pycuda 2022.2.2~dfsg-1 source package in Ubuntu
Changelog
pycuda (2022.2.2~dfsg-1) unstable; urgency=medium * New upstream release. * The CUDA toolkit autopkgtests are now in src:nvidia-cuda-samples. * Update Standards-Version to 4.6.2; no changes necessary. -- Andreas Beckmann <email address hidden> Tue, 03 Jan 2023 14:08:09 +0100
Upload details
- Uploaded by:
- Debian NVIDIA Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian NVIDIA Maintainers
- Architectures:
- any all
- Section:
- python
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pycuda_2022.2.2~dfsg-1.dsc | 2.5 KiB | f0f22d1db0322f95b7079fc1cc8292de9295bdbcf593e09061ddb87ef46b8448 |
| pycuda_2022.2.2~dfsg.orig.tar.xz | 184.7 KiB | 64b624efa4dd721ed7d602f396486ccf5bf6a39005021f2e1ef0f09397018fce |
| pycuda_2022.2.2~dfsg-1.debian.tar.xz | 22.8 KiB | f4c38f882ef76a2b6e3ce35a77bddde476c3e9f7141f715eb701df71d7b2c7d7 |
Available diffs
No changes file available.
Binary packages built by this source
- python-pycuda-doc: module to access Nvidia‘s CUDA computation API (documentation)
PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
Several wrappers of the CUDA API already exist–so what’s so special about
PyCUDA?
* Object cleanup tied to lifetime of objects. This idiom, often called
RAII in C++, makes it much easier to write correct, leak- and crash-free
code. PyCUDA knows about dependencies, too, so (for example) it won’t
detach from a context before all memory allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver. SourceModule and
pycuda.gpuarray. GPUArray make CUDA programming even more convenient than
with Nvidia’s C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA’s driver API at your
disposal, if you wish.
* Automatic Error Checking. All CUDA errors are automatically translated
into Python exceptions.
* Speed. PyCUDA’s base layer is written in C++, so all the niceties
above are virtually free.
* Helpful Documentation.
.
This package contains HTML documentation and example scripts.
- python3-pycuda: Python 3 module to access Nvidia‘s CUDA parallel computation API
PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
Several wrappers of the CUDA API already exist–so what’s so special about
PyCUDA?
* Object cleanup tied to lifetime of objects. This idiom, often called
RAII in C++, makes it much easier to write correct, leak- and crash-free
code. PyCUDA knows about dependencies, too, so (for example) it won’t
detach from a context before all memory allocated in it is also freed.
* Convenience. Abstractions like pycuda.driver. SourceModule and
pycuda.gpuarray. GPUArray make CUDA programming even more convenient than
with Nvidia’s C-based runtime.
* Completeness. PyCUDA puts the full power of CUDA’s driver API at your
disposal, if you wish.
* Automatic Error Checking. All CUDA errors are automatically translated
into Python exceptions.
* Speed. PyCUDA’s base layer is written in C++, so all the niceties
above are virtually free.
* Helpful Documentation.
.
This package contains Python 3 modules.
- python3-pycuda-dbgsym: debug symbols for python3-pycuda
