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Releases: zenml-io/zenml

0.7.0

28 Mar 14:44
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With ZenML 0.7.0, a lot has been revamped under the hood about how things are stored. Importantly what this means is that ZenML now has system-wide profiles that let you register stacks to share across several of your projects! If you still want to manage your stacks for each project folder individually, profiles still let you do that as well.

Most projects of any complexity will require passwords or tokens to access data and infrastructure, and for this purpose ZenML 0.7.0 introduces the Secrets Manager stack component to seamlessly pass around these values to your steps. Our AWS integration also allows you to use AWS Secrets Manager as a backend to handle all your secret persistence needs.

Finally, in addition to the new AzureML and Sagemaker Step Operators that version 0.6.3 brought, this release also adds the ability to run individual steps on GCP's Vertex AI.

Beyond this, some smaller bugfixes and documentation changes combine to make ZenML 0.7.0 a more pleasant user experience.

What's Changed

New Contributors

Full Changelog: 0.6.3...0.7.0rc

0.6.3

14 Mar 14:20
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0.6.3

ZenInTheClouds_3

With ZenML 0.6.3, you can now run your ZenML steps on Sagemaker and AzureML! It's normal to have certain steps that require specific hardware on which to run model training, for example, and this latest release gives you the power to switch out hardware for individual steps to support this.

We added a new Tensorboard visualisation that you can make use of when using our Kubeflow Pipelines integration. We handle the background processes needed to spin up this interactive web interface that you can use to visualise your model's performance over time.

Behind the scenes we gave our integration testing suite a massive upgrade, fixed a number of smaller bugs and made documentation updates. For a detailed look at what's changed, see below:

What's Changed

New Contributors

0.6.2

23 Feb 13:23
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ZenML0-6-2

ZenML 0.6.2 brings you the ability to serve models using MLflow deployments as well as an updated CLI interface! For a real continuous deployment cycle, we know that ZenML pipelines should be able to handle everything — from pre-processing to training to serving to monitoring and then potentially re-training and re-serving. The interfaces we created in this release are the foundation on which all of this will build.

We also improved how you interact with ZenML through the CLI. Everything looks so much smarter and readable now with the popular rich library integrated into our dependencies.

Smaller changes that you'll notice include updates to our cloud integrations and bug fixes for Windows users. For a detailed look at what's changed, see below.

What's Changed

0.6.1

07 Feb 14:41
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0.6.1

ZenML 0.6.1 is out and it's all about the cloud ☁️! We have improved AWS integration and a brand-new Azure integration! Run your pipelines on AWS and Azure now and let us know how it went on our Slack. To learn more, check out the new documentation page we just included to guide you in deploying your pipelines to AWS, GCP and/or Azure.

Smaller changes that you'll notice include much-awaited updates and fixes, including the first iterations of scheduling pipelines and tracking more reproducibility-relevant data in the metadata store.

For a detailed look at what's changed, see below.

What's Changed

Full Changelog: 0.6.0...0.6.1

0.6.0

26 Jan 15:39
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ZenML 0.6.0 is out now. We've made some big changes under the hood, but our biggest public-facing addition is our new integration to support all your data logging needs: whylogs. Our core architecture was thoroughly reworked and is now in a much better place to support our ongoing development needs.

Smaller changes that you'll notice include extensive documentation additions, updates and fixes. For a detailed look at what's changed, see below.

📊 Whylogs logging

Whylogs is an open source library that analyzes your data and creates statistical summaries called whylogs profiles. Whylogs profiles can be visualized locally or uploaded to the WhyLabs platform where more comprehensive analysis can be carried out.

ZenML integrates seamlessly with Whylogs and WhyLabs. This example shows how easy it is to enhance steps in an existing ML pipeline with Whylogs profiling features. Changes to the user code are minimal while ZenML takes care of all aspects related to Whylogs session initialization, profile serialization, versioning and persistence and even uploading generated profiles to Whylabs.

Example of the visualizations you can make from Whylogs profiles

With our WhylogsVisualizer, as described in the associated example notes, you can visualize Whylogs profiles generated as part of a pipeline.

⛩ New Core Architecture

We implemented some fundamental changes to the core architecture to solve some of the issues we previously had and provide a more extensible design to support quicker implementations of different stack components and integrations. The main change was to refactor the Repository, Stack and StackComponent architectures. These changes had a pretty wide impact so involved changes in many files throughout the codebase, especially in the CLI which makes calls to all these pieces.

We've already seen how it helps us move faster in building integrations and we hope it helps making contributions as pain-free as possible!

🗒 Documentation and Example Updates

As the codebase and functionality of ZenML grows, we always want to make sure our documentation is clear, up-to-date and easy to use. We made a number of changes in this release that will improve your experience in this regard:

  • added a number of new explainers on key ZenML concepts and how to use them in your code, notably on how to create a custom materializer and how to fetch historic pipeline runs using the StepContext
  • fixed a number of typos and broken links
  • added versioning to our API documentation so you can choose to view the reference appropriate to the version that you're using. We now use mkdocs for this so you'll notice a slight visual refresh as well.
  • added new examples highlighting specific use cases and integrations:
    • how to create a custom materializer (example)
    • how to fetch historical pipeline runs (example)
    • how to use standard interfaces for common ML patterns (example)
    • whylogs logging (example)

➕ Other updates, additions and fixes

As with most releases, we made a number of small but significant fixes and additions. The most import of these were that you can now access the metadata store via the step context. This enables a number of new possible workflows and pipeline patterns and we're really excited to have this in the release.

We added in a markdown parser for the zenml example info … command, so now when you want to use our CLI to learn more about specific examples you will see beautifully parsed text and not markdown markup.

We improved a few of our error messages, too, like for when the return type of a step function doesn’t match the expected type, or if step is called twice. We hope this makes ZenML just that little bit easier to use.

New Contributors

[Full Changelog: https://github.com/zenml-io/zenml/compare/0.5.7...0.6.0]

0.5.7

13 Jan 14:00
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0.5.7

ZenML 0.5.7 is here 💯 and it brings not one, but 🔥TWO🔥 brand new integrations 🚀! ZenML now support MLFlow for tracking pipelines as experiments and Evidently for detecting drift in your ML pipelines in production!

New Features

  • Introducing the MLFLow Tracking Integration, a first step towards our complete MLFlow Integration as described in the #115 poll. Full example found here.

Mlflow

evidently

Bugfixes

  • Prevent KFP install timeouts during stack up by @stefannica in #299
  • Prevent naming parameters same name as inputs/outputs to prevent kwargs-errors by @bcdurak in #300

What's Changed

New Contributors

0.5.6

23 Dec 17:07
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This release fixes some known bugs from previous releases and especially 0.5.5. Therefore, upgrading to 0.5.6 is a breaking change. You must do the following in order to proceed with this version:

cd zenml_enabled_repo
rm -rf .zen/

And then start again with ZenML init:

pip install --upgrade zenml
zenml init

New Features

  • Added zenml example run [EXAMPLE_RUN_NAME] feature: The ability to run an example with one command. In order to run this, do zenml example pull first and see all examples available by running zenml example list.
  • Added ability to specify a .dockerignore file before running pipelines on Kubeflow.
  • Kubeflow Orchestrator is now leaner and faster.
  • Added the describe command group to the CLI for groups stack, orchestrator, artifact-store, and metadata-store. E.g. zenml stack describe

Bug fixes and minor improvements

  • Adding StepContext to a branch now invalidates caching by default. Disable explicitly with enable_cache=True.
  • Docs updated to reflect minor changes in CLI commands.
  • CLI list commands now mentions active component. Try zenml stack list to check this out.
  • zenml version now has cooler art.

What's Changed

Full Changelog: 0.5.5...0.5.6

0.5.5

14 Dec 01:39
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ZenML 0.5.5 is jam-packed with new features to take your ML pipelines to the next level. Our three biggest new features: Kubeflow Pipelines, CLI support for our integrations and Standard Interfaces. That’s right, Standard Interfaces are back!

What's Changed

New Contributors

Full Changelog: 0.5.4...0.5.5

0.5.4

05 Dec 22:02
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0.5.4 adds a lineage tracking integration to visualize lineage of pipeline runs! It also includes numerous bug fixes and optimizations.

What's Changed

New Contributors

Full Changelog: 0.5.3...0.5.4

0.5.3

23 Nov 12:23
d4cf625
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Version 0.5.3 adds statistics visualizations, greatly improved speed for CLI commands as well as lots of small improvements to the pipeline and step interface.

What's Changed

Full Changelog: 0.5.2...0.5.3