We’re excited to announce the newest version of our Bitfusion Ubuntu 14 Chainer AMI is now available in the AWS Marketplace.
Chainer is a Python framework for developing neural networks that can leverage CUDA for GPU computation. It supports a variety of network architectures including feed-forward nets, convnets, recurrent nets and recursive nets, and focuses on an intuitive experience with easy debugging. This latest AMI release includes Chainer 0.17, which includes a variety of new features and bug fixes.
For many, Jupyter is the primary interface for performing data work. In the latest AMI, we’ve included an extensive Jupyter notebook tutorial for getting started with Chainer. If this is your first time using Chainer, or you are still experimenting, the guide does a great job walking through how to tap into the core capabilities of the framework.
Regardless of whether you prefer Python 2 or Python 3, we’ve added new and updated tools, libraries, and frameworks to support and handle both.
When working with Chainer, there are many libraries that provide complementary functionality. In order to provide a truly comprehensive data science and deep learning toolset, we’re including them all by default.
Matplotlib provides an extensive library of graphs and charts that can be implemented with just a few lines of Python.
NumPy is a Python library that provides a variety of fundamental scientific computing functions, including convenient and fast N-dimensional array manipulation.
SciPy builds on top of NumPy with modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.
Pandas is library of data structures and data analysis tools that extend Python’s data munging and prep strengths with powerful data analysis and modeling capabilities.
SymPy is a lightweight, easy-to-use Python library for symbolic mathematics that aims to provide a full-featured computer algebra system (CAS).
h5py provides a Python interface to the HDF5 binary format. It allows you to use straightforward NumPy and Python metaphors like dictionaries and NumPy arrays to interact and process terabyte scale files on disk, sits on top of the HDF5 C API which you can tap into for deeper functionality, and creates files in a standard binary format where they can be leveraged by other programs like MATLAB and IDL.
PyCUDA is rapidly becoming the leading way to interact with NVIDIA CUDA from Python due to its effective handling of memory management and overall convenience and comprehensiveness. By bundling this capability with the ability to compile CUDA Kernels directly from Jupyter notebooks, you can have the ability to interact with the CUDA parallel computation API at a very deep level while also simplicity and ease of use of a notebook user interface.
AWS recently announced their next generation GPU P2 instances, which provide up to 16 NVIDIA K80 GPUs, 64 vCPUs and 732 GiB of host memory. This is a welcome relief, given that the previous generation GPU G2 instances were around for a while and starting to show their age. In our previous release (2016.02) of the Bitfusion Chainer AMI, we included updated NVIDIA drivers, the CUDA toolkit, and CUDNN support, allowing you to tap into these new powerful instances.
Try out these new additions and enhancements in the AWS marketplace, all our AMIs come with a 5 day free trial: Bitfusion Ubuntu 14 Chainer AMI.