Releases

MLReef is currently in closed early access. Thing will change on a day to day basis!

This will guide you through the most significant changes in MLReef. We will try to keep it updated as much as we can.

Release log

r-01-04/2021

With the latest release we solved many bugs and issues during this beta phase and worked on general stability.

In addition, now you can:

  • Create multiple experiments at the time (although there is still no concurrent pipelines)
  • General stability and improved test coverage
  • Rework of the publishing process to be more light-weight and agile

    • Including publishing up to 10 versions of master and unlimited branches
  • Rework of the experiment tracking and experiment management section now including graphs and more functionality
  • Include the possibility to see the entry point script in all pipelines

r-02-01/2021

This release moves MLReef to beta stage, involving broad changes to the entire infrastructure and baseline.

Now, you can:

  • Create code projects (repositories)

    • Publish code repositories
    • Re-publish code repositories via the publishing wizard or via commit to master of your code repository master branch
    • Published code projects are stored in a docker registry and explorable / usable in the data pipelines directly
  • General stability and improved test coverage
  • Many smaller features, such as creating new files
  • Ad-hoc visualizations based on tabular data (insights/graphs)
  • Move to kubernetes cluster (for hosted version)
  • Development of "Nautilus", an offline and on-premise version of MLReef

r-01-08/2020

In this release we included:

  • Create ML projects
  • Use basic repository functions, such as creating branches, merging and reviewing MR.
  • Upload data into your ML project
  • Create basic experiments

    • You can only use one experiment at the time. Currently only ResNet 50 Model (images) and a dummy model are included.
    • Training log
    • Download experiment artifacts (such as the model binary and metric values)
    • Visualize experiment details with params and data source.
  • Create basic data processing pipeline (DataOps)

    • You can create data pipelines using 3 available data operations for images
    • Concatenate data processors in one pipeline (order is kept)
    • Creates Dataset (currently hold as a separate branch - you can merge it into your data repository)
  • Create basic data visualization using currently only one operator (t-SNE)
  • Infrastructure supports GPU and CPU execution