We’re excited to announce that the newest version of our Bitfusion Ubuntu 14 Tensorflow AMI is now available in the AWS Marketplace with Tensorflow 1.0! This Tensorflow release marks a major milestone of functionality, robustness, and -- going forward -- backwards compatibility.
We’re excited to announce the newest AMI in our lineup: Bitfusion Ubuntu 14 MXNet AMI, now available in the AWS Marketplace. Amazon CTO Werner Vogels has recently announced that MXNet is the company's official deep learning framework of choice, paving the way for fast growth and interest this previously lesser known project.
We’re excited to announce that the newest version of our Bitfusion Ubuntu 14 Tensorflow AMI is now available in the AWS Marketplace. We’ve also made some exciting new changes to our pricing model to help save you money.
Bitfusion Deep Learning AMIs including TensorFlow, Caffe, Torch, Theano, Chainer, and Digits 4 are now available on the newly announced AWS P2 Instances. Recently AWS introduced new P2 Instances which feature Nvidia K80 Accelerators with GK210 GPUs. Unlike the previous G2 instanced which were equipped with K520 cards, where each card only had 4 GiB of memory, each GPU in the P2s has 12 GiB of memory with a memory bandwidth of 240 GB/s. The table below summarizes the specifications for the new P2 Instances: Instance vCPU Count System Memory GPU Count Total GPU Memory Network p2.xlarge 4 61 GiB 1 12 GiB High p2.8xlarge 32 488 GiB 8 96 GiB 10 Gigabit p2.16xlarge 64 732 GiB 16 192 GiB 20 Gigabit The new P2 instances provide significant advantages over the last generation of instances when it comes to deep learning, including the ability to train neural networks significantly faster and to work with larger models that previously exceeded the GPU memory limits. We will be posting a follow on blog shortly detailing some performance benchmarks between the new P2 instances and the previous generation of G2 instances. In the meantime, we have qualified our deep learning AMIs on the new P2 instances and they are are available in the AWS Marketplace as follows: Bitfusion Boost Ubuntu 14 Caffe AMI Pre-installed with Ubuntu 14, Nvidia Drivers, Cuda 7.5 Toolkit, cuDNN 5.1, Caffe, pyCaffe, and Jupyter. Boost enabled for multi-node deployment. Get started with Caffe machine learning and deep learning in minutes. Launch on AWS! Bitfusion Boost Ubuntu 14 Torch 7 AMI Pre-installed with Ubuntu 14, Nvidia Drivers, Cuda 7.5 Toolkit, cuDNN 5.1, Torch 7, iTorch, and Jupyter. Boost enabled for multi-node deployment. Get started with Torch numerical computing, machine learning, and deep learning in minutes. Launch on AWS! Bitfusion Ubuntu 14 Chainer AMI Pre-installed with Nvidia Drivers, Cuda 7.5 Toolkit, cuDNN 5.1, Chainer 1.13.0, and Jupyter. Optimized to leverage Nvidia GRID as well as CPU instances. Designed for developers as well as those eager to get started with the flexible Chainer framework for neural networks. Launch on AWS! Bitfusion Ubuntu 14 Digits 4 AMI Pre-installed with the Deep Learning GPU Training System (DIGITS) from Nvidia. Leverage GPU instances to accelerate pre-installed Caffe and Torch applications. Train deep neural networks and view results directly from your browser. Launch on AWS! Bitfusion Ubuntu 14 TensorFlow AMI Pre-installed with Ubuntu 14, Nvidia Drivers, Cuda 7.5 Toolkit, cuDNN 5.1, TensorFlow, Magenta, Keras and Jupyter. Get started with TensorFlow deep learning, machine learning, and numerical computing in minutes with pre-installed tutorial collateral. Launch on AWS! Bitfusion Ubuntu 14 Theano AMI Pre-installed with Ubuntu 14, Nvidia Drivers, Cuda 7.5 Toolkit, cuDNN 5, Theano, and Jupyter. Get started with Theano deep learning, machine learning, and numerical computing, and develop interactive Theano scripts via python directly from your browser. Launch on AWS! Bitfusion Mobile Deep Learning Service AMI Pre-installed with Nvidia Drivers, Cuda 7.5 Toolkit, Caffe, GPU Rest Engine, Pre-trained Models, and a simple Rest API server. Use existing pre-trained models or train your own models and then integrate inference tasks into your applications via the provided REST API. Launch on AWS!
Last week we released an update for our popular Bitfusion Ubuntu 14 Scientific Computing AMI which upgrades all of the commonly used applications for scientific and statistical computing to the most recent versions, including:
A few weeks ago we published a tutorial on Easy TensorFlow Model Training on AWS using our Bitfusion TensorFlow AMI. This quick tutorial as well as the AMI have proven immensely popular with our users and we received various feature requests. As such this week we are releasing v0.03 of the TensorFlow AMI which introduces several new features: