45_prefatch/prefetch_caching.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"center\" style='color:blue'>Optimize tensorflow pipeline performance with prefetch and caching</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.5.0'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Prefetch</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FileDataset(tf.data.Dataset):\n",
    "    def read_file_in_batches(num_samples):\n",
    "        # Opening the file\n",
    "        time.sleep(0.03)\n",
    "\n",
    "        for sample_idx in range(num_samples):\n",
    "            # Reading data (line, record) from the file\n",
    "            time.sleep(0.015)\n",
    "\n",
    "            yield (sample_idx,)\n",
    "\n",
    "    def __new__(cls, num_samples=3):\n",
    "        return tf.data.Dataset.from_generator(\n",
    "            cls.read_file_in_batches,\n",
    "            output_signature = tf.TensorSpec(shape = (1,), dtype = tf.int64),\n",
    "            args=(num_samples,)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark(dataset, num_epochs=2):\n",
    "    for epoch_num in range(num_epochs):\n",
    "        for sample in dataset:\n",
    "            # Performing a training step\n",
    "            time.sleep(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "304 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "benchmark(FileDataset())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "238 ms ± 6.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "benchmark(FileDataset().prefetch(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240 ms ± 7.28 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "benchmark(FileDataset().prefetch(tf.data.AUTOTUNE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As you can notice above, using prefetch improves the performance from 304 ms to 238 and 240 ms**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Cache</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = tf.data.Dataset.range(5)\n",
    "dataset = dataset.map(lambda x: x**2)\n",
    "dataset = dataset.cache(\"mycache.txt\")\n",
    "# The first time reading through the data will generate the data using\n",
    "# `range` and `map`.\n",
    "list(dataset.as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4, 9, 16]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subsequent iterations read from the cache.\n",
    "list(dataset.as_numpy_iterator())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mapped_function(s):\n",
    "    # Do some hard pre-processing\n",
    "    tf.py_function(lambda: time.sleep(0.03), [], ())\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.25 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r1 -n1\n",
    "benchmark(FileDataset().map(mapped_function), 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "528 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r1 -n1\n",
    "benchmark(FileDataset().map(mapped_function).cache(), 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Further reading** https://www.tensorflow.org/guide/data_performance#caching"
   ]
  }
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