- Stay within resource usage requirements.
- Do not engage in or encourage activity that is illegal.
- Do not engage in cryptocurrency mining.
If an Environment Error occurs, please restart the lab.
Ready for the real environment experience?
TensorFlow is a popular framework used for machine learning. The Amazon Deep Learning AMI comes bundled with everything you need to start using TensorFlow from development through to production. In this Lab, you will develop, visualize, serve, and consume a TensorFlow machine learning model using the Amazon Deep Learning AMI.
Upon completion of this lab you will be able to:
- Create machine learning models in TensorFlow
- Visualize TensorFlow graphs and the learning process in TensorBoard
- Serve trained TensorFlow models with TensorFlow Serving
- Create clients that consume served TensorFlow models, all with the Amazon Deep Learning AMI
You should be familiar with:
- Working at the Linux command line
- The Python programming language
- Some linear algebra knowledge is beneficial (basic vector and matrix operations)
- Basic understanding of neural networks is beneficial, but not required
Before completing the lab instructions, the environment will look as follows:
After completing the lab instructions, the environment should look similar to:
January 17th, 2022 - Improved instructions for establishing an SSH tunnel with PuTTY for Windows users
January 12th, 2022 - Updated and added SSH tunnel instructions
January 3rd, 2022 - Updated to use Python 3
August 1st, 2021 - Fixed typo in lab instructions
December 7th. 2020 - Updated instructions to address an issue that prevented a lab step from being able to be completed successfully
April 15th, 2020 - Updated instructions to provide for a more streamlined lab experience
January 10th, 2019 - Added a validation lab Step to check the work you perform in the lab
Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Security Specialist (CKS), Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.