This repository contains a number of assets for implementing an open source MLOps approach using OpenShift Container Platform and Red Hat OpenShift Data Science (RHODS).
- RHODS Workbench Controller based on Kubeflow Notebook Controller for notebook-based development,
- Data Science Pipelines based on Kubeflow Pipelines for workflow orchestration and experiment tracking,
- RHODS Model Serving based on KServe ModelMesh for model serving.
To quickly set up a RHODS environment for fraud detection and object detection demos, follow these steps:
- (optional) Set up GPU enablement if GPUs are present in your cluster.
- Deploy the RHODS operator on your OpenShift cluster. The demo pack has been tested with RHODS 1.28.1.
- Deploy the OpenShift Pipelines operator. The demo pack has been tested with Pipelines versions 1.8 to 1.10.
- Deploy the Codeflare operator. The demo pack has been tested with Codeflare versions 0.0.4 to 0.0.6.
- Clone this repository and navigate to
manifests
. - Run
oc apply -f projects.yaml
- Run
oc apply -k .
Once the manifests have been deployed, your environment contains:
- A Minio instance as a lightweight S3 storage provider. You can manage the S3 buckets through the Minio UI through the
minio-ui
route URL in projectminio
. Useminio
andminio123
for logging in. - A Data Science Project
fraud-detection
for running the fraud detection demo. The pipeline server is instantiated and cluster storage and data connections are configured. - A Data Science Project
object-detection
for running the object detection demo. The pipeline server is instantiated and cluster storage and data connections are configured. The OVMS model server is instantiatend for model deployment. - A Data Science Project
huggingface-demo
for running the Huggingface demo. Cluster storage is configured. - A number of community workbench images.
- A number of custom serving runtimes.
To get started with your demo, instantiate the respective workbenches.
- In the RHODS dashboard, enter the
fraud-detection
project. - Create a new workbench with an arbitrary name and these parameters:
- image:
Trino
- existing cluster storage:
development
- existing data connection:
fraud-detection
- image:
- In the workbench, clone this repository, navigate to
notebooks/fraud-detection
and follow the instructions.
- In the RHODS dashboard, enter the
object-detection
project. - Create a new workbench with an arbitrary name and these parameters:
- image:
Object detection
- existing cluster storage:
development
- existing data connection:
object-detection
- image:
- In the workbench, clone this repository, navigate to
notebooks/fraud-detection
and follow the instructions.
- In the RHODS dashboard, enter the
huggingface-demo
project. - Create a new workbench with an arbitrary name and these parameters:
- image:
CodeFlare Notebook
- existing cluster storage:
development
- image:
- In the workbench, clone this repository, navigate to
notebooks/codeflare-examples/guided-demos
and follow the notebooks.