apache-tvm-ffi>=0.1.6,!=0.1.8,!=0.1.8.post0,<0.2
click
# cuda-python + nccl4py: runtime deps for the moe_ep EP transport backends
# (default since the EP install became opt-out; CUDA-13 wheels only — see
# build_backend.py). NCCL-EP is the released nccl4py wheel (`nccl.ep` API +
# bundled libnccl_ep.so); no in-tree NCCL build.
#
# nvidia-nccl-cu13 is deliberately NOT a base dep: torch's cu13 wheels pin it
# EXACTLY (e.g. ==2.29.7), so any floor here (>=2.30.7 for B200 EP) makes the
# resolver evict torch — on aarch64 it backtracks all the way to the CPU-only
# torch 2.10.0 wheel. torch supplies libnccl; the >=2.30.7 B200 floor is
# enforced at runtime in flashinfer/moe_ep/core/validation/common.py, and the
# build hook installs the newer wheel --no-deps on source installs
# (build_backend.py).
#
# The same base-deps-must-not-force-a-CUDA-major rule applies to cuda-python:
# a >=13.0 floor bulldozes CUDA-12 environments (pip reports e.g.
# "nvshmem4py-cu12 requires cuda-python<=12.9" and then re-resolves torch to
# the cu13 default build, whose cuda-toolkit[cudart] dep drops libcudart.so.13
# next to the env's libcudart.so.12 — cudnn-frontend's cuda-pathfinder loader
# then aborts with "Multiple libcudart libraries found"). With a >=12.0 floor
# a cu12 env keeps cuda-python 12.x (moe_ep is CUDA-13-only and its runtime
# validator raises a clear error there), while cu13 envs resolve 13.x and get
# the full nccl.ep stack.
cuda-python>=12.0
cuda-tile>=1.4.0
einops
nccl4py>=0.3.1
ninja
numpy
nvidia-cudnn-frontend>=1.25.0
nvidia-cutlass-dsl>=4.5.0
nvidia-ml-py
packaging>=24.2
requests
tabulate
torch
tqdm
