014 - Compiler.ipynb

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   "source": [
    "# Compiler son code Python"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivations\n",
    "\n",
    "1. Distribuer une application sans dépendre de Python\n",
    "2. Améliorer les performances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Les principaux outils"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### Cython\n",
    "\n",
    "https://cython.org/\n",
    "\n",
    "Compilateur statique, pour compiler des extensions ou une application complète, en utilisant un surensemble du langage Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numba\n",
    "\n",
    "https://numba.pydata.org/\n",
    "\n",
    "Compilateur dynamique (alias *just-in-time*) basé sur LLVM, qui permet de compiler certaines fonctions critiques en ajoutant une simple annotation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nuitka\n",
    "\n",
    "https://nuitka.net/pages/overview.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Références\n",
    "\n",
    "- https://speakerdeck.com/pycon2018/jake-vanderplas-performance-python-seven-strategies-for-optimizing-your-numerical-code?slide=74 (Cython)\n",
    "- https://speakerdeck.com/pycon2018/jake-vanderplas-performance-python-seven-strategies-for-optimizing-your-numerical-code?slide=83 (Numba)\n"
   ]
  }
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