Here you can find a list of practical examples on how you can use ZenML with brief descriptions for each example:
quickstart: The official quickstart tutorial.
aws_orchestrated: Runs pipeline on an EC2 instance on
Amazon Web Services as the orchestration backend.
batch_inference: Run an offline batch inference job.
gan: Tutorial on how to create and train a GAN model with a custom
gcp_gpu_orchestrated: Training a classifier on an (optionally preemptible) cuda-enabled Google Cloud Platform virtual machine.
gcp_kubernetes_orchestrated: Launches a Kubernetes job on a Kubernetes cluster.
gcp_orchestrated: Runs pipeline on an (optionally preemptible) virtual machine on
Google Cloud Platform as the orchestration backend.
pytorch: Showcases PyTorch support.
Note: A lot of the examples are based on Google Cloud Platform. Extensions to other cloud providers like AWS and Azure will be released over time, but the interactions will be very similar. In fact, adding support for these can a great first pull request if you would be a contributor to ZenML!
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