Docs updates (#35)

README.org

@@ -40,7 +40,9 @@ oc rollout status deployment/minio --watch
 
 ** TODO Consider creating a cluster web terminal pod
 
-** Download model from huggingface
+** TODO Create Minio Bucket `models`
+
+** Download model from huggingface into each `on prem` clusters Minio `model`'s bucket
 
 #+begin_src tmux
 HUGGINGFACE_TOKEN="HUGGINGFACE_TOKEN"
@@ -49,9 +51,13 @@ huggingface-cli login --token "${HUGGINGFACE_TOKEN}"
 git clone https://huggingface.co/instructlab/granite-7b-lab
 #+end_src
 
-** TODO Create Minio Bucket `models`
+or use this:
+https://github.com/tnscorcoran/rhods-finetunning-demo/blob/main/vllm_get_from_huggingface.ipynb
+
 
 ** Upload model to on-prem cluster minio
+Consider using this:
+https://github.com/tnscorcoran/rhods-finetunning-demo/blob/main/vllm_push_to_minio.ipynb
 
 TODO Run aws configure and pull values out of that automatically.
 

data/hackathon/scenario1.mdx

@@ -33,6 +33,7 @@ Your hackathon team are the pre-sales technical team engaging with various ACME
 ## 1.1 - Understanding the environment
 
 For this challenge your team will be given two OpenShift clusters
+
 - an AWS ROSA OpenShift 4 cluster representing the ACME cloud environment 
 - a Single Node OpenShift cluster representing the ACME on-premises environment
 
@@ -49,6 +50,8 @@ All challenge tasks must be performed on these clusters so your solutions can be
 
 Working in a small team you will have two shared clusters for team members to use. Your team will have a name allocated already.
 
+TODO Tom - update this
+
 To get underway open your web browser and navigate to these link to allocate two clusters for your team:
 1. ROSA https://demo.redhat.com/workshop/s72ya3
 2. On-premises https://demo.redhat.com/workshop/s72ya3

data/hackathon/scenario2.mdx

@@ -8,7 +8,7 @@ authors: ['default']
 summary: "How do we use GPU accelerators??"
 ---
 
-As a sales team you've got an upcoming demo with the Acme Financial Services data science team, who have asked you to show them how to enable GPU support in Red Hat OpenShift Service on AWS (ROSA).
+As a sales team you've got an upcoming demo with the Acme Financial Services data science team, who have asked you to show them how to enable GPU support on their On Premisies cluster (in your case represented by Red Hat OpenShift Service on AWS (ROSA))?
 
 You've spun up a demo environment to show them how it's done.
 

data/hackathon/scenario3.mdx

@@ -39,7 +39,7 @@ You won't need any Custom Resources for OpenShift Service Mesh and OpenShift Ser
 You will need one for OpenShift AI. A valid strategy would be to open the yaml view and go with all the defaults - the only addition to be to add this knative-serving-cert secret
         ingressGateway:
           certificate:
-            `secretName: knative-serving-cert`
+            secretName: knative-serving-cert
 
 Documentation you may find helpful is:
 - https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/2.9/html/installing_and_uninstalling_openshift_ai_self-managed/index
@@ -110,6 +110,17 @@ Documentation you may find helpful is:
 
 
 
+## 1.5 - Hints!
+The first hint is free: In scenario 6, you will need to provision 15 minutes time for synthetic data generation as well as 20 minutes for model training. You might want to make this part of your strategy to win.
+
+If you get stuck on a question, fear not, perhaps try a different approach. If you have tried everything you can think of and are still stuck you can unlock a hint for `5` points by posting a message in the `#event-anz-ocp-ai-hackathon` channel with the message:
+
+> [team name] are stuck on [exercise] and are unlocking a hint.
+
+A hackathon organiser will join your breakout room to share the hint with you 🤫.
+
+
+TODO Tom - move this to Google Docs
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

data/hackathon/scenario4.mdx

@@ -61,7 +61,22 @@ TODO PULL all Check your work sections from https://github.com/jmhbnz/workshops/
 
 
 # HINTS
-[4.2.1] TODO - move the contents of this repo to the final github location:
+
+## 1.5 - Hints!
+The first hint is free: In scenario 6, you will need to provision 15 minutes time for synthetic data generation as well as 20 minutes for model training. You might want to make this part of your strategy to win.
+
+If you get stuck on a question, fear not, perhaps try a different approach. If you have tried everything you can think of and are still stuck you can unlock a hint for `5` points by posting a message in the `#event-anz-ocp-ai-hackathon` channel with the message:
+
+> [team name] are stuck on [exercise] and are unlocking a hint.
+
+A hackathon organiser will join your breakout room to share the hint with you 🤫.
+
+
+ TODO Tom 
+ - move the hint to a Google doc
+ - move the contents of this repo to the final github location:
+
+[4.2.1]
 Solution via Helm Chart is available here: https://github.com/butler54/rhoai-custom-image
 
             TODO - MOVE TO DOCS:

data/hackathon/scenario5.mdx

@@ -54,9 +54,17 @@ This exercise is worth 25 points. The event team will reply in slack to confirm
 
 
 
+## 1.5 - Hints!
+The first hint is free: In scenario 6, you will need to provision 15 minutes time for synthetic data generation as well as 20 minutes for model training. You might want to make this part of your strategy to win.
 
+If you get stuck on a question, fear not, perhaps try a different approach. If you have tried everything you can think of and are still stuck you can unlock a hint for `5` points by posting a message in the `#event-anz-ocp-ai-hackathon` channel with the message:
 
+> [team name] are stuck on [exercise] and are unlocking a hint.
 
+A hackathon organiser will join your breakout room to share the hint with you 🤫.
+
+
+TODO Tom - move this to a Google Doc
 # HINTS
 - [5.1.2]: The actual web app yaml (already with the configuration to talk to the model server) is available here: https://raw.githubusercontent.com/rh-aiservices-bu/mad_m6_workshop/main/deployment/intelligent_application_deployment.yaml
 

data/hackathon/scenario6.mdx

@@ -64,6 +64,17 @@ If you find the answers somewhat peculiar, your mission is to fix that - should
 - Create a screenshot and post it in the slack channel.
 
 
+## 1.5 - Hints!
+The first hint is free: In scenario 6, you will need to provision 15 minutes time for synthetic data generation as well as 20 minutes for model training. You might want to make this part of your strategy to win.
+
+If you get stuck on a question, fear not, perhaps try a different approach. If you have tried everything you can think of and are still stuck you can unlock a hint for `5` points by posting a message in the `#event-anz-ocp-ai-hackathon` channel with the message:
+
+> [team name] are stuck on [exercise] and are unlocking a hint.
+
+A hackathon organiser will join your breakout room to share the hint with you 🤫.
+
+
+TODO Tom - move this to a Google Doc
 # HINTS
 - [6.1]: If you get stuck, have a closer look at: https://shonpaz.medium.com/rewiring-the-way-we-think-on-ai-part-1-model-fine-tuning-using-instructlab-ebba7017e5d5