Apache TVM Repo Skill

Use this skill when a task involves Apache TVM as a machine-learning compiler stack: installing or building TVM, compiling Relax/TIR modules, authoring TIRx kernels, scheduling/tuning tensor programs, or deploying through TVM RPC.

This is a router. Read the focused sub-skill before making detailed API, configuration, backend, or verification decisions.

Fast routing

User task signal	Read
Build from source, validate import, choose CMake/LLVM/RPC/CUDA flags, fix libtvm or tvm-ffi import failures	sub-skills/install-build/SKILL.md
Create or import Relax IR, apply optimization pipelines, call tvm.compile, export/load executables, decide target/pipeline boundaries	sub-skills/relax-compile/SKILL.md
Write TIRx TVMScript kernels, reason about execution scope, layouts, tile primitives, lowering, CUDA/Blackwell prerequisites, or TIRx GPU tests	sub-skills/tirx-kernels/SKILL.md
Use tvm.s_tir.Schedule, transformations, dlight, meta-schedule, tune_tir, builders/runners/databases, or tune CPU/GPU TIR	sub-skills/s-tir-tuning/SKILL.md
Start/query TVM RPC tracker/server/proxy, cross-compile, upload/load remote modules, debug keys/timeouts/target-host issues	sub-skills/rpc-deployment/SKILL.md

Minimal install/import orientation

• Distribution name: apache-tvm; import name: tvm.
• A package install starts with python -m pip install apache-tvm; add optional dependencies only for the selected workflow, such as meta-schedule, RPC, Torch frontend, or CUDA helper packages.
• Core runtime dependencies from package metadata include apache-tvm-ffi, numpy, ml_dtypes, and typing_extensions.
• Optional extras are workflow-specific: meta-schedule adds xgboost, rpc adds tornado, psutil, and cloudpickle, popen-pool adds psutil and cloudpickle, torch adds torch, and cuda adds cuda-bindings.
• For a source checkout, prefer a built build/ directory plus PYTHONPATH pointing at the checkout's python/ tree. Avoid editable installs when working with multiple TVM checkouts because they can silently import a different checkout's Python package.

Minimal verification from this skill directory:

python scripts/check_tvm_runtime.py --expect-backend llvm

Use scripts/check_tvm_runtime.py for a safe import/backend probe against an installed package or a caller-provided checkout. Read references/troubleshooting.md when an import, runtime library, backend, or optional dependency mismatch appears.

Backend truthfulness rules

• A CPU/LLVM import check proves only CPU/LLVM readiness. It does not prove CUDA, ROCm, Metal, Vulkan, RPC device execution, TIRx GPU execution, or Blackwell kernel-library correctness.
• TVM can compile for a target without a matching local device in some cases, but execution and timing require the target runtime/device to exist.
• TIRx Blackwell and external tirx-kernels workflows require compute capability 10.0-class NVIDIA hardware and additional packages. Do not present them as verified from an A100/CPU-only environment.

Verify before relying on this skill

Read references/repo-provenance.md before assuming this skill matches a different checkout or release. Refresh the skill when the commit, major package metadata, public API shape, or TIRx/S-TIR/RPC workflow evidence changes.

Structured router metadata lives in references/repo-routing-metadata.json.