Example notebooks (Cookbook)

LabPod Cookbook is a separate, public repository of ready-to-run environments and starter notebooks - the fastest way to go from “I have a workspace” to “I’m running something real,” across a range of research domains, not just deep learning.

Each entry in the cookbook is a small LabPod workspace-template bundle (a .tar file you import, same as any other workspace template) plus one or more notebooks or example files. The bundle and the notebook are separate on purpose - the bundle just describes the environment (base image, extra packages) and gets built once; your actual work lives in /work, which is what survives stopping and starting a workspace.

What’s there

Domain	Bundle	What it covers
Deep learning (PyTorch)	pytorch-scientific-ml	GPU basics, neural operators, physics-informed neural networks, U-Net, diffusion models, transformers, graph neural networks, reinforcement learning, multi-GPU training
Deep learning (Hugging Face)	huggingface	transformers pipelines - sentiment classification, text generation
Deep learning (MATLAB)	matlab-deep-learning	Image classification with MATLAB’s Deep Learning Toolbox - requires your own MATLAB license
Parallel programming	parallel-programming	The same problem solved with OpenMP, MPI, and CUDA - shared-memory, distributed-memory, and GPU parallelism side by side
Computational fluid dynamics	openfoam-cfd	OpenFOAM’s own lid-driven-cavity tutorial
Molecular dynamics	gromacs-md	GROMACS, pointing at the community-standard external tutorial
Quantum chemistry	quantum-chemistry	Electronic structure calculations with PySCF
Quantum computing	quantum-computing	Quantum circuit simulation with Qiskit
Cheminformatics	cheminformatics	Molecule parsing and similarity search with RDKit
Materials science	materials-science	Crystal structures and lattice optimization with ASE
Seismology	seismology	Real seismic waveform processing with ObsPy
Bioinformatics	bioinformatics-alignment	Read alignment with bwa and samtools
Statistics (R + Python)	r-statistics	Mixed models, GAMs, survival analysis, forecasting, and moving data between R and Python - with both RStudio Server and JupyterLab in one workspace

Most bundles expose a single app. r-statistics is the exception: from the same workspace you can open RStudio or JupyterLab (with R and Python kernels), over one shared Python install - so reticulate from R and the Python kernel are the same environment rather than two you maintain separately. It is CPU-only by design, since classical statistics is CPU- and memory-bound; its README shows how to add GPU PyTorch if you want it.

Each bundle is deliberately minimal - only the dependencies that specific domain needs, not one giant image with everything installed. Most work entirely offline with synthetic or bundled example data; a few (Hugging Face, bioinformatics reference genomes) need internet access or a license the first time you use them - each bundle’s own README says which.

How to use one

1. Pick a bundle from the table above and open its directory in the labpod-cookbook repo - its README has the exact details for that environment.
2. Download its .tar file (linked from the same README, or directly under dist/ in the repo) and import it in LabPod: open the Advanced tools menu → Workspace templates, open the My workspace templates tab, then press Import bundle.
3. Click Build, then Enable the workspace template, then create a workspace from it.
4. Open a terminal in the workspace and clone the repo to get the notebook(s):

   git clone https://github.com/LabPod/labpod-cookbook /work/labpod-cookbook

5. Open the notebook from /work/labpod-cookbook/<bundle>/ and run it.

See Workspaces & workspace templates for more on what importing and building a workspace template actually does.