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2025-12-16 09:23:53 +08:00
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88dd3320",
+   "metadata": {
+    "origin_pos": 0
+   },
+   "source": [
+    "# 计算性能\n",
+    ":label:`chap_performance`\n",
+    "\n",
+    "在深度学习中，数据集和模型通常都很大，导致计算量也会很大。\n",
+    "因此，计算的性能非常重要。\n",
+    "本章将集中讨论影响计算性能的主要因素：命令式编程、符号编程、\n",
+    "异步计算、自动并行和多GPU计算。\n",
+    "通过学习本章，对于前几章中实现的那些模型，可以进一步提高它们的计算性能。\n",
+    "例如，我们可以在不影响准确性的前提下，大大减少训练时间。\n",
+    "\n",
+    ":begin_tab:toc\n",
+    " - [hybridize](hybridize.ipynb)\n",
+    " - [async-computation](async-computation.ipynb)\n",
+    " - [auto-parallelism](auto-parallelism.ipynb)\n",
+    " - [hardware](hardware.ipynb)\n",
+    " - [multiple-gpus](multiple-gpus.ipynb)\n",
+    " - [multiple-gpus-concise](multiple-gpus-concise.ipynb)\n",
+    " - [parameterserver](parameterserver.ipynb)\n",
+    ":end_tab:\n"
+   ]
+  }
+ ],
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+  "language_info": {
+   "name": "python"
+  },
+  "required_libs": []
+ },
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+ "nbformat_minor": 5
+}