Keras 2.3 Quickstart

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Run in Google Colab  View source on GitHub

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In this guide we will describe how to scale out Keras 2.3 programs using Orca in 4 simple steps. (TensorFlow 1.5 and TensorFlow 2 guides are also available.)

Step 0: Prepare Environment

We recommend using conda to prepare the environment. Please refer to the install guide for more details.

conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like.
conda activate zoo
pip install analytics_zoo-${VERSION} # install either version 0.9 or latest nightly build
pip install tensorflow==1.15.0
pip install tensorflow-datasets==2.1.0
pip install psutil
pip install pandas
pip install scikit-learn

Step 1: Init Orca Context

from zoo.orca import init_orca_context, stop_orca_context

if cluster_mode == "local":  # For local machine
    init_orca_context(cluster_mode="local", cores=4, memory="10g")
elif cluster_mode == "k8s":  # For K8s cluster
    init_orca_context(cluster_mode="k8s", num_nodes=2, cores=2, memory="10g", driver_memory="10g", driver_cores=1)
elif cluster_mode == "yarn":  # For Hadoop/YARN cluster
    init_orca_context(cluster_mode="yarn", num_nodes=2, cores=2, memory="10g", driver_memory="10g", driver_cores=1)

This is the only place where you need to specify local or distributed mode. View Orca Context for more details.

Note: You should export HADOOP_CONF_DIR=/path/to/hadoop/conf/dir when running on Hadoop YARN cluster. View Hadoop User Guide for more details.

Step 2: Define the Model

You may define your model, loss and metrics in the same way as in any standard (single node) Keras program.

from tensorflow import keras

model = keras.Sequential(
    [keras.layers.Conv2D(20, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
                         input_shape=(28, 28, 1), padding='valid'),
     keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
     keras.layers.Conv2D(50, kernel_size=(5, 5), strides=(1, 1), activation='tanh',
                         padding='valid'),
     keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),
     keras.layers.Flatten(),
     keras.layers.Dense(500, activation='tanh'),
     keras.layers.Dense(10, activation='softmax'),
     ]
)

model.compile(optimizer=tf.keras.optimizers.RMSprop(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

Step 3: Define Train Dataset

You can define the dataset using standard tf.data.Dataset. Orca also supports Spark DataFrame and Orca XShards.

import tensorflow as tf
import tensorflow_datasets as tfds

def preprocess(data):
    data['image'] = tf.cast(data["image"], tf.float32) / 255.
    return data['image'], data['label']

# get DataSet
mnist_train = tfds.load(name="mnist", split="train", data_dir=dataset_dir)
mnist_test = tfds.load(name="mnist", split="test", data_dir=dataset_dir)

mnist_train = mnist_train.map(preprocess)
mnist_test = mnist_test.map(preprocess)

Step 4: Fit with Orca Estimator

First, create an Estimator.

from zoo.orca.learn.tf.estimator import Estimator

est = Estimator.from_keras(keras_model=model)

Next, fit and evaluate using the Estimator.

est.fit(data=mnist_train,
        batch_size=320,
        epochs=5,
        validation_data=mnist_test)

result = est.evaluate(mnist_test)
print(result)

That’s it, the same code can run seamlessly in your local laptop and the distribute K8s or Hadoop cluster.

Note: You should call stop_orca_context() when your program finishes.