Using Scikit-learn with the SageMaker Python SDK

With Scikit-learn Estimators, you can train and host Scikit-learn models on Amazon SageMaker.

For information about supported versions of Scikit-learn, see the AWS documentation. We recommend that you use the latest supported version because that’s where we focus most of our development efforts.

For more information about the framework, see the Scikit-Learn repository. For general information about using the SageMaker Python SDK, see Using the SageMaker Python SDK.

Train a Model with Scikit-learn

To train a Scikit-learn model by using the SageMaker Python SDK:

  1. Prepare a training script

  2. Create a sagemaker.sklearn.SKLearn Estimator

  3. Call the estimator’s fit method

Prepare a Scikit-learn Training Script

Your Scikit-learn training script must be a Python 3.7 compatible source file.

The training script is similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables. For example:

  • SM_MODEL_DIR: A string representing the path to the directory to write model artifacts to. These artifacts are uploaded to S3 for model hosting.

  • SM_OUTPUT_DATA_DIR: A string representing the filesystem path to write output artifacts to. Output artifacts may include checkpoints, graphs, and other files to save, not including model artifacts. These artifacts are compressed and uploaded to S3 to the same S3 prefix as the model artifacts.

Supposing two input channels, ‘train’ and ‘test’, were used in the call to the Scikit-learn estimator’s fit() method, the following will be set, following the format “SM_CHANNEL_[channel_name]”:

  • SM_CHANNEL_TRAIN: A string representing the path to the directory containing data in the ‘train’ channel

  • SM_CHANNEL_TEST: Same as above, but for the ‘test’ channel.

A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to model_dir so that it can be hosted later. Hyperparameters are passed to your script as arguments and can be retrieved with an argparse.ArgumentParser instance. For example, a training script might start with the following:

import argparse
import os

if __name__ =='__main__':

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument('--epochs', type=int, default=50)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--learning-rate', type=float, default=0.05)

    # Data, model, and output directories
    parser.add_argument('--output-data-dir', type=str, default=os.environ.get('SM_OUTPUT_DATA_DIR'))
    parser.add_argument('--model-dir', type=str, default=os.environ.get('SM_MODEL_DIR'))
    parser.add_argument('--train', type=str, default=os.environ.get('SM_CHANNEL_TRAIN'))
    parser.add_argument('--test', type=str, default=os.environ.get('SM_CHANNEL_TEST'))

    args, _ = parser.parse_known_args()

    # ... load from args.train and args.test, train a model, write model to args.model_dir.

Because the SageMaker imports your training script, you should put your training code in a main guard (if __name__=='__main__':) if you are using the same script to host your model, so that SageMaker does not inadvertently run your training code at the wrong point in execution.

For more on training environment variables, please visit SageMaker Training Toolkit.


The sagemaker-containers repository has been deprecated, however it is still used to define Scikit-learn and XGBoost environment variables.

Save the Model

In order to save your trained Scikit-learn model for deployment on SageMaker, your training script should save your model to a certain filesystem path called model_dir. This value is accessible through the environment variable SM_MODEL_DIR. The following code demonstrates how to save a trained Scikit-learn model named model as model.joblib at the end of training:

from sklearn.externals import joblib
import argparse
import os

if __name__=='__main__':
    # default to the value in environment variable `SM_MODEL_DIR`. Using args makes the script more portable.
    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
    args, _ = parser.parse_known_args()

    # ... train classifier `clf`, then save it to `model_dir` as file 'model.joblib'
    joblib.dump(clf, os.path.join(args.model_dir, "model.joblib"))

After your training job is complete, SageMaker will compress and upload the serialized model to S3, and your model data will available in the s3 output_path you specified when you created the Scikit-learn Estimator.

Using third-party libraries

When running your training script on SageMaker, it has access to some pre-installed third-party libraries including scikit-learn, numpy, and pandas. For more information on the runtime environment, including specific package versions, see SageMaker Scikit-learn Docker Container.

If there are other packages you want to use with your script, you can include a requirements.txt file in the same directory as your training script to install other dependencies at runtime. Both requirements.txt and your training script should be put in the same folder. You must specify this folder in source_dir argument when creating a Scikit-learn estimator. A requirements.txt file is a text file that contains a list of items that are installed by using pip install. You can also specify the version of an item to install. For information about the format of a requirements.txt file, see Requirements Files in the pip documentation.

Create an Estimator

You run Scikit-learn training scripts on SageMaker by creating SKLearn Estimators. Call the fit method on a SKLearn Estimator to start a SageMaker training job. The following code sample shows how you train a custom Scikit-learn script named “”, passing in three hyperparameters (‘epochs’, ‘batch-size’, and ‘learning-rate’), and using two input channel directories (‘train’ and ‘test’).

sklearn_estimator = SKLearn('',
                            hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1}){'train': 's3://my-data-bucket/path/to/my/training/data',
                        'test': 's3://my-data-bucket/path/to/my/test/data'})

Call the fit Method

You start your training script by calling fit on a SKLearn Estimator. fit takes both required and optional arguments.

fit Required arguments

  • inputs: This can take one of the following forms: A string s3 URI, for example s3://my-bucket/my-training-data. In this case, the s3 objects rooted at the my-training-data prefix will be available in the default train channel. A dict from string channel names to s3 URIs. In this case, the objects rooted at each s3 prefix will available as files in each channel directory.

For example:


fit Optional arguments

  • wait: Defaults to True, whether to block and wait for the training script to complete before returning.

  • logs: Defaults to True, whether to show logs produced by training job in the Python session. Only meaningful when wait is True.

Deploy a Scikit-learn Model

After you fit a Scikit-learn Estimator, you can host the newly created model in SageMaker.

After you call fit, you can call deploy on an SKLearn estimator to create a SageMaker endpoint. The endpoint runs a SageMaker-provided Scikit-learn model server and hosts the model produced by your training script, which was run when you called fit. This was the model you saved to model_dir.

deploy returns a Predictor object, which you can use to do inference on the Endpoint hosting your Scikit-learn model. Each Predictor provides a predict method which can do inference with numpy arrays or Python lists. Inference arrays or lists are serialized and sent to the Scikit-learn model server by an InvokeEndpoint SageMaker operation.

predict returns the result of inference against your model. By default, the inference result a NumPy array.

# Train my estimator
sklearn_estimator = SKLearn(entry_point='',

# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = sklearn_estimator.deploy(instance_type='ml.m4.xlarge',

# `data` is a NumPy array or a Python list.
# `response` is a NumPy array.
response = predictor.predict(data)

You use the SageMaker Scikit-learn model server to host your Scikit-learn model when you call deploy on an SKLearn Estimator. The model server runs inside a SageMaker Endpoint, which your call to deploy creates. You can access the name of the Endpoint by the name property on the returned Predictor.

SageMaker Scikit-learn Model Server

The Scikit-learn Endpoint you create with deploy runs a SageMaker Scikit-learn model server. The model server loads the model that was saved by your training script and performs inference on the model in response to SageMaker InvokeEndpoint API calls.

You can configure two components of the SageMaker Scikit-learn model server: Model loading and model serving. Model loading is the process of deserializing your saved model back into an Scikit-learn model. Serving is the process of translating InvokeEndpoint requests to inference calls on the loaded model.

You configure the Scikit-learn model server by defining functions in the Python source file you passed to the Scikit-learn constructor.

Load a Model

Before a model can be served, it must be loaded. The SageMaker Scikit-learn model server loads your model by invoking a model_fn function that you must provide in your script. The model_fn should have the following signature:

def model_fn(model_dir):

SageMaker will inject the directory where your model files and sub-directories, saved by save, have been mounted. Your model function should return a model object that can be used for model serving.

SageMaker provides automated serving functions that work with Gluon API net objects and Module API Module objects. If you return either of these types of objects, then you will be able to use the default serving request handling functions.

The following code-snippet shows an example model_fn implementation. This loads returns a Scikit-learn Classifier from a model.joblib file in the SageMaker model directory model_dir.

from sklearn.externals import joblib
import os

def model_fn(model_dir):
    clf = joblib.load(os.path.join(model_dir, "model.joblib"))
    return clf

Serve a Model

After the SageMaker model server has loaded your model by calling model_fn, SageMaker will serve your model. Model serving is the process of responding to inference requests, received by SageMaker InvokeEndpoint API calls. The SageMaker Scikit-learn model server breaks request handling into three steps:

  • input processing,

  • prediction, and

  • output processing.

In a similar way to model loading, you configure these steps by defining functions in your Python source file.

Each step involves invoking a python function, with information about the request and the return-value from the previous function in the chain. Inside the SageMaker Scikit-learn model server, the process looks like:

# Deserialize the Invoke request body into an object we can perform prediction on
input_object = input_fn(request_body, request_content_type)

# Perform prediction on the deserialized object, with the loaded model
prediction = predict_fn(input_object, model)

# Serialize the prediction result into the desired response content type
output = output_fn(prediction, response_content_type)

The above code-sample shows the three function definitions:

  • input_fn: Takes request data and deserializes the data into an object for prediction.

  • predict_fn: Takes the deserialized request object and performs inference against the loaded model.

  • output_fn: Takes the result of prediction and serializes this according to the response content type.

The SageMaker Scikit-learn model server provides default implementations of these functions. You can provide your own implementations for these functions in your hosting script. If you omit any definition then the SageMaker Scikit-learn model server will use its default implementation for that function.

The Predictor used by Scikit-learn in the SageMaker Python SDK serializes NumPy arrays to the NPY format by default, with Content-Type application/x-npy. The SageMaker Scikit-learn model server can deserialize NPY-formatted data (along with JSON and CSV data).

If you rely solely on the SageMaker Scikit-learn model server defaults, you get the following functionality:

  • Prediction on models that implement the __call__ method

  • Serialization and deserialization of NumPy arrays.

The default input_fn and output_fn are meant to make it easy to predict on NumPy arrays. If your model expects a NumPy array and returns a NumPy array, then these functions do not have to be overridden when sending NPY-formatted data.

In the following sections we describe the default implementations of input_fn, predict_fn, and output_fn. We describe the input arguments and expected return types of each, so you can define your own implementations.

Process Input

When an InvokeEndpoint operation is made against an Endpoint running a SageMaker Scikit-learn model server, the model server receives two pieces of information:

  • The request Content-Type, for example “application/x-npy”

  • The request data body, a byte array

The SageMaker Scikit-learn model server will invoke an “input_fn” function in your hosting script, passing in this information. If you define an input_fn function definition, it should return an object that can be passed to predict_fn and have the following signature:

def input_fn(request_body, request_content_type):

where request_body is a byte buffer and request_content_type is a Python string.

The SageMaker Scikit-learn model server provides a default implementation of input_fn. This function deserializes JSON, CSV, or NPY encoded data into a NumPy array.

Default NPY deserialization requires request_body to follow the NPY format. For Scikit-learn, the Python SDK defaults to sending prediction requests with this format.

Default json deserialization requires request_body contain a single json list. Sending multiple json objects within the same request_body is not supported. The list must have a dimensionality compatible with the model loaded in model_fn. The list’s shape must be identical to the model’s input shape, for all dimensions after the first (which first dimension is the batch size).

Default csv deserialization requires request_body contain one or more lines of CSV numerical data. The data is loaded into a two-dimensional array, where each line break defines the boundaries of the first dimension.

The example below shows a custom input_fn for preparing pickled NumPy arrays.

import numpy as np

def input_fn(request_body, request_content_type):
    """An input_fn that loads a pickled numpy array"""
    if request_content_type == "application/python-pickle":
        array = np.load(StringIO(request_body))
        return array
        # Handle other content-types here or raise an Exception
        # if the content type is not supported.

Get Predictions

After the inference request has been deserialized by input_fn, the SageMaker Scikit-learn model server invokes predict_fn on the return value of input_fn.

As with input_fn, you can define your own predict_fn or use the SageMaker Scikit-learn model server default.

The predict_fn function has the following signature:

def predict_fn(input_object, model):

Where input_object is the object returned from input_fn and model is the model loaded by model_fn.

The default implementation of predict_fn invokes the loaded model’s predict function on input_object, and returns the resulting value. The return-type should be a NumPy array to be compatible with the default output_fn.

The example below shows an overridden predict_fn for a Logistic Regression classifier. This model accepts a Python list and returns a tuple of predictions and prediction probabilities from the model in a NumPy array. This predict_fn can rely on the default input_fn and output_fn because input_data is a NumPy array, and the return value of this function is a NumPy array.

import sklearn
import numpy as np

def predict_fn(input_data, model):
    prediction = model.predict(input_data)
    pred_prob = model.predict_proba(input_data)
    return np.array([prediction, pred_prob])

If you implement your own prediction function, you should take care to ensure that:

  • The first argument is expected to be the return value from input_fn. If you use the default input_fn, this will be a NumPy array.

  • The second argument is the loaded model.

  • The return value should be of the correct type to be passed as the first argument to output_fn. If you use the default output_fn, this should be a NumPy array.

Process Output

After invoking predict_fn, the model server invokes output_fn, passing in the return-value from predict_fn and the InvokeEndpoint requested response content-type.

The output_fn has the following signature:

def output_fn(prediction, content_type):

Where prediction is the result of invoking predict_fn and content_type is the InvokeEndpoint requested response content-type. The function should return a byte array of data serialized to content_type.

The default implementation expects prediction to be an NumPy and can serialize the result to JSON, CSV, or NPY. It accepts response content types of “application/json”, “text/csv”, and “application/x-npy”.

Working with existing model data and training jobs

Attach to Existing Training Jobs

You can attach an Scikit-learn Estimator to an existing training job using the attach method.

my_training_job_name = "MyAwesomeSKLearnTrainingJob"
sklearn_estimator = SKLearn.attach(my_training_job_name)

After attaching, if the training job is in a Complete status, it can be deployed to create a SageMaker Endpoint and return a Predictor. If the training job is in progress, attach will block and display log messages from the training job, until the training job completes.

The attach method accepts the following arguments:

  • training_job_name (str): The name of the training job to attach to.

  • sagemaker_session (sagemaker.Session or None): The Session used to interact with SageMaker

Deploy an Endpoint from Model Data

As well as attaching to existing training jobs, you can deploy models directly from model data in S3. The following code sample shows how to do this, using the SKLearnModel class.

sklearn_model = SKLearnModel(model_data="s3://bucket/model.tar.gz",

predictor = sklearn_model.deploy(instance_type="ml.c4.xlarge", initial_instance_count=1)

To see what arguments are accepted by the SKLearnModel constructor, see sagemaker.sklearn.model.SKLearnModel.

Your model data must be a .tar.gz file in S3. SageMaker Training Job model data is saved to .tar.gz files in S3, however if you have local data you want to deploy, you can prepare the data yourself.

Assuming you have a local directory containing your model data named “my_model”, you can tar and gzip compress the file and upload to S3 using the following commands:

tar -czf model.tar.gz my_model
aws s3 cp model.tar.gz s3://my-bucket/my-path/model.tar.gz

This uploads the contents of my_model to a gzip compressed tar file to S3 in the bucket “my-bucket”, with the key “my-path/model.tar.gz”.

To run this command, you’ll need the AWS CLI tool installed. Please refer to our FAQ for more information on installing this.

Scikit-learn Training Examples

To find example notebooks that demonstrate end-to-end training on Amazon SageMaker using Scikit-learn, see the Amazon SageMaker example notebooks repository.

These are also available in SageMaker Notebook Instance hosted Jupyter notebooks under the “sample notebooks” folder.

SageMaker Scikit-learn Classes

For information about the different Scikit-learn classes in the SageMaker Python SDK, see

SageMaker Scikit-learn Docker Containers

To find the SageMaker-managed Scikit-learn containers, visit the SageMaker Scikit-Learn containers repository.