Table of Contents
Explore the data science toolbox that are in reach when you have Red Hat OpenShift on AWS.
The scenario we use was to train a model that could predict suspect attributes from an unknown fingerprint. For example, in the data center or in the field, this model could help down-select possible suspects given an unseen fingerprint. Since we only had public data, the predictions are basic, but the possibilities are what we intend to inspire.
This demo covers several topics across the lifecycle for extending Red Hat OpenShift to perform common data science tasks from data ingestion to inference monitoring.
Training Notebook | Inference UI |
---|---|
See the Getting Started to get started.
- Red Hat OpenShift Self-Managed on AWS
- AWS SageMaker Notebooks
- NVIDIA Triton Inference Server
- Gradio User Interface
- AWS Controller for Kubernetes Operators: IAM, EC2, S3, SageMaker
- Hardware Acceleration
If you have the demo installed, start at the ./notebooks/fingerprint/model_train_s3_leftright.ipynb
.
If not, see the Prerequisites.
- Red Hat OpenShift Cluster 4.10+
- Cluster admin permissions
-
oc
cli installed locally -
python3.x
installed
# login to openshift w/ cluster-admin
oc login --token=sha256~<your_token>
# clone this repo for the bootstrap scripts
git clone https://github.com/redhat-na-ssa/demo-rosa-sagemaker.git
cd demo-rosa-sagemaker/
# run bootstrap to provision the demo on your cluster
./scripts/bootstrap.sh
# optional
# source ./scripts/bootstrap.sh and run commands individually, i.e.
setup_demo
delete_demo
Intended to be run on Red Hat OpenShift Container Platform on AWS (self-managed). Alternatively, Red Hat OpenShift on AWS (managed). Extend RHOCP with AWS capabilities.
- create branch
RHODS
- use RHODS notebook
- use elyra
- ModelMesh with Intel OpenVINO for serving
- create branch
ODH
- use ODH notebook
- use airflow
- use FastAPI for serving
- create branch
edge
- deploy the tflite model to edge device with Ansible
- test fingerprint
- create branch
djl
- use djl.ai for model dev