Creating and granting access to Amazon S3 bucket

• Creating the Amazon S3 bucket
• Granting access to the Amazon S3 bucket

• Met all prerequisites
• Have AWS access key and secret access key

1. Now log into your cluster and go to the namespace where your notebook is located: oc project rhods-notebooks
2. Run the following command and make sure that your pods are running: oc get pods
3. Once you have the name of the pod (in this case it is called jupyter-nb-admin-0), execute into it: 
   oc exec -it jupyter-nb-admin-0 -- /bin/bash

4. Next, let's install the AWS CLI in that pod:

   curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
   unzip awscliv2.zip
   ./aws/install -i ~/.local/aws-cli -b ~/.local/bin

5. Then modify your PATH environment: export PATH=~/.local/bin:$PATH
6. Once it is correctly installed, be sure that you have your AWS Access Key ID and AWS Secret Access Key ready, and run the following command: aws configure
7. Select the region where your cluster is located. You could verify the configuration by running simple command such as listing the S3 buckets: aws s3 ls
8. Once the credentials matter is sorted, let's create an S3 bucket in your AWS account. Again, there are many ways to go about this. The easiest would be to go to your AWS console and create the bucket in your region from there and leave all the settings to default. Alternatively, you can run this command to create a bucket (in this case, it’s called llm-bucket-dsari, and the cluster region is us-west-2): aws s3 mb s3://llm-bucket-dsari --region us-west-2

Once these steps are complete, you’re ready to begin training the LLM model in the next resource. 

• Troubleshoot with Red Hat support