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November 24, 2019 13:57
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Error while trying to set up tensorflow with nGraph backend with PlaidML backend.
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:48.583699Z", | |
| "start_time": "2019-11-24T13:51:47.017524Z" | |
| }, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "['CPU', 'INTERPRETER', 'NOP', 'PLAIDML']\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "import ngraph_bridge\n", | |
| "print(ngraph_bridge.list_backends())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:48.661715Z", | |
| "start_time": "2019-11-24T13:51:48.585719Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ngraph_bridge.set_backend('PLAIDML')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:48.666569Z", | |
| "start_time": "2019-11-24T13:51:48.663965Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import tensorflow as tf" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:48.672412Z", | |
| "start_time": "2019-11-24T13:51:48.669082Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "__config = tf.ConfigProto(allow_soft_placement=True,\n", | |
| " log_device_placement=True,\n", | |
| " inter_op_parallelism_threads=4)\n", | |
| "\n", | |
| "config_ngraph_enabled = ngraph_bridge.update_config(__config)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:48.917820Z", | |
| "start_time": "2019-11-24T13:51:48.675066Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:48.926653Z", | |
| "start_time": "2019-11-24T13:51:48.920258Z" | |
| } | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "session = tf.Session(config=config_ngraph_enabled)\n", | |
| "tf.keras.backend.set_session(session)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:49.120846Z", | |
| "start_time": "2019-11-24T13:51:48.928569Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING:tensorflow:From ./venv/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", | |
| "Instructions for updating:\n", | |
| "Call initializer instance with the dtype argument instead of passing it to the constructor\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model = tf.keras.models.Sequential([\n", | |
| " tf.keras.layers.Flatten(input_shape=(28, 28)),\n", | |
| " tf.keras.layers.Dense(128, activation='relu'),\n", | |
| " tf.keras.layers.Dropout(0.2),\n", | |
| " tf.keras.layers.Dense(10, activation='softmax')\n", | |
| "])\n", | |
| "\n", | |
| "model.compile(optimizer='adam',\n", | |
| " loss='sparse_categorical_crossentropy',\n", | |
| " metrics=['accuracy'])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2019-11-24T13:51:49.871254Z", | |
| "start_time": "2019-11-24T13:51:49.123259Z" | |
| } | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Epoch 1/20\n" | |
| ] | |
| }, | |
| { | |
| "ename": "InternalError", | |
| "evalue": "Caught exception while executing nGraph computation: Second argument of index must be an integer\n\n\t [[{{node ngraph_cluster_17}}]]", | |
| "output_type": "error", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mInternalError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m<ipython-input-9-da57676bce5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/Workspace/Personal/ngraphtf/venv/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[0mvalidation_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m 781\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 782\u001b[0m def evaluate(self,\n", | |
| "\u001b[0;32m~/Workspace/Personal/ngraphtf/venv/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[0;31m# Get outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m~/Workspace/Personal/ngraphtf/venv/lib/python3.7/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 3290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3291\u001b[0m fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3292\u001b[0;31m run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m 3293\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3294\u001b[0m output_structure = nest.pack_sequence_as(\n", | |
| "\u001b[0;32m~/Workspace/Personal/ngraphtf/venv/lib/python3.7/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1456\u001b[0m ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m 1457\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1458\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 1459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1460\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;31mInternalError\u001b[0m: Caught exception while executing nGraph computation: Second argument of index must be an integer\n\n\t [[{{node ngraph_cluster_17}}]]" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "model.fit(x_train, y_train, epochs=20)\n", | |
| "model.evaluate(x_test, y_test, verbose=2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
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| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "ngraphtf-venv", | |
| "language": "python", | |
| "name": "ngraphtf-venv" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.5" | |
| }, | |
| "latex_envs": { | |
| "bibliofile": "biblio.bib", | |
| "cite_by": "apalike", | |
| "current_citInitial": 1, | |
| "eqLabelWithNumbers": true, | |
| "eqNumInitial": 0 | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
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