Memory Profiling

memory_profiling.ipynb

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        "<a href=\"https://colab.research.google.com/gist/muellerzr/3777fac2613eba1da9995acf916830d3/scratchpad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "!pip install fastai --upgrade -qqq"
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      "cell_type": "markdown",
      "metadata": {
        "id": "x51rJjbnOcfF"
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      "source": [
        "Note: The memory profiling sections are from Andres Babino's notebook [here](https://gist.github.com/ababino/cbe0c1d55573dcfe6e3b9bfad9dfa872)"
      ]
    },
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      "metadata": {
        "id": "dVG2lJJiJijS"
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      "source": [
        "import gc\n",
        "from fastcore import *\n",
        "from fastai.vision.all import *\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "def get_cudas():\n",
        "    '''Returns the number of tensors in cuda device.'''\n",
        "    n = 0\n",
        "    for o in gc.get_objects():\n",
        "        if torch.is_tensor(o):\n",
        "            o = maybe_attr(o, 'data')\n",
        "            if o.is_cuda: n += 1\n",
        "    return n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "class GatherCudas(Callback):\n",
        "    def __init__(self): self.cudas = []\n",
        "    def after_batch(self): self.cudas.append(get_cudas())"
      ],
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      "outputs": []
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        "@patch\n",
        "def forward(self:SequentialEx, x):\n",
        "        res = x\n",
        "        for l in self.layers:\n",
        "            res.orig = x\n",
        "            nres = l(res)\n",
        "            # We have to remove res.orig and nres.orig to avoid hanging refs and therefore memory leaks\n",
        "            nres.orig, res.orig = None, None\n",
        "            res = nres\n",
        "        return res"
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      "source": [
        "path = untar_data(URLs.CAMVID_TINY)\n",
        "dls = SegmentationDataLoaders.from_label_func(\n",
        "    path, bs=8, fnames = get_image_files(path/\"images\"),\n",
        "    label_func = lambda o: path/'labels'/f'{o.stem}_P{o.suffix}',\n",
        "    codes = np.loadtxt(path/'codes.txt', dtype=str), num_workers=0\n",
        ")\n",
        "gccb = GatherCudas()\n",
        "learn = unet_learner(dls, resnet18, cbs=gccb)\n",
        "learn.fine_tune(2)"
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            "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-5c106cde.pth\n"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>0</td>\n",
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            "/usr/local/lib/python3.6/dist-packages/torch/distributed/distributed_c10d.py:126: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead\n",
            "  warnings.warn(\"torch.distributed.reduce_op is deprecated, please use \"\n"
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aDHaO2JJqGj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}