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The Ultimate Reinforcement Learning Bible: Concepts, Code, and Applications
1. Introduction to Reinforcement Learning
1.1 What is Reinforcement Learning?
Imagine teaching a dog new tricks. You don't explicitly tell the dog exactly how to sit or roll over - instead, you reward good behavior with treats and perhaps gently discourage unwanted behavior. Over time, the dog learns what actions lead to treats and begins to make better decisions.
This is exactly how reinforcement learning works in the world of artificial intelligence!
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Pod Anti-Affinity: The podAntiAffinity rule prevents the ODF pods from being scheduled on the same node. This ensures high availability and fault tolerance by distributing the pods across different nodes.
Node Affinity: The nodeAffinity rule specifies that the ODF pods should only be scheduled on the specified nodes (worker-odf-01.acmhub2.narlabs.io, worker-odf-02.acmhub2.narlabs.io, worker-odf-03.acmhub2.narlabs.io). This guarantees that the ODF pods run only on these designated nodes, which may have specific configurations or resources required by the application.
Additional Considerations
Resource Requests and Limits: The resources requested and limited for the containers ensure that each pod has sufficient CPU and memory. Adjust these values based on actual workload requirements.
Persistent Volume and PVC: The configuration includes a sample PersistentVolume and PersistentVolumeClaim for storage. Customize these to fit the specific storage needs and
(2) Create a config map from the downloaded file in the openshift-config-managed namespace:
oc create configmap nvidia-dcgm-exporter-dashboard -n openshift-config-managed --from-file=dcgm-exporter-dashboard.json
(3) Label the config map to expose the dashboard in the Administrator perspective of the web console:
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