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#PyTorch

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IT News<p>How To Train A New Voice For Piper With Only A Single Phrase - [Cal Bryant] hacked together a home automation system years ago, which more recent... - <a href="https://hackaday.com/2025/07/09/how-to-train-a-new-voice-for-piper-with-only-a-single-phrase/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">hackaday.com/2025/07/09/how-to</span><span class="invisible">-train-a-new-voice-for-piper-with-only-a-single-phrase/</span></a> <a href="https://schleuss.online/tags/artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>artificialintelligence</span></a> <a href="https://schleuss.online/tags/speechsynthesis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>speechsynthesis</span></a> <a href="https://schleuss.online/tags/texttospeech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>texttospeech</span></a> <a href="https://schleuss.online/tags/chatterbox" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>chatterbox</span></a> <a href="https://schleuss.online/tags/pipervoice" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pipervoice</span></a> <a href="https://schleuss.online/tags/pytorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pytorch</span></a> <a href="https://schleuss.online/tags/whisper" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>whisper</span></a> <a href="https://schleuss.online/tags/gpu" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gpu</span></a></p>
Towards Data Science<p>Turbo-charge your everyday Python scripts by tapping the idle NVIDIA GPU sitting in your rig. Learn how Thomas Reid uses <a href="https://hachyderm.io/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a> to offload non-ML computations with minimal code, slashing runtimes and boosting throughput — read the full breakdown FREE now.</p><p><a href="https://towardsdatascience.com/use-pytorch-to-easily-access-your-gpu/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/use-pyt</span><span class="invisible">orch-to-easily-access-your-gpu/</span></a></p>
Towards Data Science<p>Are your GPUs sitting idle between ML model training runs? Thomas Reid demonstrates how <a href="https://hachyderm.io/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a> can dramatically accelerate everyday computational tasks with minimal code modifications. </p><p><a href="https://towardsdatascience.com/use-pytorch-to-easily-access-your-gpu/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/use-pyt</span><span class="invisible">orch-to-easily-access-your-gpu/</span></a></p>
Towards Data Science<p>Got an NVIDIA GPU? Thomas Reid's article reveals an absurdly easy way to accelerate your non-ML Python code using <a href="https://hachyderm.io/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a>! See 10x+ speedups for numerical ops compared to NumPy. </p><p><a href="https://towardsdatascience.com/use-pytorch-to-easily-access-your-gpu/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">towardsdatascience.com/use-pyt</span><span class="invisible">orch-to-easily-access-your-gpu/</span></a></p>
LavX News<p>Accelerating Graph Learning with PyTorch: Unleashing the Power of torch.compile</p><p>Discover how to enhance the performance of your PyTorch graph machine learning models by leveraging the latest advancements in torch.compile. This article dives into practical techniques that achieved...</p><p><a href="https://news.lavx.hu/article/accelerating-graph-learning-with-pytorch-unleashing-the-power-of-torch-compile" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.lavx.hu/article/accelerat</span><span class="invisible">ing-graph-learning-with-pytorch-unleashing-the-power-of-torch-compile</span></a></p><p><a href="https://ioc.exchange/tags/news" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>news</span></a> <a href="https://ioc.exchange/tags/tech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tech</span></a> <a href="https://ioc.exchange/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a> <a href="https://ioc.exchange/tags/GraphNeuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GraphNeuralNetworks</span></a> <a href="https://ioc.exchange/tags/torchcompile" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>torchcompile</span></a></p>
LavX News<p>vLLM Joins PyTorch Foundation: A New Era for Large Language Model Inference</p><p>The PyTorch Foundation has officially welcomed vLLM, a high-throughput inference engine for large language models, marking a significant step in AI innovation. This integration promises enhanced perfo...</p><p><a href="https://news.lavx.hu/article/vllm-joins-pytorch-foundation-a-new-era-for-large-language-model-inference" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">news.lavx.hu/article/vllm-join</span><span class="invisible">s-pytorch-foundation-a-new-era-for-large-language-model-inference</span></a></p><p><a href="https://ioc.exchange/tags/news" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>news</span></a> <a href="https://ioc.exchange/tags/tech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tech</span></a> <a href="https://ioc.exchange/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a> <a href="https://ioc.exchange/tags/AIInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIInference</span></a> <a href="https://ioc.exchange/tags/vLLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>vLLM</span></a></p>
TOV<p>If you want to work with audio files in Python, here are some helpful libraries.</p><p><a href="https://fosstodon.org/tags/Pydub" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pydub</span></a> allows you to play, slice, concatenate and edit audio files effortlessly.</p><p><a href="https://fosstodon.org/tags/SoundDevice" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SoundDevice</span></a> allows you to play and record audio files.</p><p><a href="https://fosstodon.org/tags/SoundFile" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SoundFile</span></a> allows you to read and write audio files in various formats.</p><p><a href="https://fosstodon.org/tags/Librosa" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Librosa</span></a> allpwsbyou to <a href="https://fosstodon.org/tags/analysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>analysis</span></a> music and audio files.</p><p><a href="https://fosstodon.org/tags/TorchAudio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TorchAudio</span></a> allows you to <a href="https://fosstodon.org/tags/process" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>process</span></a> audio <a href="https://fosstodon.org/tags/signals" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>signals</span></a> with <a href="https://fosstodon.org/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a></p>
Janne Moren<p>I wish <a href="https://fosstodon.org/tags/pytorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pytorch</span></a> wasn't CUDA/ROCm only :(</p><p>I know I *can* use the nodes at work, but that's not the point. I want to use my own new toy, not somebody else's.</p><p>Any DL framework out there with good support for <a href="https://fosstodon.org/tags/Vulkan" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Vulkan</span></a> or <a href="https://fosstodon.org/tags/Opencl" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Opencl</span></a> ?</p>
obrhoff<p>Anyone have some good experience with a M4 Max and PyTorch? Can it compete against a NVIDIA GPU? (training and not inference)<br><a href="https://mastodon.social/tags/AppleSilicon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AppleSilicon</span></a> <a href="https://mastodon.social/tags/PyTorch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyTorch</span></a> <a href="https://mastodon.social/tags/maschinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>maschinelearning</span></a></p>
Towards Data Science

Is your metric collection slowing down your training? Chaim Rand explores how inefficient metric computation can impact performance and provides optimization techniques using TorchMetrics and #PyTorch Profiler.

towardsdatascience.com/efficie

Towards Data Science · Efficient Metric Collection in PyTorch: Avoiding the Performance Pitfalls of TorchMetrics | Towards Data ScienceMetric collection is an essential part of every machine learning project, enabling us to track model performance and monitor training progress. Ideally, metrics should be collected and computed without introducing any additional overhead to the training process. However, just like other components of the training loop, inefficient metric computation can introduce unnecessary overhead, increase training-step […]
Towards Data Science

NaNs in Deep Learning workloads are amongst the most frustrating issues to encounter. Say goodbye to NaN frustration 👋 Chaim Rand's newest article provides practical solutions and code examples for tackling NaN errors in #PyTorch, helping you get your deep learning projects back on track.

towardsdatascience.com/debuggi

Towards Data Science · Debugging the Dreaded NaN | Towards Data ScienceCapturing and reproducing failures in PyTorch training with Lightning
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