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.
IMPORTANT: We are no longer supporting and updating this repository. We are still actively working on this project but on our main repo at GitLab.
MLReef is a ML/DL development platform containing four main sections:
To find out more about how MLReef can streamline your Machine Learning Development Lifecycle visit our homepage
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:
The MLReef ML components within the ML life cycle:
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.
To start developing, continue with the developer guide
The canonical source of MLReef where all development takes place is hosted on gitLab.com/mlreef/mlreef.
MIT License (see the License for more information)
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]
Merge Requests are always welcomed ❤️ See more details in the MLReef Contribution Guidelines.