New AWS Deep Learning AMIs for Machine Learning Practitioners
We’re excited to announce the availability of two new versions of the AWS Deep Learning AMI. The first is a Conda-based AMI with separate Python environments for deep learning frameworks created using Conda—a popular open source package and environment management tool. The second is a Base AMI with GPU drivers and libraries to deploy your own customized deep learning models.
Deep learning technology is evolving at a rapid pace—everything from frameworks and algorithms to new methods and theories from academia and industry. All of this causes complexity for developers who need tools for quickly and securely testing algorithms, optimizing for specific versions of frameworks, running tests and benchmarks, or collaborating on projects starting with a blank canvas. Virtual environments provide the freedom and flexibility to do all this, which is why we’re adding it to the AWS Deep Learning AMIs today. We’ve also set up new developer resources to help you learn more about the AMIs, choose the right AMI for your project and dive into hands-on tutorials.
New Conda-based Deep Learning AMI
The Conda-based AMI comes pre-installed with Python environments for deep learning created using Conda. Each Conda-based Python environment is configured to include the official pip package of a popular deep learning framework, and its dependencies. Think of it as a fully baked virtual environment ready to run your deep learning code, for example, to train a neural network model. Our step-by-step guide provides instructions on how to activate an environment with the deep learning framework of your choice or swap between environments using simple one-line commands.
But the benefits of the AMI don’t stop there. The environments on the AMI operate as mutually-isolated, self-contained sandboxes. This means when you run your deep learning code inside the sandbox, you get full visibility and control of its run-time environment. You can install a new software package, upgrade an existing package or change an environment variable—all without worrying about interrupting other deep learning environments on the AMI. This level of flexibility and fine-grained control over your execution environment also means you can now run tests, and benchmark the performance of your deep learning models in a manner that is consistent and reproducible over time.
Finally, the AMI provides a visual interface that plugs straight into your Jupyter notebooks so you can switch in and out of environments, launch a notebook in an environment of your choice, and even reconfigure your environment—all with a single click, right from your Jupyter notebook browser. Our step-by-step guide walks you through these integrations and other Jupyter notebooks and tutorials.
The new Conda-based Deep Learning AMI comes packaged with the latest official releases of the following deep learning frameworks:
- Apache MXNet 0.12 with Gluon
- TensorFlow 1.4
- Caffe2 0.8.1
- PyTorch 0.2
- CNTK 2.2
- Theano 0.9
- Keras 1.2.2 and Keras 2.0.9
This AMI also includes the following libraries and drivers for GPU acceleration on the cloud:
- CUDA 8 and 9
- cuDNN 6 and 7
- NCCL 2.0.5 libraries
- NVidia Driver 384.81
New Deep Learning Base AMI
The Base AMI comes pre-installed with the foundational building blocks for deep learning. This includes NVIDIA CUDA libraries, GPU drivers, and system libraries to speed up and scale machine learning on Amazon Elastic Compute Cloud (EC2) instances. Think of the Base AMI as a clean slate to deploy your customized deep learning set up.
For example, for developers contributing to open source deep learning framework enhancements or even building a new deep learning engine, the Base AMI provides the foundation to install your own custom configurations and code repositories to test out new framework features. The Base AMI comes with the CUDA 9 environment installed by default, however you can also switch to a CUDA 8 environment using simple one-line commands given in our step-by-step user guide.
The Base AMI provides the following GPU drivers and libraries:
- CUDA 8 and 9
- CuBLAS 8 and 9
- CuDNN 6 and 7
- glibc 2.18
- OpenCV 3.2.0
- NVIDIA driver 384.81
- NCCL 2.0.5
- Python 2 and 3
Deep Learning AMIs with source code
In addition to the two new AMIs available today, we continue to support the AMIs that install all the popular deep learning frameworks from source in a unified Python environment, and include their source code on the AMI. This AMI is great if you want to try out and compare multiple frameworks in a shared base environment or you need quick access to source code on the AMI itself to recompile a framework with your custom set of build options.
The AMI comes in CUDA 8 and CUDA 9 versions to meet your specific needs of the AWS EC2 instance you want to use for deep learning.
Deep Learning AMIs cheat sheet
We now have three types of AWS Deep Learning AMIs available in the AWS Marketplace to support the various needs of machine learning practitioners. Don’t forget to check out our AMI selection guide, simple tutorials, and more deep learning resources in our developer guide!
|Conda-based AMI||Base AMI||AMIs with source code|
|For developers who want pre-installed pip packages of deep learning frameworks in separate virtual environments||For developers who want a clean slate to set up private deep learning engine repositories or custom builds of deep learning engines||For developers who want pre-installed deep learning frameworks and their source code in a shared python environment|
Deep Learning AMI (Ubuntu)
Deep Learning AMI (Amazon Linux)
Deep Learning Base AMI (Ubuntu)
Deep Learning Base AMI (Amazon Linux)
For P3 instances:
Deep Learning AMI with Source Code (CUDA 9, Ubuntu)
Deep Learning AMI with Source Code (CUDA 9, Amazon Linux)
For P2 instances:
Deep Learning AMI with Source Code (CUDA 8, Ubuntu)
Deep Learning AMI with Source Code (CUDA 8, Amazon Linux)
About the Author
Cynthya Peranandam is a Principal Marketing Manager for AWS artificial intelligence solutions, helping customers use deep learning to provide business value. In her spare time she likes to run and listen to music.