amd-nerfacc
NerfAcc is a PyTorch Nerf acceleration toolbox for both training and inference. It focus on efficient sampling in the volumetric rendering pipeline of radiance fields, which is universal and plug-and-play for most of the NeRFs. With minimal modifications to the existing codebases, Nerfacc provides significant speedups in training various recent NeRF papers. And it is pure Python interface with flexible APIs!
Information
- Homepage: https://github.com/rocm/nerfacc
- License: Apache 2.0
Installation
pip install amd-nerfacc
Metadata
Release files for amd-nerfacc 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| amd_nerfacc-0.0.1.tar.gz | 1.4 kB | Without provenance | Details for amd_nerfacc-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| amd_nerfacc-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for amd_nerfacc-0.0.1-py3-none-any.whl |
Total release size: 2.8 kB
Release files / amd_nerfacc-0.0.1.tar.gz
| Download URL | amd_nerfacc-0.0.1.tar.gz |
|---|---|
| Size | 1.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
ca5e6039094c06766cfee3d03adbbb58683bf4ec2d2e84c536cb1fee2c5bff3b
|
|
BLAKE2b-256 checksum How to use checksums |
6c01478cf29b87dd62915f8785a9ee9b2b2ba1c43fd7550b7c4d2490f7ba568e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.3
|
Release files / amd_nerfacc-0.0.1-py3-none-any.whl
| Download URL | amd_nerfacc-0.0.1-py3-none-any.whl |
|---|---|
| Size | 1.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
115e276c740e2ae4322f09020099ac2600429eb0280e966566dd2645b2653e82
|
|
BLAKE2b-256 checksum How to use checksums |
80fd48340698ffa0277929202e0e3073092ef8c26592944332649549980eb717
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.3
|
Security for amd-nerfacc 0.0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
