sagemaker
Open source library for training and deploying models on Amazon SageMaker.
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Description
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====================
SageMaker Python SDK
====================
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SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
With the SDK, you can train and deploy models using popular deep learning frameworks **Apache MXNet** and **PyTorch**.
You can also train and deploy models with **Amazon algorithms**,
which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training.
If you have **your own algorithms** built into SageMaker compatible Docker containers, you can train and host models using these as well.
To install SageMaker Python SDK, see `Installing SageMaker Python SDK <#installing-the-sagemaker-python-sdk>`_.
āš„ SageMaker V3 Release
-------------------------
Version 3.0.0 represents a significant milestone in our product's evolution. This major release introduces a modernized architecture, enhanced performance, and powerful new features while maintaining our commitment to user experience and reliability.
**Important: Please review these breaking changes before upgrading.**
* Older interfaces such as Estimator, Model, Predictor and all their subclasses will not be supported in V3.
* Please see our `V3 examples folder <https://github.com/aws/sagemaker-python-sdk/tree/master/v3-examples>`__ for example notebooks and usage patterns.
Migrating to V3
----------------
**Upgrading to 3.x**
To upgrade to the latest version of SageMaker Python SDK 3.x:
::
pip install --upgrade sagemaker
If you prefer to downgrade to the 2.x version:
::
pip install sagemaker==2.*
See `SageMaker V2 Examples <#sagemaker-v2-examples>`__ for V2 documentation and examples.
**Key Benefits of 3.x**
* **Modular Architecture**: Separate PyPI packages for core, training, and serving capabilities
* `sagemaker-core <https://pypi.org/project/sagemaker-core/>`__
* `sagemaker-train <https://pypi.org/project/sagemaker-train/>`__
* `sagemaker-serve <https://pypi.org/project/sagemaker-serve/>`__
* `sagemaker-mlops <https://pypi.org/project/sagemaker-mlops/>`__
* **Unified Training & Inference**: Single classes (ModelTrainer, ModelBuilder) replace multiple framework-specific classes
* **Object-Oriented API**: Structured interface with auto-generated configs aligned with AWS APIs
* **Simplified Workflows**: Reduced boilerplate and more intuitive interfaces
**Training Experience**
V3 introduces the unified ModelTrainer class to reduce complexity of initial setup and deployment for model training. This replaces the V2 Estimator class and framework-specific classes (PyTorchEstimator, SKLearnEstimator, etc.).
This example shows how to train a model using a custom training container with training data from S3.
*SageMaker Python SDK 2.x:*
.. code:: python
from sagemaker.estimator import Estimator
estimator = Estimator(
image_uri="my-training-image",
role="arn:aws:iam::123456789012:role/SageMakerRole",
instance_count=1,
instance_type="ml.m5.xlarge",
output_path="s3://my-bucket/output"
)
estimator.fit({"training": "s3://my-bucket/train"})
*SageMaker Python SDK 3.x:*
.. code:: python
from sagemaker.train import ModelTrainer
from sagemaker.train.configs import InputData
trainer = ModelTrainer(
training_image="my-training-image",
role="arn:aws:iam::123456789012:role/SageMakerRole"
)
train_data = InputData(
channel_name="training",
data_source="s3://my-bucket/train"
)
trainer.train(input_data_config=[train_data])
**See more examples:** `SageMaker V3 Examples <#sagemaker-v3-examples>`__
**Inference Experience**
V3 introduces the unified ModelBuilder class for model deployment and inference. This replaces the V2 Model class and framework-specific classes (PyTorchModel, TensorFlowModel, SKLearnModel, XGBoostModel, etc.).
This example shows how to deploy a trained model for real-time inference.
*SageMaker Python SDK 2.x:*
.. code:: python
from sagemaker.model import Model
from sagemaker.predictor import Predictor
model = Model(
image_uri="my-inference-image",
model_data="s3://my-bucket/model.tar.gz",
role="arn:aws:iam::123456789012:role/SageMakerRole"
)
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.m5.xlarge"
)
result = predictor.predict(data)
*SageMaker Python SDK 3.x:*
.. code:: python
from sagemaker.serve import ModelBuilder
model_builder = ModelBuilder(
model="my-model",
model_path="s3://my-bucket/model.tar.gz"
)
endpoint = model_builder.build()
result = endpoint.invoke(...)
**See more examples:** `SageMaker V3 Examples <#sagemaker-v3-examples>`__
SageMaker V3 Examples
---------------------
**Training Examples**
#. `Custom Distributed Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/custom-distributed-training-example.ipynb>`__
#. `Distributed Local Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/distributed-local-training-example.ipynb>`__
#. `Hyperparameter Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/hyperparameter-training-example.ipynb>`__
#. `JumpStart Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/jumpstart-training-example.ipynb>`__
#. `Local Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/local-training-example.ipynb>`__
**Inference Examples**
#. `HuggingFace Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/huggingface-example.ipynb>`__
#. `In-Process Mode Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/in-process-mode-example.ipynb>`__
#. `Inference Spec Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/inference-spec-example.ipynb>`__
#. `JumpStart E2E Training Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/jumpstart-e2e-training-example.ipynb>`__
#. `JumpStart Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/jumpstart-example.ipynb>`__
#. `Local Mode Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/local-mode-example.ipynb>`__
#. `Optimize Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/optimize-example.ipynb>`__
#. `Train Inference E2E Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/inference-examples/train-inference-e2e-example.ipynb>`__
**ML Ops Examples**
#. `V3 Hyperparameter Tuning Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-hyperparameter-tuning-example/v3-hyperparameter-tuning-example.ipynb>`__
#. `V3 Hyperparameter Tuning Pipeline <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-hyperparameter-tuning-example/v3-hyperparameter-tuning-pipeline.ipynb>`__
#. `V3 Model Registry Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-model-registry-example/v3-model-registry-example.ipynb>`__
#. `V3 PyTorch Processing Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-processing-job-pytorch/v3-pytorch-processing-example.ipynb>`__
#. `V3 Pipeline Train Create Registry <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-pipeline-train-create-registry.ipynb>`__
#. `V3 Processing Job Sklearn <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-processing-job-sklearn.ipynb>`__
#. `V3 SageMaker Clarify <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-sagemaker-clarify.ipynb>`__
#. `V3 Transform Job Example <https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/ml-ops-examples/v3-transform-job-example.ipynb>`__
**Looking for V2 Examples?** See `SageMaker V2 Examples <#sagemaker-v2-examples>`__ below.
Installing the SageMaker Python SDK
-----------------------------------
The SageMaker Python SDK is built to PyPI and the latest version of the SageMaker Python SDK can be installed with pip as follows
::
pip install sagemaker==<Latest version from pyPI from https://pypi.org/project/sagemaker/>
You can install from source by cloning this repository and running a pip install command in the root directory of the repository:
::
git clone https://github.com/aws/sagemaker-python-sdk.git
cd sagemaker-python-sdk
pip install .
Supported Operating Systems
~~~~~~~~~~~~~~~~~~~~~~~~~~~
SageMaker Python SDK supports Unix/Linux and Mac.
Supported Python Versions
~~