Add aws and docker content

Signed-off-by: Michael Yuan <michael@secondstate.io>

docs/node-guide/tasks/aws.md

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+---
+sidebar_position: 2
+---
+
+# Start a node on AWS using AMI images
+
+We have created a series of public AMIs for you to start GaiaNet nodes in AWS with just a few clicks.
+
+## Running an Nvidia GPU-enabled AWS instance
+
+Load the [AWS console](https://aws.amazon.com/console/) and sign into your account. Go to EC2 | instances and 
+click on the "Launch instance" button.
+
+In the "Application and OS Images" section, search the AMI catalog and select the image named `GaiaNet_ubuntu22.04_amd64_cuda12`.
+
+![](aws_ami.png)
+
+In the "Instance type" section, select any of the `g4dn` types. Those are EC2 VMs with Nvidia T4 GPUs.
+
+![](aws_instance_type.png)
+
+In the "Network settings", make sure that you allow SSH connections.
+
+![](aws_network.png)
+
+Click on the "Launch instance" button and wait for instance to start up. Once the instance is ready, SSH
+into its public IP address. Once you are in the VM, run the following two commands.
+
+```
+gaianet init
+gaianet start
+```
+
+The node is ready when it shows `The GaiaNet node is started at: https://...` on the console.
+You can go to that URL from your browser to interact with the GaiaNet node.
+
+You can [customize your GaiaNet node](../customize.md) with your own choice of LLMs and knowledge base snapshots.
+
+## Running a CPU-only AWS instance
+
+Load the [AWS console](https://aws.amazon.com/console/) and sign into your account. Go to EC2 | instances and 
+click on the "Launch instance" button.
+
+In the "Application and OS Images" section, search the AMI catalog and select the image named 
+
+* `GaiaNet_ubuntu22.04_amd64` for x86 CPU machines
+* `GaiaNet_ubuntu22.04_arm64` for ARM CPU machines
+
+In the "Instance type" section, select an instance with at least 8GB of RAM. For example, we recommend `t2.large` or `t2.xlarge` instances.
+
+In the "Network settings", make sure that you allow SSH connections.
+
+Click on the "Launch instance" button and wait for instance to start up. Once the instance is ready, SSH
+into its public IP address. Once you are in the VM, run the following two commands.
+
+```
+gaianet init
+gaianet start
+```
+
+The node is ready when it shows `The GaiaNet node is started at: https://...` on the console.
+You can go to that URL from your browser to interact with the GaiaNet node.
+
+You can [customize your GaiaNet node](../customize.md) with your own choice of LLMs and knowledge base snapshots.
+
+Good luck!
+

docs/node-guide/tasks/cuda.md

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-sidebar_position: 1
+sidebar_position: 101
 ---
 
 # Install CUDA on Linux
@@ -76,7 +76,7 @@ Cuda compilation tools, release 12.2, V12.2.140
 Build cuda_12.2.r12.2/compiler.33191640_0
 ```
 
-After that, use the following two commands to set up the environment path. You should probably add these two lines to your `~/.bashrc` or `~/.zshrc` files so that new terminals and future logins will still be able to find these CUDA library files.
+After that, use the following two commands to set up the environment path. You should probably add these two lines to your `~/.bashrc` and `~/.bash_profile` (or `~/.zshrc` and `~/.profile`) files so that new terminals and future logins will still be able to find these CUDA library files.
 
 ```
 export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH}

docs/node-guide/tasks/docker.md

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+---
+sidebar_position: 1
+---
+
+# Start a node with Docker
+
+You can run all the commands in this document without any change on any machine with the latest Docker and at least 8GB of RAM available to the container.
+By default, the container uses the CPU to perform computations, which could be slow for large LLMs. For GPUs,
+
+* Mac: Everything here works on [Docker Desktop for Mac](https://docs.docker.com/desktop/install/mac-install/). However, the Apple GPU cores will not be available inside Docker containers until [WebGPU is supported by Docker](https://github.com/LlamaEdge/LlamaEdge/blob/main/docker/webgpu.md) later in 2024.
+* Windows and Linux with Nvidia GPU: You will need to install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installation) for Docker. In the instructions below, replace the `latest` tag with `cuda12` or `cuda11` to use take advantage of the GPU, and add the `--device nvidia.com/gpu=all` flag. If you need to build the images yourself, replace `Dockerfile` with `Dockerfile.cuda12` or `Dockerfile.cuda11`.
+
+Find [GaiaNet Docker images](https://hub.docker.com/?namespace=gaianet) you can run!
+
+## Quick start
+
+Start a Docker container for the GaiaNet node. It will print running logs from the GaiaNet node in this terminal. 
+
+```
+docker run --name gaianet \
+  -p 8080:8080 \
+  -v $(pwd)/qdrant_storage:/root/gaianet/qdrant/storage:z \
+  gaianet/phi-3-mini-instruct-4k_paris:latest
+```
+
+The node is ready when it shows `The GaiaNet node is started at: https://...` on the console.
+You can go to that URL from your browser to interact with the GaiaNet node.
+
+The docker image contains the LLM and embedding models required by the node. However, the vector
+collection snapshot (i.e., knowledge base) is downloaded and imported at the time when the node
+starts up. That is because the knowledge based could be updated frequently. The `qdrant_storage`
+directory on the host machine stores the vector database content.
+
+Alternatively, the command to run the GaiaNet on your Nvidia CUDA 12 machine is as follows.
+
+```
+docker run --name gaianet \
+  -p 8080:8080 --device nvidia.com/gpu=all \
+  -v $(pwd)/qdrant_storage:/root/gaianet/qdrant/storage:z \
+  gaianet/phi-3-mini-instruct-4k_paris:cuda12
+```
+
+## Stop and re-start
+
+You can stop and re-start the node as follows. Every time you re-start, it will re-initailize the vector
+collection (knowledge base).
+
+```
+docker stop gaianet
+docker start gaianet
+```
+
+NOTE: When you restart the node, the log messages will no longer be printed to the console.
+You will need to wait for a few minutes before the restarted node comes back online. You can still see
+the logs by logging into the container as follows.
+
+```
+docker exec -it gaianet /bin/bash
+tail -f /root/gaianet/log/start-llamaedge.log
+```
+
+You can also delete the node if you no longer needs it.
+
+```
+docker stop gaianet
+docker rm gaianet
+```
+
+## Make changes to the node
+
+You can update the configuration parameters of the node, such as context size for the models, by
+executing the `config` command on the `gaianet` program inside the container.
+For example, the following command changes the chat LLM's context size to 8192 tokens.
+
+```
+docker exec -it gaianet /root/gaianet/bin/gaianet config --chat-ctx-size 8192
+```
+
+Then, restart the node for the new configuration to take effect.
+You will need to wait for a few minutes for the server to start again, or you can monitor
+the log files inside the container as discussed above.
+
+```
+docker stop gaianet
+docker start gaianet
+```
+
+## Change the node ID
+
+You can update the node ID (Ethereum address) associated with the node. Start the node and copy the `nodeid.json`
+file, as well as the keystore file defined in `nodeid.json` into the container.
+
+```
+docker cp /local/path/to/nodeid.json gaianet:/root/gaianet/nodeid.json
+docker cp /local/path/to/1234-abcd-key-store gaianet:/root/gaianet/1234-abcd-key-store
+```
+
+THen, restart the node for the new address and keystore to take effect.
+
+```
+docker stop gaianet
+docker start gaianet
+```
+
+## Build a node image locally
+
+Each GaiaNet is defined by a `config.json` file. It defines the node's required
+LLM and embedding models, model parameters,
+prompts, and vector snapshots (e.g., knowledge base). 
+The following command builds a Docker image with two platforms 
+for a node based on the specified `config.json` file. 
+
+```
+docker buildx build . --platform linux/arm64,linux/amd64 \
+  --tag gaianet/phi-3-mini-instruct-4k_paris:latest -f Dockerfile \
+  --build-arg CONFIG_URL=https://raw.githubusercontent.com/GaiaNet-AI/gaianet-node/main/config.json
+```
+
+> The `Dockerfile` is available [here](https://raw.githubusercontent.com/GaiaNet-AI/gaianet-node/main/docker/Dockerfile). Feel free to change it to Nvidia [CUDA versions](https://raw.githubusercontent.com/GaiaNet-AI/gaianet-node/main/docker/Dockerfile.cuda12) if your Docker is enabled with the [Nvidia container toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
+
+You can publish your node for other people to use it.
+
+```
+docker push gaianet/phi-3-mini-instruct-4k_paris:latest
+```
+
+Good luck!
+

docs/node-guide/tasks/local.md

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-sidebar_position: 4
+sidebar_position: 104
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 # Run a local-only node

docs/node-guide/tasks/multiple.md

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-sidebar_position: 3
+sidebar_position: 103
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 # Install multiple nodes on a single machine

docs/node-guide/tasks/protect.md

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-sidebar_position: 5
+sidebar_position: 105
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 # Protect the server process