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How to install prime-rl and its optional dependencies. Use when setting up the project, installing extras like DeepEP for multi-node expert parallelism, or troubleshooting dependency issues.

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prime-rl
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Install
$ npx -y skills add primeintellect-ai/prime-rl --skill install --agent claude-code

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How to install prime-rl and its optional dependencies. Use when setting up the project, installing extras like DeepEP for multi-node expert parallelism, or troubleshooting dependency issues.

SKILL.md

install.SKILL.md
name: install
description: How to install prime-rl and its optional dependencies. Use when setting up the project, installing extras like DeepEP for multi-node expert parallelism, or troubleshooting dependency issues.

Install

Clone + submodules

prime-rl is a monorepo with submodules. Use the install script when bootstrapping a fresh machine:

bash scripts/install.sh   # clones, inits submodules, installs uv, runs `uv sync --all-extras`

For an existing clone, init submodules explicitly:

git submodule update --init --recursive

Sync

uv sync                                    # slim
uv sync --group dev                        # + pytest, ruff, pre-commit
uv sync --all-extras                       # + extras (flash-attn, flash-attn-cute, …)
uv sync --all-extras --all-packages        # + all env packages (needed to train on them)
uv sync --package prime-rl --package gsm8k  # core + just one env

`uv sync --group dev` installs the `pre-commit` package but leaves the git hook inert until it's wired up — run `uv run pre-commit install` once per clone (see `README.md`'s Development section).

Environment packages are uv **workspace members**. Those under `deps/prime-envs/environments/*/*` are auto-discovered — adding a new env there needs no `pyproject.toml` change. verifiers' example envs are enumerated explicitly in `[tool.uv.workspace].members` (only the ones prime-rl trains or tests on) — to use another, add its path to the list. Members are opt-in: a plain `uv sync` / `--all-extras` does not install them (and would remove them if already present — re-run with `--all-packages`, or `--inexact` to keep them). Install all with `--all-packages`, or a subset with repeated `--package <env>` (include `--package prime-rl` to keep the core). If two envs pin conflicting transitive versions (all members share one lock), add the loser to `[tool.uv.workspace].exclude`.

When bumping a package past the workspace-wide `exclude-newer = "7 days"` window, add it (and any newly-required transitives) to `[tool.uv.exclude-newer-package]` before refreshing `uv.lock`.

Optional extras

CUDA kernels

Prebuilt wheels, pinned at a release in `[tool.uv.sources]`:

uv sync --extra kernels

No plain sync compiles CUDA — building from source stays an explicit, manual step (needs `nvcc` whose CUDA major matches torch's and the `deps/prime-kernels` submodule initialized), and overrides the wheel until the next sync:

git submodule update --init deps/prime-kernels
uv pip install --no-build-isolation -e deps/prime-kernels

See the `kernels` skill.

NemotronH (Mamba SSD kernels)

uv sync --extra gpu

Native NemotronH uses Mamba2 Triton SSD kernels and FLA's packed causal convolution. The pinned `mamba-ssm` source installs without `nvcc`; no separate `causal-conv1d` installation is needed. `uv sync --all-extras` includes these dependencies.

Trainer DeepEP backend

The `disagg` extra installs the prebuilt DeepEP wheel pinned in `[tool.uv.sources]`:

uv sync --extra disagg

The prime-kernels repository owns source builds for DeepEP, DeepGEMM, and TorchAO. Use its scripts when a local rebuild is required:

git submodule update --init deps/prime-kernels
bash deps/prime-kernels/scripts/install_ep_kernels.sh --wheel-dir /tmp/prime-kernels-wheels
uv pip install --reinstall --no-deps /tmp/prime-kernels-wheels/deep_ep-*.whl

The build needs a CUDA toolkit whose version matches torch. Set `TORCH_CUDA_ARCH_LIST` when the build host has no GPU or when the wheel must support more than the local GPU architecture.

Verify: `uv run python -c 'import deep_ep; print(deep_ep.__file__)'`.

llm-d router backend

Multi-node / disaggregated deployments can route through the upstream llm-d Endpoint Picker instead of `vllm-router` (set `[inference.router] type = "llm-d"`). It needs three native binaries — install once:

bash scripts/install_llmd.sh   # builds epp + pd-sidecar from a pinned llm-d-router commit (vendored Go), fetches envoy

Binaries land in `third_party/llmd/bin/{epp,envoy,pd-sidecar}` (a shared path, so SLURM nodes see them). `epp` is pinned to the commit that includes the `vllmhttp-parser` (PR #1248) so prime-rl's renderer/TITO `/inference/v1/generate` path routes correctly. Override the pin with `LLMD_ROUTER_REF=<sha>`. The EPP + Envoy + endpoints configs are rendered from `templates/llmd/*.yaml.j2` (included into the SLURM script); only the per-node IPv4 addresses are filled in inline at launch time.

Key files

  • `pyproject.toml` — dependencies, extras, dependency groups
  • `uv.lock` — pinned lockfile (refresh with `uv sync --all-extras`)
  • `scripts/install.sh` — bootstrap installer
  • `deps/prime-kernels/scripts/` — native wheel build scripts
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