https://bugzilla.redhat.com/show_bug.cgi?id=2272375
Bug ID: 2272375
Summary: Review Request: python-torchaudio - Audio signal
processing, powered by PyTorch
Product: Fedora
Version: rawhide
OS: Linux
Status: NEW
Component: Package Review
Severity: medium
Assignee: nobody(a)fedoraproject.org
Reporter: trix(a)redhat.com
QA Contact: extras-qa(a)fedoraproject.org
CC: package-review(a)lists.fedoraproject.org
Target Milestone: ---
Classification: Fedora
Spec URL: https://trix.fedorapeople.org/python-torchaudio.spec
SPRM URL:
https://trix.fedorapeople.org/python-torchaudio-2.3.0%5egit20240318.17a7081…
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting
PyTorch, torchaudio follows the same philosophy of providing strong GPU
acceleration, having a focus on trainable features through the autograd
system, and having consistent style (tensor names and dimension names).
Therefore, it is primarily a machine learning library and not a general
signal processing library. The benefits of PyTorch can be seen in torchaudio
through having all the computations be through PyTorch operations which
makes it easy to use and feel like a natural extension.
Reproducible: Always
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https://bugzilla.redhat.com/show_bug.cgi?id=2274979
Bug ID: 2274979
Summary: Review Request: python-torchrl - A PyTorch library for
Reinforcement Learning
Product: Fedora
Version: rawhide
OS: Linux
Status: NEW
Component: Package Review
Severity: medium
Assignee: nobody(a)fedoraproject.org
Reporter: trix(a)redhat.com
QA Contact: extras-qa(a)fedoraproject.org
CC: package-review(a)lists.fedoraproject.org
Target Milestone: ---
Classification: Fedora
Spec URL: https://trix.fedorapeople.org/python-torchrl.spec
SRPM URL: https://trix.fedorapeople.org/python-torchrl-0.3.1-1.fc41.src.rpm
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.
It provides PyTorch and python-first, low and high level abstractions for
RL that are intended to be efficient, modular, documented and properly
tested. The code is aimed at supporting research in RL. Most of it is
written in python in a highly modular way, such that researchers can easily
swap components, transform them or write new ones with little effort.
Reproducible: Always
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https://bugzilla.redhat.com/show_bug.cgi?id=2274979
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