Install & Compatibility
Where this runs
tested against v0.0.0a0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
installs and imports cleanly · install 0.0s · import 0.000s · 17.8MB
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 4.1s · import 0.000s · 316MB
164MB installed
● package 164MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
nvcc_path
✓ from nvidia import nvcc_path
✗ from nvidia.cuda.nvcc import nvcc_path
Locate the bundled nvcc and compile a minimal CUDA kernel.
from nvidia.cuda.nvcc import nvcc_path
import subprocess
import os
# Get the path to the bundled nvcc
nvcc = nvcc_path()
print(f"Using nvcc at: {nvcc}")
# Simple compile and run a CUDA kernel
code = '''
__global__ void add(int *a, int *b, int *c) {
*c = *a + *b;
}
'''
with open('kernel.cu', 'w') as f:
f.write(code)
result = subprocess.run([nvcc, 'kernel.cu', '-o', 'kernel'], capture_output=True, text=True)
if result.returncode == 0:
print("Compilation succeeded")
else:
print("Compilation failed:", result.stderr)
nvcc --version
Upgrade
Version history
13.3.33latest on PyPI · released May 26, 2026
Audit
Dependencies
nvidia-cuda-runtime-cu12requiredProvides the CUDA runtime libraries required for nvcc compilation.
nvidia-cuda-nvrtc-cu12optionalOptional for runtime compilation, but commonly needed for JIT.