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Releases: zenml-io/zenml

0.2.0

22 Jan 16:20
@htahir1 htahir1
0.2.0
23441ef
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0.2.0

This new release is a major one. Its the first to introduce our new integrations system, which is meant to be used to extend ZenML with various other ML/MLOps libraries easily. The first big advantage one gets is 🚀 PyTorch Support 🚀!

pip install --upgrade zenml

And to enable the PyTorch extension:

pip install zenml[pytorch]

New Features

  • Introduced integrations for ZenML with the extra_requires setuptools paradigm.
  • Added PyTorchTrainer support with easily extendable TorchBaseTrainer example.
  • Restructured trainer steps to be more intuitive to extend from Tensorflow and PyTorch. Now, we have a TrainerStep, followed by TFBaseTrainerStep and TorchBaseTrainerStep.
  • The input_fn of the TorchTrainer have implemented in a way that it can ingest from a tfrecords file. This marks one of the few projects out there
    that have native support for ingesting the TFRecords format into PyTorch directly.

Bug Fixes

  • Fixed an issue with Repository.get_zenml_dir() that caused any pipeline creates below root level to fail on creation.

Documentation Annoucement

The docs are almost complete! We are at 80% completion. Keep an eye out as we update with more details on how to use/extend ZenML and let us know via slack if there is something missing!

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0.1.5

19 Jan 20:45
@htahir1 htahir1
0.1.5
7b32b66
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0.1.5

New Features

  • Added Kubernetes Orchestrator to run pipelines on a kubernetes cluster.
  • Added timeseries support with StandardSequencerStep.
  • Added more [CLI groups] such as step, datasource and pipelines. E.g. zenml pipeline list gives list of pipelines in current repo.
  • Completed a significant portion of the Docs.
  • Refactored Step Interfaces for easier integrations into other libraries.
  • Added a GAN Example to showcase ImageDatasource.
  • Set up base for more Trainer Interfaces like PyTorch, scikit etc.
  • Added ability to see historical steps.

Bug Fixes

  • All files except YAML files picked up while parsing pipelines_dir, in reference to concerns raised in #13.

Upcoming changes

  • Next release will be a major one and will involve refactoring of design decisions that might cause backward incompatible changes to existing ZenML repos.
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0.1.4

08 Jan 13:37
@htahir1 htahir1
0.1.4
d91381e
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0.1.4

0.1.4

New Features

  • Ability to add a custom image to Dataflow ProcessingBackend.

Bug Fixes

  • Fixed requirements.txt and setup.py to enable local build.
  • Pip package should install without any requirement conflicts now.
  • Added custom docs made by Jupyter book in the docs/book folder.
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0.1.3

27 Dec 09:13
@hamzamaiot hamzamaiot
0.1.3
d4f0f3b
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0.1.3

New Features

  • Launch GCP preemptible VM instances to orchestrate pipelines with OrchestratorGCPBackend. See full example here.
  • Train using Google Cloud AI Platform with SingleGPUTrainingGCAIPBackend. See full example here
  • Use Dataflow for distributed preprocessing. See full example here.
  • Run pipelines locally with SQLite Metadata Store, local Artifact Store, and local Pipelines Directory.
  • Native Git integration: All steps are pinned with the Git SHA of the code when the pipelines it was used in is run. See details here.
  • All pipelines run are reproducible with a unique combination of the Metadata Store, Artifact Store and the Pipelines Directory.

Bug Fixes

  • Metadata Store and Artifact Store specified in pipelines disassociated from default .zenml_config file.
  • Fixed typo in default docker images constants.
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