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Open source library for creating containers to run on Amazon SageMaker.

Project description

WARNING: This package has been deprecated. Please use the SageMaker Training Toolkit for model training and the SageMaker Inference Toolkit for model serving.

SageMaker Containers

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SageMaker Containers gives you tools to create SageMaker-compatible Docker containers, and has additional tools for letting you create Frameworks (SageMaker-compatible Docker containers that can run arbitrary Python or shell scripts).

Currently, this library is used by the SageMaker Scikit-learn containers.

Getting Started

Creating a container using SageMaker Containers

Here we’ll demonstrate how to create a Docker image using SageMaker Containers in order to show the simplicity of using this library.

Let’s suppose we need to train a model with the following training script using TF 2.0 in SageMaker:

import tensorflow as tf

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')

              metrics=['accuracy']), y_train, epochs=1)

model.evaluate(x_test, y_test)

The Dockerfile

We then create a Dockerfile with our dependencies and define the program that will be executed in SageMaker:

FROM tensorflow/tensorflow:2.0.0a0

RUN pip install sagemaker-containers

# Copies the training code inside the container
COPY /opt/ml/code/

# Defines as script entry point

More documentation on how to build a Docker container can be found here

Building the container

We then build the Docker image using docker build:

docker build -t tf-2.0 .

Training with Local Mode

We can use Local Mode to test the container locally:

from sagemaker.estimator import Estimator

estimator = Estimator(image_name='tf-2.0',

After using Local Mode, we can push the image to ECR and run a SageMaker training job. To see a complete example on how to create a container using SageMaker Container, including pushing it to ECR, see the example notebook tensorflow_bring_your_own.ipynb.

How a script is executed inside the container

The training script must be located under the folder /opt/ml/code and its relative path is defined in the environment variable SAGEMAKER_PROGRAM. The following scripts are supported:

  • Python scripts: uses the Python interpreter for any script with .py suffix

  • Shell scripts: uses the Shell interpreter to execute any other script

When training starts, the interpreter executes the entry point, from the example above:


Mapping hyperparameters to script arguments

Any hyperparameters provided by the training job will be passed by the interpreter to the entry point as script arguments. For example the training job hyperparameters:

{"HyperParameters": {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}}

Will be executed as:

./ --batch-size 256 --learning_rate 0.0001 --communicator pure_nccl

The entry point is responsible for parsing these script arguments. For example, in a Python script:

import argparse

if __name__ == '__main__':
  parser = argparse.ArgumentParser()

  parser.add_argument('--learning-rate', type=int, default=1)
  parser.add_argument('--batch-size', type=int, default=64)
  parser.add_argument('--communicator', type=str)
  parser.add_argument('--frequency', type=int, default=20)

  args = parser.parse_args()

Reading additional information from the container

Very often, an entry point needs additional information from the container that is not available in hyperparameters. SageMaker Containers writes this information as environment variables that are available inside the script. For example, the training job below includes the channels training and testing:

from sagemaker.pytorch import PyTorch

estimator = PyTorch(entry_point='', ...){'training': 's3://bucket/path/to/training/data',
               'testing': 's3://bucket/path/to/testing/data'})

The environment variable SM_CHANNEL_{channel_name} provides the path were the channel is located:

import argparse
import os

if __name__ == '__main__':
  parser = argparse.ArgumentParser()


  # reads input channels training and testing from the environment variables
  parser.add_argument('--training', type=str, default=os.environ['SM_CHANNEL_TRAINING'])
  parser.add_argument('--testing', type=str, default=os.environ['SM_CHANNEL_TESTING'])

  args = parser.parse_args()

When training starts, SageMaker Containers will print all available environment variables.


These environment variables are those that you’re likely to use when writing a user script. A full list of environment variables is given below.



When the training job finishes, the container will be deleted including its file system with exception of the /opt/ml/model and /opt/ml/output folders. Use /opt/ml/model to save the model checkpoints. These checkpoints will be uploaded to the default S3 bucket. Usage example:

import os

# using it in argparse
parser.add_argument('model_dir', type=str, default=os.environ['SM_MODEL_DIR'])

# using it as variable
model_dir = os.environ['SM_MODEL_DIR']

# saving checkpoints to model dir in chainer
serializers.save_npz(os.path.join(os.environ['SM_MODEL_DIR'], 'model.npz'), model)

For more information, see: How Amazon SageMaker Processes Training Output.



Contains the list of input data channels in the container.

When you run training, you can partition your training data into different logical “channels”. Depending on your problem, some common channel ideas are: “training”, “testing”, “evaluation” or “images” and “labels”.

SM_CHANNELS includes the name of the available channels in the container as a JSON encoded list. Usage example:

import os
import json

# using it in argparse
parser.add_argument('channel_names', default=json.loads(os.environ['SM_CHANNELS'])))

# using it as variable
channel_names = json.loads(os.environ['SM_CHANNELS']))



Contains the directory where the channel named channel_name is located in the container. Usage examples:

import os
import json

parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAINING'])
parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TESTING'])

args = parser.parse_args()

train_file = np.load(os.path.join(args.train, 'train.npz'))
test_file = np.load(os.path.join(args.test, 'test.npz'))


SM_HPS='{"batch-size": "256", "learning-rate": "0.0001","communicator": "pure_nccl"}'

Contains a JSON encoded dictionary with the user provided hyperparameters. Example usage:

import os
import json

hyperparameters = json.loads(os.environ['SM_HPS']))
# {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}



Contains value of the hyperparameter named hyperparameter_name. Usage examples:

learning_rate = float(os.environ['SM_HP_LEARNING-RATE'])
batch_size = int(os.environ['SM_HP_BATCH-SIZE'])
comminicator = os.environ['SM_HP_COMMUNICATOR']



The name of the current container on the container network. Usage example:

import os

# using it in argparse
parser.add_argument('current_host', type=str, default=os.environ['SM_CURRENT_HOST'])

# using it as variable
current_host = os.environ['SM_CURRENT_HOST']



JSON encoded list containing all the hosts . Usage example:

import os
import json

# using it in argparse
parser.add_argument('hosts', type=str, default=json.loads(os.environ['SM_HOSTS']))

# using it as variable
hosts = json.loads(os.environ['SM_HOSTS'])



The number of gpus available in the current container. Usage example:

import os

# using it in argparse
parser.add_argument('num_gpus', type=int, default=os.environ['SM_NUM_GPUS'])

# using it as variable
num_gpus = int(os.environ['SM_NUM_GPUS'])

List of provided environment variables by SageMaker Containers



The number of cpus available in the current container. Usage example:

# using it in argparse
parser.add_argument('num_cpus', type=int, default=os.environ['SM_NUM_CPUS'])

# using it as variable
num_cpus = int(os.environ['SM_NUM_CPUS'])



The current log level in the container. Usage example:

import os
import logging

logger = logging.getLogger(__name__)

logger.setLevel(int(os.environ.get('SM_LOG_LEVEL', logging.INFO)))



Name of the network interface, useful for distributed training. Usage example:

# using it in argparse
parser.add_argument('network_interface', type=str, default=os.environ['SM_NETWORK_INTERFACE_NAME'])

# using it as variable
network_interface = os.environ['SM_NETWORK_INTERFACE_NAME']



JSON encoded list with the script arguments provided for training.



The path of the input directory, e.g. /opt/ml/input/ The input_dir, e.g. /opt/ml/input/, is the directory where SageMaker saves input data and configuration files before and during training.



The path of the input configuration directory, e.g. /opt/ml/input/config/. The directory where standard SageMaker configuration files are located, e.g. /opt/ml/input/config/.

SageMaker training creates the following files in this folder when training starts:

  • hyperparameters.json: Amazon SageMaker makes the hyperparameters in a CreateTrainingJob request available in this file.

  • inputdataconfig.json: You specify data channel information in the InputDataConfig parameter in a CreateTrainingJob request. Amazon SageMaker makes this information available in this file.

  • resourceconfig.json: name of the current host and all host containers in the training.

More information about this files can be find here:



The dir to write non-model training artifacts (e.g. evaluation results) which will be retained by SageMaker, e.g. /opt/ml/output/data.

As your algorithm runs in a container, it generates output including the status of the training job and model and output artifacts. Your algorithm should write this information to the this directory.



The contents from /opt/ml/input/config/resourceconfig.json. It has the following keys:

  • current_host: The name of the current container on the container network. For example, 'algo-1'.

  • hosts: The list of names of all containers on the container network, sorted lexicographically. For example, ['algo-1', 'algo-2', 'algo-3'] for a three-node cluster.

For more information about resourceconfig.json:


    "testing": {
        "RecordWrapperType": "None",
        "S3DistributionType": "FullyReplicated",
        "TrainingInputMode": "File"
    "training": {
        "RecordWrapperType": "None",
        "S3DistributionType": "FullyReplicated",
        "TrainingInputMode": "File"

Input data configuration from /opt/ml/input/config/inputdataconfig.json.

For more information about inpudataconfig.json:


    "channel_input_dirs": {
        "test": "/opt/ml/input/data/testing",
        "train": "/opt/ml/input/data/training"
    "current_host": "algo-1",
    "framework_module": "",
    "hosts": [
    "hyperparameters": {
        "batch-size": 10000,
        "epochs": 1
    "input_config_dir": "/opt/ml/input/config",
    "input_data_config": {
        "test": {
            "RecordWrapperType": "None",
            "S3DistributionType": "FullyReplicated",
            "TrainingInputMode": "File"
        "train": {
            "RecordWrapperType": "None",
            "S3DistributionType": "FullyReplicated",
            "TrainingInputMode": "File"
    "input_dir": "/opt/ml/input",
    "job_name": "preprod-chainer-2018-05-31-06-27-15-511",
    "log_level": 20,
    "model_dir": "/opt/ml/model",
    "module_dir": "s3://sagemaker-{aws-region}-{aws-id}/{training-job-name}/source/sourcedir.tar.gz",
    "module_name": "user_script",
    "network_interface_name": "ethwe",
    "num_cpus": 4,
    "num_gpus": 1,
    "output_data_dir": "/opt/ml/output/data/algo-1",
    "output_dir": "/opt/ml/output",
    "resource_config": {
        "current_host": "algo-1",
        "hosts": [

Provides the entire training information as a JSON-encoded dictionary.

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