Who is this for?
This is an introductory topic for software developers interested in automation for Machine Learning (ML) tasks.
What will you learn?
Upon completion of this learning path, you will be able to:
- Set up an Arm-hosted GitHub runner.
- Train and test a PyTorch ML model with the German Traffic Sign Recognition Benchmark (GTSRB) dataset.
- Compare the performance of two trained PyTorch ML models; one model compiled with OpenBLAS (Open Basic Linear Algebra Subprograms Library) and oneDNN (Deep Neural Network Library), and the other model compiled with Arm Compute Library (ACL).
- Containerize a ML model and push the container to DockerHub.
- Automate steps in an ML workflow using GitHub Actions.
Prerequisites
Before starting, you will need the following:
- A GitHub account with access to Arm-hosted GitHub runners.
- A Docker Hub account for storing container images.
- Familiarity with the concepts of ML and continuous integration and deployment (CI/CD).