# The Ultimate Beginner’s Guide to FAU HPC: From Zero to A100 So, you’ve received an invitation to use the High-Performance Computing (HPC) cluster at FAU (likely a Tier3 project). You want to run Deep Learning, VLM, or RL experiments, but you are staring at a black terminal screen and don't know where to start. Don't worry. I went through the exact same struggle—configuring SSH keys, getting "Permission Denied" errors, and wondering why PyTorch couldn't see the GPU. This guide will take you from **receiving the email** to **training on an NVIDIA A100**, step-by-step. --- ## Step 1: Accept the Invitation 1. Log in to the [**FAU HPC Portal**](https://portal.hpc.fau.de/) using your standard IdM credentials (e.g., `abc1234d`). 2. Go to **User** -> **Your Invitations** and accept the project invitation. 3. **Wait overnight.** The system runs a synchronization script every night. You usually cannot log in immediately after accepting; your home directory needs time to be created. --- ## Step 2: Generate and Upload Your SSH Key HPC systems don't use passwords; they use "keys." You need to generate a lock (Public Key) and a key (Private Key). ### For Windows (PowerShell) / Mac / Linux: Open your terminal and run: ```bash ssh-keygen -t rsa -b 4096 ``` 1. Press **Enter** to save it in the default location. 2. Press **Enter** twice to skip setting a password (useful for automation). 3. Display your public key: * **Windows:** `type %userprofile%\.ssh\id_rsa.pub` * **Mac/Linux:** `cat ~/.ssh/id_rsa.pub` 4. **Copy everything** (starting with `ssh-rsa` and ending with your username). ### Upload to Portal: 1. Go back to the [HPC Portal](https://portal.hpc.fau.de/). 2. Go to **User** -> **Your Accounts**. 3. Find your HPC account (e.g., `abc1234d`), click on it, and paste your key into the **"Add new SSH Key"** section. 4. **Wait 15-20 minutes** for the key to sync to the servers. --- ## Step 3: Configure VS Code (The "Pro" Setup) **Do not** try to use the raw terminal for everything. Use **VS Code** with the **Remote - SSH** extension. It allows you to edit code on the server as if it were on your laptop. ### The "ProxyJump" Trick Direct access to GPU nodes (like `tinyx`) is often blocked from outside the university network. We need to jump through a "gatekeeper" server called `csnhr`. 1. In VS Code, install the **Remote - SSH** extension. 2. Click the blue `><` icon (bottom left) -> **Open Configuration File** -> Select your `.ssh/config`. 3. Paste the following configuration (Replace `abc1234d` with **YOUR** HPC username): ```text # 1. The Gatekeeper (Jump Host) Host csnhr HostName csnhr.nhr.fau.de User abc1234d IdentityFile ~/.ssh/id_rsa IdentitiesOnly yes PasswordAuthentication no # 2. Woody (CPU Frontend - Good for data transfer) Host woody HostName woody.nhr.fau.de User abc1234d ProxyJump csnhr IdentityFile ~/.ssh/id_rsa IdentitiesOnly yes # 3. TinyX (Tier3 GPU Frontend - RUN YOUR EXPERIMENTS HERE) Host tinyx HostName tinyx.nhr.fau.de User abc1234d ProxyJump csnhr IdentityFile ~/.ssh/id_rsa IdentitiesOnly yes ``` 4. Save the file. 5. Click the blue `><` icon -> **Connect to Host** -> Select **tinyx**. --- ## Step 4: Know Your Territory ($HOME vs $WORK) Once logged in, you need to know where to put your files. This is the most common mistake beginners make. * **$HOME (**`/home/hpc/...`): * **Size:** Very small (100GB). * **Use for:** Config files, scripts, source code. * **NEVER put:** Datasets, Conda environments, or Model checkpoints here. You will run out of space immediately. * **$WORK (**`/home/woody/...`): * **Size:** Huge (1TB+?). * **Use for:** **EVERYTHING BIG.** Install Miniforge here. Download datasets here. * **How to find it:** Run `echo $WORK` in the terminal. **Always switch to WORK before doing anything:** ```bash cd $WORK ``` --- ## Step 5: Setting Up the Environment (The Right Way) Do not use the default Python. Do not use Anaconda (it's too bloated). Use **Miniforge**. 1. **Download and Install (in the terminal on** `tinyx`): ```bash cd $WORK wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh bash Miniforge3-Linux-x86_64.sh ``` * **Crucial:** When asked for the installation path, ensure it says `/home/woody/...`. If it says `/home/hpc/...`, edit it manually! * Type `yes` to initialize. 2. **Restart your terminal** (close and reopen the terminal pane in VS Code). 3. **Create your Environment:** (e.g., vlm\_env) ```bash mamba create -n vlm_env python=3.10 mamba activate vlm_env ``` --- ## Step 6: Installing PyTorch (The Trap!) Here is where many fail. 1. **Compute nodes (GPUs) have NO INTERNET.** You must install packages on the **Login Node (**`tinyx`). 2. **Mamba sometimes defaults to CPU versions.** Use `pip` to force the CUDA version. **The Golden Command (run this on** `tinyx`): ```bash mamba activate vlm_env pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 pip install transformers accelerate bitsandbytes ``` *Note: We verify installation on the Login Node, but we run it on the Compute Node.* --- ## Step 7: Accessing GPUs You are currently on `tinyx` (a shared login node). **DO NOT** run training here. You must request a **Compute Node**. ### Option A: Interactive Mode (Debugging/Testing) Use `salloc` to get a GPU for a short time (e.g., 30 mins). **To check what GPUs are available:** ```bash sinfo -o "%20P %G" ``` *(You might see partitions like* `a100`, `v100`, `rtx3080`). **To request an A100 (The Beast):** ```bash salloc --partition=a100 --gres=gpu:a100:1 --time=00:30:00 ``` **To request a V100 (Reliable):** ```bash salloc --partition=v100 --gres=gpu:v100:1 --time=00:30:00 ``` Once inside (prompt changes to `tgXXX`), **reactivate your environment** and test: ```bash mamba activate vlm_env python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}'); print(f'Device: {torch.cuda.get_device_name(0)}')" ``` If it says `True` and `NVIDIA A100`, you win! ### Option B: Batch Jobs (Real Training) For long training runs (e.g., 24 hours), create a script called `run.sh`: ```bash #!/bin/bash #SBATCH --job-name=vlm_train #SBATCH --output=logs/%j.out #SBATCH --partition=a100 # or v100 #SBATCH --gres=gpu:a100:1 # or gpu:v100:1 #SBATCH --time=24:00:00 source $WORK/miniforge3/bin/activate vlm_env python train.py ``` Submit it with: `sbatch run.sh` --- ## Summary Cheat Sheet 1. **Connect:** VS Code -> `tinyx`. 2. **Workspace:** `cd $WORK`. 3. **Install:** Run installs on `tinyx` (Login Node). 4. **Debug:** `salloc ...` to get an interactive GPU. 5. **Train:** `sbatch run.sh` for long jobs. Good luck with your experiments! 🚀