manual, update new pr
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- [UFW Basics](system_administrators/computer_it_basics/firewall_basics/ufw_basics.md)
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- [Firewalld Basics](system_administrators/computer_it_basics/firewall_basics/firewalld_basics.md)
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- [File Transfer](system_administrators/computer_it_basics/file_transfer.md)
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- [Screenshots](system_administrators/computer_it_basics/screenshots.md)
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- [Advanced](system_administrators/advanced/advanced.md)
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- [Token Transfer Keygenerator](system_administrators/advanced/token_transfer_keygenerator.md)
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- [Cancel Contracts](system_administrators/advanced/cancel_contracts.md)
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@ -251,6 +252,7 @@
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- [IPFS](system_administrators/advanced/ipfs/ipfs_toc.md)
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- [IPFS on a Full VM](system_administrators/advanced/ipfs/ipfs_fullvm.md)
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- [IPFS on a Micro VM](system_administrators/advanced/ipfs/ipfs_microvm.md)
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- [AI & ML Workloads](system_administrators/advanced/ai_ml_workloads.md)
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- [ThreeFold Token](threefold_token/threefold_token.md)
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- [TFT Bridges](threefold_token/tft_bridges/tft_bridges.md)
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- [TFChain-Stellar Bridge](threefold_token/tft_bridges/tfchain_stellar_bridge.md)
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@ -12,3 +12,4 @@ In this section, we delve into sophisticated topics and powerful functionalities
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- [IPFS](ipfs_toc.md)
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- [IPFS on a Full VM](ipfs_fullvm.md)
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- [IPFS on a Micro VM](ipfs_microvm.md)
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- [AI & ML Workloads](ai_ml_workloads.md)
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collections/system_administrators/advanced/ai_ml_workloads.md
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<h1> AI & ML Workloads </h1>
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<h2> Table of Contents </h2>
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- [Introduction](#introduction)
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- [Prerequisites](#prerequisites)
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- [Prepare the System](#prepare-the-system)
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- [Install the GPU Driver](#install-the-gpu-driver)
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- [Set a Python Virtual Environment](#set-a-python-virtual-environment)
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- [Install PyTorch and Test Cuda](#install-pytorch-and-test-cuda)
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- [Set and Access Jupyter Notebook](#set-and-access-jupyter-notebook)
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- [Run AI/ML Workloads](#run-aiml-workloads)
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***
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## Introduction
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We present a basic method to deploy artificial intelligence (AI) and machine learning (ML) on the TFGrid. For this, we make use of dedicated nodes and GPU support.
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In the first part, we show the steps to install the Nvidia driver of a GPU card on a full VM Ubuntu 22.04 running on the TFGrid.
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In the second part, we show how to use PyTorch to run AI/ML tasks.
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## Prerequisites
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You need to reserve a [dedicated GPU node](../../dashboard/deploy/node_finder.md#dedicated-nodes) on the ThreeFold Grid.
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## Prepare the System
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- Update the system
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```
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dpkg --add-architecture i386
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apt-get update
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apt-get dist-upgrade
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reboot
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```
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- Check the GPU info
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```
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lspci | grep VGA
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lshw -c video
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```
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## Install the GPU Driver
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- Download the latest Nvidia driver
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- Check which driver is recommended
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```
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apt install ubuntu-drivers-common
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ubuntu-drivers devices
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```
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- Install the recommended driver (e.g. with 535)
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```
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apt install nvidia-driver-535
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```
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- Reboot and reconnect to the VM
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- Check the GPU status
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```
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nvidia-smi
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```
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Now that the GPU node is set, let's work on setting PyTorch to run AI/ML workloads.
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## Set a Python Virtual Environment
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Before installing Python package with pip, you should create a virtual environment.
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- Install the prerequisites
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```
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apt update
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apt install python3-pip python3-dev
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pip3 install --upgrade pip
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pip3 install virtualenv
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```
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- Create a virtual environment
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```
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mkdir ~/python_project
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cd ~/python_project
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virtualenv python_project_env
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source python_project_env/bin/activate
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```
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## Install PyTorch and Test Cuda
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Once you've created and activated a virtual environment for Pyhton, you can install different Python packages.
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- Install PyTorch and upgrade Numpy
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```
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pip3 install torch
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pip3 install numpy --upgrade
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```
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Before going further, you can check if Cuda is properly installed on your machine.
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- Check that Cuda is available on Python with PyTorch by using the following lines:
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```
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import torch
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torch.cuda.is_available()
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torch.cuda.device_count() # the output should be 1
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torch.cuda.current_device() # the output should be 0
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torch.cuda.device(0)
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torch.cuda.get_device_name(0)
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```
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## Set and Access Jupyter Notebook
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You can run Jupyter Notebook on the remote VM and access it on your local browser.
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- Install Jupyter Notebook
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```
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pip3 install notebook
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```
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- Run Jupyter Notebook in no-browser mode and take note of the URL and the token
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```
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jupyter notebook --no-browser --port=8080 --ip=0.0.0.0
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```
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- On your local machine, copy and paste on a browser the given URL but make sure to change `127.0.0.1` with the WireGuard IP (here it is `10.20.4.2`) and to set the correct token.
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```
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http://10.20.4.2:8080/tree?token=<insert_token>
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```
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## Run AI/ML Workloads
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After following the steps above, you should now be able to run Python codes that will make use of your GPU node to compute AI and ML workloads.
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Feel free to explore different ways to use this feature. For example, the [HuggingFace course](https://huggingface.co/learn/nlp-course/chapter1/1) on natural language processing is a good introduction to machine learning.
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@ -12,4 +12,5 @@ In this section, tailored specifically for system administrators, we'll delve in
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- [Firewall Basics](firewall_basics.md)
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- [UFW Basics](ufw_basics.md)
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- [Firewalld Basics](firewalld_basics.md)
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- [File Transfer](file_transfer.md)
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- [File Transfer](file_transfer.md)
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- [Screenshots](screenshots.md)
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<h1> Screenshots </h1>
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<h2>Table of Contents</h2>
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- [Introduction](#introduction)
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- [Linux](#linux)
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- [MAC](#mac)
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- [Windows](#windows)
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***
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## Introduction
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In this section, we show how to easily take screenshots on Linux, MAC and Windows.
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## Linux
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- Copy to the clipboard a full screenshot
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```
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PrintScreen
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```
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- Copy to the clipboard a screenshot of an active window
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```
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Alt + PrintScreen
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```
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- Copy to the clipboard a screenshot of an active app
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```
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Control + Alt + PrintScreen
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```
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- Copy to the clipboard a screenshot of a selected area
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```
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Shift + PrintScreen
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```
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## MAC
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- Save to the desktop a full screenshot
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```
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Shift + Command (⌘) + 3
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```
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- Save to the desktop a screenshot of an active window
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```
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Shift + Command (⌘) + 4 + Spacebar
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```
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- Copy to the clipboard a screenshot of an active window
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```
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Shift + Control + Command (⌘) + 3
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```
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- Save to the desktop a screenshot of a selected area
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```
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Shift + Command (⌘) + 4
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```
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- Copy to the clipboard a screenshot of a selected area
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```
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Shift + Control + Command (⌘) + 4
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```
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## Windows
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- Copy to the clipboard a full screenshot
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```
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PrintScreen
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```
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- Save to the pictures directory a full screenshot
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```
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Windows key + PrintScreen
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```
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- Copy to the clipboard a screenshot of an active window
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```
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Alt + PrintScreen
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```
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- Copy to the clipboard a selected area of the screen
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```
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Windows key + Shift + S
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```
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- [API](#api)
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- [Message System](#message-system)
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- [Inspecting Node Keys](#inspecting-node-keys)
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- [Permanently Enable Mycelium](#permanently-enable-mycelium)
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***
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@ -150,4 +151,32 @@ Where the output could be something like this:
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```sh
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Public key: a47c1d6f2a15b2c670d3a88fbe0aeb301ced12f7bcb4c8e3aa877b20f8559c02
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Address: 27f:b2c5:a944:4dad:9cb1:da4:8bf7:7e65
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```
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## Permanently Enable Mycelium
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It is possible to permenently enable Mycelium on your client.
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For Linux, we use systemd to manage the mycelium daemon in `/storage/`. Here's an example:
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```
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[Unit]
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Description=End-2-end encrypted IPv6 overlay network
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Wants=network.target
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After=network.target
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Documentation=https://github.com/threefoldtech/mycelium
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[Service]
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ProtectHome=true
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ProtectSystem=true
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SyslogIdentifier=mycelium
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CapabilityBoundingSet=CAP_NET_ADMIN
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StateDirectory=mycelium
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StateDirectoryMode=0700
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ExecStartPre=+-/sbin/modprobe tun
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ExecStart=/storage/mycelium --no-tun --disable-peer-discovery -k %S/mycelium/key.bin --peers tcp://[2a01:4f8:221:1e0b::2]:9651 tcp://[2a01:4f8:212:fa6::2]:9651 tcp://[2a02:1802:5e:0:8478:51ff:fee2:3331]:9651 tcp://[2a02:1802:5e:0:8c9e:7dff:fec9:f0d2]:9651 tcp://[2a01:4f9:5a:1042::2]:9651
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Restart=always
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RestartSec=5
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TimeoutStopSec=5
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[Install]
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WantedBy=multi-user.target
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```
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- [UFW Basics](ufw_basics.md)
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- [Firewalld Basics](firewalld_basics.md)
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- [File Transfer](file_transfer.md)
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- [Screenshots](screenshots.md)
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- [Advanced](advanced.md)
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- [Token Transfer Keygenerator](token_transfer_keygenerator.md)
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- [Cancel Contracts](cancel_contracts.md)
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- [Redis](grid3_redis.md)
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- [IPFS](ipfs_toc.md)
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- [IPFS on a Full VM](ipfs_fullvm.md)
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- [IPFS on a Micro VM](ipfs_microvm.md)
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- [IPFS on a Micro VM](ipfs_microvm.md)
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- [AI & ML Workloads](ai_ml_workloads.md)
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