Accelerate TensorFlow with AWS SageMaker: Step-by-Step Guide

Accelerate TensorFlow with AWS SageMaker: Step-by-Step Guide

Table of Contents:

1. Introduction
2. What is AWS?
3. Why do we need AWS for GPU code examples?
4. Creating an AWS Account
5. Logging into the AWS Console
6. Searching for SageMaker in the AWS Console
7. Creating a Notebook Instance
8. Requesting Service Quota for Accelerated Computing Instances
9. Creating a Notebook Instance for ML.G4DN
10. Starting the Notebook Instance
11. Opening Jupyter Lab in the Notebook Instance
12. Importing TensorFlow and Checking its Version
13. Checking for GPU Availability
14. Conclusion
15. Resources

🚀 Introduction

In this article, we will explore how to run TensorFlow GPU code examples on AWS SageMaker. If you are not familiar with AWS, it stands for Amazon Web Services, which is a cloud computing service platform that allows you to run high-performance computing jobs on the cloud. Utilizing AWS is crucial when it comes to GPU-intensive tasks, as it significantly reduces training time. So, let's dive into the AWS console and get started!

What is AWS?

AWS, short for Amazon Web Services, is a cloud computing service platform developed by Amazon. It offers a wide range of services, including storage, database management, machine learning, and more. With AWS, users can securely store data, access computing power, and deploy applications globally without the need for physical infrastructure.

Why do we need AWS for GPU code examples?

When it comes to running GPU code examples, having access to powerful hardware is essential. Training complex machine learning models can be time-consuming and resource-intensive. AWS provides a cloud-based solution that allows you to leverage their high-performance computing capabilities, such as GPU instances, to speed up the training process and reduce costs.

Creating an AWS Account

To begin, you need to create your AWS account. If you don't have an account, visit the AWS website and sign up. Once you have created an account, proceed to the next step.

Logging into the AWS Console

After creating your AWS account, log in to the AWS console using your account credentials. Once logged in, you will be directed to the AWS console homepage, where you can access various services.

Searching for SageMaker in the AWS Console

To access SageMaker, a service that enables you to build, train, and deploy machine learning models, use the search bar at the top of the AWS console page. Type "SageMaker" in the search bar, and click on it from the search results.

Creating a Notebook Instance

Within the SageMaker service, you can create a notebook instance to perform your machine learning tasks. On the left side of the page, click on "Create notebook instance." This will allow you to configure your notebook instance settings.

Requesting Service Quota for Accelerated Computing Instances

Before you can create a notebook instance for accelerated computing instances, you need to request a service quota increase. By default, the quota for accelerated computing instances is set to zero. To request the increase, search for "service quotas" in the search bar. Select "AWS service quotas" under the AWS service and scroll through the list to find the "ml.g4" instance type. Copy the instance type and request the increase, specifying a quota value of 1.

Creating a Notebook Instance for ML.G4DN

Now that you have the service quota approved, you can finally create a notebook instance. In the SageMaker page, click on "Create notebook instance." Give your instance a name and select "ml.g4dn.xlarge" as the instance type. Keep the other settings as default and click on "Create notebook instance." Once the instance is created, you can start it.

Starting the Notebook Instance

When you first create the notebook instance, its status will be "stopped." To start the instance, simply click on the "Start" button. The instance will transition to the "pending" state and will take a few minutes to complete.

Opening Jupyter Lab in the Notebook Instance

Once the notebook instance is up and running, you can open Jupyter Lab by clicking on the "Open Jupyter Lab" button. SageMaker takes care of the GPU drivers and environment setup, allowing you to directly start coding without worrying about those details.

Importing TensorFlow and Checking its Version

In the Jupyter Lab notebook, the first step is to import the TensorFlow library and check its version. By running the appropriate code, you can confirm that TensorFlow is installed and verify the version being used.

Checking for GPU Availability

Since you are using the "g4dn.xlarge" instance type, which supports GPU acceleration, you should have GPU devices available. You can verify this by running a command to list the physical devices using the TensorFlow configuration. Filtering the results will allow you to determine if a GPU device is Present.

Conclusion

In this article, we have explored how to run TensorFlow GPU code examples on AWS SageMaker. By utilizing AWS's cloud computing capabilities, specifically GPU instances, you can significantly reduce training time for machine learning models. AWS provides a seamless environment for developing and deploying machine learning solutions, making it a powerful tool in the field of AI.

Resources

• AWS Official Website
• AWS SageMaker Documentation
• TensorFlow Official Documentation
• Jupyter Lab Official Documentation