Import your docker image into CML to use your new ML Runtime.
ActiveState + Cloudera
At Cloudera and ActiveState, we strongly believe that open source security and innovation can coexist. This joint mission is why we have partnered to bring trusted, open-source ML Runtimes to Cloudera Machine Learning (CML). Unlike other ML platforms, which rely solely on insecure public sources like PyPI or Conda Forge for extensibility, Cloudera customers can now enjoy supply chain security across the entire open source Python ecosystem.
CML customers can be confident that their AI projects are secure from concept to deployment by leveraging the ActiveState Platform.
Running Spark 2 workloads? Python 3.7 is now officially end of life. Contact us to get extended support.
Cloudera Machine Learning, Securely Extended
With the ActiveState Platform, you can easily generate ML Runtimes to securely extend your Cloudera Machine Learning (CML) environment with the latest Data Science and Machine Learning tools and frameworks.
How it works
Step One
Sign up for a free ActiveState Platform account using your GitHub credentials, create a new Python project and select Linux as the operating system. Add the cloudera-ml-runtime package to your project to ensure your Python runtime is set up properly. Add any additional dependencies you want to bring to CML.
Step Two
Enable the Docker Image deployment option from the Platforms Modal on your project’s configuration tab. Your ML Runtime will be packaged in a CML-compatible docker image before import.
Step Three
Go to the downloads builds tab and follow the instructions to download your docker image and upload the image to your docker registry.
Step Four
Try the Cloudera ML and ActiveState integration today
Cloudera provides a cloud-based data science platform (similar to some Amazon AWS or Azure services) based on Jupyter Notebooks called CDP Machine Learning (CML). It includes popular Machine learning tools, data pipelines (such as spark, Apache Hadoop, etc), and scalable compute resources that can take data science projects from concept to production. All you need to bring are your big data datasets.
CML Customers need to bring their own open source Python Machine Learning framework and libraries to CML since only a minimal set is provided by CML as part of their default ML Runtime.
The ActiveState Platform lets you create a secure Python runtime for Microsoft Windows, Mac or Linux on demand for a wide range of popular Machine Learning and data science use cases, and then package it in a Docker container, ready for upload to CML. After creating your runtime via our Command Line Interface (CLI) or Web GUI, you can:
- Understand their full manifest of Python dependencies and transitive dependencies in our runtime environment
- Track and audit dependencies over time as they become outdated/vulnerable
- Obtain software attestations and SBOMs for your Python runtime environments via our API