Keras-bug-dispatch.ipynb

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     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([autoencoder.AutoEncoder, sklearn.base.BaseEstimator, object],\n",
       " [keras.engine.training.Model,\n",
       "  keras.engine.network.Network,\n",
       "  keras.engine.base_layer.Layer,\n",
       "  object])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from distributed import Client\n",
    "client = Client()\n",
    "# noisy, clean = autoencoder.get_noisy_mnist(n=64)\n",
    "\n",
    "import autoencoder\n",
    "from distributed.protocol import register_attributes\n",
    "register_attributes(autoencoder.AutoEncoder)\n",
    "\n",
    "model = autoencoder.AutoEncoder()\n",
    "type(model).mro(), type(model.autoencoder).mro()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "distributed.protocol.core - CRITICAL - Failed to Serialize\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 53, in dumps\n",
      "    for key, value in data.items()\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 54, in <dictcomp>\n",
      "    if type(value) is Serialize}\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/serialize.py\", line 153, in serialize\n",
      "    raise TypeError(msg)\n",
      "TypeError: Could not serialize object of type Model\n",
      "distributed.comm.utils - ERROR - Could not serialize object of type Model\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/comm/utils.py\", line 41, in _to_frames\n",
      "    context=context))\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 53, in dumps\n",
      "    for key, value in data.items()\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 54, in <dictcomp>\n",
      "    if type(value) is Serialize}\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/serialize.py\", line 153, in serialize\n",
      "    raise TypeError(msg)\n",
      "TypeError: Could not serialize object of type Model\n"
     ]
    }
   ],
   "source": [
    "import pytest\n",
    "with pytest.raises(TypeError):\n",
    "    model = client.scatter(model.autoencoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup(return_x_y=False):\n",
    "    from keras.datasets import mnist\n",
    "    from keras.models import Sequential\n",
    "    from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
    "    from keras import backend as K\n",
    "    import keras\n",
    "    img_rows, img_cols = 28, 28\n",
    "    (x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "    x_train = x_train.astype('float32') / 255\n",
    "    x_test = x_test.astype('float32') / 255\n",
    "    y_train = keras.utils.to_categorical(y_train, 10)\n",
    "    y_test = keras.utils.to_categorical(y_test, 10)\n",
    "    if return_x_y:\n",
    "        return x_train, y_train\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                     activation='relu',\n",
    "                     input_shape=input_shape))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.25))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(128, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(10, activation='softmax'))\n",
    "    return model, x_train, y_train"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.protocol.core - CRITICAL - Failed to Serialize\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 53, in dumps\n",
      "    for key, value in data.items()\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 54, in <dictcomp>\n",
      "    if type(value) is Serialize}\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/serialize.py\", line 153, in serialize\n",
      "    raise TypeError(msg)\n",
      "TypeError: Could not serialize object of type Sequential\n",
      "distributed.comm.utils - ERROR - Could not serialize object of type Sequential\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/comm/utils.py\", line 41, in _to_frames\n",
      "    context=context))\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 53, in dumps\n",
      "    for key, value in data.items()\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/core.py\", line 54, in <dictcomp>\n",
      "    if type(value) is Serialize}\n",
      "  File \"/Users/ssievert/Developer/mrocklin/distributed/distributed/protocol/serialize.py\", line 153, in serialize\n",
      "    raise TypeError(msg)\n",
      "TypeError: Could not serialize object of type Sequential\n"
     ]
    }
   ],
   "source": [
    "m, _, _ = setup()\n",
    "with pytest.raises(TypeError):\n",
    "    client.scatter(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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Keras-serialize-bug-dispatch.ipynb

I think this gist is with modifying the decorator at https://github.com/dask/distributed/blob/4e36660e84b0eb20c1bc4127ba07a1876aefd5c5/distributed/protocol/keras.py#L8 to be

@dask_serialize.register(
    (
        keras.Model,
        keras.engine.training.Model,
        keras.models.Model,
        keras.model.Sequential

    )
)

and likewise for serialization.