Launch a GPU workspace with PyTorch, TensorFlow, or the CUDA stack ready to go - then open it in JupyterLab, VS Code, or a terminal. Pick a template and you're training in minutes.
Save a GPU workspace as a template and hand it to a labmate - everyone starts from the same PyTorch, the same CUDA, the same packages. Your conda env and dotfiles persist across every restart.
A web console for the shared GPU in the corner - launch your PyTorch or CUDA environment, keep it across restarts, and see who's on the card right now.
Built-in templates include a CUDA / cuDNN base with Conda and JupyterLab. Bring the rest with pip or conda - or build your own image.
A workspace is a deep-learning environment you launch in a click, keep across restarts, and hand to the whole lab - bounded by fair CPU, GPU, and memory limits.
Pick a PyTorch, TensorFlow, or CUDA template and it launches GPU-ready - open it in JupyterLab, VS Code, or a terminal. Bring or build your own image whenever you need to.
Per-workspace home keeps conda envs, dotfiles, pip installs, and shell history across every stop and start. /work survives re-creation; the HF cache downloads once.
Export a workspace template to a labmate, mount shared read-only datasets, and expose your own Gradio or Streamlit through a named LabPod port.
Give a user a whole GPU, or split one card by memory and compute so several share it at once on supported NVIDIA hosts.
Per-user CPU, memory, and GPU quotas, enforced by cgroups - not an honor system. Disk and image storage are tracked and shown. Admins decide how much; researchers decide what to run.
Rootless Podman per Linux account - no Docker group, no root-equivalent access. Capability drop, no-new-privileges, localhost bind.
LabPod solves the shared-workstation problem: a 5 to 10 person research group needs browser access, fair resource boundaries, and real visibility without turning one server into an infrastructure project.
It is deliberately narrower than a scheduler, notebook platform, or container admin UI. One install command, one binary, one SQLite file, and rootless Podman workspaces owned by real Linux users.
Your hardware, image, packages, files, and long-running jobs stay on the lab workstation.
JupyterLab is one launcher. The same workspace can also expose VS Code, TensorBoard, MLflow, terminals, and custom services.
No queue for interactive research. Quotas, live occupancy, and admin force-stop handle coordination on one node.
Researchers launch workspaces and apps, not raw container primitives. Admins still get policy, monitoring, and audit controls.
Start simple with whole-GPU passthrough; add fractional sharing when the lab grows. The same workspace, different boundaries.
One or more GPUs assigned to a user, with locks so nothing double-books. Single-GPU training and multi-GPU DDP both work out of the box.
Share a single GPU between several researchers by capping memory and compute per workspace on supported NVIDIA hosts. Where the hardware supports partitioning, slices are fully isolated.
Cookbook templates make the first run concrete: Hugging Face notebooks, PyTorch scientific ML with TensorBoard logs, experiment tracking, and code editing all live behind the same LabPod session.
Run a Hugging Face starter notebook from the LabPod Cookbook, with model weights cached under /work/.hf-cache.
The PyTorch Scientific ML cookbook logs training curves to /work/runs, ready for the TensorBoard launcher.
Track cookbook-style sweeps and compare runs from inside the same workspace, exposed through LabPod's protected proxy.
Edit scripts such as the cookbook's multi-GPU DDP example and run them from a persistent browser terminal.
Who holds which GPU, how much memory is live, which workspaces look idle, how much image storage each user takes - on one page.
Research images often have a shell but no web terminal - or none of the usual launchers at all. LabPod injects a minimal, read-only runtime so a browser terminal and live monitoring just work.
Every plan is licensed per GPU server. Start with a full-featured 90-day trial, then license the box when you are ready.
Evaluate on your box · Any use
Full features, no card required
Personal · Academic · Non-profit
Billed per server, per year
Thanks. We will email you when checkout opens for the Standard plan.
Business · Research institutes · For-profit
Billed per server, per year
Thanks. We will email you when checkout opens for the Pro plan.
Standard is licensed for personal, academic, and non-profit use. Companies, for-profit organizations, and research institutes need Pro.
Need more GPUs, multiple servers, or a perpetual license? Talk to us.
One idempotent script sets up Podman and the LabPod service. Then your lab logs in and starts launching workspaces.
$ curl -fsSL https://labpod.ai/install.sh | sudo bash