Install & Compatibility
Where this runs
tested against v1.5.3 · 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
py 3.10
✕ build_error
✓ 80.9s
py 3.11
✕ build_error
✓ 72.7s
py 3.12
✕ build_error
✓ 60.3s
py 3.13
✕ build_error
✓ 58.2s
py 3.9
✕ build_error
✕ timeout
4890MB installed
● package 4890MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
DefaultStrategy
✓ from gsplat import DefaultStrategy
✗ import gsplat; gsplat.rasterization(...)
Strategy
✓ from gsplat import Strategy
accumulate
✓ from gsplat import accumulate
Minimal example of rasterization with dummy data. Requires CUDA for performance.
import torch
import gsplat
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# dummy data: batch of 3D Gaussians (N, 14)
gaussians = torch.randn(100, 14, device=device)
viewmat = torch.eye(4, device=device).unsqueeze(0) # (1, 4, 4)
fx, fy, cx, cy = 100.0, 100.0, 50.0, 50.0
img_height, img_width = 100, 100
rendered = gsplat.rasterization(
means=gaussians[:, :3],
quats=gaussians[:, 3:7],
scales=torch.exp(gaussians[:, 7:10]),
opacities=torch.sigmoid(gaussians[:, 10:11]),
colors=torch.sigmoid(gaussians[:, 11:14]),
viewmat=viewmat,
K=torch.tensor([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], device=device),
width=img_width,
height=img_height,
)
print(rendered.keys())
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Version history
1.5.3latest on PyPI · released Jul 4, 2025
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Dependencies
torchrequiredgsplat depends on PyTorch for tensor operations and CUDA support.