Creating a Sagemaker Tensorflow model can be a great way to improve the performance of your machine learning models. In this blog post, we’ll show you how to create a Sagemaker Tensorflow model and deploy it to a SageMaker endpoint.
For more information check out this video:
This guide walks you through the process of creating a Tensorflow model using Amazon Sagemaker. It covers downloading and preparing the data, creating and training the model, and deploying the model to make predictions.
What is TensorFlow?
TensorFlow is an open source machine learning platform for numerical computation and large-scale machine learning. It is used by researchers and developers to build custom machine learning models for a variety of tasks such as image classification, natural language processing, and predictive analytics. TensorFlow can be used on a variety of hardware platforms, including CPUs, GPUs, and TPUs.
What is Amazon SageMaker?
Amazon SageMaker is a cloud machine learning platform that enables developers and data scientists to build, train, and deploy machine learning models at scale. Amazon SageMaker removes the undifferentiated heavy lifting associated with building, training, and deploying machine learning models, so developers and scientists can focus on the core Amazon SageMaker is fully managed end-to-end solution that includes common ML development tools and efficient utilization of compute resources. It is purpose-built for ML development with Jupyter notebooks for coding, an easy interface for managing training jobs and model deployment, pre-built algorithms and integrations with many popular deep learning frameworks.
With Amazon SageMaker, data scientists and developers can quickly build and train models using their own choice of popular deep learning frameworks, including TensorFlow, Apache MXNet, PyTorch, Chainer, Scikit-learn,and XGBoost. They can also bring their own algorithms built in any framework to Amazon SageMaker.
Setting up the environment
In order to use TensorFlow with SageMaker, we’ll need to set up a few things. Firstly, we’ll need to install the sagemaker python SDK and the tensorflow library.
Secondly, we’ll need to create a TensorFlow Estimator that will be used to deploy our model onto an EC2 instance. This Estimator will take care of everything for us, from downloading the model binaries from our S3 bucket to creating the necessary Amazon SageMaker containers and configuring them for predictions.
Lastly, we’ll need to build a simple web app that will interact with our deployed model and provide predictions.
Creating the TensorFlow model
In order to create a TensorFlow model, you will need to have the TensorFlow SDK installed on your computer. You can find instructions for doing so here: https://www.tensorflow.org/install/.
Once you have the TensorFlow SDK installed, you will need to create a new file called “model.py”. In this file, you will need to import the following packages:
import tensorflow as tf
“””An input function for serving predictions.
params (obj): A dictionary of parameter values (strings) provided when making a prediction request.
tf.estimator.export.ServingInputReceiver object containing features and placeholders needed for prediction later (these placeholders will be fed using the params argument)
# This function needs to take in params and return a ServingInputReceiver object with feature placeholders and feature Tensors created from those placeholders
return tf.estimator.export.ServingInputReceiver(features, placeholder) # features is a dict of Tensors and placeholder is a dict of placeholder Tensors
Training the model
Now that we have our data ready, it’s time to train the model. We’ll use Amazon SageMaker to set up and train our TensorFlow model. Amazon SageMaker is a fully-managed platform that enables developers to build, train, and deploy machine learning models at scale.
To get started, we first need to create an Amazon SageMaker notebook instance. This instance will be used to prepare our data, write our TensorFlow code, and train our model. Sagemaker provides several machine learning algorithms and packages pre-installed, so we won’t need to worry about provisioning and managing our own infrastructure.
Once our notebook instance is up and running, we can upload our training data to an Amazon S3 bucket. Amazon S3 is a highly scalable, reliable, and fast storage service designed for storing large amounts of data. Once our data is in S3, we can create a TensorFlow Estimator, which is a SageMaker API for training TensorFlow models. The Estimator will take care of provisioning the necessary infrastructure and performing the training for us.
Once the training job is complete, we can deploy our trained model to an Amazon SageMaker endpoint. This endpoint can be used to make predictions on new data (in this case, images of handwritten digits). We can also update the endpoint with new versions of our trained model as we continue to improve it.
Deploying the model
After you have trained and evaluated your TensorFlow model, you can deploy it to Amazon SageMaker.Deploying the model to SageMaker hosting saves the trained model in the S3 storage bucket and creates an endpoint. An endpoint is an URL that is used to access the models predictions, inferences, or other functionality.
To deploy the TensorFlow model to Amazon SageMaker, you need to:
1. Specify the S3 location of the trained model artifacts
2. Define a TensorFlow inference script that loads the model and serves requests
3. Configure a SageMaker Model resource that describes the model for deployment
4. Deploy the model to SageMaker hosting by creating an inference endpoint
The following sections walk you through these steps.
Predicting with the model
Once you have your TensorFlow model built, you can use Amazon SageMaker to host your model and perform predictions. This section shows you how to deploy your TensorFlow model to an Amazon SageMaker endpoint and use the endpoint for predictions.
SageMaker manages the hosting for you and provides scalability and high availability. You don’t need to provision or manage any infrastructure. You pay only for the resources that you use. For more information about Amazon SageMaker, see What is Amazon SageMaker?
To get started, do the following:
Set up an Amazon SageMaker notebook instance. For more information, see Create an Amazon SageMaker Notebook Instance.
Open the Amazon SageMaker notebook instance that you created and upload the TensorFlow model that you built in Create a TensorFlow Model.
Create an Amazon SageMaker Python SDK TensorFlow estimator object. The estimator object provides convenient methods that simplify deploying models to Amazon SageMaker endpoints. Configure the estimator with your training data placeholders, hyperparameters, FeatureDefinition objects (optional), directory (optional) containing custom code (optional), and subnets in which to deployed training and inference endpoints (required). For more information about how these parameters affect deployments, see Deploy models with Amazon SageMaker Estimators .
Now that you have created your sagemaker tensorflow model and have trained it on your dataset, it is time to deploy it. This can be done in a number of ways, but the easiest way is to use the sagemaker tensorflow serving container. This container will take care of all the required steps to deploy your model, including creating an endpoint and loading your model into the container.
Once your model is deployed, you can test it by sending requests to the endpoint using the sagemaker tensorflow serving client. The client will send requests to your deployed model and return the results. You can also use the sagemaker tensorflow serving container to create a batch transform job. This job will take your input data and run it through your deployed model. The output of the batch transform job will be a collection of files containing your predictions.
If you want to learn more about creating a Sagemaker Tensorflow model, we suggest checking out the following resources:
-The official Tensorflow documentation on developing and training models: https://www.tensorflow.org/guide/developing_models
-A tutorial on using Tensorflow with Sagemaker: https://aws.amazon.com/blogs/machine-learning/training-tensorflow-models-on-amazon-sagemaker/
Keyword: How to Create a Sagemaker Tensorflow Model