SageMaker Training Toolkit <https://github.com/aws/sagemaker-training-toolkit> for model training and the SageMaker Inference Toolkit <https://github.com/aws/sagemaker-inference-toolkit> for model serving... _header-n957:
.. image:: https://img.shields.io/badge/code_style-black-000000.svg :target: https://github.com/python/black :alt: Code style: black
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 <https://github.com/aws/sagemaker-scikit-learn-container>__.
.. contents::
.. _header-n1174:
.. _header-n962:
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 train.py using TF 2.0 in SageMaker:
.. code:: python
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.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=1)
model.evaluate(x_test, y_test)
.. _header-n965:
The Dockerfile
We then create a Dockerfile with our dependencies and define the
program that will be executed in SageMaker:
.. code:: docker
FROM tensorflow/tensorflow:2.0.0a0
RUN pip install sagemaker-containers
# Copies the training code inside the container
COPY train.py /opt/ml/code/train.py
# Defines train.py as script entry point
ENV SAGEMAKER_PROGRAM train.py
More documentation on how to build a Docker container can be found `here <https://docs.docker.com/get-started/part2/#define-a-container-with-dockerfile>`__
.. _header-n968:
Building the containerWe then build the Docker image using docker build:
.. code:: shell
docker build -t tf-2.0 .
.. _header-n971:
Training with Local Mode
We can use `Local
Mode <https://sagemaker.readthedocs.io/en/stable/overview.html#local-mode>`__
to test the container locally:
.. code:: python
from sagemaker.estimator import Estimator
estimator = Estimator(image_name='tf-2.0',
role='SageMakerRole',
train_instance_count=1,
train_instance_type='local')
estimator.fit()
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 <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/tensorflow_bring_your_own/tensorflow_bring_your_own.ipynb>`__.
.. _header-n975:
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:
.. code:: python
python train.py
.. _header-n984:
Mapping hyperparameters to script argumentsAny 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:
.. code:: python
{"HyperParameters": {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}}
Will be executed as:
.. code:: shell
./user_script.sh --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:
.. code:: python
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()
..... _header-n991:
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**:
.. code:: python
from sagemaker.pytorch import PyTorch
estimator = PyTorch(entry_point='train.py', ...)
estimator.fit({'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:
.. code:: python
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.
.. _header-n997:
IMPORTANT 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.
.. _header-n999:
SM_MODEL_DIR.. code:: shell
SM_MODEL_DIR=/opt/ml/model
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:
.. code:: python
import os
parser.add_argument('model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
model_dir = os.environ['SM_MODEL_DIR']
serializers.save_npz(os.path.join(os.environ['SM_MODEL_DIR'], 'model.npz'), model)
For more information, see: How Amazon SageMaker Processes Training Output <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-envvariables>__.
.. _header-n1004:
SM_CHANNELS
.. code:: shell
SM_CHANNELS='["testing","training"]'
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:
.. code:: python
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']))
.. _header-n1010:
SM_CHANNEL_{channel_name}.. code:: shell
SM_CHANNEL_TRAINING='/opt/ml/input/data/training' SM_CHANNEL_TESTING='/opt/ml/input/data/testing'
Contains the directory where the channel named channel_name is
located in the container. Usage examples:
.. code:: python
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'))
.. _header-n1014:
SM_HPS
.. code:: shell
SM_HPS='{"batch-size": "256", "learning-rate": "0.0001","communicator": "pure_nccl"}'
Contains a JSON encoded dictionary with the user provided
hyperparameters. Example usage:
.. code:: python
import os
import json
hyperparameters = json.loads(os.environ['SM_HPS']))
# {"batch-size": 256, "learning-rate": 0.0001, "communicator": "pure_nccl"}
.. _header-n1020:
SM_HP_{hyperparameter_name}.. code:: shell
SM_HP_LEARNING-RATE=0.0001 SM_HP_BATCH-SIZE=10000 SM_HP_COMMUNICATOR=pure_nccl
Contains value of the hyperparameter named hyperparameter_name.
Usage examples:
.. code:: python
learning_rate = float(os.environ['SM_HP_LEARNING-RATE']) batch_size = int(os.environ['SM_HP_BATCH-SIZE']) comminicator = os.environ['SM_HP_COMMUNICATOR']
.. _header-n1026:
SM_CURRENT_HOST
.. code:: shell
SM_CURRENT_HOST=algo-1
The name of the current container on the container network. Usage
example:
.. code:: python
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']
.. _header-n1032:
SM_HOSTS.. code:: shell
SM_HOSTS='["algo-1","algo-2"]'
JSON encoded list containing all the hosts . Usage example:
.. code:: python
import os import json
parser.add_argument('hosts', type=str, default=json.loads(os.environ['SM_HOSTS']))
hosts = json.loads(os.environ['SM_HOSTS'])
.. _header-n1038:
SM_NUM_GPUS
.. code:: shell
SM_NUM_GPUS=1
The number of gpus available in the current container. Usage example:
.. code:: python
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'])
.. _header-n1042:
List of provided environment variables by SageMaker Containers
--------------------------------------------------------------
.. _header-n1043:
SM_NUM_CPUS.. code:: shell
SM_NUM_CPUS=32
The number of cpus available in the current container. Usage example:
.. code:: python
parser.add_argument('num_cpus', type=int, default=os.environ['SM_NUM_CPUS'])
num_cpus = int(os.environ['SM_NUM_CPUS'])
.. _header-n1047:
SM_LOG_LEVEL
.. code:: shell
SM_LOG_LEVEL=20
The current log level in the container. Usage example:
.. code:: python
import os
import logging
logger = logging.getLogger(__name__)
logger.setLevel(int(os.environ.get('SM_LOG_LEVEL', logging.INFO)))
.. _header-n1053:
SM_NETWORK_INTERFACE_NAME.. code:: shell
SM_NETWORK_INTERFACE_NAME=ethwe
Name of the network interface, useful for distributed training. Usage example:
.. code:: python
parser.add_argument('network_interface', type=str, default=os.environ['SM_NETWORK_INTERFACE_NAME'])
network_interface = os.environ['SM_NETWORK_INTERFACE_NAME']
.. _header-n1057:
SM_USER_ARGS
.. code:: shell
SM_USER_ARGS='["--batch-size","256","--learning_rate","0.0001","--communicator","pure_nccl"]'
JSON encoded list with the script arguments provided for training.
.. _header-n1060:
SM_INPUT_DIR.. code:: shell
SM_INPUT_DIR=/opt/ml/input/
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.
.. _header-n1063:
SM_INPUT_CONFIG_DIR
.. code:: shell
SM_INPUT_CONFIG_DIR=/opt/ml/input/config
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:
https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html
.. _header-n1068:
SM_OUTPUT_DATA_DIR.. code:: shell
SM_OUTPUT_DATA_DIR=/opt/ml/output/data/algo-1
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.
.. _header-n1072:
SM_RESOURCE_CONFIG
.. code:: shell
SM_RESOURCE_CONFIG='{"current_host":"algo-1","hosts":["algo-1","algo-2"]}'
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``:
https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-running-container-dist-training
.. _header-n1081:
SM_INPUT_DATA_CONFIG.. code:: shell
SM_INPUT_DATA_CONFIG='{ "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:
https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html#your-algorithms-training-algo-running-container-dist-training
.. _header-n1085:
SM_TRAINING_ENV
.. code:: shell
SM_TRAINING_ENV='
{
"channel_input_dirs": {
"test": "/opt/ml/input/data/testing",
"train": "/opt/ml/input/data/training"
},
"current_host": "algo-1",
"framework_module": "sagemaker_chainer_container.training:main",
"hosts": [
"algo-1",
"algo-2"
],
"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": [
"algo-1",
"algo-2"
]
}
}'
Provides the entire training information as a JSON-encoded dictionary.