# kbv-multiturn GPU box, core (installed into a fresh venv /opt/kbv/v WITHOUT system site-packages, so torch and every
# CUDA component live in one site-packages/nvidia/cu13 tree that becomes CUDA_HOME). torch and the nvidia-* pins are
# copied from projects/compute-labor-rct/runs/rehearsal-v2-launch/remote/requirements-gpu.txt (torch 2.14.1, cu130).
cuda-bindings==13.4.3
cuda-pathfinder==1.8.3
numpy==2.5.3
nvidia-cublas==13.1.1.3
nvidia-cuda-cupti==13.0.85
nvidia-cuda-nvrtc==13.0.88
nvidia-cuda-runtime==13.0.96
nvidia-cudnn-cu13==9.24.0.43
nvidia-cufft==12.0.0.61
nvidia-cufile==1.15.1.6
nvidia-curand==10.4.0.35
nvidia-cusolver==12.0.4.66
nvidia-cusparse==12.6.3.3
nvidia-cusparselt-cu13==0.8.1
nvidia-nccl-cu13==2.30.7
nvidia-nvjitlink==13.4.92
nvidia-nvshmem-cu13==3.4.5
nvidia-nvtx==13.0.85
torch==2.14.1
triton==3.8.0
# nvcc + headers for torch.utils.cpp_extension.load_inline (offline in the sandbox). Same 13.0.3 toolkit release as
# the runtime pins above: nvcc/crt/nvvm 13.0.88, cccl 13.0.85 (checked against the cuda-toolkit 13.0.3.0 wheel metadata).
cuda-toolkit[nvcc,crt,nvvm,cccl,cudart]==13.0.3.0
ninja==1.13.2
pydantic==2.13.5
pydra_config==0.0.17.post1
# pydra (KBV's vendor CLI) imports dill and requires dill>=0.3.7,<0.4 (bring-up kbv-w01: datasets had pulled 0.4.1)
dill>=0.3.7,<0.4
packaging==26.3
