MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine Learning work with thousands of other users.

Overview

The collaboration platform for Machine Learning

MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine Learning work with thousands of other users.


MLReef

MLReef is a ML/DL development platform containing four main sections:

  • Data-Management - Fully versioned data hosting and processing infrastructure
  • Publishing code repositories - Containerized and versioned script repositories for immutable use in data pipelines
  • Experiment Manager - Experiment tracking, environments and results
  • ML-Ops - Pipelines & Orchestration solution for ML/DL jobs (K8s / Cloud / bare-metal)


To find out more about how MLReef can streamline your Machine Learning Development Lifecycle visit our homepage

Data Management

  • Host your data using git / git LFS repositories.
    • Work concurrently on data
    • Fully versioned or LFS version control
    • Full view on data processing and visualization history
  • Connect your external storage to MLReef and use your data directly in pipelines
  • Data set management (access, history, pipelines)

Publishing Code

Adding only parameter annotations to your code...

# example of parameter annotation for a image crop function
 @data_processor(
        name="Resnet50",
        author="MLReef",
        command="resnet50",
        type="ALGORITHM",
        description="CNN Model resnet50",
        visibility="PUBLIC",
        input_type="IMAGE",
        output_type="MODEL"
    )
    @parameter(name='input-path', type='str', required=True, defaultValue='train', description="input path")
    @parameter(name='output-path', type='str', required=True, defaultValue='output', description="output path")
    @parameter(name='height', type='int', required=True, defaultValue=224, description="height of cropped images in px")
    @parameter(name='width', type='int', required=True, defaultValue=224, description="width of cropped images in px")
    def init_params():
        pass

...and publishing your scripts gets you the following:

  • Containerization of your scripts
    • Always working scripts including easy hyperparameter access in pipelines
    • Execution environment (including specific packages & versions)
    • Hyper-parameters
      • ArgParser for command line parameters with currently used values
      • Explicit parameters dictionary
      • Input validation and guides
  • Multiple containers based on version and code branches

Experiment Manager

  • Complete experiment setup log
    • Full source control info including non-committed local changes
    • Execution environment (including specific packages & versions)
    • Hyper-parameters
  • Full experiment output automatic capture
    • Artifacts storage and standard-output logs
    • Performance metrics on individual experiments and comparative graphs for all experiments
    • Detailed view on logs and outputs generated
  • Extensive platform support and integrations

ML-Ops

  • Concurrent computing pipelining
  • Governance and control
    • Access and user management
    • Single permission management
    • Resource management
  • Model management

MLReef Architecture

The MLReef ML components within the ML life cycle:

  • Data Storage components based currently on Git and Git LFS.
  • Model development based on working modules (published by the community or your team), data management, data processing / data visualization / experiment pipeline on hosted or on-prem and model management.
  • ML-Ops orchestration, experiment and workflow reproducibility, and scalability.

Why MLReef?

MLReef is our solution to a problem we share with countless other researchers and developers in the machine learning/deep learning universe: Training production-grade deep learning models is a tangled process. MLReef tracks and controls the process by associating code version control, research projects, performance metrics, and model provenance.

We designed MLReef on best data science practices combined with the knowleged gained from DevOps and a deep focus on collaboration.

  • Use it on a daily basis to boost collaboration and visibility in your team
  • Create a job in the cloud from any code repository with a click of a button
  • Automate processes and create pipelines to collect your experimentation logs, outputs, and data
  • Make you ML life cycle transparent by cataloging it all on the MLReef platform

Getting Started as a Developer

To start developing, continue with the developer guide

Canonical source

The canonical source of MLReef where all development takes place is hosted on gitLab.com/mlreef/mlreef.

License

MIT License (see the License for more information)

Documentation, Community and Support

More information in the official documentation and on Youtube.

For examples and use cases, check these use cases or start the tutorial after registring:

If you have any questions: post on our Slack channel, or tag your questions on stackoverflow with 'mlreef' tag.

For feature requests or bug reports, please use GitLab issues.

Additionally, you can always reach out to us via [email protected]

Contributing

Merge Requests are always welcomed ❤️ See more details in the MLReef Contribution Guidelines.

You might also like...
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models tabular data.

AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just

Graphsignal is a machine learning model monitoring platform.
Graphsignal is a machine learning model monitoring platform.

Graphsignal is a machine learning model monitoring platform. It helps ML engineers, MLOps teams and data scientists to quickly address issues with data and models as well as proactively analyze model performance and availability.

This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform.

Zillow-Houses This repository contains full machine learning pipeline of the Zillow Houses competition on Kaggle platform. Pipeline is consists of 10

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.

Python Extreme Learning Machine (ELM) Python Extreme Learning Machine (ELM) is a machine learning technique used for classification/regression tasks.

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

CD) in machine learning projectsImplementing continuous integration & delivery (CI/CD) in machine learning projects

CML with cloud compute This repository contains a sample project using CML with Terraform (via the cml-runner function) to launch an AWS EC2 instance

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.
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

Comments
  • Review

    Review

    I am writing a review after trying my hands on new MLReef project.

    The project went smoothly as I created and executed different data sets and paths.

    I was work on the data in the project for detecting masks on people's face and it shows promising outputs.

    I am looking forward work more and improve my skills on this.

    Thank you

    opened by christianokoye 3
  • MLReef review

    MLReef review

    The whole idea of collaborating machine learning algorithm and reusing them is cool. As a first time user, I did not understand the basics though, so I found it really difficult to experiment and learn.

    opened by Aparichit0000 1
  • Made my day

    Made my day

    A few days ago, I searched for a system to detect face masks to control the unmasked people from entering a shop. Just found this on MLReef! That's fantastic and I hope I will use this platform again for my next project.

    opened by soulium 0
  • Interesting Open Source Platform for ML

    Interesting Open Source Platform for ML

    MLReef was recommended by a friend of mine and no doubt this is one of the best open source platform to look around different project and learn from them. Moreover, I was able to write a test project with ease. MLReef will help me my other project as well.

    opened by PikaFromMars 0
Owner
MLReef
Your entire Machine Learning life cycle in one platform.
MLReef
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions

A library for debugging/inspecting machine learning classifiers and explaining their predictions

null 154 Dec 17, 2022
Evidently helps analyze machine learning models during validation or production monitoring

Evidently helps analyze machine learning models during validation or production monitoring. The tool generates interactive visual reports and JSON profiles from pandas DataFrame or csv files. Currently 6 reports are available.

Evidently AI 3.1k Jan 7, 2023
Python module for data science and machine learning users.

dsnk-distributions package dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calcu

Emmanuel ASIFIWE 1 Nov 23, 2021
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 5, 2023
Pytools is an open source library containing general machine learning and visualisation utilities for reuse

pytools is an open source library containing general machine learning and visualisation utilities for reuse, including: Basic tools for API developmen

BCG Gamma 26 Nov 6, 2022
Data Version Control or DVC is an open-source tool for data science and machine learning projects

Continuous Machine Learning project integration with DVC Data Version Control or DVC is an open-source tool for data science and machine learning proj

Azaria Gebremichael 2 Jul 29, 2021
SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.

SageMaker Python SDK SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the S

Amazon Web Services 1.8k Jan 1, 2023
Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

GENDIS GENetic DIscovery of Shapelets In the time series classification domain, shapelets are small subseries that are discriminative for a certain cl

IDLab Services 90 Oct 28, 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
Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validation, deployment and inference in production. Liminal provides a Domain Specific Language to build ML workflows on top of Apache Airflow.

The Apache Software Foundation 121 Dec 28, 2022