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>
 

collections/system_administrators/getstarted/TF_Token/tft_ethereum/tft_ethereum.md

@@ -13,7 +13,7 @@
 
 ## Introduction
 
-The TFT Stellar-Ethereum bridge serves as a vital link between the Stellar and Ethereum blockchains, enabling the seamless transfer of TFT tokens between these two networks. This bridge enhances interoperability and expands the utility of TFT by allowing users to leverage the strengths of both platforms. With the bridge in place, TFT holders can convert their tokens from the Stellar network to the Ethereum network and vice versa, unlocking new possibilities for engagement with decentralized applications, smart contracts, and the vibrant Ethereum ecosystem. This bridge promotes liquidity, facilitates cross-chain transactions, and encourages collaboration between the Stellar and Ethereum communities.
+The TFT Stellar-Ethereum bridge serves as a vital link between the Stellar and Ethereum blockchains, enabling the seamless transfer of TFT tokens between these two networks. This bridge enhances interoperability by allowing users to leverage the strengths of both platforms. With the bridge in place, TFT holders can convert their tokens from the Stellar network to the Ethereum network and vice versa, unlocking new possibilities for engagement with decentralized applications, smart contracts, and the vibrant Ethereum ecosystem. This bridge promotes liquidity, facilitates cross-chain transactions, and encourages collaboration between the Stellar and Ethereum communities.
 
 ***
 

collections/system_administrators/getstarted/TF_Token/tft_lobstr/tft_lobstr.md

@@ -16,11 +16,7 @@
 
 ## Introduction
 
-The Threefold token (TFT) is the utility token of the Threefold Grid, a decentralized and open-source project offering network, compute and storage capacity.
-
-Threefold Tokens (TFT) are created (minted) by the ThreeFold Blockchain (TFChain) only when new Internet capacity is added to the ThreeFold Grid by farmers. For this reason, TFT is a pure utility token as minting is solely the result of farming on the Threefold Grid.
-
-* To **farm** TFT, read the [complete farming guide](https://forum.threefold.io/t/threefold-farming-guide-part-1/2989).
+The Threefold token (TFT) is the token of the Threefold Grid, a decentralized and open-source project offering network, compute and storage capacity.
 
 * To **buy** TFT, follow this guide.