Cleanup readme

README.md

@@ -1,91 +1,108 @@
-## Hypha Helm Chart
+# Hypha Helm Chart
 
 This document details how to add the Hypha Helm chart repository, install the Hypha chart, manage values files, generate secrets, and check available chart versions. Additionally, it covers dependencies and the configuration for the Triton Inference Server, including how to load models from an S3-compatible service like Minio.
 
-Adding the Helm Chart Repository
+## Adding the Helm Chart Repository
 
 To add the Hypha Helm chart repository, run the following command:
 
-"""
+```
 helm repo add hypha https://amun-ai.github.io/hypha-helm-charts/
-"""
+```
 
 ## Installing the Helm Chart
 
 You can install the Hypha Helm chart manually by specifying the chart directory and updating dependencies as follows:
 
-
+```
 helm install --generate-name hypha/hypha --dependency-update
+```
 
 ## Using a Values File
 
 To customize the installation, pass in a custom values.yaml file:
 
+```
 helm install --values values.yaml --generate-name hypha/hypha --dependency-update
-
+```
 
 ## Generating a Secret
 
 For creating secrets, such as passwords or keys, use:
 
-"""
+```
 openssl rand -base64 32
-"""
+```
 
 ## Checking Available Versions
 
 To see what versions of the Hypha chart are available, use:
 
-"""
+```
 helm search repo hypha --versions
-"""
+```
 
 # Dependencies
 
 The chart has dependencies on Minio, Redis, and Triton. Note that these dependencies need to be installed as part of the chart installation; pre-existing installations cannot be used directly.
 
-Triton Inference Server
-Getting Models from S3
+# Triton Inference Server
+## Getting Models from S3
 
 Models can be stored on an S3-compatible service like Minio. They can be loaded using a path format like s3::/minio/model-repository.
 
-Configuration
+## Configuration
 
 In the Triton configuration, you can customise the initContainers and volumes to load models from a storage service.
 
 Example of mounting a volume for the Triton cache:
 
-"""
+```
 volumeMounts:
   - mountPath: /model-repository
     name: triton-cache
     subPath: model-repository
-"""
+```
 
 Example of an initContainer to synchronise models from an S3-compatible service:
 
-"""
-initContainers:
-  - name: sync
-    image: minio/mc
-    command: ["/bin/sh"]
-    args:
-    - "-c"
-    - |
-      mc alias set bucket s3://bucket/model-repository
-      mc mirror --overwrite bucket/model-repository/ /model-repository
-    volumeMounts:
-    - mountPath: /model-repository
-      name: triton-cache
-      subPath: model-repository
-"""
-
-## Manually uploading models to the Bioengine
+```
+  initContainers:
+    - name: sync
+      image: minio/mc
+      command: ["/bin/sh"]
+      args:
+      - "-c"
+      - |
+        mc alias set bucket s3://bucket/model-repository
+        mc mirror --overwrite bucket/model-repository/ /model-repository
+      volumeMounts:
+      - mountPath: /model-repository
+        name: triton-cache
+        subPath: model-repository
+```
+
+# Manually uploading models to the Bioengine
 
 For situations where models need to be updated or added manually without using an S3-compatible service, you can use the kubectl cp command.
 This allows you to copy model directories directly from your local machine to the Triton Inference Server pod.
 This is not extremely 
 
+## Manually Uploading Models to Triton Inference Server (Discouraged Method)
+
+Directly uploading models to a pod can be quick but is generally discouraged due to the lack of persistence. If the pod restarts or is redeployed, any uploaded models will be lost. This method should only be used for temporary testing or in situations where persistence is managed externally.
+
+### Steps for Uploading Models Directly to a Pod
+Identify the Pod Name: Use kubectl get pods to find the Triton Inference Server pod.
+
+```
+kubectl get pods
+```
+
+```
+kubectl cp <local_model_directory> <pod_name>:/models/<model_directory> -n <namespace>
+```
+
 ## Cloud Providers
 
 The chart supports deployment on cloud platforms, including GCP.