## Support Python wheel build
This PR modernizes the Python packaging for MSCCL++ by defining
dependencies and optional extras in `pyproject.toml`, enabling proper
wheel builds with `pip install ".[cuda12]"`.
### Changes
**`pyproject.toml`**
- Add `dependencies` (numpy, blake3, pybind11, sortedcontainers)
- Add `optional-dependencies` for platform-specific CuPy (`cuda11`,
`cuda12`, `cuda13`, `rocm6`), `benchmark`, and `test` extras
- Bump minimum Python version from 3.8 to 3.10
**`test/deploy/setup.sh`**
- Use `pip install ".[<platform>,benchmark,test]"` instead of separate
`pip install -r requirements_*.txt` + `pip install .` steps
- Add missing CUDA 13 case
**`docs/quickstart.md`**
- Update install instructions to use extras (e.g., `pip install
".[cuda12]"`)
- Document all available extras and clarify that `rocm6` builds CuPy
from source
- Update Python version references to 3.10
**`python/csrc/CMakeLists.txt`**, **`python/test/CMakeLists.txt`**
- Update `find_package(Python)` from 3.8 to 3.10
### Notes
- The `requirements_*.txt` files are kept for Docker base image builds
where only dependencies (not the project itself) should be installed.
- CuPy is intentionally not in base dependencies — users must specify a
platform extra to get the correct pre-built wheel (or source build for
ROCm).
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
## Problem
`nccl-test.yml` was the only CI template calling `deploy.yml` without
passing `gpuArch`. Since the CI build machine has no GPU, CMake fell
back to building for **all** supported architectures (`80;90;100;120`),
unnecessarily slowing down CI builds.
## Fix
- Add `gpuArch` parameter to `nccl-test.yml` and forward it to
`deploy.yml`
- Pass `gpuArch: '80'` (A100) and `gpuArch: '90'` (H100) from
`nccl-api-test.yml`
All other templates were already passing `gpuArch` correctly.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
## Summary
- **Multi-node H100 CI setup**: Improve architecture detection and GPU
configuration
- **Remove hardcoded VMSS hostnames** from deploy files
- **Fix CUDA compat library issue**: Remove stale compat paths from
Docker image for CUDA 12+. Instead, `peer_access_test` now returns a
distinct exit code (2) for CUDA init failure, and `setup.sh`
conditionally adds compat libs only when needed. This fixes
`cudaErrorSystemNotReady` (error 803) when the host driver is newer than
the container's compat libs.
- **Speed up deploy**: Replace recursive `parallel-scp` with
tar+scp+untar to avoid per-file SSH overhead.
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This pull request updates the deployment pipeline to allow custom CMake
arguments to be passed to the pip install process on remote VMs.
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
## Summary
- Fix `run-remote.sh` to correctly execute multi-command scripts (e.g.,
multiple `mpirun` calls)
- The old approach piped decoded script through `base64 -d | bash`,
which feeds the script via bash's **stdin**. When `mpirun` (or its child
processes) runs, it can consume the remaining stdin, causing bash to
never see subsequent commands — only the first command would execute.
- The fix decodes the script to a **temp file** and runs `bash -euxo
pipefail "$TMP"` instead, so bash reads commands from the file and
`mpirun` consuming stdin has no effect.
- Applied to both the docker path (pssh + docker exec) and the
non-docker path (pssh only).
🤖 Generated with [Claude Code](https://claude.com/claude-code)
- Removes the GTest dependency, replacing it with a minimal custom
framework (`test/framework.*`) that covers only what the tests actually
use — a unified `TEST()` macro with SFINAE-based fixture auto-detection,
`EXPECT_*`/`ASSERT_*` assertions, environments, and setup/teardown.
- `--exclude-perf-tests` flag and substring-based negative filtering
- `MSCCLPP_ENABLE_COVERAGE` CMake option with gcov/lcov; CI uploads to
Codecov
- Merges standalone `test/perf/` into main test targets
- Refactors Azure pipelines to reduce redundancies & make more readable
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Changho Hwang <changhohwang@microsoft.com>
* Add a FIFO test code that reproduced a correctness issue
* Fix the correctness issue by using pinned memory instead of cudaMemcpy
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
1. use `fence+relaxed` to replace `release` for fifo. `fence+relax` is
more efficient on A100
2. Update the deviceSyncer. Previous one cannot handle threadBlock
number change correctly. Use three counters to solve this issue. Reset
previous counter before sync on current counter.
3. Introduce relaxedWait which can be used with relaxedSignal for case
doesn't need guarantee the memory visibility
Now build images by a script with a shared Dockerfile template
---------
Co-authored-by: Binyang Li <binyli@microsoft.com>
Co-authored-by: Saeed Maleki <saemal@microsoft.com>
- remove `#include <cstdint>` from `poll.hpp`. To make it only contains
device-side code
- Fix compilation issue, which will cause pytest fail randomly. Reuse
the compiled result for same kernel with different arguments
