In the previous post, we learnt how to build a PyTorch image and running the image to train the model on your laptop using
Once a model has been trained, we will now begin to package this model and deploy the model onto Kuberentes.
The repository used in this post is available here.
Kubernetes (Greek for “helmsman” or “pilot” or “governor”) is an open-source system for automating deployment, scaling, and management of containerized applications. …
In the previous post, we talked about the benefits of container images and why they provide a consistent runtime environment for your AI workload. In part 2 of this series, we will explore tools such as
podman to build OCI compatible images.
The source code used in this post is available on my Git Hub repository.
In this series of blog posts, I will introduce how containers can be used in data scientist workflow, how to build and push them into a container registry, and how to use a container for training and deploy them onto Kubernetes
The first part of the series is focused on why containers for data science.
Containers are lightweight software packages that only contain the application and runtime. These containers share the operating system kernel, therefore it doesn’t need its own Operating System, unlike virtual machines.
As such, a container size is smaller, which allows them to be shipped and deployed much faster than virtual machines. …