flox-cuda

To set up a CUDA environment with Flox, authenticate with Flox and initialize a CUDA-enabled Linux environment. The skill provisions NVIDIA CUDA toolkits, libraries, and runtimes while enforcing explicit version pinning and driver compatibility checks for reproducible configurations.

Yes, you can integrate PyTorch and TensorFlow with CUDA in a Flox environment. The skill provisions GPU-enabled frameworks alongside CUDA toolkits and runtime libraries like cuBLAS to support machine learning experimentation and reproducible research environments.

Flox CUDA supports multiple CUDA versions for reproducible development by enforcing explicit version pinning. You can discover and pin specific CUDA toolkits like nvcc and runtime libraries to ensure consistent configurations across Linux distributions.

Before provisioning CUDA toolkits in Flox, you need to verify NVIDIA driver compatibility on your Linux system. The skill performs driver compatibility checks and requires a Linux-only environment to successfully integrate GPU libraries and runtimes.

Flox CUDA enforces explicit version pinning for GPU libraries to maintain reproducible environment configurations. This ensures that CUDA toolkits, cudart, and cuBLAS versions remain consistent across different Linux distributions during ML experimentation and development.

The primary limitation of provisioning CUDA runtimes with Flox is that it supports Linux-only environments. CUDA development setup is restricted to Linux targets, meaning Windows or macOS users cannot use this skill to enable GPU-accelerated development.