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name flox-cuda description CUDA and GPU development with Flox. Use for NVIDIA CUDA setup, GPU computing, deep learning frameworks, cuDNN, and cross-platform GPU/CPU development.
For the human maintainer of this environment. Do not run these commands
on the user's behalf — relay them.
DEPRECATED: this skill is frozen and has moved to
flox/flox-skills .
flox/flox-agentic receives no further updates and will be archived on
2026-09-30 .
The seven skills here were consolidated into flox (reproducible
environments, with reference guides for services, builds, containers,
publishing, sharing, and CUDA). The maintained plugin also ships floxify
for onboarding an existing repo to Flox.
To migrate, a person should run claude plugin marketplace add flox/flox-skills and claude plugin install flox@flox-skills, then remove
this one with claude plugin uninstall flox@flox-agentic and claude plugin marketplace remove flox-agentic.
Flox CUDA Development Guide
Prerequisites & Authentication
Sign up for early access at https://flox.dev
Authenticate with flox auth login
Linux-only : CUDA packages only work on ["aarch64-linux", "x86_64-linux"]All CUDA packages are prefixed with flox-cuda/ in the catalog
No macOS support : Use Metal alternatives on Darwin
Core Commands
flox search cudatoolkit --all | grep flox-cuda
flox search nvcc --all | grep 12_8
Package Discovery
flox search cudatoolkit --all | grep flox-cuda
flox search nvcc --all | grep 12_8
flox show flox-cuda/cudaPackages.cudatoolkit
flox search libcublas --all | grep flox-cuda
flox search cudnn --all | grep flox-cuda
Essential CUDA Packages Package Pattern Purpose Example cudaPackages_X_Y.cudatoolkitMain CUDA Toolkit cudaPackages_12_8.cudatoolkitcudaPackages_X_Y.cuda_nvccNVIDIA C++ Compiler cudaPackages_12_8.cuda_nvcccudaPackages.cuda_cudartCUDA Runtime API cuda_cudartcudaPackages_X_Y.libcublasLinear algebra cudaPackages_12_8.libcublascudaPackages_X_Y.libcufftFast Fourier Transform cudaPackages_12_8.libcufftcudaPackages_X_Y.libcurandRandom number generation cudaPackages_12_8.libcurandcudaPackages_X_Y.cudnn_9_11Deep neural networks cudaPackages_12_8.cudnn_9_11cudaPackages_X_Y.ncclMulti-GPU communication cudaPackages_12_8.nccl
Critical: Conflict Resolution CUDA packages have LICENSE file conflicts requiring explicit priorities:
[install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cuda_nvcc.priority = 1
cuda_cudart.pkg-path = "flox-cuda/cudaPackages.cuda_cudart"
cuda_cudart.systems = ["aarch64-linux" , "x86_64-linux" ]
cuda_cudart.priority = 2
cudatoolkit.pkg-path = "flox-cuda/cudaPackages_12_8.cudatoolkit"
cudatoolkit.systems = ["aarch64-linux" , "x86_64-linux" ]
cudatoolkit.priority = 3
gcc.pkg-path = "gcc"
gcc-unwrapped.pkg-path = "gcc-unwrapped"
gcc-unwrapped.priority = 5
CUDA Version Selection
CUDA 12.x (Current) [install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cudatoolkit.pkg-path = "flox-cuda/cudaPackages_12_8.cudatoolkit"
cudatoolkit.priority = 3
cudatoolkit.systems = ["aarch64-linux" , "x86_64-linux" ]
CUDA 11.x (Legacy Support) [install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_11_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cudatoolkit.pkg-path = "flox-cuda/cudaPackages_11_8.cudatoolkit"
cudatoolkit.priority = 3
cudatoolkit.systems = ["aarch64-linux" , "x86_64-linux" ]
Cross-Platform GPU Development Dual CUDA/CPU packages for portability (Linux gets CUDA, macOS gets CPU fallback):
[install]
cuda-pytorch.pkg-path = "flox-cuda/python3Packages.torch"
cuda-pytorch.systems = ["x86_64-linux" , "aarch64-linux" ]
cuda-pytorch.priority = 1
pytorch.pkg-path = "python313Packages.pytorch"
pytorch.systems = ["x86_64-darwin" , "aarch64-darwin" ]
pytorch.priority = 6
GPU Detection Pattern Dynamic CPU/GPU package installation in hooks:
setup_gpu_packages "$FLOX_ENV_CACHE /venv"
if [ ! -f "$FLOX_ENV_CACHE /.deps_installed" ]; then
if lspci 2>/dev/null | grep -E 'NVIDIA|AMD' > /dev/null; then
echo "GPU detected, installing CUDA packages"
uv pip install --python "$venv /bin/python" \
torch torchvision --index-url https://download.pytorch.org/whl/cu129
else
echo "No GPU detected, installing CPU packages"
uv pip install --python "$venv /bin/python" \
torch torchvision --index-url https://download.pytorch.org/whl/cpu
fi
touch "$FLOX_ENV_CACHE /.deps_installed"
fi
}
Complete CUDA Environment Examples
Basic CUDA Development [install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cuda_cudart.pkg-path = "flox-cuda/cudaPackages.cuda_cudart"
cuda_cudart.priority = 2
cuda_cudart.systems = ["aarch64-linux" , "x86_64-linux" ]
gcc.pkg-path = "gcc"
gcc-unwrapped.pkg-path = "gcc-unwrapped"
gcc-unwrapped.priority = 5
[vars]
CUDA_VERSION = "12.8"
CUDA_HOME = "$FLOX_ENV"
[hook]
echo "CUDA $CUDA_VERSION environment ready"
echo "nvcc: $(nvcc --version | grep release)"
Deep Learning with PyTorch [install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cuda_cudart.pkg-path = "flox-cuda/cudaPackages.cuda_cudart"
cuda_cudart.priority = 2
cuda_cudart.systems = ["aarch64-linux" , "x86_64-linux" ]
libcublas.pkg-path = "flox-cuda/cudaPackages_12_8.libcublas"
libcublas.priority = 2
libcublas.systems = ["aarch64-linux" , "x86_64-linux" ]
cudnn.pkg-path = "flox-cuda/cudaPackages_12_8.cudnn_9_11"
cudnn.priority = 2
cudnn.systems = ["aarch64-linux" , "x86_64-linux" ]
python313Full.pkg-path = "python313Full"
uv.pkg-path = "uv"
gcc-unwrapped.pkg-path = "gcc-unwrapped"
gcc-unwrapped.priority = 5
[vars]
CUDA_VERSION = "12.8"
PYTORCH_CUDA_ALLOC_CONF = "max_split_size_mb:128"
[hook]
setup_pytorch_cuda() {
venv ="$FLOX_ENV_CACHE/venv"
if [ ! -d "$venv" ]
uv venv "$venv" --python python3
fi
if [ -f "$venv/bin/activate" ]
source "$venv/bin/activate"
fi
if [ ! -f "$FLOX_ENV_CACHE/.deps_installed" ]
uv pip install --python "$venv/bin/python" \
torch torchvision torchaudio \
--index-url https://download.pytorch.org/whl/cu129
touch "$FLOX_ENV_CACHE/.deps_installed"
fi
}
setup_pytorch_cuda
TensorFlow with CUDA [install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cuda_cudart.pkg-path = "flox-cuda/cudaPackages.cuda_cudart"
cuda_cudart.priority = 2
cuda_cudart.systems = ["aarch64-linux" , "x86_64-linux" ]
cudnn.pkg-path = "flox-cuda/cudaPackages_12_8.cudnn_9_11"
cudnn.priority = 2
cudnn.systems = ["aarch64-linux" , "x86_64-linux" ]
python313Full.pkg-path = "python313Full"
uv.pkg-path = "uv"
[hook]
setup_tensorflow() {
venv ="$FLOX_ENV_CACHE/venv"
[ ! -d "$venv" ] && uv venv "$venv" --python python3
[ -f "$venv/bin/activate" ] && source "$venv/bin/activate"
if [ ! -f "$FLOX_ENV_CACHE/.tf_installed" ]
uv pip install --python "$venv/bin/python" tensorflow[and-cuda]
touch "$FLOX_ENV_CACHE/.tf_installed"
fi
}
setup_tensorflow
Multi-GPU Development [install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
nccl.pkg-path = "flox-cuda/cudaPackages_12_8.nccl"
nccl.priority = 2
nccl.systems = ["aarch64-linux" , "x86_64-linux" ]
libcublas.pkg-path = "flox-cuda/cudaPackages_12_8.libcublas"
libcublas.priority = 2
libcublas.systems = ["aarch64-linux" , "x86_64-linux" ]
[vars]
CUDA_VISIBLE_DEVICES = "0,1,2,3"
NCCL_DEBUG = "INFO"
Modular CUDA Environments
Base CUDA Environment
[install]
cuda_nvcc.pkg-path = "flox-cuda/cudaPackages_12_8.cuda_nvcc"
cuda_nvcc.priority = 1
cuda_nvcc.systems = ["aarch64-linux" , "x86_64-linux" ]
cuda_cudart.pkg-path = "flox-cuda/cudaPackages.cuda_cudart"
cuda_cudart.priority = 2
cuda_cudart.systems = ["aarch64-linux" , "x86_64-linux" ]
gcc.pkg-path = "gcc"
gcc-unwrapped.pkg-path = "gcc-unwrapped"
gcc-unwrapped.priority = 5
[vars]
CUDA_VERSION = "12.8"
CUDA_HOME = "$FLOX_ENV"
CUDA Math Libraries
[include]
environments = [{ remote = "team/cuda-base" }]
[install]
libcublas.pkg-path = "flox-cuda/cudaPackages_12_8.libcublas"
libcublas.priority = 2
libcublas.systems = ["aarch64-linux" , "x86_64-linux" ]
libcufft.pkg-path = "flox-cuda/cudaPackages_12_8.libcufft"
libcufft.priority = 2
libcufft.systems = ["aarch64-linux" , "x86_64-linux" ]
libcurand.pkg-path = "flox-cuda/cudaPackages_12_8.libcurand"
libcurand.priority = 2
libcurand.systems = ["aarch64-linux" , "x86_64-linux" ]
CUDA Debugging Tools
[install]
cuda-gdb.pkg-path = "flox-cuda/cudaPackages_12_8.cuda-gdb"
cuda-gdb.systems = ["aarch64-linux" , "x86_64-linux" ]
nsight-systems.pkg-path = "flox-cuda/cudaPackages_12_8.nsight-systems"
nsight-systems.systems = ["aarch64-linux" , "x86_64-linux" ]
[vars]
CUDA_LAUNCH_BLOCKING = "1"
Layer for Development
flox activate -r team/cuda-base
flox activate -r team/cuda-base -- flox activate -r team/cuda-debug
Testing CUDA Installation
Verify CUDA Compiler
Check GPU Availability
Compile Test Program cat > hello_cuda.cu << 'EOF'
__global__ void hello printf ("Hello from GPU!\n" );
}
int main 1,1 >>>();
cudaDeviceSynchronize();
return 0;
}
EOF
nvcc hello_cuda.cu -o hello_cuda
./hello_cuda
Test PyTorch CUDA import torch
print (f"CUDA available: {torch.cuda.is_available()} " )
print (f"CUDA version: {torch.version.cuda} " )
print (f"GPU count: {torch.cuda.device_count()} " )
if torch.cuda.is_available():
print (f"GPU name: {torch.cuda.get_device_name(0 )} " )
Best Practices
Always Use Priority Values CUDA packages have predictable conflicts - assign explicit priorities
Version Consistency Use specific versions (e.g., _12_8) for reproducibility. Don't mix CUDA versions.
Modular Design Split base CUDA, math libs, and debugging into separate environments for flexibility
Test Compilation Verify nvcc hello.cu -o hello works after setup
Platform Constraints Always include systems = ["aarch64-linux", "x86_64-linux"]
Memory Management Set appropriate CUDA memory allocator configs:
[vars]
PYTORCH_CUDA_ALLOC_CONF = "max_split_size_mb:128"
CUDA_LAUNCH_BLOCKING = "0"
Common CUDA Gotchas
CUDA Toolkit ≠ Complete Toolkit The cudatoolkit package doesn't include all libraries. Add what you need:
libcublas for linear algebra
libcufft for FFT
cudnn for deep learning
License Conflicts Every CUDA package may need explicit priority due to LICENSE file conflicts
No macOS Support CUDA is Linux-only. Use Metal-accelerated packages on Darwin when available
Version Mixing Don't mix CUDA versions. Use consistent _X_Y suffixes across all CUDA packages
Python Virtual Environments CUDA Python packages (PyTorch, TensorFlow) should be installed in venv with correct CUDA version
Driver Requirements Ensure NVIDIA driver supports your CUDA version. Check with nvidia-smi
Troubleshooting
CUDA Not Found
echo $CUDA_HOME
which nvcc
nvcc --version
echo $LD_LIBRARY_PATH
PyTorch Not Using GPU import torch
print (torch.cuda.is_available())
print (torch.version.cuda)
Compilation Errors
gcc --version
g++ --version
flox list | grep gcc-unwrapped
echo $CPATH
echo $LIBRARY_PATH
Runtime Errors
echo $CUDA_VISIBLE_DEVICES
nvidia-smi
CUDA_LAUNCH_BLOCKING=1 python my_script.py
Related Skills
flox-environments - Setting up development environmentsflox-sharing - Composing CUDA base with project environmentsflox-containers - Containerizing CUDA environments for deploymentflox-services - Running CUDA workloads as services 