![code](docs/imgs/code.png)\n\nGraph\n\n![graph](docs/imgs/graph.png)\n\n[More screenshots](docs/screenshots.md)\n\n# Installation\n\n1. Install MLComp package\n\n    ```bash\n    pip install mlcomp\n    mlcomp init\n    ```\n\n2. Setup your environment. Please consider [Environment variables](#environment-variables) section\n\n3. Run db, redis, mlcomp-server, mlcomp-workers:\n\n    **Variant 1: minimal (if you have 1 computer)**\n\n    Run all necessary (mlcomp-server, mlcomp-workers, redis-server), it uses SQLITE:\n\n    ```bash\n    mlcomp-server start\n    ```\n\n    **Variant 2: full**\n\n    a. Change your [Environment variables](#environment-variables) to use PostgreSql\n\n    b. Install rsync on each work computer\n\n    ```.env\n    sudo apt-get install rsync\n    ```\n\n    Ensure that every computer is available by SSH protocol with IP/PORT you specified\n     in the [Environment variables](#environment-variables) file.\n\n     rsync will perform the following commands:\n\n     to upload\n     ```bash\n     rsync -vhru -e \"ssh -p {target.port} -o StrictHostKeyChecking=no\" \\\n     {folder}/ {target.user}@{target.ip}:{folder}/ --perms  --chmod=777\n     ```\n     to download\n\n     ```.env\n     rsync -vhru -e \"ssh -p {source.port} -o StrictHostKeyChecking=no\" \\\n     {source.user}@{source.ip}:{folder}/ {folder}/ --perms  --chmod=777\n     ```\n\n    c. Install [apex](https://github.com/NVIDIA/apex#quick-start) for distributed learning\n\n    d. To Run postgresql, redis-server, mlcomp-server, execute on your server-computer:\n\n     ```bash\n    cd ~/mlcomp/configs/\n    docker-compose -f server-compose.yml up -d\n    ```\n\n    e. Run on each worker-computer:\n\n    ```bash\n    mlcomp-worker start\n    ```\n\n # UI\n\nWeb site is available at http://{WEB_HOST}:{WEB_PORT}\n\nBy default, it is http://localhost:4201\n\nThe front is built with AngularJS.\n\nIn case you desire to change it, please consider [front's Readme page](mlcomp/server/front/README.md)\n\n # Usage\n\nRun\n ```bash\nmlcomp dag PATH_TO_CONFIG.yml\n```\n\nThis command copies files of the directory to the database.\n\nThen, the server schedules the DAG considering free resources. \n\nFor more information, please consider [Docs](https://catalyst-team.github.io/mlcomp/usage.html)\n\n# Docs and examples\n\nAPI documentation and an overview of the library can be\n found here [![Docs](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fmlcomp%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://catalyst-team.github.io/mlcomp/index.html)\n\nYou can find advanced tutorials and MLComp best practices in the [examples](examples/) folder of the repository.\n\n# Environment variables\n\nThe single file to setup your computer environment is located at ~/mlcomp/configs/.env\n\n- ROOT_FOLDER - folder to save MLComp files: configs, db, tasks, etc.\n- TOKEN - site security token. Please change it to any string\n- DB_TYPE. Either SQLITE or POSTGRESQL\n- POSTGRES_DB. PostgreSql db name\n- POSTGRES_USER. PostgreSql user\n- POSTGRES_PASSWORD. PostgreSql password\n- POSTGRES_HOST. PostgreSql host\n- PGDATA. PostgreSql db files location\n- REDIS_HOST. Redis host\n- REDIS_PORT. Redis port\n- REDIS_PASSWORD. Redis password\n- WEB_HOST. MLComp site host. 0.0.0.0 means it is available from everywhere\n- WEB_PORT. MLComp site port\n- CONSOLE_LOG_LEVEL. log level for output to the console\n- DB_LOG_LEVEL. log level for output to the database\n- IP. Ip of a work computer. The work computer must be accessible from other work computers by these IP/PORT\n- PORT. Port of a work computer. 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