updated faq, tft

collections/system_administrators/advanced/advanced.md

@@ -10,9 +10,12 @@ In this section, we delve into sophisticated topics and powerful functionalities
 - [Listing Free Public IPs](list_public_ips.md)
 - [Cloud Console](cloud_console.md)
 - [Redis](grid3_redis.md)
-- [IPFS](ipfs_toc.md)
-  - [IPFS on a Full VM](ipfs_fullvm.md)
-  - [IPFS on a Micro VM](ipfs_microvm.md)
+- [IPFS](ipfs/ipfs_toc.md)
 - [MinIO Operator with Helm3](minio_helm3.md)
-- [AI & ML Workloads](ai_ml_workloads.md)
-- [Hummingbot](hummingbot.md)
+- [Hummingbot](hummingbot.md)
+- [AI & ML Workloads](ai_ml_workloads/ai_ml_workloads_toc.md)
+- [Ecommerce](ecommerce/ecommerce.md)
+- [HTTPS with Caddy](https_caddy.md)
+- [Node Status Bot](node_status_bot.md)
+- [Minetest](minetest.md)
+- [Remote Desktop and GUI](remote-desktop_gui.md)

collections/system_administrators/advanced/ai_ml_workloads/ai_ml_workloads_toc.md

@@ -0,0 +1,7 @@
+# AI & ML Workloads
+
+<h2>Table of Contents</h2>
+
+- [CPU and Llama](cpu_and_llama.md)
+- [GPU and Pytorch](gpu_and_pytorch.md)
+- [GPU and Fooocus](gpu_and_fooocus.md)

collections/system_administrators/advanced/ai_ml_workloads/cpu_and_llama.md

@@ -0,0 +1,105 @@
+<h1> AI & ML Workloads: CPU and Llama </h1>
+
+<h2>Table of Contents</h2>
+
+- [Introduction](#introduction)
+- [Prerequisites](#prerequisites)
+- [Deploy a Full VM](#deploy-a-full-vm)
+- [Preparing the VM](#preparing-the-vm)
+- [Setting OpenWebUI](#setting-openwebui)
+- [Pull a Model](#pull-a-model)
+- [Using Llama](#using-llama)
+- [References](#references)
+
+---
+
+## Introduction
+
+We present a simple guide on how to deploy large language models on the grid using CPU. For this guide, we will be deploying Llama on a full VM using OpenWebUI bundled with Ollama support.
+
+Llama is a large language model trained by Meta AI. It is an open-source model, meaning that it is free to use and customize for various applications. This LLM is designed to be a more conversational AI allowing users to engage in natural-sounding conversations. Llama is trained on a massive dataset of text from the internet and can generate responses to a wide range of topics and questions.
+
+Ollama is an open-source project that allows users to run large language models (LLMs) on their local machine.
+
+OpenWebUI is one of many front ends for Ollama, providing a convenient and user friendly way to load weights and chat with the bot.
+
+## Prerequisites
+
+- [A TFChain account](dashboard@@wallet_connector)
+- TFT in your TFChain account
+  - [Buy TFT](threefold_token@@buy_sell_tft)
+  - [Send TFT to TFChain](threefold_token@@tfchain_stellar_bridge)
+
+## Deploy a Full VM
+
+We start by deploying a full VM on the ThreeFold Dashboard. The more cores we set to the machine, the faster the model will be.
+
+* On the [Threefold Dashboard](https://dashboard.grid.tf/#/), go to the [full virtual machine deployment page](https://dashboard.grid.tf/#/deploy/virtual-machines/full-virtual-machine/)
+* Deploy a full VM (Ubuntu 22.04) with only `Wireguard` as the network
+  * Vcores: 8 vcores
+  * MB of RAM: 4096 GB
+  * GB of storage: 100 GB
+* After deployment, [set the Wireguard configurations](ssh_wireguard.md)
+* Connect to the VM via SSH
+  * ``` 
+    ssh root@VM_Wireguard_Address
+    ```
+
+## Preparing the VM
+
+We prepare the full VM to run Llama.
+
+* Install Docker
+  * ```
+    wget -O docker.sh get.docker.com
+    bash docker.sh
+    ```
+
+## Setting OpenWebUI
+
+We now install OpenWebUI with bundled Ollama support. Note that you might need to use another port than `3000` if this port is already in use on your local machine.
+
+* For CPU only
+   ```
+   docker run -d -p 3000:8080 -v ollama:/root/.ollama -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:ollama
+   ```
+* Once the container is fully loaded and running, go to your browser to access OpenWebUI using the Wireguard address:
+  * ```
+    10.20.4.2:3000
+    ```
+
+You should now see the OpenWebUI page. You can register by entering your email and setting a password. This information will stay on the machine running OpenWebUI.
+
+<p align="center">
+  <img src="./img/openwebui_page.png" />
+</p>
+
+## Pull a Model
+
+Once you've access OpenWebUI, you need to download a LLM model before using it.
+
+- Click on the bottom left button displaying your username
+- Click on `Settings`, then `Admin Settings` and `Models`
+- Under `Pull a model from Ollama.com`, enter the LLM model you want to use
+  - In our case we will use `llama3`
+- Click on the button on the right to pull the image
+
+![](./img/openwebui_model.png)
+
+## Using Llama
+
+Let's now use Llama!
+
+- Click on `New Chat` on the top left corner
+- Click on `Select a model` and select the model you downloaded
+  - You can click on `Set as default` for convenience
+
+![](./img/openwebui_set_model.png)
+
+- You can now `Send a Message` to Llama and interact with it!
+
+That's it. You now have a running LLM instance on the grid.
+
+## References
+
+For any advanced configurations, you may refer to the [OpenWebUI documentation](https://github.com/open-webui/open-webui).

collections/system_administrators/advanced/ai_ml_workloads/gpu_and_fooocus.md

@@ -0,0 +1,109 @@
+<h1>AI & ML Workloads: GPU and Fooocus</h1>
+
+<h2>Table of Contents</h2>
+
+- [Introduction](#introduction)
+- [Prerequisites](#prerequisites)
+- [Prepare the System](#prepare-the-system)
+- [Install the GPU Driver](#install-the-gpu-driver)
+- [Install the Prerequisites and Launch Fooocus](#install-the-prerequisites-and-launch-fooocus)
+- [Use Fooocus](#use-fooocus)
+
+---
+
+## Introduction
+
+We show how to use a ThreeFold GPU node on the grid to deploy AI workloads for graphics and image generation. We will be using [Fooocus](https://github.com/lllyasviel/Fooocus) and a full virtual machine with a GPU card.
+
+## Prerequisites
+
+- [A TFChain account](dashboard@@wallet_connector)
+- TFT in your TFChain account
+  - [Buy TFT](threefold_token@@buy_sell_tft)
+  - [Send TFT to TFChain](threefold_token@@tfchain_stellar_bridge)
+- Reserve a [dedicated GPU node](dashboard@@node_finder) on the ThreeFold Grid
+- Deploy a full VM running Ubuntu 22.04
+- [SSH](ssh_guide.md) into the node with [Wireguard](ssh_wireguard.md) 
+
+## Prepare the System
+
+- Update the system
+    ```
+    dpkg --add-architecture i386
+    apt-get update
+    apt-get dist-upgrade
+    reboot
+    ```
+- Check the GPU info
+    ```
+    lspci | grep VGA
+    lshw -c video
+    ```
+
+## Install the GPU Driver
+
+- Download the latest Nvidia driver
+  - Check which driver is recommended
+      ```
+      apt install ubuntu-drivers-common
+      ubuntu-drivers devices
+      ```
+  - Install the recommended driver (e.g. with 535)
+      ```
+      apt install nvidia-driver-535
+      ```
+- Check the GPU status
+    ```
+    nvidia-smi
+    ```
+
+Now that the GPU node is set, let's install and launch Fooocus.
+
+## Install the Prerequisites and Launch Fooocus
+
+We install the prerequisites, including Miniconda, clone the repository, download the models and launch Fooocus.
+
+- Install the prerequisites
+  ```
+  apt update
+  apt install python3-pip python3-dev
+  pip3 install --upgrade pip
+  wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+  bash Miniconda3-latest-Linux-x86_64.sh
+  ```
+- Reload the shell to enable Conda
+- Clone the Fooocus directory and install the Python requirements with Miniconda
+  ```
+  git clone https://github.com/lllyasviel/Fooocus.git
+  cd Fooocus
+  conda env create -f environment.yaml
+  conda activate fooocus
+  pip install -r requirements_versions.txt
+  ```
+- Download the models with conda and deploy Fooocus
+  ```
+  conda activate fooocus
+  python entry_with_update.py
+  ```
+- Create an SSH tunnel to the VM
+  ```
+  ssh -4 -L 7865:127.0.0.1:7865 root@10.20.4.2
+  ```
+
+## Use Fooocus
+
+You can then access Fooocus:
+
+```
+127.0.0.1:7865
+```
+
+Here are some image generated with Fooocus:
+
+![](./img/fooocus_dog_ocean.png)
+
+![](./img/fooocus_ship.png)
+
+![](./img/fooocus_jungle.png)
+
+![](./img/fooocus_stadium.png)

collections/system_administrators/advanced/ai_ml_workloads/gpu_and_pytorch.md

@@ -1,4 +1,4 @@
-<h1> AI & ML Workloads </h1>
+<h1> AI & ML Workloads: GPU and Pytorch</h1>
 
 <h2> Table of Contents </h2>