Amazon SageMaker JumpStart models and algorithms now available via API

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment.

Previously, all JumpStart content was available only through Amazon SageMaker Studio, which provides a user-friendly graphical interface to interact with the feature. Today, we’re excited to announce the launch of easy-to-use JumpStart APIs as an extension of the SageMaker Python SDK. These APIs allow you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. This launch unlocks the usage of JumpStart capabilities in your code workflows, MLOps pipelines, and anywhere else you’re interacting with SageMaker via SDK.

In this post, we provide an update on the current state of JumpStart’s capabilities and guide you through the usage flow of the JumpStart API with an example use case.

JumpStart overview

JumpStart is a multi-faceted product that includes different capabilities to help get you quickly started with ML on SageMaker. At the time of writing, JumpStart enables you to do the following:

• Deploy pre-trained models for common ML tasks – JumpStart enables you to solve common ML tasks with no development effort by providing easy deployment of models pre-trained on publicly available large datasets. The ML research community has put a large amount of effort into making a majority of recently developed models publicly available for use. JumpStart hosts a collection of over 300 models, spanning the 15 most popular ML tasks such as object detection, text classification, and text generation, making it easy for beginners to use them. These models are drawn from popular model hubs, such as TensorFlow, PyTorch, Hugging Face, and MXNet Hub.
• Fine-tune pre-trained models – JumpStart allows you to fine-tune pre-trained models with no need to write your own training algorithm. In ML, the ability to transfer the knowledge learned in one domain to another domain is called transfer learning. You can use transfer learning to produce accurate models on your smaller datasets, with much lower training costs than the ones involved in training the original model from scratch. JumpStart also includes popular training algorithms based on LightGBM, CatBoost, XGBoost, and Scikit-learn that you can train from scratch for tabular data regression and classification.
• Use pre-built solutions – JumpStart provides a set of 17 pre-built solutions for common ML use cases, such as demand forecasting and industrial and financial applications, which you can deploy with just a few clicks. The solutions are end-to-end ML applications that string together various AWS services to solve a particular business use case. They use AWS CloudFormation templates and reference architectures for quick deployment, which means they are fully customizable.
• Use notebook examples for SageMaker algorithms – SageMaker provides a suite of built-in algorithms to help data scientists and ML practitioners get started with training and deploying ML models quickly. JumpStart provides sample notebooks that you can use to quickly use these algorithms.
• Take advantage of training videos and blogs – JumpStart also provides numerous blog posts and videos that teach you how to use different functionalities within SageMaker.

JumpStart accepts custom VPC settings and KMS encryption keys, so that you can use the available models and solutions securely within your enterprise environment. You can pass your security settings to JumpStart within SageMaker Studio or through the SageMaker Python SDK.

JumpStart-supported ML tasks and API example Notebooks

JumpStart currently supports 15 of the most popular ML tasks; 13 of them are vision and NLP-based tasks, of which 8 support no-code fine-tuning. It also supports four popular algorithms for tabular data modeling. The tasks and links to their sample notebooks are summarized in the following table.

Depending on the task, the sample notebooks linked in the preceding table can guide you on all or a subset of the following processes:

• Select a JumpStart supported pre-trained model for your specific task.
• Host a pre-trained model, get predictions from it in real-time, and adequately display the results.
• Fine-tune a pre-trained model with your own selection of hyperparameters and deploy it for inference.

Fine-tune and deploy an object detection model with JumpStart APIs

In the following sections, we provide a step-by-step walkthrough of how to use the new JumpStart APIs on the representative task of object detection. We show how to use a pre-trained object detection model to identify objects from a predefined set of classes in an image with bounding boxes. Finally, we show how to fine-tune a pre-trained model on your own dataset to detect objects in images that are specific to your business needs, simply by bringing your own data. We provide an accompanying notebook for this walkthrough.

We walk through the following high-level steps:

1. Run inference on the pre-trained model.
   1. Retrieve JumpStart artifacts and deploy an endpoint.
   2. Query the endpoint, parse the response, and display model predictions.
2. Fine-tune the pre-trained model on your own dataset.
   1. Retrieve training artifacts.
   2. Run training.

Run inference on the pre-trained model

In this section, we choose an appropriate pre-trained model in JumpStart, deploy this model to a SageMaker endpoint, and show how to run inference on the deployed endpoint. All the steps are available in the accompanying Jupyter notebook.

Retrieve JumpStart artifacts and deploy an endpoint

SageMaker is a platform based on Docker containers. JumpStart uses the available framework-specific SageMaker Deep Learning Containers (DLCs). We fetch any additional packages, as well as scripts to handle training and inference for the selected task. Finally, the pre-trained model artifacts are separately fetched with model_uris, which provides flexibility to the platform. You can use any number of models pre-trained for the same task with a single training or inference script. See the following code:

infer_model_id, infer_model_version  = "pytorch-od-nvidia-ssd", "*"

# Retrieve the inference docker container uri. This is the base container PyTorch image for the model selected above. 
deploy_image_uri = image_uris.retrieve(region=None, framework=None, image_scope="inference",model_id=infer_model_id, model_version=infer_model_version, instance_type=inference_instance_type)

# Retrieve the inference script uri. This includes all dependencies and scripts for model loading, inference handling etc.
deploy_source_uri = script_uris.retrieve(model_id=infer_model_id, model_version=infer_model_version, script_scope="inference")

# Retrieve the base model uri. This includes the pre-trained nvidia-ssd model and parameters.
base_model_uri = model_uris.retrieve(model_id=infer_model_id, model_version=infer_model_version, model_scope="inference")

Next, we feed the resources into a SageMaker Model instance and deploy an endpoint:

# Create the SageMaker model instance
model = Model(image_uri=deploy_image_uri, source_dir=deploy_source_uri, model_data=base_model_uri, entry_point="inference.py", role=aws_role, predictor_cls=Predictor, name=endpoint_name)

# deploy the Model. Note that we need to pass Predictor class when we deploy model through Model class for being able to run inference through the sagemaker API.
base_model_predictor = model.deploy(initial_instance_count=1, instance_type=inference_instance_type, predictor_cls=Predictor, endpoint_name=endpoint_name)

Endpoint deployment may take a few minutes to complete.

Query the endpoint, parse the response, and display predictions

To get inferences from a deployed model, an input image needs to be supplied in binary format along with an accept type. In JumpStart, you can define the number of bounding boxes returned. In the following code snippet, we predict ten bounding boxes per image by appending ;n_predictions=10 to Accept. To predict xx boxes, you can change it to ;n_predictions=xx , or get all the predicted boxes by omitting ;n_predictions=xx entirely.

def query(model_predictor, image_file_name):

    with open(image_file_name, "rb") as file:
        input_img_rb = file.read()

    return model_predictor.predict(input_img_rb,{
            "ContentType": "application/x-image",
            "Accept": "application/json;verbose;n_predictions=10"})

query_response = query(base_model_predictor, Naxos_Taverna)

The following code snippet gives you a glimpse of what object detection looks like. The probability predicted for each object class is visualized, along with its bounding box. We use the parse_response and display_predictions helper functions, which are defined in the accompanying notebook.

normalized_boxes, classes_names, confidences = parse_response(query_response)
display_predictions(Naxos_Taverna, normalized_boxes, classes_names, confidences)

The following screenshot shows the output of an image with prediction labels and bounding boxes.

Fine-tune a pre-trained model on your own dataset

Existing object detection models in JumpStart are pre-trained either on the COCO or the VOC datasets. However, if you need to identify object classes that don’t exist in the original pre-training dataset, you have to fine-tune the model on a new dataset that includes these new object types. For example, if you need to identify kitchen utensils and run inference on a deployed pre-trained SSD model, the model doesn’t recognize any characteristics of the new image types and therefore the output is incorrect.

In this section, we demonstrate how easy it is to fine-tune a pre-trained model to detect new object classes using JumpStart APIs. The full code example with more details is available in the accompanying notebook.

Retrieve training artifacts

Training artifacts are similar to the inference artifacts discussed in the preceding section. Training requires a base Docker container, namely the MXNet container in the following example code. Any additional packages required for training are included with the training scripts in train_sourcer_uri. The pre-trained model and its parameters are packaged separately.

train_model_id, train_model_version, train_scope = "mxnet-od-ssd-512-vgg16-atrous-coco","*","training"
training_instance_type = "ml.p2.xlarge"

# Retrieve the docker image. This is the base container MXNet image for the model selected above. 
train_image_uri = image_uris.retrieve(region=None, framework=None, 
                            model_id=train_model_id, model_version=train_model_version,
                            image_scope=train_scope,instance_type=training_instance_type)

# Retrieve the training script and dependencies. This contains all the necessary files including data processing, model training etc.
train_source_uri = script_uris.retrieve(model_id=train_model_id, model_version=train_model_version, script_scope=train_scope)

# Retrieve the pre-trained model tarball to further fine-tune
train_model_uri = model_uris.retrieve(
model_id=train_model_id, model_version=train_model_version, model_scope=train_scope)

Run training

To run training, we simply feed the required artifacts along with some additional parameters to a SageMaker Estimator and call the .fit function:

# Create SageMaker Estimator instance
od_estimator = Estimator(
    role=aws_role,
    image_uri=train_image_uri,
    source_dir=train_source_uri,
    model_uri=train_model_uri,
    entry_point="transfer_learning.py",  # Entry-point file in source_dir and present in train_source_uri.
    instance_count=1,
    instance_type=training_instance_type,
    max_run=360000,
    hyperparameters=hyperparameters,
    output_path=s3_output_location,
)

# Launch a SageMaker Training job by passing s3 path of the training data
od_estimator.fit({"training": training_dataset_s3_path}, logs=True)

While the algorithm trains, you can monitor its progress either in the SageMaker notebook where you’re running the code itself, or on Amazon CloudWatch. When training is complete, the fine-tuned model artifacts are uploaded to the Amazon Simple Storage Service (Amazon S3) output location specified in the training configuration. You can now deploy the model in the same manner as the pre-trained model. You can follow the rest of the process in the accompanying notebook.

Conclusion

In this post, we described the value of the newly released JumpStart APIs and how to use them. We provided links to 17 example notebooks for the different ML tasks supported in JumpStart, and walked you through the object detection notebook.

We look forward to hearing from you as you experiment with JumpStart.

About the Authors

Dr. Vivek Madan is an Applied Scientist with the Amazon SageMaker JumpStart team. He got his PhD from University of Illinois at Urbana-Champaign and was a Post-Doctoral Researcher at Georgia Tech. He is an active researcher in machine learning and algorithm design, and has published papers in EMNLP, ICLR, COLT, FOCS, and SODA conferences.

João Moura is an AI/ML Specialist Solutions Architect at Amazon Web Services. He is mostly focused on NLP use-cases and helping customers optimize Deep Learning model training and deployment.

Dr. Ashish Khetan is a Senior Applied Scientist with Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms and helps develop machine learning algorithms. He is an active researcher in machine learning and statistical inference and has published many papers in NeurIPS, ICML, ICLR, JMLR, and ACL conferences.

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