Train and Host a Keras Model with Pipe Mode and Horovod on Amazon SageMaker

Amazon SageMaker is a fully-managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high-quality models. The SageMaker Python SDK makes it easy to train and deploy models in Amazon SageMaker with several different machine learning and deep learning frameworks, including TensorFlow and Keras.

In this notebook, we train and host a Keras Sequential model on SageMaker. The model used for this notebook is a simple deep convolutional neural network (CNN) that was extracted from the Keras examples.

For training our model, we also demonstrate distributed training with Horovod and Pipe Mode. Amazon SageMaker’s Pipe Mode streams your dataset directly to your training instances instead of being downloaded first, which translates to training jobs that start sooner, finish quicker, and need less disk space.
Instance Type and Pricing:

This notebook was trained using the Python 3 (TensorFlow CPU Optimized) kernel using the ml.p3.2xlarge compute instance type in the us-west-2 region. Training time is approximately 70 minutes with the aforementioned hardware specifications.

Price per hour depends on your region and instance type. You can reference prices on the SageMaker pricing page.

Setup

First, we define a few variables that are be needed later in the example.

[ ]:
import sagemaker
from sagemaker import get_execution_role

sagemaker_session = sagemaker.Session()

role = get_execution_role()

We also install a couple libraries we need for visualizing our model’s prediction results.

[ ]:
!pip install matplotlib seaborn

The CIFAR-10 dataset

The CIFAR-10 dataset is one of the most popular machine learning datasets. It consists of 60,000 32x32 images belonging to 10 different classes (6,000 images per class). Here are the classes in the dataset, as well as 10 random images from each:

cifar10

Prepare the dataset for training

To use the CIFAR-10 dataset, we first download it and convert it to TFRecords. This step takes around 5 minutes.

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!python generate_cifar10_tfrecords.py --data-dir ./data

Next, we upload the data to Amazon S3:

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from sagemaker.s3 import S3Uploader

bucket = sagemaker_session.default_bucket()
dataset_uri = S3Uploader.upload("data", "s3://{}/tf-cifar10-example/data".format(bucket))

display(dataset_uri)

Train the model

In this tutorial, we train a deep CNN to learn a classification task with the CIFAR-10 dataset. We compare three different training jobs: a baseline training job, training with Pipe Mode, and distributed training with Horovod.

Configure metrics

In addition to running the training job, Amazon SageMaker can retrieve training metrics directly from the logs and send them to CloudWatch metrics. Here, we define metrics we would like to observe:

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metric_definitions = [
    {"Name": "train:loss", "Regex": ".*loss: ([0-9\\.]+) - accuracy: [0-9\\.]+.*"},
    {"Name": "train:accuracy", "Regex": ".*loss: [0-9\\.]+ - accuracy: ([0-9\\.]+).*"},
    {
        "Name": "validation:accuracy",
        "Regex": ".*step - loss: [0-9\\.]+ - accuracy: [0-9\\.]+ - val_loss: [0-9\\.]+ - val_accuracy: ([0-9\\.]+).*",
    },
    {
        "Name": "validation:loss",
        "Regex": ".*step - loss: [0-9\\.]+ - accuracy: [0-9\\.]+ - val_loss: ([0-9\\.]+) - val_accuracy: [0-9\\.]+.*",
    },
    {
        "Name": "sec/steps",
        "Regex": ".* - \d+s (\d+)[mu]s/step - loss: [0-9\\.]+ - accuracy: [0-9\\.]+ - val_loss: [0-9\\.]+ - val_accuracy: [0-9\\.]+",
    },
]

Run a baseline training job on SageMaker

The SageMaker Python SDK’s sagemaker.tensorflow.TensorFlow estimator class makes it easy for us to interact with SageMaker. Here, we create one to configure a training job. Some parameters worth noting:

  • entry_point: our training script (adapted from this Keras example).

  • metric_definitions: the metrics (defined above) that we want sent to CloudWatch.

  • train_instance_count: the number of training instances. Here, we set it to 1 for our baseline training job.

For more details about the TensorFlow estimator class, see the API documentation.

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from sagemaker.tensorflow import TensorFlow

hyperparameters = {"epochs": 10, "batch-size": 256}
tags = [{"Key": "Project", "Value": "cifar10"}]

estimator = TensorFlow(
    entry_point="keras_cifar10.py",
    source_dir="source",
    metric_definitions=metric_definitions,
    hyperparameters=hyperparameters,
    role=role,
    framework_version="1.15.2",
    py_version="py3",
    instance_count=1,
    instance_type="ml.p3.2xlarge",
    base_job_name="cifar10-tf",
    tags=tags,
)

Once we have our estimator, we call fit() to start the SageMaker training job and pass the inputs that we uploaded to Amazon S3 earlier. We pass the inputs as a dictionary to define different data channels for training.

[ ]:
inputs = {
    "train": "{}/train".format(dataset_uri),
    "validation": "{}/validation".format(dataset_uri),
    "eval": "{}/eval".format(dataset_uri),
}

estimator.fit(inputs)

View the job training metrics

We can now view the metrics from the training job directly in the SageMaker console.

Log into the SageMaker console, choose the latest training job, and scroll down to the monitor section. Alternatively, the code below uses the region and training job name to generate a URL to CloudWatch metrics.

Using CloudWatch metrics, you can change the period and configure the statistics.

[ ]:
from urllib import parse

from IPython.core.display import Markdown

region = sagemaker_session.boto_region_name
cw_url = parse.urlunparse(
    (
        "https",
        "{}.console.aws.amazon.com".format(region),
        "/cloudwatch/home",
        "",
        "region={}".format(region),
        "metricsV2:namespace=/aws/sagemaker/TrainingJobs;dimensions=TrainingJobName;search={}".format(
            estimator.latest_training_job.name
        ),
    )
)

display(
    Markdown(
        "CloudWatch metrics: [link]({}). After you choose a metric, "
        "change the period to 1 Minute (Graphed Metrics -> Period).".format(cw_url)
    )
)

Train on SageMaker with Pipe Mode

Here we train our model using Pipe Mode. With Pipe Mode, SageMaker uses Linux named pipes to stream the training data directly from S3 instead of downloading the data first.

In our script, we enable Pipe Mode using the following code:

from sagemaker_tensorflow import PipeModeDataset

dataset = PipeModeDataset(channel=channel_name, record_format='TFRecord')

When we create our estimator, the only difference from before is that we also specify input_mode='Pipe':

[ ]:
pipe_mode_estimator = TensorFlow(
    entry_point="keras_cifar10.py",
    source_dir="source",
    metric_definitions=metric_definitions,
    hyperparameters=hyperparameters,
    role=role,
    framework_version="1.15.2",
    py_version="py3",
    instance_count=1,
    instance_type="ml.p3.2xlarge",
    input_mode="Pipe",
    base_job_name="cifar10-tf-pipe",
    tags=tags,
)
[ ]:
pipe_mode_estimator.fit(inputs)

Distributed training with Horovod

Horovod is a distributed training framework based on MPI. To use Horovod, we make the following changes to our training script:

  1. Enable Horovod:

import horovod.keras as hvd

hvd.init()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = str(hvd.local_rank())
K.set_session(tf.Session(config=config))
  1. Add these callbacks:

hvd.callbacks.BroadcastGlobalVariablesCallback(0)
hvd.callbacks.MetricAverageCallback()
  1. Configure the optimizer:

opt = Adam(lr=learning_rate * size, decay=weight_decay)
opt = hvd.DistributedOptimizer(opt)

To configure the training job, we specify the following for the distribution:

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distribution = {
    "mpi": {
        "enabled": True,
        "processes_per_host": 1,  # Number of Horovod processes per host
    }
}

This is then passed to our estimator:

[ ]:
dist_estimator = TensorFlow(
    entry_point="keras_cifar10.py",
    source_dir="source",
    metric_definitions=metric_definitions,
    hyperparameters=hyperparameters,
    distribution=distribution,
    role=role,
    framework_version="1.15.2",
    py_version="py3",
    instance_count=2,
    instance_type="ml.p3.2xlarge",
    base_job_name="cifar10-tf-dist",
    tags=tags,
)
[ ]:
dist_estimator.fit(inputs)

Deploy the trained model

After we train our model, we can deploy it to a SageMaker Endpoint, which serves prediction requests in real-time. To do so, we simply call deploy() on our estimator, passing in the desired number of instances and instance type for the endpoint.

Because we’re using TensorFlow Serving for deployment, our training script saves the model in TensorFlow’s SavedModel format.

We don’t need accelerated computing power for inference, so let’s switch over to a ml.m4.xlarge instance type.

For more information about deploying Keras and TensorFlow models in SageMaker, see this blog post.

[ ]:
predictor = estimator.deploy(initial_instance_count=1, instance_type="ml.m4.xlarge")

Invoke the endpoint

To verify the that the endpoint is in service, we generate some random data in the correct shape and get a prediction.

[ ]:
import numpy as np

data = np.random.randn(1, 32, 32, 3)
print("Predicted class: {}".format(np.argmax(predictor.predict(data)["predictions"])))

Now let’s use the test dataset for predictions.

[ ]:
from keras.datasets import cifar10

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

With the data loaded, we can use it for predictions:

[ ]:
from keras.preprocessing.image import ImageDataGenerator


def predict(data):
    predictions = predictor.predict(data)["predictions"]
    return predictions


predicted = []
actual = []
batches = 0
batch_size = 128

datagen = ImageDataGenerator()
for data in datagen.flow(x_test, y_test, batch_size=batch_size):
    for i, prediction in enumerate(predict(data[0])):
        predicted.append(np.argmax(prediction))
        actual.append(data[1][i][0])

    batches += 1
    if batches >= len(x_test) / batch_size:
        break

With the predictions, we calculate our model accuracy and create a confusion matrix.

[ ]:
from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_pred=predicted, y_true=actual)
display("Average accuracy: {}%".format(round(accuracy * 100, 2)))
[ ]:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_pred=predicted, y_true=actual)
cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
sn.set(rc={"figure.figsize": (11.7, 8.27)})
sn.set(font_scale=1.4)  # for label size
sn.heatmap(cm, annot=True, annot_kws={"size": 10})  # font size

Aided by the colors of the heatmap, we can use this confusion matrix to understand how well the model performed for each label.

Cleanup

To avoid incurring extra charges to your AWS account, let’s delete the endpoint we created:

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predictor.delete_endpoint()