{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9TnJztDZGw-n" }, "source": [ "# Text classification with an RNN" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lUWearf0Gw-p" }, "source": [ "This text classification tutorial trains a [recurrent neural network](https://developers.google.com/machine-learning/glossary/#recurrent_neural_network) on the [IMDB large movie review dataset](http://ai.stanford.edu/~amaas/data/sentiment/) for sentiment analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from kubeflow import fairing\n", "from kubeflow.fairing import TrainJob\n", "import importlib\n", "import argparse\n", "import tensorflow as tf\n", "import tensorflow_datasets as tfds\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def data_loader(hyperparams, local_data_dir):\n", " dataset, info = tfds.load('imdb_reviews/subwords8k', \n", " data_dir=local_data_dir,\n", " with_info=True,\n", " as_supervised=True)\n", " train_dataset, test_dataset = dataset['train'], dataset['test']\n", " encoder = info.features['text'].encoder\n", " train_dataset = train_dataset.shuffle(hyperparams['BUFFER_SIZE'])\n", " train_dataset = train_dataset.padded_batch(hyperparams['BATCH_SIZE'], padded_shapes=None)\n", " test_dataset = test_dataset.padded_batch(hyperparams['BATCH_SIZE'], padded_shapes=None)\n", " return train_dataset, test_dataset, encoder" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def define_model(encoder):\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Embedding(encoder.vocab_size, 64),\n", " tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.Dense(1)\n", " ])\n", " return model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class MovieReviewClassification(object):\n", " def __init__(self, learning_rate=1e-4, batch_size=64, epochs=2, local_data_dir='/app/tensorflow_datasets'):\n", " hyperparams = {'BUFFER_SIZE': 10000, 'BATCH_SIZE': batch_size}\n", " self.model_file = \"lstm_trained\"\n", " self.learning_rate = learning_rate\n", " self.epochs = epochs\n", " self.train_dataset, self.test_dataset, self.encoder = data_loader(hyperparams, local_data_dir)\n", " \n", " def train(self):\n", " model = define_model(self.encoder)\n", " model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam(self.learning_rate),\n", " metrics=['accuracy'])\n", " history = model.fit(self.train_dataset, epochs=self.epochs,\n", " validation_data=self.test_dataset,\n", " validation_steps=30)\n", " model.save(self.model_file)\n", " test_loss, test_acc = model.evaluate(self.test_dataset)\n", " print('Test Loss: {}'.format(test_loss))\n", " print('Test Accuracy: {}'.format(test_acc))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[W 200703 10:01:34 tasks:54] Using default base docker image: registry.hub.docker.com/library/python:3.6.9\n", "[W 200703 10:01:34 tasks:62] Using builder: \n", "[I 200703 10:01:34 tasks:66] Building the docker image.\n", "[I 200703 10:01:34 cluster:46] Building image using cluster builder.\n", "[W 200703 10:01:34 base:94] /home/jovyan/.local/lib/python3.6/site-packages/kubeflow/fairing/__init__.py already exists in Fairing context, skipping...\n", "[I 200703 10:01:34 base:107] Creating docker context: /tmp/fairing_context_fh52_czq\n", "[W 200703 10:01:34 base:94] /home/jovyan/.local/lib/python3.6/site-packages/kubeflow/fairing/__init__.py already exists in Fairing context, skipping...\n", "[W 200703 10:01:48 manager:298] Waiting for fairing-builder-64vxg-8d45k to start...\n", "[W 200703 10:01:48 manager:298] Waiting for fairing-builder-64vxg-8d45k to start...\n", "[W 200703 10:01:48 manager:298] Waiting for fairing-builder-64vxg-8d45k to start...\n", "[I 200703 10:01:50 manager:304] Pod started running True\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "E0703 10:01:54.197364 1 aws_credentials.go:77] while getting AWS credentials NoCredentialProviders: no valid providers in chain. Deprecated.\n", "\tFor verbose messaging see aws.Config.CredentialsChainVerboseErrors\n", "E0703 10:01:54.207742 1 metadata.go:248] Failed to unmarshal scopes: json: cannot unmarshal string into Go value of type []string\n", "\u001b[36mINFO\u001b[0m[0004] Retrieving image manifest registry.hub.docker.com/library/python:3.6.9\n", "E0703 10:01:54.657863 1 metadata.go:154] while reading 'google-dockercfg' metadata: http status code: 404 while fetching url http://metadata.google.internal./computeMetadata/v1/instance/attributes/google-dockercfg\n", "E0703 10:01:54.659838 1 metadata.go:166] while reading 'google-dockercfg-url' metadata: http status code: 404 while fetching url http://metadata.google.internal./computeMetadata/v1/instance/attributes/google-dockercfg-url\n", "\u001b[36mINFO\u001b[0m[0007] Retrieving image manifest registry.hub.docker.com/library/python:3.6.9\n", "\u001b[36mINFO\u001b[0m[0009] Built cross stage deps: map[]\n", "\u001b[36mINFO\u001b[0m[0009] Retrieving image manifest registry.hub.docker.com/library/python:3.6.9\n", "\u001b[36mINFO\u001b[0m[0010] Retrieving image manifest registry.hub.docker.com/library/python:3.6.9\n", "\u001b[36mINFO\u001b[0m[0012] Executing 0 build triggers\n", "\u001b[36mINFO\u001b[0m[0012] Checking for cached layer gcr.io/gsoc-kf-example/fairing-job/fairing-job/cache:0a3fe1c6468b990b27e36abd2581cb9bd0e838665ca411450642850790442267...\n", "\u001b[36mINFO\u001b[0m[0013] Using caching version of cmd: COPY /app//requirements.txt /app/\n", "\u001b[36mINFO\u001b[0m[0013] Checking for cached layer gcr.io/gsoc-kf-example/fairing-job/fairing-job/cache:ae1e04d1ff2efdc261e5028eb5ffff3a00d517fdd90b69ecdb59ab681994b3f0...\n", "\u001b[36mINFO\u001b[0m[0013] Using caching version of cmd: RUN if [ -e requirements.txt ];then pip install --no-cache -r requirements.txt; fi\n", "\u001b[36mINFO\u001b[0m[0014] Checking for cached layer gcr.io/gsoc-kf-example/fairing-job/fairing-job/cache:6651f01a1777ae2b1297b5eeb158cc3599b8f57d815b4c69d79e1a83b4a5adb4...\n", "\u001b[36mINFO\u001b[0m[0014] No cached layer found for cmd COPY /app/ /app/\n", "\u001b[36mINFO\u001b[0m[0014] Unpacking rootfs as cmd COPY /app/ /app/ requires it.\n", "\u001b[36mINFO\u001b[0m[0034] WORKDIR /app/\n", "\u001b[36mINFO\u001b[0m[0034] cmd: workdir\n", "\u001b[36mINFO\u001b[0m[0034] Changed working directory to /app/\n", "\u001b[36mINFO\u001b[0m[0034] Creating directory /app/\n", "\u001b[36mINFO\u001b[0m[0034] Resolving 1 paths\n", "\u001b[36mINFO\u001b[0m[0034] Taking snapshot of files...\n", "\u001b[36mINFO\u001b[0m[0034] ENV FAIRING_RUNTIME 1\n", "\u001b[36mINFO\u001b[0m[0034] No files changed in this command, skipping snapshotting.\n", "\u001b[36mINFO\u001b[0m[0034] COPY /app//requirements.txt /app/\n", "\u001b[36mINFO\u001b[0m[0034] Found cached layer, extracting to filesystem\n", "\u001b[36mINFO\u001b[0m[0035] RUN if [ -e requirements.txt ];then pip install --no-cache -r requirements.txt; fi\n", "\u001b[36mINFO\u001b[0m[0035] Found cached layer, extracting to filesystem\n", "\u001b[36mINFO\u001b[0m[0070] COPY /app/ /app/\n", "\u001b[36mINFO\u001b[0m[0071] Resolving 24 paths\n", "\u001b[36mINFO\u001b[0m[0071] Taking snapshot of files...\n", "\u001b[36mINFO\u001b[0m[0075] Pushing layer gcr.io/gsoc-kf-example/fairing-job/fairing-job/cache:6651f01a1777ae2b1297b5eeb158cc3599b8f57d815b4c69d79e1a83b4a5adb4 to cache now\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[W 200703 10:03:30 job:101] The job fairing-job-qwn9z launched.\n", "[W 200703 10:03:30 manager:298] Waiting for fairing-job-qwn9z-5qkpt to start...\n", "[W 200703 10:03:30 manager:298] Waiting for fairing-job-qwn9z-5qkpt to start...\n", "[W 200703 10:03:30 manager:298] Waiting for fairing-job-qwn9z-5qkpt to start...\n", "[I 200703 10:04:10 manager:304] Pod started running True\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TFDS datasets with text encoding are deprecated and will be removed in a future version. Instead, you should use the plain text version and tokenize the text using `tensorflow_text` (See: https://www.tensorflow.org/tutorials/tensorflow_text/intro#tfdata_example)\n", "Load dataset info from /app/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0\n", "Reusing dataset imdb_reviews (/app/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0)\n", "Constructing tf.data.Dataset for split None, from /app/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0\n", "2020-07-03 10:04:13.872255: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", "2020-07-03 10:04:13.872495: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: UNKNOWN ERROR (303)\n", "2020-07-03 10:04:13.872534: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fairing-job-qwn9z-5qkpt): /proc/driver/nvidia/version does not exist\n", "2020-07-03 10:04:13.872942: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n", "2020-07-03 10:04:13.885057: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2200000000 Hz\n", "2020-07-03 10:04:13.886099: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f6310000b20 initialized for platform Host (this does not guarantee that XLA will be used). 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'tensorflow_datasets/imdb_reviews/subwords8k/1.0.0/dataset_info.json',\n", " 'tensorflow_datasets/imdb_reviews/subwords8k/1.0.0/imdb_reviews-test.tfrecord-00000-of-00001',\n", " 'tensorflow_datasets/imdb_reviews/subwords8k/1.0.0/imdb_reviews-train.tfrecord-00000-of-00001',\n", " 'tensorflow_datasets/imdb_reviews/subwords8k/1.0.0/imdb_reviews-unsupervised.tfrecord-00000-of-00001',\n", " 'tensorflow_datasets/imdb_reviews/subwords8k/1.0.0/label.labels.txt',\n", " 'tensorflow_datasets/imdb_reviews/subwords8k/1.0.0/text.text.subwords',\n", " 'requirements.txt']\n", "\n", "train_job = TrainJob(MovieReviewClassification,\n", " input_files=data_files, \n", " docker_registry=DOCKER_REGISTRY, \n", " backend=BackendClass(build_context_source=BuildContext))\n", "train_job.submit()\n" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "text_classification_rnn.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", 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