0.3.2

Earlier release to get the PostgreSQL datasource out quicker.

To upgrade:

pip install --upgrade zenml

New Features

• sci-kit learn example.
• PostgreSQL Datasource added.

Bug Fixes + Refactor

• Slight change to telemetry utils -> Now opt-out also sends a signal.

0.3.1

This release is a big design change and refactor. It involves a significant change in the Configuration file structure, meaning this is a breaking upgrade. For those upgrading from 0.2.0, we ask to please delete their old pipelines dir and .zenml folders and start afresh with a zenml init.

If only working locally, this is as simple as:

cd zenml_enabled_repo
rm -rf pipelines/
rm -rf .zenml/

And then another init:

pip install --upgrade zenml
zenml init

New Features

• BatchInferencePipeline added for offline batch inference use-cases.
• Google Cloud Platform Bootstrapping Terraform script added for one-command bootstrapping of ZenML on GCP.
• DeployPipeline added to deploy a pipeline directly without having to create a TrainingPipeline.

Bug Fixes + Refactor

• Now you can run pipelines from within any subdirectory in the repo.
• Relaxed restriction on custom steps having sub-directories with their module.
• Relationship between Datasource and Data Step refined.
• Numerous small bugs and refinements to facilitate flexible API design.

Note: Future releases are also expected to be breaking. Until announced, please expect that upgrading ZenML versions may cause older-ZenML generated pipelines to behave unexpectedly.

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!

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.

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.

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.