(kempner_cluster:new_user_checklist)= # New User Checklist Use this checklist when onboarding to the Kempner AI cluster for the first time. It is intended as a step-by-step path through the main setup tasks, with links to the handbook sections that explain each topic in more detail. ## Before Requesting Access - [ ] Confirm that your work is eligible for Kempner AI cluster access. See {doc}`Overview of Cluster `. - [ ] Review the expected use cases, fair-use expectations, GPU-only policy, and communication norms in {doc}`Cluster Usage Policies `. ## Request Your FASRC and Kempner AI Cluster Accounts ### FASRC Account If you already have a FASRC account set up, you may jump to the *Kempner AI Cluster Account* section below. - [ ] Request a FASRC account through the [FASRC account request portal](https://portal.rc.fas.harvard.edu/request/account/new). - [ ] Select the correct PI and approver for your role. See {doc}`Introduction and Basics `. - [ ] Wait for your approver to approve the account request in the FASRC portal. - [ ] Set your FASRC account password when your account is created. ### Kempner AI Cluster Account Once you have an active FASRC account, you can request access to the Kempner AI cluster: - [ ] Reach out to your PI and fill out the Kempner AI cluster account request form. Your PI will guide you through the form. See {doc}`Introduction and Basics ` for whom to contact. - [ ] Allow up to two business days for the PI to confirm approval and for the account to be set up on the Kempner AI cluster. - [ ] If you have not heard back, email [rchelp@rc.fas.harvard.edu](mailto:rchelp@rc.fas.harvard.edu) with the subject `kempner cluster account`. ## Set Up Required Authentication - [ ] Install and configure OpenAuth two-factor authentication. See {ref}`kempner_cluster:installing_openauth_2fa`. - [ ] Install the Cisco Secure Client (formerly AnyConnect) if you plan to use Open OnDemand or other VPN-only FASRC services. - [ ] Configure the FASRC VPN using `vpn.rc.fas.harvard.edu`. See {doc}`Accessing the Cluster `. - [ ] Confirm that you can generate an OpenAuth code from your phone, password manager, Duo Mobile, or Java desktop app. ## Log In for the First Time - [ ] Connect by SSH: ```bash ssh @login.rc.fas.harvard.edu ``` See {ref}`ssh_access`. - [ ] Confirm that you can log in with your FASRC password and OpenAuth code. - [ ] Remember that login nodes are for file management, job submission, and lightweight tasks only. Do not run compute-heavy code on login nodes. - [ ] If you prefer a browser interface, connect to the VPN and open [Open OnDemand](https://vdi.rc.fas.harvard.edu/). See {ref}`ondemand_access` and {ref}`general_hpc_concepts:open_ondemand`. ## Learn Where Files Should Go - [ ] Find your home directory (`/n/home/`) and understand its 100 GB persistent storage limit. Check current usage with `df -h ~/`. See {doc}`Storage Options <../storage_and_data_transfer/understanding_storage_options>`. - [ ] Find your lab directory at `/n/holylabs/LABS/` for persistent lab storage (4 TB per lab). See {doc}`Storage Options <../storage_and_data_transfer/understanding_storage_options>`. - [ ] Find your scratch directory under `$SCRATCH/` (typically `/n/netscratch/`) for active high-performance work (50 TB per lab), and review the 90-day scratch retention policy. See {doc}`Storage Options <../storage_and_data_transfer/understanding_storage_options>`. - [ ] Choose an appropriate data transfer method before moving files: `scp` or `rsync` for smaller transfers, and Globus for large transfers. See {ref}`storage_and_data_transfer:data_transfer`. ## Set Up a Working Environment - [ ] Learn how to inspect and load software modules with `module avail`, `module load`, `module list`, and `module purge`. See {ref}`development_and_runtime_envs:software_module_and_environment_management`. - [ ] Configure conda to use `conda-forge` as the default channel. See {ref}`development_and_runtime_envs:using_conda_env:conda_forge_default`. - [ ] Create a project-specific conda environment. See {ref}`development_and_runtime_envs:using_conda_env:creation`. - [ ] If you will use Jupyter or JupyterLab, install `ipykernel` in your conda environment. See {ref}`development_and_runtime_envs:using_conda_env:jupyter`. - [ ] If you will use VSCode, review the remote development workflow in {ref}`development_and_runtime_envs:using_vscode_for_remote_development`. ## Run a First GPU Job - [ ] Learn the basics of SLURM partitions, accounts, and job submission. The Kempner partitions are GPU-only, so every job must request a GPU with `--gres=gpu:`. See {doc}`Understanding SLURM <../general_hpc_concepts/understanding_slurm>`. - [ ] Identify which SLURM (fairshare) account you should charge jobs to. If you are in multiple groups, confirm the right account before submitting jobs. - [ ] Start with a small interactive GPU allocation to test your environment, then connect to the allocated node. See {doc}`Job Submission Basics <../general_hpc_concepts/job_submission_basics>`. ```bash salloc --partition=kempner --account= --time=0-01:00 --mem=64G --gres=gpu:1 --cpus-per-task=16 ``` - [ ] Submit a small batch job with `sbatch` after your interactive test works. See {ref}`resource_management:job_submission_basics:batch_jobs`. - [ ] Check that your job requests a GPU and uses Kempner resources responsibly. Revisit {doc}`Cluster Usage Policies ` before scaling up. ## Get Help and Stay Connected - [ ] Join the `#cluster-users` channel in the Kempner Slack space. - [ ] Use the Kempner Slack channel for Kempner-specific workflow questions, community advice, and handbook update suggestions. - [ ] Review FASRC training and support options in {doc}`Support and Troubleshooting <../../s8_support/README>`. - [ ] Direct your questions to [rchelp@rc.fas.harvard.edu](mailto:rchelp@rc.fas.harvard.edu) with the subject line containing the word `kempner`, such as `kempner account setup`.