mlreef brand

HostCreateRunBuildManageScaleShareyour Machine Learning development in one platform

MLReef is an open source MLOps platform that helps you collaborate, reproduce and share you Machine Learning work.

    @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")
    @parameter(name='width', type='int', required=True, defaultValue=224, description="width")
    @parameter(name='epochs', type='int', required=True, defaultValue=5, description="epochs")
    @parameter(name='channels', type='int', required=False, defaultValue=3, description="channels")
    @parameter(name='use-pretrained', type='str', required=False, defaultValue='True', description="pretrained")
    @parameter(name='class-mode', type='str', required=True,defaultValue='sparse',description="classmode")
    @parameter(name='batch-size', type='int', required=False, defaultValue=32, description="batch-size")
    @parameter(name='validation-split', type='float', required=False, defaultValue=.2, description="validationsplit")
    @parameter(name='learning-rate', type='float', required=False, defaultValue=.0001, description="learning rate")
    @parameter(name='loss', type='str', required=False, defaultValue="sparse_categorical_crossentropy",
               description="loss")
    def init_params():
        pass
See the source file

Create or upload your scripts and add parameter decorators before publishing your code. The publishing will create a docker image so that your code is directly accessible within an interactive UI.

Pileline example

Each published module will be available as working code for you, your team or the entire MLReef community. Change its parameters and execute them in the built-in pipelines.

Experiment example

Track all jobs and experiments with GIT to reproduce data, code, hyperparameter values and environment settings used. Compare metrics and keep track of your progress.

Why MLReef?

Super-fast
Super-fast
Prototyping
In-depth
In-depth
Collaboration
Effortless
Effortless
Sharing
Granular
Granular
Reproducibility
Complete
Complete
Ownership
Free CPU / GPU
Free CPU / GPU
Computing

Supported and powered by

View source code & contribute

A structured and containerized approach to Machine Learning development

Fast-track your ML work with a contextually structured development environment using built-in piplines and containerized scripts. Use CI/CD principles to efficiently operationalize your ML workflow.

A structured and containerized approach to Machine Learning development
A structured and containerized approach to Machine Learning development

Collaborative, modular and reusable ML content

Access community repositories with data sets, models, data operations and visualizations and reuse them to jumpstart your ML projects!

a preview of some of the most popular ML projects
a preview of some of the most popular ML projects
a preview of some of the most popular ML projects
a preview of some of the most popular ML projects

A full set of tools for your ML work!

Instead of disconnected toolchains, use MLReef to run your pipelines, process your data, version and track your experiments and model, manage your teams and members, ... and much more..

Manage
Create
Build
Share
Select a feature from above to discover what it does

MLReef is a holistic and open source platform with an ever expanding set of features to meet your ML requirements.

for everyone
for everyone

MLReef supports open source for free. Get involved to perfect your craft and be part of something big.

for individuals
for individuals

Use MLReef for your own ML projects, from experimental to hosting your life´s work.

for teams
for teams

Business of all sizes use MLReef to securely and efficiently develop their ML projects.

Stay informed and don't miss out!

Keep informed about news and development progress in our newsletter!

No worries, we don't spam - just the most relevant news to keep up to date.

Thank you for signing up!

The latest from our blog