Building from Source
Build Dynamo from source when you want to contribute code, test features on the development branch, or customize the build. If you just want to run Dynamo, the Local Installation guide is faster.
This guide covers Ubuntu and macOS. For a containerized dev environment that handles all of this automatically, see DevContainer.
1. Install System Libraries
Ubuntu:
Dynamo needs protoc 3.15 or newer:
- Ubuntu 24.04: the
protobuf-compilerpackage above is enough. - Ubuntu 22.04: also install protoc 30.2 from the protobuf releases:
macOS:
2. Install Rust
3. Create a Python Virtual Environment
Install uv if you don’t have it:
Create and activate a Python 3.12 virtual environment. If Python 3.12 is not installed, uv downloads it.
4. Install Build Tools
Maturin is the Rust-Python bindings build tool. The patchelf extra lets maturin patch native extension library paths during the build.
5. Build the Rust Bindings
Run one of these builds:
The minimal build leaves out the release wheel features: the standalone KV indexer, slot tracker and select service (some router tests start these), multimodal KV routing, the AISimulate perf model and S3 request traces.
To annotate Nsight Systems profiles, add nvtx to the feature list.
6. Install GPU Memory Service
7. Install the Wheel
Install Dynamo with a backend extra to pull the inference engine and its CUDA dependencies. Choose the backend you intend to run:
The base uv pip install -e . installs only the Dynamo runtime and frontend. A backend extra ([vllm], or [sglang]) will install the relevant framework dependencies to run an inference worker. For the TensorRT-LLM backend, use the tensorrtllm-runtime container instead of installing via uv pip to ensure the right dependencies are installed. See Local Installation for more details.
8. Verify the Build
You should see the frontend command help output.
DevContainer
VSCode and Cursor users can skip manual setup using pre-configured development containers. The DevContainer installs all toolchains, builds the project, and sets up the Python environment automatically.
Framework-specific containers are available for vLLM, SGLang, and TensorRT-LLM. See the DevContainer README for setup instructions.
Set Up Pre-commit Hooks
Before submitting PRs, install the pre-commit hooks to ensure your code passes CI checks:
Run checks manually on all files:
Troubleshooting
Missing system packages
If maturin develop fails with linker errors, verify all system dependencies are installed. On Ubuntu:
protoc failed: relay.proto: This file contains proto3 optional fields
Install protoc 30.2 as shown in step 1, then check that protoc --version prints libprotoc 30.2.
dynamo.indexer is not available in this build
Rebuild the bindings with the release-wheel --features list from step 5.
Virtual environment not activated
Maturin builds against the active Python interpreter. If you see errors about Python or site-packages, ensure your virtual environment is activated:
Disk space
The Rust target/ directory can grow to 10+ GB during development. If builds fail with disk space errors, clean the build cache:
vLLM worker fails to start: FlashInfer sampler JIT and CUDA 13 wheels
When you run a vLLM worker from a CUDA 13 source install, the worker can abort during startup with a FlashInfer JIT error:
The CUDA wheels resolved for a CUDA 13 install can be version-skewed: torch pins the runtime headers to 13.0, while vLLM’s tilelang dependency pulls nvidia-cuda-nvcc 13.2. FlashInfer compiles its sampler kernel with nvcc against those headers, and the version mismatch fails the build. This is tracked upstream at flashinfer#3493.
Set VLLM_USE_FLASHINFER_SAMPLER=0 so vLLM falls back to its native sampler:
Next Steps
- Contribution Guide — Workflow for contributing code
- Examples — Explore the codebase
- Good First Issues — Find a task to work on