Collaborative Machine Learning development

    @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.

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.

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.

Newcomer

MLReef gives you an entry into Machine Learning. Dive into ML on a no-code basis or explore the depths on a source-code level.

Experienced

Share your ML work with your team and the community through a structured, non-intrusive and open development environment.

Enterprise

MLReef operationalizes and lets you scales your AI/ML activities on the cloud or on-premise. Gain full insights and control over all processes with one unified tool.

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.

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 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