cuda-spatial-intelligence-lab

Working with NVIDIA Cosmos World Foundation Models (Cosmos-Predict, Cosmos-Reason, Cosmos-Transfer) on DGX Spark — pulling containers, running baseline inference, fine-tuning on custom data, and exporting for production. Use when the user is on Lab 11+ or otherwise interacting with Cosmos.

Step-by-step workflow for writing a new CUDA kernel from spec to profile-validated implementation on DGX Spark (sm_121). Use when starting a new kernel, refactoring an existing one, or porting Python/PyTorch logic to CUDA.

The master 4-month cuda-spatial-intelligence-lab curriculum. Use when the user asks about the overall plan, what week they are on, what to study next, or for the rubric used to grade weekly checkpoints. Read the relevant month file for week-level detail.

Bootstrap a fresh NVIDIA DGX Spark for this curriculum — verify hardware, log into NGC, pull baseline ARM64 containers, install CUDA-13/CMake/Ninja/Nsight host tooling, and prove the toolchain works with the standard CMake template. Use when starting work on a new Spark.

How to author a rigorous report/LAB.md for a curriculum lab — the writeup that is graded on the Writeup axis of the rubric and shipped publicly with the repo. Use when running /lab-report or finishing a lab.

Build the Month-4 NextJS application backed by a LangChain DeepAgent that consumes home cameras/sensors and routes inference to Spark (Triton) or AWS (SageMaker/Bedrock). Use when scaffolding the app, defining tools, wiring SSE streaming, or persisting agent memory.

Canonical pattern for wrapping our CUDA kernels as PyTorch custom ops via torch.utils.cpp_extension. Use proactively when a lab from Lab 02 onward needs a python/ wrapper, or when a production kernel needs to survive torch.compile / TensorRT export.

Deploy a model trained on DGX Spark to AWS — either as a SageMaker BYOC real-time/async endpoint, or as a Bedrock custom-model import (for supported architectures). Covers ECR, container contract, autoscaling, and verifying the same model produces consistent outputs as the local Spark Triton deployment.

Reference of modern C++20 idioms used throughout the lab — RAII wrappers for CUDA resources, concept-constrained templates, ranges-based pipelines, std::span/mdspan views, designated-initializer config structs, and std::expected-style error handling. Use when writing or reviewing host-side C++.

The lab's standard Nsight Systems + Nsight Compute workflow for any CUDA kernel claiming a performance number. Use after a kernel passes correctness tests and before any "this is fast" claim is made.

The 5-axis rubric and grading workflow used at the end of every week (and stricter in checkpoint weeks 4/8/12/16). Use when running /checkpoint or evaluating whether the user is ready to advance.