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    "# 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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