With libraries such as Tensorflow, PyTorch, scikit-learn, and MXNet being released it is easier than ever to start a deep learning project. Unfortunately, it is still difficult to manage scaling and reproduction of training for these projects. Mourad Mourafiq built Polyaxon on top of Kubernetes to address this shortcoming. In this episode he shares his reasons for starting the project, how it works, and how you can start using it today.
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- Your host as usual is Tobias Macey and today I’m interviewing Mourad Mourafiq about Polyaxon, a platform for building, training and monitoring large scale deep learning applications.
- How did you get introduced to Python?
- Can you give a quick overview of what Polyaxon is and your motivation for creating it?
- What is a typical workflow for building and testing a deep learning application?
- How is Polyaxon implemented?
- How has the internal architecture evolved since you first started working on it?
- What is unique to deep learning workloads that makes it necessary to have a dedicated tool for deploying them?
- What does Polyaxon add on top of the existing functionality in Kubernetes?
- It can be difficult to build a docker container that holds all of the necessary components for a complex application. What are some tips or best practices for creating containers to be used with Polyaxon?
- What are the relative tradeoffs of the various deep learning frameworks that you support?
- For someone who is getting started with Polyaxon what does the workflow look like?
- What is involved in migrating existing projects to run on Polyaxon?
- What have been the most challenging aspects of building Polyaxon?
- What are your plans for the future of Polyaxon?
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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA