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Posts tagged with: maciej

Bitfusion Presenting at Supercomputing Conference 2016

Join us November 15-17 in Salt Lake City, Utah as we partake in the 2016 Supercomputing Conference.  In conjunction with introducing the latest version of our revolutionary Bitfusion Boost software, which now comes with a free trial for up to 4 node deployments, we will be presenting a talk titled: GPU Virtualization: Optimizing Performance and Cost of your Applications, on Wednesday November 16 at 11am, Nimbix Booth #4072.
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Quick Comparison of TensorFlow GPU Performance on AWS P2 and G2 Instances

TensorFlow GPU performance on AWS p2 instances is between 2x-3x faster when compared to previous generation g2 instances across a variety of convolutional neural networks. Recently, we made our Bitfusion Deep Learning  AMIs available on the newly announced AWS P2 instances. Naturally, one of the first questions that arises is, how does the performance of the new P2 instances compare to that of the the previous generation G2 instances. In this post we take a quick look at single-GPU performance across a variety of convolutional neural networks. To keep things consistent we start each EC2 instance with the exact same AMI, thus keeping the driver, cuda, cudnn, and framework the same across the instances. TensorFlow GPU Performance To evaluate TensorFlow performance we utilized the Bitfusion TensorFlow AMI along with the convnet-benchmark to measure for forward and backward propagation times for some of the more well known convolutional neural networks including AlexNet, Overfeat, VGG, and GoogleNet. Because of the much larger GPU memory of 12 GiB, the P2 instances can accommodate much larger batch sizes than the G2 instances. For the purpose of the benchmarks below, the batch sizes were selected for each network type such that they could run on the G2 as well as on the P2 instances. The Tables below summarize the results obtained for G2 and P2 instances:   Bitfusion Ubuntu 14 TensorFlow AMI Launch on AWS!   g2.2xlarge - Nvidia K520 Network Batch Size Forward Pass (ms) Backward Pass (ms) Total Time (ms) AlexNet 512 502 914 1416 Overfeat 256 1134 2934 4068 VGG 64 750 2550 3300 GoogleNet 128 600 1587 2187     p2.xlarge - Nvidia K80 Network Batch Size Forward Pass (ms) Backward Pass (ms) Total Time (ms) AlexNet 512 254 462 716 Overfeat 256 427 847 1274 VGG 64 423 869 1292 GoogleNet 128 341 783 1124         Averaging the speedup across all four types of networks, the results show an approximate ~2.42x improvement in performance - not bad for an instance which is only ~1.39 more expensive on an hourly on demand basis. We have several other Deep learning AMIs available in the AWS Marketplace including Caffe, Chainer, Theano, Torch, and Digits.  If you are interested in seeing GPU Performance benchmarks for any of the above drop us a note. Are you currently developing AI applications, but spending too much time wrangling machines and setting up your infrastructure? We are currently offering a Free 30-Day Trial of Bitfusion Flex!
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Bitfusion Deep Learning AMIs Now Available on AWS P2 Instances

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!  
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Bitfusion Presenting at the GPU Technology Conference Europe 2016

Join us September 28-29 in Amsterdam, Netherlands at the GPU Technology Conference Europe as we showcase how Bitfusion Boost can enable GPU virtualization in the datacenter with ease. Whether you are going for efficiency and utilization or outright performance, we can help with both. Be sure to attend session "HPC 12: Breaking New Database Performance Records with GPUs" on Thursday September 29th, 09:30 - 10:00, where along with IBM Cloud and MapD we will be discussing in detail how these remarkable results were achieved.
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Bitfusion Scientific Computing AMI 2016.08

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:
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TensorFlow 0.9 AMI with Keras, cuDNN 5, and 30-40% faster

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:
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New Blender AMI with Mate Desktop, Turbo VNC Server, and VirtualGL

Last week we released several media related AMIs featuring a RESTful API interfaces. We received feedback, particularly on our Blender rendering AMI, that some of you would like to try and work directly with Blender via a remote session and a Desktop environment. Always listening to user feedback, this week we are releasing a Bitfusion Ubuntu 14 Blender AMI pre-installed with Nvidia Drivers, Cuda 7.5 Toolkit, Blender 2.77, and a complete Linux desktop environment including Mate Desktop, TurboVNC Server and VirtualGL for full 3D hardware acceleration of OpenGL applications when using remote display software.
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New Bitfusion Deep Learning and Media AMIs with REST APIs for Developers

Today we are introducing four new AMIs targeted at developers that want to offload compute intensive applications or tasks from their thin clients, laptops, or even mobile devices into the cloud where they can utilize vastly more powerful systems to get these tasks done orders of magnitude faster. The four new AMIs are as follows: Bitfusion Mobile Deep Learning Service, Bitfusion Mobile Image Manipulation Service, Bitfusion Mobile Rendering Service, and Bitfusion Mobile Video Processing Service. Each AMI comes with a simple REST API which can be used as is and for which we provide simple example scripts. Alternatively, you can build on top of our API and provide your own services or integrate these AMIs into your applications. Here are the details for each new AMI:
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Deploy Bitfusion Boost on AWS faster than ever

Enabling development, deployment, and acceleration of multi-node GPU applications from deep learning to oil exploration. Back in March, we first described how to deploy Bitfusion Boost on AWS to create a 16 GPU cluster. We received a lot of customer feedback since then, in particular we paid attention to issues that tripped you up in order to make the experience more seamless. With that in mind, we engaged the AWS Marketplace team to integrate Bitfusion Boost directly into our products, enabling you to spin-up Bitfusion Boost GPU clusters directly from the AWS Marketplace with just a few clicks. Some of the major improvements include: Run multi-gpu enabled applications across multiple GPU instances without any additional configurations or code changes Boost enabled AMIs can be launched in cluster-mode directly from the AWS Marketplace Boost enabled AMI clusters can be launched in all AWS regions that contain GPU instances AMI opt-in process is identical for single-instance and cluster-mode AMI launches Monthly cluster cost estimates are provided directly in the AWS Marketplace Simplified cluster launch parameters for CFNs enable easier cluster customization   Summary Launching a Bitfusion Boost Cluster now entails only 4 easy steps: Locate a Bitfusion Boost enabled AMI in the AWS Marketplace Select a Bitfusion Boost Cluster configuration Fine-tune the Bitfusion Boost Cluster launch parameters Launch the Bitfusion Boost Cluster and verify proper operation   Detailed Instructions Locate a Bitfusion Boost enabled AMI You can locate all Bitfusion Boost enabled AMIs in the AWS marketplace by clicking here. Alternatively, below are direct links to our AMIs which are presently Boost enabled. If you don't already have an AWS account, you can create one by clicking here. Bitfusion Boost Ubuntu 14 Cuda 7 Bitfusion Boost Ubuntu 14 Cuda 7.5 Bitfusion Boost Ubuntu 14 Caffe Bitfusion Boost Ubuntu 14 Torch Select a Bitfusion Boost cluster configuration For this example we are using the Bitfusion Boost Ubuntu Cuda 7.5 AMI, and we will launch an 8 GPU cluster. The image below has several color-coded boxes: Blue Box: Shows detailed descriptions of the available deployment (delivery) options for this AMI. Green Box: Selection box where you can pick the cluster you want to create. Pick the GPU Optimized Cluster here. Yellow Box: Estimated costs for the cluster if you were going to run it 24/7 for an entire month. Even though the cost of the infrastructure is shown for a month, the actual charges will be calculated based on hourly usage. Once you have selected the GPU Optimized Cluster option, click on the large Continue button above it, and you will be forwarded to the Launch on EC2 page shown below. The important sections are once again highlighted by color-coded boxes: Blue Box: Select the AWS region in which you would like to launch the Bitfusion Boost Cluster. Green Box: Click this button to proceed and fine-tune the cluster parameters. Fine-Tune the Bitfusion Boost Cluster parameters After you click the Launch with CloudFormation Console button you will be taken to the Select Template AWS page. Simply click the Next button on the bottom right and you will be presented with several options to fine-tune the cluster you are about to launch. All the available options are described in detail in our Boost on AWS Documentation, however, to launch the 8 GPU cluster we only need to specify two options as highlighted in the figure below: Blue Box: Select a key name which you will use to SSH into the instance. If you have not create an AWS key before you can create one by following the AWS directions here. After you create the key, return to the fine-tuning page where the key needs to be selected, refresh the page, and then select they key you just created. Green Box: You must enter here the IP address from which you will be connecting to the EC2 instance. For now enter 0.0.0.0/0 to keeps things simple, however, for future clusters consider setting a specific IP from which you will be connecting to increase the security of the cluster even further. Once you set these two fields, click the Next button on the bottom right and you will be forwarded to the Options pages. Nothing needs to be set here, so simply click the Next button again to go to the Review page. Launch the Bitfusion Boost Cluster One the Review page you must click the check-box next to the "I acknowledge that this template might cause AWS CloudFormation to create IAM resources" text at the very bottom of the page to enable our template to provision the cluster for you. Only thing left to do is clicking the Create button, and your cluster will be created! At this point you are forwarded to the Stack Management page on AWS. It will most likely be blank initially, but after a couple minutes you will see a stack being created as shown in the image below. You can click the check-box next to the stack to obtain additional information about the stack. You will see that the status is shown as CREATE_IN_PROGRESS. The creation of the cluster can take anywhere from 5 to 10 minutes. If you are curious about all the details that we are taking care of simply click on the events tab. Eventually you will see the status change to CREATE_COMPLETE - time to log in to the cluster and verify that everything is working as expected. To log in to the instance you need to obtain the instance IP address. You can find this information by navigating to your AWS Console, clicking on EC2, and then clicking on running instances. In case you have other instance running, filter the instances by "bitfusion-boost" and you should see two instances as shown below. Blue Box: Select the AWS instance that contains the cuda75 in the name. This is the application instance into which you will log in, and from which you will execute your Cuda / GPU applications. The instances below it, with gpunode in the name, is the instance hosting the additional GPUs. Depending on how many additional GPUs you selected when creating your cluster, you may have multiple of these instances. Green Box: Note down the Public DNS address listed in this box for your instance. You will use this address in the commands below to access the instance and execute applications. To access the instance application instance execute the following command: ssh -i {path to your pem file} [email protected]{public dns address} Once you are logged in execute the following command to verify that all 8 GPUs are available to your application: bfboost client /usr/local/cuda-7.5/samples/bin/x86_64/linux/release/deviceQuery You should see obtain the following output: deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 8, Device0 = GRID K520, Device1 = GRID K520, Device2 = GRID K520, Device3 = GRID K520, Device4 = GRID K520, Device5 = GRID K520, Device6 = GRID K520, Device7 = GRID K520 Result = PASS BFBoost run complete. You are all set. Happy coding and development on your 8+ GPU Bitfusion Boost Cluster.
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Bitfusion Presenting at Data By the Bay Conference - May 19, 2016

Please join us on Thursday May 19, 2016 at 10:40am at the Data By the Bay Conference as we present on the Promise of Heterogeneous computing.
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