Amazon Web Services has announced the availability of two new versions of the AWS Deep Learning AMI: Conda-based AMI and Base AMI.
The Conda-based AMI comes pre-installed with separate Python environments for deep learning frameworks created using Conda, while the Base AMI comes pre-installed with the foundational building blocks for deep learning.
“Think of the Conda-based AMI as a fully baked virtual environment ready to run your deep learning code, for example, to train a neural network model. Think of the Base AMI as a clean slate to deploy your customized deep learning set up,” Amazon said in its announcement.
Conda-based Deep Learning AMI
The Conda-based AMI has Python environments for deep learning created using Conda—a popular open source package and environment management tool. In addition to the flexibility at the run-time environment, the AMI provides a visual interface that plugs straight into the 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,” Amazon said.
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
The 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
Deep Learning Base AMI
The new Base AMI comes with GPU drivers and libraries to deploy your own customized deep learning models. If you are a developer who is contributing to open source deep learning framework enhancements or even creating a new deep learning engine, the Base AMI will provide the foundation to install your own custom configurations and code repositories to test out new framework features.
By default, the AMI is configured with an NVidia CUDA 9 environment. However, we can switch to a CUDA 8 environment by reconfiguring the environment variable LD_LIBRARY_PATH. Simply replace the CUDA 9 portion of the environment variable string with its CUDA 8 equivalent.
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
Amazon has set up new developer resources to help select the right AMI for your project. The AMI selection guide can be accessed here.