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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"معايير الأداء","local":"معايير-الأداء","sections":[{"title":"كيفية قياس أداء نماذج 🤗 Transformers","local":"كيفية-قياس-أداء-نماذج--transformers","sections":[],"depth":2},{"title":"أفضل الممارسات في اختبار الأداء","local":"أفضل-الممارسات-في-اختبار-الأداء","sections":[],"depth":2},{"title":"مشاركة نتائج اختبار الأداء الخاص بك","local":"مشاركة-نتائج-اختبار-الأداء-الخاص-بك","sections":[],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/transformers/pr_28250/ar/_app/immutable/chunks/EditOnGithub.98bf070f.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"معايير الأداء","local":"معايير-الأداء","sections":[{"title":"كيفية قياس أداء نماذج 🤗 Transformers","local":"كيفية-قياس-أداء-نماذج--transformers","sections":[],"depth":2},{"title":"أفضل الممارسات في اختبار الأداء","local":"أفضل-الممارسات-في-اختبار-الأداء","sections":[],"depth":2},{"title":"مشاركة نتائج اختبار الأداء الخاص بك","local":"مشاركة-نتائج-اختبار-الأداء-الخاص-بك","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="معايير-الأداء" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#معايير-الأداء"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>معايير الأداء</span></h1> <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"><p data-svelte-h="svelte-sa70wz">أدوات قياس الأداء من Hugging Face أصبحت قديمة،ويُنصح باستخدام مكتبات خارجية لقياس سرعة وتعقيد الذاكرة لنماذج Transformer.</p></div> <div class="flex space-x-1 absolute z-10 right-0 top-0"> <div class="relative colab-dropdown "> <button class=" " type="button"> <img alt="Open In Colab" class="!m-0" src="https://colab.research.google.com/assets/colab-badge.svg"> </button> </div> <div class="relative colab-dropdown "> <button class=" " type="button"> <img alt="Open In Studio Lab" class="!m-0" src="https://studiolab.sagemaker.aws/studiolab.svg"> </button> </div></div> <p data-svelte-h="svelte-6xcuon">لنلق نظرة على كيفية تقييم أداء نماذج 🤗 Transformers، وأفضل الممارسات، ومعايير الأداء المتاحة بالفعل.</p> <p data-svelte-h="svelte-n8jgs2">يُمكن العثور على دفتر ملاحظات يشرح بالتفصيل كيفية قياس أداء نماذج 🤗 Transformers <a href="https://github.com/huggingface/notebooks/tree/main/examples/benchmark.ipynb" rel="nofollow">هنا</a>.</p> <h2 class="relative group"><a id="كيفية-قياس-أداء-نماذج--transformers" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#كيفية-قياس-أداء-نماذج--transformers"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>كيفية قياس أداء نماذج 🤗 Transformers</span></h2> <p data-svelte-h="svelte-1bx7ar6">تسمح الفئتان <code>PyTorchBenchmark</code> و <code>TensorFlowBenchmark</code> بتقييم أداء نماذج 🤗 Transformers بمرونة. تتيح لنا فئات التقييم قياس الأداء قياس <em>الاستخدام الأقصى للذاكرة</em> و <em>الوقت اللازم</em> لكل من <em>الاستدلال</em> و <em>التدريب</em>.</p> <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p data-svelte-h="svelte-up5889">هنا، ييُعرَّف <em>الاستدلال</em> بأنه تمريرة أمامية واحدة، ويتم تعريف <em>التدريب</em> بأنه تمريرة أمامية واحدة وتمريرة خلفية واحدة.</p></div> <p data-svelte-h="svelte-10c8gct">تتوقع فئات تقييم الأداء <code>PyTorchBenchmark</code> و <code>TensorFlowBenchmark</code> كائنًا من النوع <code>PyTorchBenchmarkArguments</code> و <code>TensorFlowBenchmarkArguments</code>، على التوالي، للتنفيذ. <code>PyTorchBenchmarkArguments</code> و <code>TensorFlowBenchmarkArguments</code> هي فئات بيانات وتحتوي على جميع التكوينات ذات الصلة لفئة تقييم الأداء المقابلة. في المثال التالي، يتم توضيح كيفية تقييم أداء نموذج BERT من النوع <em>bert-base-cased</em>.</p> <div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4"> <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg> <span>Pytorch</span></div> <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> <span>Hide Pytorch content</span></div></div> <div class="framework-content"> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PyTorchBenchmark, PyTorchBenchmarkArguments

	<span class="hljs-meta">>>> </span>args = PyTorchBenchmarkArguments(models=[<span class="hljs-string">"google-bert/bert-base-uncased"</span>], batch_sizes=[<span class="hljs-number">8</span>], sequence_lengths=[<span class="hljs-number">8</span>, <span class="hljs-number">32</span>, <span class="hljs-number">128</span>, <span class="hljs-number">512</span>])
	<span class="hljs-meta">>>> </span>benchmark = PyTorchBenchmark(args)<!-- HTML_TAG_END --></pre></div></div></div> <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg> <span>TensorFlow</span></div> <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> <span>Hide TensorFlow content</span></div></div> <div class="framework-content"> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TensorFlowBenchmark, TensorFlowBenchmarkArguments

	<span class="hljs-meta">>>> </span>args = TensorFlowBenchmarkArguments(
	<span class="hljs-meta">... </span> models=[<span class="hljs-string">"google-bert/bert-base-uncased"</span>], batch_sizes=[<span class="hljs-number">8</span>], sequence_lengths=[<span class="hljs-number">8</span>, <span class="hljs-number">32</span>, <span class="hljs-number">128</span>, <span class="hljs-number">512</span>]
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>benchmark = TensorFlowBenchmark(args)<!-- HTML_TAG_END --></pre></div> </div></div> </div> <p data-svelte-h="svelte-1vvmk00">هنا، يتم تمرير ثلاثة معامﻻت إلى فئات بيانات حجة قياس الأداء، وهي <code>models</code> و <code>batch_sizes</code> و <code>sequence_lengths</code>. المعامل <code>models</code> مطلوبة وتتوقع <code>قائمة</code> من بمعرّفات النموذج من <a href="https://huggingface.co/models" rel="nofollow">مركز النماذج</a> تحدد معامﻻت القائمة <code>batch_sizes</code> و <code>sequence_lengths</code> حجم <code>input_ids</code> الذي يتم قياس أداء النموذج عليه. هناك العديد من المعلمات الأخرى التي يمكن تكوينها عبر فئات بيانات معال قياس الأداء. لمزيد من التفاصيل حول هذه المعلمات، يمكنك إما الرجوع مباشرة إلى الملفات <code>src/transformers/benchmark/benchmark_args_utils.py</code>، <code>src/transformers/benchmark/benchmark_args.py</code> (لـ PyTorch) و <code>src/transformers/benchmark/benchmark_args_tf.py</code> (لـ Tensorflow). أو، بدلاً من ذلك، قم بتشغيل أوامر shell التالية من المجلد الرئيسي لطباعة قائمة وصفية بجميع المعلمات القابلة للتكوين لـ PyTorch و Tensorflow على التوالي.</p> <div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4"> <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg> <span>Pytorch</span></div> <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> <span>Hide Pytorch content</span></div></div> <div class="framework-content"> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->python examples/pytorch/benchmarking/run_benchmark.py --<span class="hljs-built_in">help</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1ajlu1q">يُمكن ببساطة تشغيل كائن التقييم الذي تم تهيئته عن طريق استدعاء <code>benchmark.run()</code>.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>results = benchmark.run()
	<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(results)
	==================== INFERENCE - SPEED - RESULT ====================
	--------------------------------------------------------------------------------
	Model Name Batch Size Seq Length Time <span class="hljs-keyword">in</span> s
	--------------------------------------------------------------------------------
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">8</span> <span class="hljs-number">0.006</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">32</span> <span class="hljs-number">0.006</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">0.018</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">0.088</span>
	--------------------------------------------------------------------------------

	==================== INFERENCE - MEMORY - RESULT ====================
	--------------------------------------------------------------------------------
	Model Name Batch Size Seq Length Memory <span class="hljs-keyword">in</span> MB
	--------------------------------------------------------------------------------
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">8</span> <span class="hljs-number">1227</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">32</span> <span class="hljs-number">1281</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">1307</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">1539</span>
	--------------------------------------------------------------------------------

	==================== ENVIRONMENT INFORMATION ====================

	- transformers_version: <span class="hljs-number">2.11</span><span class="hljs-number">.0</span>
	- framework: PyTorch
	- use_torchscript: <span class="hljs-literal">False</span>
	- framework_version: <span class="hljs-number">1.4</span><span class="hljs-number">.0</span>
	- python_version: <span class="hljs-number">3.6</span><span class="hljs-number">.10</span>
	- system: Linux
	- cpu: x86_64
	- architecture: 64bit
	- date: <span class="hljs-number">2020</span>-06-<span class="hljs-number">29</span>
	- time: 08:<span class="hljs-number">58</span>:<span class="hljs-number">43.371351</span>
	- fp16: <span class="hljs-literal">False</span>
	- use_multiprocessing: <span class="hljs-literal">True</span>
	- only_pretrain_model: <span class="hljs-literal">False</span>
	- cpu_ram_mb: <span class="hljs-number">32088</span>
	- use_gpu: <span class="hljs-literal">True</span>
	- num_gpus: <span class="hljs-number">1</span>
	- gpu: TITAN RTX
	- gpu_ram_mb: <span class="hljs-number">24217</span>
	- gpu_power_watts: <span class="hljs-number">280.0</span>
	- gpu_performance_state: <span class="hljs-number">2</span>
	- use_tpu: <span class="hljs-literal">False</span><!-- HTML_TAG_END --></pre></div></div></div> <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg> <span>TensorFlow</span></div> <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> <span>Hide TensorFlow content</span></div></div> <div class="framework-content"> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->python examples/tensorflow/benchmarking/run_benchmark_tf.py --<span class="hljs-built_in">help</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-4htuj4">يُمكن بعد ذلك تشغيل كائن قياس الأداء الذي تم تهيئته عن طريق استدعاء <code>benchmark.run()</code>.</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>results = benchmark.run()
	<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(results)
	<span class="hljs-meta">>>> </span>results = benchmark.run()
	<span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(results)
	==================== INFERENCE - SPEED - RESULT ====================
	--------------------------------------------------------------------------------
	Model Name Batch Size Seq Length Time <span class="hljs-keyword">in</span> s
	--------------------------------------------------------------------------------
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">8</span> <span class="hljs-number">0.005</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">32</span> <span class="hljs-number">0.008</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">0.022</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">0.105</span>
	--------------------------------------------------------------------------------

	==================== INFERENCE - MEMORY - RESULT ====================
	--------------------------------------------------------------------------------
	Model Name Batch Size Seq Length Memory <span class="hljs-keyword">in</span> MB
	--------------------------------------------------------------------------------
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">8</span> <span class="hljs-number">1330</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">32</span> <span class="hljs-number">1330</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">1330</span>
	google-bert/bert-base-uncased <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">1770</span>
	--------------------------------------------------------------------------------

	==================== ENVIRONMENT INFORMATION ====================

	- transformers_version: <span class="hljs-number">202.11</span><span class="hljs-number">.0</span>
	- framework: Tensorflow
	- use_xla: <span class="hljs-literal">False</span>
	- framework_version: <span class="hljs-number">2.2</span><span class="hljs-number">.0</span>
	- python_version: <span class="hljs-number">3.6</span><span class="hljs-number">.10</span>
	- system: Linux
	- cpu: x86_64
	- architecture: 64bit
	- date: <span class="hljs-number">2020</span>-06-<span class="hljs-number">29</span>
	- time: 09:<span class="hljs-number">26</span>:<span class="hljs-number">35.617317</span>
	- fp16: <span class="hljs-literal">False</span>
	- use_multiprocessing: <span class="hljs-literal">True</span>
	- only_pretrain_model: <span class="hljs-literal">False</span>
	- cpu_ram_mb: <span class="hljs-number">32088</span>
	- use_gpu: <span class="hljs-literal">True</span>
	- num_gpus: <span class="hljs-number">1</span>
	- gpu: TITAN RTX
	- gpu_ram_mb: <span class="hljs-number">24217</span>
	- gpu_power_watts: <span class="hljs-number">280.0</span>
	- gpu_performance_state: <span class="hljs-number">2</span>
	- use_tpu: <span class="hljs-literal">False</span><!-- HTML_TAG_END --></pre></div> </div></div> </div> <p data-svelte-h="svelte-1q06oin">بشكل افتراضي، يتم تقييم <em>الوقت</em> و <em>الذاكرة المطلوبة</em> لـ <em>الاستدلال</em>. في مثال المخرجات أعلاه، يُظهر القسمان الأولان النتيجة المقابلة لـ <em>وقت الاستدلال</em> و <em>ذاكرة الاستدلال</em>. بالإضافة إلى ذلك، يتم طباعة جميع المعلومات ذات الصلة حول بيئة الحوسبة، على سبيل المثال نوع وحدة معالجة الرسومات (GPU)، والنظام، وإصدارات المكتبة، وما إلى ذلك، في القسم الثالث تحت <em>معلومات البيئة</em>. يمكن حفظ هذه المعلومات بشكل اختياري في ملف <em>.csv</em> عند إضافة المعامل <code>save_to_csv=True</code> إلى <code>PyTorchBenchmarkArguments</code> و <code>TensorFlowBenchmarkArguments</code> على التوالي. في هذه الحالة، يتم حفظ كل قسم في ملف <em>.csv</em> منفصل. يمكن اختيارًا تحديد مسار كل ملف <em>.csv</em> عبر فئات بيانات معامل قياس الأداء.</p> <p data-svelte-h="svelte-j785ca">بدلاً من تقييم النماذج المدربة مسبقًا عبر معرّف النموذج، على سبيل المثال <code>google-bert/bert-base-uncased</code>، يُمكن للمستخدم بدلاً من ذلك قياس أداء تكوين عشوائي لأي فئة نموذج متاحة. في هذه الحالة، يجب إدراج “قائمة” من التكوينات مع معامل قياس الأداء كما هو موضح أدناه.</p> <div class="space-y-10 py-6 2xl:py-8 2xl:-mx-4"> <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><defs><clipPath id="a"><rect x="3.05" y="0.5" width="25.73" height="31" fill="none"></rect></clipPath></defs><g clip-path="url(#a)"><path d="M24.94,9.51a12.81,12.81,0,0,1,0,18.16,12.68,12.68,0,0,1-18,0,12.81,12.81,0,0,1,0-18.16l9-9V5l-.84.83-6,6a9.58,9.58,0,1,0,13.55,0ZM20.44,9a1.68,1.68,0,1,1,1.67-1.67A1.68,1.68,0,0,1,20.44,9Z" fill="#ee4c2c"></path></g></svg> <span>Pytorch</span></div> <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> <span>Hide Pytorch content</span></div></div> <div class="framework-content"> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PyTorchBenchmark، PyTorchBenchmarkArguments، BertConfig

	<span class="hljs-meta">>>> </span>args = PyTorchBenchmarkArguments(
	<span class="hljs-meta">... </span> models=[<span class="hljs-string">"bert-base"</span>، <span class="hljs-string">"bert-384-hid"</span>، <span class="hljs-string">"bert-6-lay"</span>]، batch_sizes=[<span class="hljs-number">8</span>]، sequence_lengths=[<span class="hljs-number">8</span>، <span class="hljs-number">32</span>، <span class="hljs-number">128</span>، <span class="hljs-number">512</span>]
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>config_base = BertConfig()
	<span class="hljs-meta">>>> </span>config_384_hid = BertConfig(hidden_size=<span class="hljs-number">384</span>)
	<span class="hljs-meta">>>> </span>config_6_lay = BertConfig(num_hidden_layers=<span class="hljs-number">6</span>)

	<span class="hljs-meta">>>> </span>benchmark = PyTorchBenchmark(args، configs=[config_base، config_384_hid، config_6_lay])
	<span class="hljs-meta">>>> </span>benchmark.run()
	==================== INFERENCE - SPEED - RESULT ====================
	--------------------------------------------------------------------------------
	Model Name Batch Size Seq Length Time <span class="hljs-keyword">in</span> s
	--------------------------------------------------------------------------------
	bert-base <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">0.006</span>
	bert-base <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">0.006</span>
	bert-base <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">0.018</span>
	bert-base <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">0.088</span>
	bert-<span class="hljs-number">384</span>-hid <span class="hljs-number">8</span> <span class="hljs-number">8</span> <span class="hljs-number">0.006</span>
	bert-<span class="hljs-number">384</span>-hid <span class="hljs-number">8</span> <span class="hljs-number">32</span> <span class="hljs-number">0.006</span>
	bert-<span class="hljs-number">384</span>-hid <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">0.011</span>
	bert-<span class="hljs-number">384</span>-hid <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">0.054</span>
	bert-<span class="hljs-number">6</span>-lay <span class="hljs-number">8</span> <span class="hljs-number">8</span> <span class="hljs-number">0.003</span>
	bert-<span class="hljs-number">6</span>-lay <span class="hljs-number">8</span> <span class="hljs-number">32</span> <span class="hljs-number">0.004</span>
	bert-<span class="hljs-number">6</span>-lay <span class="hljs-number">8</span> <span class="hljs-number">128</span> <span class="hljs-number">0.009</span>
	bert-<span class="hljs-number">6</span>-lay <span class="hljs-number">8</span> <span class="hljs-number">512</span> <span class="hljs-number">0.044</span>
	--------------------------------------------------------------------------------

	==================== INFERENCE - MEMORY - RESULT ====================
	--------------------------------------------------------------------------------
	Model Name Batch Size Seq Length Memory <span class="hljs-keyword">in</span> MB
	<span class="hljs-comment">## نتائج اختبار الأداء</span>

	في هذا القسم، يتم قياس _وقت الاستدلال_ و _الذاكرة المطلوبة_ للاستدلال، لمختلف تكوينات `BertModel`. يتم عرض النتائج في جدول، مع تنسيق مختلف قليلاً لكل من PyTorch و TensorFlow.

	--------------------------------------------------------------------------------
	\| اسم النموذج \| حجم الدفعة \| طول التسلسل \| الذاكرة بالميغابايت \|
	--------------------------------------------------------------------------------
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">1277</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">1281</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">1307</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">1539</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">1005</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">1027</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">1035</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">1255</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">1097</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">1101</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">1127</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">1359</span> \|
	--------------------------------------------------------------------------------

	==================== معلومات البيئة ====================

	- transformers_version: <span class="hljs-number">2.11</span><span class="hljs-number">.0</span>
	- framework: PyTorch
	- use_torchscript: <span class="hljs-literal">False</span>
	- framework_version: <span class="hljs-number">1.4</span><span class="hljs-number">.0</span>
	- python_version: <span class="hljs-number">3.6</span><span class="hljs-number">.10</span>
	- system: Linux
	- cpu: x86_64
	- architecture: 64bit
	- date: <span class="hljs-number">2020</span>-06-<span class="hljs-number">29</span>
	- time: 09:<span class="hljs-number">35</span>:<span class="hljs-number">25.143267</span>
	- fp16: <span class="hljs-literal">False</span>
	- use_multiprocessing: <span class="hljs-literal">True</span>
	- only_pretrain_model: <span class="hljs-literal">False</span>
	- cpu_ram_mb: <span class="hljs-number">32088</span>
	- use_gpu: <span class="hljs-literal">True</span>
	- num_gpus: <span class="hljs-number">1</span>
	- gpu: TITAN RTX
	- gpu_ram_mb: <span class="hljs-number">24217</span>
	- gpu_power_watts: <span class="hljs-number">280.0</span>
	- gpu_performance_state: <span class="hljs-number">2</span>
	- use_tpu: <span class="hljs-literal">False</span><!-- HTML_TAG_END --></pre></div></div></div> <div class="border border-gray-200 rounded-xl px-4 relative"><div class="flex h-[22px] mt-[-12.5px] justify-between leading-none"><div class="flex px-1 items-center space-x-1 bg-white dark:bg-gray-950"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="0.94em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 274"><path d="M145.726 42.065v42.07l72.861 42.07v-42.07l-72.86-42.07zM0 84.135v42.07l36.43 21.03V105.17L0 84.135zm109.291 21.035l-36.43 21.034v126.2l36.43 21.035v-84.135l36.435 21.035v-42.07l-36.435-21.034V105.17z" fill="#E55B2D"></path><path d="M145.726 42.065L36.43 105.17v42.065l72.861-42.065v42.065l36.435-21.03v-84.14zM255.022 63.1l-36.435 21.035v42.07l36.435-21.035V63.1zm-72.865 84.135l-36.43 21.035v42.07l36.43-21.036v-42.07zm-36.43 63.104l-36.436-21.035v84.135l36.435-21.035V210.34z" fill="#ED8E24"></path><path d="M145.726 0L0 84.135l36.43 21.035l109.296-63.105l72.861 42.07L255.022 63.1L145.726 0zm0 126.204l-36.435 21.03l36.435 21.036l36.43-21.035l-36.43-21.03z" fill="#F8BF3C"></path></svg> <span>TensorFlow</span></div> <div class="cursor-pointer flex items-center justify-center space-x-1 text-sm px-2 bg-white dark:bg-gray-950 hover:underline leading-none"><svg class="" width="0.9em" height="0.9em" viewBox="0 0 10 9" fill="currentColor" xmlns="http://www.w3.org/2000/svg"><path d="M1.39125 1.9725L0.0883333 0.669997L0.677917 0.0804138L8.9275 8.33041L8.33792 8.91958L6.95875 7.54041C6.22592 8.00523 5.37572 8.25138 4.50792 8.25C2.26125 8.25 0.392083 6.63333 0 4.5C0.179179 3.52946 0.667345 2.64287 1.39167 1.9725H1.39125ZM5.65667 6.23833L5.04667 5.62833C4.81335 5.73996 4.55116 5.77647 4.29622 5.73282C4.04129 5.68918 3.80617 5.56752 3.62328 5.38463C3.44039 5.20175 3.31874 4.96663 3.27509 4.71169C3.23144 4.45676 3.26795 4.19456 3.37958 3.96125L2.76958 3.35125C2.50447 3.75187 2.38595 4.2318 2.4341 4.70978C2.48225 5.18777 2.6941 5.63442 3.0338 5.97411C3.37349 6.31381 3.82015 6.52567 4.29813 6.57382C4.77611 6.62197 5.25605 6.50345 5.65667 6.23833ZM2.83042 1.06666C3.35 0.862497 3.91625 0.749997 4.50792 0.749997C6.75458 0.749997 8.62375 2.36666 9.01583 4.5C8.88816 5.19404 8.60119 5.84899 8.1775 6.41333L6.56917 4.805C6.61694 4.48317 6.58868 4.15463 6.48664 3.84569C6.3846 3.53675 6.21162 3.256 5.98156 3.02594C5.7515 2.79588 5.47075 2.6229 5.16181 2.52086C4.85287 2.41882 4.52433 2.39056 4.2025 2.43833L2.83042 1.06708V1.06666Z" fill="currentColor"></path></svg> <span>Hide TensorFlow content</span></div></div> <div class="framework-content"> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig

	<span class="hljs-meta">>>> </span>args = TensorFlowBenchmarkArguments(
	<span class="hljs-meta">... </span> models=[<span class="hljs-string">"bert-base"</span>, <span class="hljs-string">"bert-384-hid"</span>, <span class="hljs-string">"bert-6-lay"</span>], batch_sizes=[<span class="hljs-number">8</span>], sequence_lengths=[<span class="hljs-number">8</span>, <span class="hljs-number">32</span>, <span class="hljs-number">128</span>, <span class="hljs-number">512</span>]
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>config_base = BertConfig()
	<span class="hljs-meta">>>> </span>config_384_hid = BertConfig(hidden_size=<span class="hljs-number">384</span>)
	<span class="hljs-meta">>>> </span>config_6_lay = BertConfig(num_hidden_layers=<span class="hljs-number">6</span>)

	<span class="hljs-meta">>>> </span>benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
	<span class="hljs-meta">>>> </span>benchmark.run()
	==================== نتائج السرعة في الاستدلال ====================
	--------------------------------------------------------------------------------
	\| اسم النموذج \| حجم الدفعة \| طول التسلسل \| الوقت بالثانية \|
	--------------------------------------------------------------------------------
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">0.005</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">0.008</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">0.022</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">0.106</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">0.005</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">0.007</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">0.018</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">0.064</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">0.002</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">0.003</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">0.0011</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">0.074</span> \|
	--------------------------------------------------------------------------------

	==================== نتائج الذاكرة في الاستدلال ====================
	--------------------------------------------------------------------------------
	\| اسم النموذج \| حجم الدفعة \| طول التسلسل \| الذاكرة بالميغابايت \|
	--------------------------------------------------------------------------------
	\| اسم النموذج \| حجم الدفعة \| طول التسلسل \| الذاكرة بالميغابايت \|
	--------------------------------------------------------------------------------
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">1330</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">1330</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">1330</span> \|
	\| bert-base \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">1770</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">1330</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">1330</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">1330</span> \|
	\| bert-<span class="hljs-number">384</span>-hid \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">1540</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">8</span> \| <span class="hljs-number">1330</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">32</span> \| <span class="hljs-number">1330</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">128</span> \| <span class="hljs-number">1330</span> \|
	\| bert-<span class="hljs-number">6</span>-lay \| <span class="hljs-number">8</span> \| <span class="hljs-number">512</span> \| <span class="hljs-number">1540</span> \|
	--------------------------------------------------------------------------------

	==================== معلومات البيئة ====================

	- transformers_version: <span class="hljs-number">2.11</span><span class="hljs-number">.0</span>
	- framework: Tensorflow
	- use_xla: <span class="hljs-literal">False</span>
	- framework_version: <span class="hljs-number">2.2</span><span class="hljs-number">.0</span>
	- python_version: <span class="hljs-number">3.6</span><span class="hljs-number">.10</span>
	- system: Linux
	- cpu: x86_64
	- architecture: 64bit
	- date: <span class="hljs-number">2020</span>-06-<span class="hljs-number">29</span>
	- time: 09:<span class="hljs-number">38</span>:<span class="hljs-number">15.487125</span>
	- fp16: <span class="hljs-literal">False</span>
	- use_multiprocessing: <span class="hljs-literal">True</span>
	- only_pretrain_model: <span class="hljs-literal">False</span>
	- cpu_ram_mb: <span class="hljs-number">32088</span>
	- use_gpu: <span class="hljs-literal">True</span>
	- num_gpus: <span class="hljs-number">1</span>
	- gpu: TITAN RTX
	- gpu_ram_mb: <span class="hljs-number">24217</span>
	- gpu_power_watts: <span class="hljs-number">280.0</span>
	- gpu_performance_state: <span class="hljs-number">2</span>
	- use_tpu: <span class="hljs-literal">False</span><!-- HTML_TAG_END --></pre></div> </div></div> </div> <p data-svelte-h="svelte-122bxr8">مرة أخرى، يتم قياس <em>وقت الاستدلال</em> و <em>الذاكرة المطلوبة</em> للاستدلال، ولكن هذه المرة لتكوينات مخصصة لـ <code>BertModel</code>. يمكن أن تكون هذه الميزة مفيدة بشكل خاص عند اتخاذ قرار بشأن التكوين الذي يجب تدريب النموذج عليه.</p> <h2 class="relative group"><a id="أفضل-الممارسات-في-اختبار-الأداء" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#أفضل-الممارسات-في-اختبار-الأداء"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>أفضل الممارسات في اختبار الأداء</span></h2> <p data-svelte-h="svelte-b225op">يسرد هذا القسم بعض أفضل الممارسات التي يجب مراعاتها عند إجراء اختبار الأداء لنموذج ما.</p> <ul data-svelte-h="svelte-1uhq69v"><li>حالياً، يتم دعم اختبار الأداء على جهاز واحد فقط. عند إجراء الاختبار على وحدة معالجة الرسوميات (GPU)، يوصى بأن يقوم المستخدم بتحديد الجهاز الذي يجب تشغيل التعليمات البرمجية عليه من خلال تعيين متغير البيئة <code>CUDA_VISIBLE_DEVICES</code> في الشل، على سبيل المثال <code>export CUDA_VISIBLE_DEVICES=0</code> قبل تشغيل التعليمات البرمجية.</li> <li>يجب تعيين الخيار <code>no_multi_processing</code> إلى <code>True</code> فقط لأغراض الاختبار والتصحيح. ولضمان قياس الذاكرة بدقة، يوصى بتشغيل كل اختبار ذاكرة في عملية منفصلة والتأكد من تعيين <code>no_multi_processing</code> إلى <code>True</code>.</li> <li>يجب دائمًا ذكر معلومات البيئة عند مشاركة نتائج تقييم النموذج. يُمكن أن تختلف النتائج اختلافًا كبيرًا بين أجهزة GPU المختلفة وإصدارات المكتبات، وما إلى ذلك، لذلك فإن نتائج الاختبار بمفردها ليست مفيدة جدًا للمجتمع.</li></ul> <h2 class="relative group"><a id="مشاركة-نتائج-اختبار-الأداء-الخاص-بك" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#مشاركة-نتائج-اختبار-الأداء-الخاص-بك"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>مشاركة نتائج اختبار الأداء الخاص بك</span></h2> <p data-svelte-h="svelte-r8at8i">في السابق، تم إجراء اختبار الأداء لجميع النماذج الأساسية المتاحة (10 في ذلك الوقت) لقياس <em>وقت الاستدلال</em>، عبر العديد من الإعدادات المختلفة: باستخدام PyTorch، مع TorchScript وبدونها، باستخدام TensorFlow، مع XLA وبدونه. تم إجراء جميع هذه الاختبارات على وحدات المعالجة المركزية (CPU) (باستثناء XLA TensorFlow) ووحدات معالجة الرسوميات (GPU).</p> <p data-svelte-h="svelte-u6lb2k">يتم شرح هذا النهج بالتفصيل في <a href="https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2" rel="nofollow">منشور المدونة هذا</a> وتتوفر النتائج <a href="https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing" rel="nofollow">هنا</a>.</p> <p data-svelte-h="svelte-18njil1">مع أدوات اختبار الأداء الجديدة، أصبح من الأسهل من أي وقت مضى مشاركة نتائج اختبار الأداء الخاص بك مع المجتمع:</p> <ul data-svelte-h="svelte-19e0l7c"><li><a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/benchmarking/README.md" rel="nofollow">نتائج اختبار الأداء في PyTorch</a>.</li> <li><a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/benchmarking/README.md" rel="nofollow">نتائج اختبار الأداء في TensorFlow</a>.</li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/ar/benchmarks.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p>

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