curriculum-plan

name	curriculum-plan
description	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.

4-Month Curriculum: CUDA + C++20 + Spatial Intelligence

Audience and intent

A strong Python/PyTorch engineer with deep ML-systems experience goes from zero to advanced in C++20 and CUDA on an NVIDIA DGX Spark, lands in Spatial Intelligence (NVIDIA Cosmos + Gaussian splatting + multi-view geometry), and ships a NextJS app driven by a LangChain DeepAgent that talks to home cameras/sensors and serves models from Spark or AWS.

Bias: rigor over reproducibility. Depth over breadth. Applied over academic, but every applied lab is anchored to a primary text or paper.

Time budget

• ~20 hrs/week of focused work (3 hrs/weekday + a long weekend session).
• 4 months = 16 weeks = 16 labs.
• Weeks 4, 8, 12, 16 are integration/checkpoint weeks where the deliverable is harder and the rubric is stricter.

The arc

Month	Theme	Big deliverable
1	C++20 + CUDA foundations	Tiled FP32 GEMM hitting ≥ 70% of cuBLAS on Spark
2	Advanced CUDA + CV foundations	Tensor-core (BF16) GEMM ≥ 60% of cuBLAS, fused attention kernel, GPU image rectification
3	Multi-view geometry + Cosmos	8-point algorithm on GPU, fine-tuned Cosmos-Predict on a custom video dataset, Gaussian-splatting scene reconstruction
4	Production + DeepAgents	Cosmos served via TensorRT-LLM + Triton on Spark, dual-target deploy (Spark + SageMaker/Bedrock), NextJS DeepAgent answering grounded questions about a real home scene

Weekly index

Month 1 — Foundations (month-1-foundations.md)

• Week 1: Modern C++20 essentials + CUDA Hello (PMPP Ch 1-3, Iglberger Ch 1-3, Stroustrup PPP3 selected)
• Week 2: Memory hierarchy + tiled GEMM (PMPP Ch 4-5)
• Week 3: Reductions, scans, atomics, warp primitives (PMPP Ch 6, 10, 11)
• Week 4 (checkpoint): GEMM target — ≥ 70% of cuBLAS FP32

Month 2 — Advanced CUDA + CV foundations (month-2-cuda-advanced.md)

• Week 5: Tensor cores via WMMA + CUTLASS (PMPP Ch 16, CUTLASS docs)
• Week 6: Fused attention kernel from scratch (FlashAttention paper)
• Week 7: Multi-stream / async / cuda::pipeline + (optional) two-Spark NCCL
• Week 8 (checkpoint): CV foundations — Torralba Ch 1-6, Hartley §1-6; lab: GPU image rectification + undistortion at 4K30

Month 3 — Multi-view geometry + Cosmos (month-3-spatial-intelligence.md)

• Week 9: Two-view geometry + 8-point on GPU (Hartley §11)
• Week 10: Stereo + simple SfM (Hartley §11-13, Szeliski §11-12)
• Week 11: NVIDIA Cosmos-Predict / Cosmos-Reason — fine-tune on a custom dataset
• Week 12 (checkpoint): Train a Gaussian-splatting scene from your own captured data; render at ≥ 30 FPS at 1080p on Spark

Month 4 — Production + DeepAgents (month-4-production-deepagents.md)

• Week 13: TensorRT / TensorRT-LLM optimization, FP8 quantization on Blackwell
• Week 14: Dual deploy — Triton on Spark + SageMaker (BYOC) endpoint with the same model
• Week 15: NextJS + LangChain DeepAgents app, sensor ingestion (RTSP / RealSense)
• Week 16 (capstone): End-to-end home spatial-intelligence agent, written up paper-style

The rubric (every lab graded on this)

5 axes, 0–4 each, 20 total, 14 to advance. Weeks 4/8/12/16 require 17.

Axis	4 (Excellent)	2 (Pass)	0 (Fail)
Correctness	Tests pass at all sizes incl. edge cases; max-error within 2× theoretical	Tests pass at the lab's required sizes	Tests fail or no tests exist
Performance	Exceeds the lab's perf target	Hits the perf target	Below 50% of target
Idiom	C++20 (concepts/ranges/RAII), CUDA (cooperative groups, async copies, tensor cores where relevant)	Modern but not exemplary	Raw new, no concepts, blocking memcpys
Profile evidence	Nsight Systems trace + Nsight Compute report committed; bottleneck named	One report committed	No profiling artifacts
Writeup	report/LAB.md with hypothesis → method → results → next steps; cites primary sources	Has results	Missing or shallow

Daily loop in Cursor

1. /start-lab N → curriculum-mentor opens the lab.
2. Read the assigned chapters first. Do not write code before reading.
3. Implement in labs/lab-NN-*/. Use cpp20-tutor and cuda-tutor as you go. Follow the ## Plan of work checklist in the lab's LAB.md.
4. After it compiles, /review-cuda.
5. After it passes tests, /profile-kernel.
6. After perf target hit, /lab-report.
7. End of lab: /checkpoint.

Stretch tracks

If you blow through the schedule:

• Compiler track: read the PTX ISA + write a Triton kernel and compare to your hand CUDA.
• Distributed track: pair two Sparks via ConnectX-7, run FSDP on a 70B model with expert parallelism.
• Robotics track: integrate Isaac Lab simulator and have the Month-4 DeepAgent control a simulated robot in addition to home sensors.

Reading order quick-reference

Weeks 1-4: PMPP Ch 1-12, Iglberger Ch 1-7, Stroustrup PPP3 Ch 4-7, 12-22. Weeks 5-8: PMPP Ch 13-17, Iglberger Ch 8-end, Torralba Ch 1-6, Hartley §1-6. Weeks 9-12: Hartley §11-15, Szeliski §11-13, Cosmos paper(s), 3DGS paper. Weeks 13-16: TensorRT-LLM docs, Triton docs, SageMaker BYOC docs, Bedrock custom-model-import docs, LangChain DeepAgents docs, Next.js 15 + Vercel AI SDK docs.