投稿時間:2023-06-24 20:14:46 RSSフィード2023-06-24 20:00 分まとめ(15件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
python	Pythonタグが付けられた新着投稿 - Qiita	「Webアプリ簡単作成法」をWinPythonで起動する	https://qiita.com/76r6qo698/items/ef307c6bf989947f698c	okoppe	2023-06-24 19:35:49
python	Pythonタグが付けられた新着投稿 - Qiita	KivyのLabelウィジェットで日本語を扱う	https://qiita.com/reimy_yamanouchi/items/17ee38ae84de490bc7e0	label	2023-06-24 19:31:02
python	Pythonタグが付けられた新着投稿 - Qiita	Qiita 投稿のことはじめ	https://qiita.com/konzarefu/items/42a596eb88a9928d200b	qiita	2023-06-24 19:26:43
js	JavaScriptタグが付けられた新着投稿 - Qiita	【Three.js】blenderで作成した3DモデルをWebページ上に表示する方法	https://qiita.com/enumura1/items/c303dd5e759ab02b3345	yukidarum	2023-06-24 19:05:42
海外TECH	MakeUseOf	Apple Store vs. Apple App Store: What’s the Difference?	https://www.makeuseof.com/apple-store-vs-apple-app-store/	apple	2023-06-24 10:15:18
海外TECH	DEV Community	Memory Management using PYTORCH_CUDA_ALLOC_CONF	https://dev.to/shittu_olumide_/memory-management-using-pytorchcudaallocconf-5afh	Memory Management using PYTORCH CUDA ALLOC CONFLike an orchestra conductor carefully allocating resources to each musician memory management is the hidden maestro that orchestrates the performance of software applications It is the art and science of efficiently organizing and utilizing a computer s memory to optimize performance enhance security and unleash the full potential of our programs In deep learning where models are becoming increasingly complex and datasets larger than ever efficient memory management is crucial in achieving optimal performance The memory requirements of deep learning models can be immense often surpassing the capabilities of the available hardware which is why in this article we explore a powerful tool called PYTORCH CUDA ALLOC CONF that addresses these memory management challenges when using PyTorch and CUDA PyTorch a popular deep learning framework and CUDA a parallel computing platform provide developers with the tools to leverage the power of GPUs for accelerated training and inference However managing GPU memory efficiently is essential for preventing out of memory errors maximizing hardware utilization and achieving faster computation times Overview of PYTORCH CUDA ALLOC CONFPYTORCH CUDA ALLOC CONF is a configuration option introduced in PyTorch to enhance memory management and allocation for deep learning applications utilizing CUDA It is designed to optimize GPU memory allocation and improve performance during training and inference processes It enables users to fine tune the memory management behavior by configuring various aspects of CUDA memory allocation By adjusting these configurations developers can optimize memory utilization and minimize unnecessary memory transfers improving training and inference efficiency The configuration options provided by PYTORCH CUDA ALLOC CONF allow users to control parameters such as the caching algorithm the maximum GPU memory capacity the allocation granularity and the memory pool management strategy These configurations can be adjusted based on the specific requirements of the deep learning model and the available GPU resources One key advantage of PYTORCH CUDA ALLOC CONF is its ability to dynamically allocate and manage memory based on memory usage patterns during runtime It supports dynamic memory allocation allowing the framework to allocate memory on demand and release it when it is no longer needed This dynamic allocation approach helps avoid unnecessary memory waste and efficiently utilizes GPU resources Similarly PYTORCH CUDA ALLOC CONF incorporates memory recycling techniques where memory blocks no longer in use can be recycled and reused for subsequent computations Reusing memory reduces the frequency of memory allocations and deallocations which can be time consuming This recycling mechanism further enhances memory management efficiency and contributes to improved performance How does PYTORCH CUDA ALLOC CONF work As discussed earlier PYTORCH CUDA ALLOC CONF is a PyTorch environment variable that allows us to configure memory allocation behavior for CUDA tensors It controls memory allocation strategies enabling users to optimize memory usage and improve performance in deep learning tasks When set PYTORCH CUDA ALLOC CONF overrides the default memory allocator in PyTorch and introduces more efficient memory management techniques PYTORCH CUDA ALLOC CONF operates by utilizing different memory allocation algorithms and strategies It provides several configuration options including heuristic This option enables PyTorch to automatically select the best memory allocation strategy based on heuristics and runtime conditions It dynamically adjusts memory allocation parameters to optimize performance for different scenarios nmalloc This option specifies the number of memory allocation attempts before an out of memory error is raised It allows users to control the number of attempts made by PyTorch to allocate memory caching allocator This option enables a caching memory allocator which improves performance by reusing previously allocated memory blocks It reduces the overhead of memory allocation and deallocation operations pooled This option activates pooled memory allocation which allocates memory in fixed size blocks or pools It improves memory utilization by reducing fragmentation and overhead associated with variable sized memory allocations ImplementationIn this section we will look at how we use PYTORCH CUDA ALLOC CONF for memory management in PyTorch import torchimport os Set PYTORCH CUDA ALLOC CONF environment variableos environ PYTORCH CUDA ALLOC CONF caching allocator Explanation By setting PYTORCH CUDA ALLOC CONF to caching allocator we enable the caching memory allocator which improves memory management efficiency Create a CUDA tensorx torch randn cuda Explanation Here we create a CUDA tensor using the torch randn function Since PYTORCH CUDA ALLOC CONF is set the tensor will be allocated using the caching allocator Perform some computationsy x x t z torch matmul y y Explanation We perform some computations on the CUDA tensor The caching allocator manages the memory allocation and reuse efficiently reducing the overhead of memory allocation and deallocation operations Clear memory explicitly optional del x y z Explanation Clearing the variables is optional but it can help release GPU memory before subsequent operations to avoid excessive memory usage Reset PYTORCH CUDA ALLOC CONF environment variable optional os environ PYTORCH CUDA ALLOC CONF Explanation Resetting PYTORCH CUDA ALLOC CONF to an empty string restores the default memory allocator behavior in PyTorch Continue with other operationsExplanation The code sets the environment variable PYTORCH CUDA ALLOC CONF to caching allocator This activates the caching memory allocator which improves memory management efficiency by reusing previously allocated memory blocks A CUDA tensor x of size x is created using torch randn Since PYTORCH CUDA ALLOC CONF is set the tensor will be allocated using the caching allocator Computation operations y x x t and z torch matmul y y are performed on the CUDA tensor The caching allocator manages memory allocation and reuse efficiently reducing the overhead of memory allocation and deallocation operations The del statement is used to explicitly clear the variables x y and z This step is optional but can help release GPU memory before subsequent operations to avoid excessive memory usage The PYTORCH CUDA ALLOC CONF environment variable is reset to an empty string if desired This restores the default memory allocator behavior in PyTorch Further operations can be performed using PyTorch as needed Advantages and benefits of using PYTORCH CUDA ALLOC CONF Improved performance PYTORCH CUDA ALLOC CONF offers various memory allocation strategies to significantly enhance performance in deep learning tasks By optimizing memory usage it reduces memory fragmentation and improves overall memory management efficiency This in turn leads to faster computation and better utilization of GPU resources Reduced memory fragmentation occurs when memory blocks become scattered and inefficiently utilized leading to wasted memory PYTORCH CUDA ALLOC CONF helps mitigate fragmentation by implementing pooling and caching strategies This ensures more effective memory reuse and reduces the likelihood of memory fragmentation resulting in better memory utilization Customizable allocation behavior PYTORCH CUDA ALLOC CONF allows users to customize memory allocation behavior according to their specific requirements Users can adapt memory allocation strategies to their particular models data sizes and hardware configurations by choosing different options and configurations leading to optimal performance Error control The nmalloc option in PYTORCH CUDA ALLOC CONF allows users to set the maximum number of memory allocation attempts This feature can prevent excessive memory allocation attempts and prevent the program from getting stuck in an allocation loop It provides control and error handling when dealing with memory allocation issues Compatibility and ease of use PYTORCH CUDA ALLOC CONF seamlessly integrates with PyTorch a widely used deep learning framework It can be easily set as an environment variable allowing users to enable and configure memory allocation behavior without complex code modifications This ensures compatibility across different PyTorch versions and simplifies the implementation of memory management optimizations ConclusionIn summary PYTORCH CUDA ALLOC CONF provides a valuable tool for developers working with PyTorch and CUDA offering a range of configuration options to optimize memory allocation and utilization By leveraging this feature deep learning practitioners can effectively manage memory resources reduce memory related bottlenecks and ultimately improve the efficiency and performance of their models	2023-06-24 10:28:03
海外科学	BBC News - Science & Environment	Rules on pollution blocking housebuilding, says minister	https://www.bbc.co.uk/news/uk-politics-65999226?at_medium=RSS&at_campaign=KARANGA	baroness	2023-06-24 10:25:48
海外ニュース	Japan Times latest articles	Putin vows to crush ‘armed mutiny’ after Russian mercenary boss seizes southern city	https://www.japantimes.co.jp/news/2023/06/24/world/russia-infighting-wagner-group/	Putin vows to crush armed mutiny after Russian mercenary boss seizes southern cityThe feud between Wagner s Yevgeny Prigozhin and the Russian military has escalated into a confrontation setting up the biggest challenge to Putin s authority since the	2023-06-24 19:33:34
海外ニュース	Japan Times latest articles	What is Russia’s Wagner Group and why is it accused of mutiny?	https://www.japantimes.co.jp/news/2023/06/24/world/russia-wagner-group-explainer/	ukraine	2023-06-24 19:23:12
ニュース	BBC News - Home	South East Water blames working from home for hosepipe ban	https://www.bbc.co.uk/news/uk-england-66007675?at_medium=RSS&at_campaign=KARANGA	claims	2023-06-24 10:07:44
ニュース	BBC News - Home	SNP convention to discuss new independence strategy	https://www.bbc.co.uk/news/uk-scotland-scotland-politics-65998210?at_medium=RSS&at_campaign=KARANGA	yousaf	2023-06-24 10:03:56
ニュース	BBC News - Home	Is this a coup? What is Prigozhin doing in Russia?	https://www.bbc.co.uk/news/world-europe-66006880?at_medium=RSS&at_campaign=KARANGA	leadership	2023-06-24 10:11:55
ニュース	BBC News - Home	Comments on Kyrgios 'misinterpreted' as racist - Tsitsipas	https://www.bbc.co.uk/sport/tennis/66007433?at_medium=RSS&at_campaign=KARANGA	Comments on Kyrgios x misinterpreted x as racist TsitsipasStefanos Tsitsipas says comments he made towards Nick Kyrgios at Wimbledon in have been misinterpreted after they were perceived as racist on social media	2023-06-24 10:25:23
ニュース	BBC News - Home	Women's Ashes 2023: First-ball drama as Natalie Sciver-Brunt survives wicket after review	https://www.bbc.co.uk/sport/av/cricket/66008279?at_medium=RSS&at_campaign=KARANGA	Women x s Ashes First ball drama as Natalie Sciver Brunt survives wicket after reviewWatch as Natalie Sciver Brunt is given out with the first ball of day three but survives after a successful review	2023-06-24 10:18:17
海外TECH	reddit	/r/WorldNews Live Thread: Russian Invasion of Ukraine Day 486, Part 3 (Thread #629)	https://www.reddit.com/r/worldnews/comments/14hp2a1/rworldnews_live_thread_russian_invasion_of/	r WorldNews Live Thread Russian Invasion of Ukraine Day Part Thread submitted by u WorldNewsMods to r worldnews link comments	2023-06-24 10:02:43