This repository contains the basic steps to start running scripts and notebooks on the EPFL RCP cluster. We provide scripts that make your life easier by automating most of the boilerplate. The setup is loosely based on infrastructure from TML/CLAIRE and earlier scripts by Atli.

The RCP cluster provides:

• GPUs: A100 (40GB/80GB), H100 (80GB), H200 (140GB), V100
• Stack: Docker (containers), Kubernetes (orchestration), run:ai (scheduler)

• FAQ: Check the frequently asked questions page
• Slack: Reach out on #-cluster or #-it channels
• Resources: See quick links below

Content overview:

• Quick Start
• Setup Guide
  • 1. Pre-setup (Access & Repository)
  • 2. Setup Tools on Your Machine
  • 3. Login to the Cluster
  • 4. Configure Your .env File
  • 5. Start Your First Job
• Using VS Code
• Recommended Workflow
• csub.py Usage and Arguments
• Advanced Topics
• Reference

# Start an interactive job with 1 GPU python csub.py -n sandbox # Connect to your job runai exec sandbox -it -- zsh # Run your codecd /mloscratch/homes/<your_username> python main.py # Or start a training job in one command python csub.py -n experiment --train --command "cd /mloscratch/homes/<your_username>/<your_code> python main.py"

1. Request cluster access

Ask Jennifer or Martin to add you to the runai-mlo group: https://groups.epfl.ch/

2. Prepare your code repository

While waiting for access, create a GitHub repository for your code. This is best practice regardless of our cluster setup.

3. Set up experiment tracking (optional)

• Weights & Biases: Create an account at wandb.ai and get your API key
• Hugging Face: Create an account at huggingface.co and get your token (if using their models)

Download and install kubectl v1.30.11 (matching the cluster version):

# macOS with Apple Silicon curl -LO "https://dl.k8s.io/release/v1.30.11/bin/darwin/arm64/kubectl"# Linux (AMD64)# curl -LO "https://dl.k8s.io/release/v1.30.11/bin/linux/amd64/kubectl"# Install chmod +x ./kubectl sudo mv ./kubectl /usr/local/bin/kubectl sudo chown root: /usr/local/bin/kubectl

See https://kubernetes.io/docs/tasks/tools/install-kubectl/ for other platforms.

Download the kube config file to ~/.kube/config:

curl -o ~/.kube/config https://raw.githubusercontent.com/epfml/getting-started/main/kubeconfig.yaml

Download and install the run:ai CLI:

# macOS with Apple Silicon wget --content-disposition https://rcp-caas-prod.rcp.epfl.ch/cli/darwin # Linux (replace 'darwin' with 'linux')# wget --content-disposition https://rcp-caas-prod.rcp.epfl.ch/cli/linux# Install chmod +x ./runai sudo mv ./runai /usr/local/bin/runai sudo chown root: /usr/local/bin/runai

runai login

# List available projects runai list projects # Set your default project runai config project mlo-$GASPAR_USERNAME

kubectl get nodes

You should see the RCP cluster nodes listed.

This setup keeps all personal configuration and secrets in a local .env file (never committed to git).

git clone https://github.com/epfml/getting-started.git cd getting-started cp user.env.example .env

Open .env in an editor and configure:

To ensure correct file permissions:

# SSH into HaaS machine (use your Gaspar password) ssh <your_gaspar_username>@haas001.rcp.epfl.ch # Get your UID id

Copy the number after uid= (e.g., uid=123456) into LDAP_UID in your .env file.

Optionally configure in .env:

• WANDB_API_KEY – Weights & Biases API key
• HF_TOKEN – Hugging Face token
• GIT_USER_NAME / GIT_USER_EMAIL – Git identity for commits
• GitHub SSH keys (auto-loaded from ~/.ssh/github if empty):
  • GITHUB_SSH_KEY_PATH / GITHUB_SSH_PUBLIC_KEY_PATH (to override default paths)

The secret is automatically synced when starting a job. To manually sync:

python csub.py --sync-secret-only

python csub.py -n sandbox

This can take a few minutes. Monitor the status:

# List all jobs runai list # Check specific job status runai describe job sandbox

Once the status shows Running:

runai exec sandbox -it -- zsh

You should now be inside a terminal on the cluster! 🎉

Inside the pod, clone your code into your scratch home folder:

cd /mloscratch/homes/<your_username> git clone https://github.com/<your_username>/<your_repo>.git cd<your_repo>

The default image includes uv as the recommended package manager (pip also works):

# Create and activate virtual environment uv venv .venv source .venv/bin/activate # Install dependencies uv pip install -r requirements.txt

python main.py

If you configured WANDB_API_KEY or HF_TOKEN in .env, authentication should work automatically.

For remote development on the cluster:

1. Install extensions

   • Kubernetes
   • Dev Containers

2. Attach to your pod

   • Navigate to: Kubernetes → rcp-cluster → Workloads → Pods
   • Right-click your pod → Attach Visual Studio Code
   • Open /mloscratch/homes/<your_username> in the remote session

For detailed instructions, see the Managing Workflows guide.

For detailed workflow guidance, see the Managing Workflows guide.

The csub.py script is a thin wrapper around the run:ai CLI that simplifies job submission by:

• Reading configuration and secrets from .env
• Syncing Kubernetes secrets automatically
• Constructing and executing runai submit commands

python csub.py -n <job_name> -g <num_gpus> -t <time> --command "<cmd>" [--train]

# CPU-only pod for development python csub.py -n dev-cpu # Interactive development pod with 1 GPU python csub.py -n dev-gpu -g 1 # Training job with 4 A100 GPUs python csub.py -n experiment --train -g 4 --command "cd /mloscratch/homes/user/code; python train.py"# Use specific GPU type python csub.py -n my-job -g 2 --node-type h100 --train --command "..."# Dry run (see command without executing) python csub.py -n test --dry --command "..."

After submitting, csub.py prints useful follow-up commands:

runai describe job <name># Check job status runai logs <name># View logs runai exec<name> -it -- zsh # Connect to pod runai delete job <name># Delete job

Run python csub.py -h for the complete help text.

For detailed guides on day-to-day operations, see the Managing Workflows guide:

• Pod management – Commands to list, describe, delete jobs
• Important workflow notes – Job types, GPU selection, best practices
• HaaS machine – File transfer between storage systems
• File management – Understanding storage (mloscratch, mlodata1, mloraw1)

• Run:ai CLI directly: See docs/runai_cli.md for using run:ai without csub.py
• Custom Docker images: See Creating Custom Images
• Distributed training: See docs/multinode.md for multi-node jobs

If you need custom dependencies:

1. Get registry access

   • Login at https://ic-registry.epfl.ch/ and verify you see the MLO project
   • The runai-mlo group should already have access

2. Install Docker

   brew install --cask docker # macOS

   If you get "Cannot connect to the Docker daemon", run Docker Desktop GUI first.

3. Login to registry

   docker login ic-registry.epfl.ch # Use GASPAR credentials

4. Modify and publish

   • Edit docker/Dockerfile as needed
   • Use docker/publish.sh to build and push
   • Important: Rename your image (e.g., mlo/<your-username>:tag) to avoid overwriting the default

Example workflow:

docker build . -t <your-tag> docker tag <your-tag> ic-registry.epfl.ch/mlo/<your-tag> docker push ic-registry.epfl.ch/mlo/<your-tag>

See also Matteo's custom Docker example.

To access services running in your pod (e.g., Jupyter):

kubectl get pods kubectl port-forward <pod_name> 8888:8888

Then access at http://localhost:8888

For multi-node training across several compute nodes, see the detailed guide:

• Documentation: docs/multinode.md
• Official docs: https://docs.run.ai/v2.13/Researcher/cli-reference/runai-submit-dist-pytorch/

├── csub.py # Job submission wrapper (wraps runai submit) ├── utils.py # Python helpers for csub.py ├── user.env.example # Template for .env (copy and configure) ├── docker/ │ ├── Dockerfile # uv-enabled base image (RCP template) │ ├── entrypoint.sh # Runtime bootstrap script │ └── publish.sh # Build and push Docker images ├── kubeconfig.yaml # Kubeconfig template for ~/.kube/config └── docs/ ├── faq.md # Frequently asked questions ├── managing_workflows.md # Day-to-day operations guide ├── README.md # Architecture deep dive ├── runai_cli.md # Alternative run:ai CLI workflows ├── multinode.md # Multi-node/distributed training └── how_to_use_k8s_secret.md # Kubernetes secrets reference 

For technical details about the Docker image, entrypoint script, environment variables, and secret management:

Read the architecture explainer: docs/README.md

Topics covered:

• Runtime environment and entrypoint
• Permissions model and shared caches
• uv-based Python workflow
• Images and publishing
• Secrets, SSH, and Kubernetes integration

RCP Resources

• RCP Main Page
• Documentation
• Dashboard
• Docker Registry
• Quick Start Guide

run:ai Documentation

• Official run:ai docs

Related Resources

• Compute and Storage @ CLAIRE – Similar setup by colleagues

MLO Cluster Repositories (OUTDATED)

These repositories contain shared tooling and infrastructure (by previous PhD students). Contact Martin for editor access. They are outdated and not maintained anymore.

• epfml/epfml-utils – Python package for shared tooling (pip install epfml-utils)
• epfml/mlocluster-setup – Base images and setup for semi-permanent machines