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Update AWS documentation for AI and Jupyter setup
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ai/aws.md

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[ai index src](https://github.com/robfatland/nexus/blob/gh-pages/ai/index.md)
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# Content sections
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- AI on AWS pointers
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- EC2 working environment build with Q Developer on VS Code Server
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- Example Python Streamlit application: Uses 4 models, 2 AWS-hosted, 2 copied from HF
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- Artificial Intelligence on AWS: pointers
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- Build an AWS VM (EC2) work environment that includes Q Developer and VS Code Server
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- Example Web Application: Access to 4 AI models { 2 AWS-hosted and 2 from Hugging Face }
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## AI on AWS pointers
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## Artificial Intelligence on AWS pointers
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- [Instructions](https://catalog.workshops.aws/building-agentic-workflows/en-US/bedrock-api)
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- Log in to an active account
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# Running Jupyter on an AWS EC2 VM
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# Running `jupyter lab` on an AWS EC2 VM including `git`
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## Set up Jupyter and connect
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This uses port forwarding: From a laptop to the
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EC2 VM running Jupyter Lab. One can also use an `ssh` tunnel,
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EC2 VM running Jupyter Lab. One can also use an `ssh` tunnel which is considered
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more secure. The **nexus** writeup for `ssh` tunnels is
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found [here](https://github.com/robfatland/nexus/blob/gh-pages/bash/tunneling.md).
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It is also more secure to modify the communication
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protocol from `http` to `https`.
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[here](https://github.com/robfatland/nexus/blob/gh-pages/bash/tunneling.md).
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It is also more secure to modify the communication protocol from `http` to `https`.
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- `pip3 install jupyter`
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- `Source: Your IP address (or 0.0.0.0/0 for any IP)`
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## Authenticate to GitHub
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First method: Generate / grab a valid GitHub access token. With this in hand run...
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```
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git config --global credential.helper store
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git push # Enter username and token (will be cached)
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```
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Second method: Use `git config` to claim your identity and then authenticate (MFA)
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when executing a push
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```
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git config --global user.name "Your Name"
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git config --global user.email "[email protected]"
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git pull
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git add .
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git commit -m "Use double quotes for this comment"
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git push
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<authentication process ensues>
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```
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