nvidia-cuopt-developer
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA,
nvidia-cuopt-install
Install cuOpt for Python, C, or as a server (pip, conda, Docker) — system
nvidia-cuopt-numerical-optimization-api-c
LP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is
nvidia-cuopt-numerical-optimization-api-cli
LP, MILP, and QP (beta) with cuOpt — CLI only (MPS files, cuopt_cli).
nvidia-cuopt-numerical-optimization-api-python
Solve Linear Programming (LP), Mixed-Integer Linear Programming (MILP),
nvidia-cuopt-numerical-optimization-formulation
Numerical optimization (LP, MILP, QP) — concepts, problem-text parsing,
nvidia-cuopt-routing-api-python
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when
nvidia-cuopt-routing-formulation
Vehicle routing (VRP, TSP, PDP) — problem types and data requirements.
nvidia-cuopt-server-api-python
cuOpt REST server — start server, endpoints, Python/curl client examples.
nvidia-cuopt-server-common
cuOpt REST server — what it does and how requests flow. Domain concepts;
nvidia-cuopt-skill-evolution
After solving a non-trivial problem, detect generalizable learnings and
nvidia-cuopt-user-rules
Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server).
nvidia-dali-dynamic-mode
Use when writing DALI data loading or preprocessing code with `nvidia.dali.experimental.dynamic`
nvidia-deepstream-dev
NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API.
nvidia-megatron-bridge-adding-model-support
Guide for adding support for new LLM or VLM models in Megatron-Bridge.
nvidia-megatron-bridge-build-and-dependency
Dev environment setup for Megatron Bridge — container-based development,
nvidia-megatron-bridge-bump-dependency
Bump a pinned dependency (TransformerEngine, Megatron-LM, NRX, etc.),
nvidia-megatron-bridge-cicd
CI/CD reference for Megatron Bridge — pipeline structure, commit and
nvidia-megatron-bridge-linting-and-formatting
Code style and quality rules for Megatron Bridge — ruff configuration,
nvidia-megatron-bridge-mlm-bridge-training
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real
nvidia-megatron-bridge-multi-node-slurm
Convert single-node scripts to multi-node Slurm sbatch jobs and debug
nvidia-megatron-bridge-nemo-rl-e2e-testing
External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider
nvidia-megatron-bridge-parity-testing
Structured framework for verifying numerical parity of HF-to-MCore weight
nvidia-megatron-bridge-perf-activation-recompute
Validate and use selective and full activation recompute in Megatron
nvidia-megatron-bridge-perf-cpu-offloading
Validate and use CPU offloading in Megatron Bridge, including layer-level
nvidia-megatron-bridge-perf-cuda-graphs
Validate and use CUDA graph capture in Megatron Bridge, including local
nvidia-megatron-bridge-perf-expert-parallel-overlap
Validate and use MoE expert-parallel communication overlap in Megatron-Bridge,
nvidia-megatron-bridge-perf-hierarchical-context-parallel
Operational guide for enabling hierarchical context parallelism in Megatron-Bridge,
nvidia-megatron-bridge-perf-megatron-fsdp
Operational guide for enabling Megatron FSDP in Megatron-Bridge, including
nvidia-megatron-bridge-perf-memory-tuning
Techniques for reducing peak GPU memory in Megatron Bridge — expandable
nvidia-megatron-bridge-perf-moe-comm-overlap
MoE expert-parallel communication overlap in Megatron Bridge. Covers
nvidia-megatron-bridge-perf-moe-dispatcher-selection
Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP)
nvidia-megatron-bridge-perf-moe-hardware-configs
Representative MoE training playbooks by hardware platform and model
nvidia-megatron-bridge-perf-moe-long-context
Long-context MoE training guidance for Megatron Bridge. Covers CP sizing,
nvidia-megatron-bridge-perf-moe-optimization-workflow
Systematic workflow for MoE training optimization in Megatron Bridge,
nvidia-megatron-bridge-perf-moe-vlm-training
Practical guidance for training MoE VLMs in Megatron Bridge. Compares
nvidia-megatron-bridge-perf-parallelism-strategies
Operational guide for choosing and combining parallelism strategies in
nvidia-megatron-bridge-perf-sequence-packing
Validate and use packed sequences and long-context training in Megatron-Bridge,
nvidia-megatron-bridge-perf-tp-dp-comm-overlap
Operational guide for enabling TP, DP, and PP communication overlap in
nvidia-megatron-bridge-recipe-recommender
Recommend and customize Megatron Bridge recipes for a user's model, GPU
nvidia-megatron-bridge-resiliency
Resiliency features in Megatron Bridge including fault tolerance, straggler
nvidia-megatron-bridge-testing
Testing reference for Megatron Bridge — unit and functional test layout,
nvidia-megatron-bridge-verl-e2e-testing
External verl end-to-end validation workflow for Megatron-Bridge model/provider
nvidia-megatron-core-build-and-dependency
Container-based dev environment setup and dependency management for Megatron-LM.
nvidia-megatron-core-bump-base-image
Bump the NVIDIA PyTorch base image using the nvcr.io/nvidia/pytorch YY.MM py3 tag
nvidia-megatron-core-cicd
CI/CD reference for Megatron-LM. Covers CI pipeline structure, PR scope
nvidia-megatron-core-create-issue
Investigate a failing GitHub Actions run or job and create a GitHub issue
nvidia-megatron-core-linting-and-formatting
Linting and formatting for Megatron-LM. Covers running autoformat.sh,
nvidia-megatron-core-nightly-sync
Domain knowledge for the nightly main-to-dev sync workflow. Covers merge
nvidia-megatron-core-onboard-gb200-1node-tests
Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped
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