Martin de La Gorce
Martin de La Gorce
In the tests I have the lines a = randn(3, 3); f = @(x) a + x ; CheckAutoDiffJacobian(f, randn(3, 3), 1e-7); The array a is a normal matlab array...
a possible implementation #351
I realized the problem is in the `nerfacc` library, not in `gsplat`. The installation using pip install `nerfacc` uses the pipy.org file `nerfacc-0.5.3-py3-none-any.whl` and not `nerfacc-0.5.3.tar.gz` and as a results...
actually the problem also appears with `gsplat` if one used `pip install gsplat` because the published wheel does not come with precompiled CUDA binaries and the installation from the wheel...
Cl.exe is the executable used to run the Microsoft visual studio compiler and linker. I misinterpreted the need for cl when running some of the tests as being due to...
I realized the problem is in the `nerfacc` library, not in `gsplat`. The installation using `pip install nerfacc` uses the pipy.org file `nerfacc-0.5.3-py3-none-any.whl` and not `nerfacc-0.5.3.tar.gz` and as a results...
following the approach used by `cupy` to specify the cuda version (https://pypi.org/project/cupy/) we could publish the packages `gsplat-cuda12x` and `gsplat-cuda11x` with precompiled wheels and continue using the package `gsplat` for...
In order to help the conversation I tried to list different options with potential drawbacks. ## Option 1 We make explicit the `pytorch` and CUDA version in the package version...
It seems that some use case require jit compilation (for example the tests test_rasterize_to_pixels https://github.com/nerfstudio-project/gsplat/blob/45d196a3611e627c498f237c27014cdaf42d94d2/tests/test_basic.py#L442 ), is that correct? In that case providing pre-compiled wheels might be a limited interest....
I experimented with the solution 1 listed above by publishing the precompiled wheels directly in the github repo releases and got it working after many iterations :) Draft PR https://github.com/nerfstudio-project/gsplat/pull/365...