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Latest Bitfusion Tensorflow AMI and New Pricing

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.
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Latest Bitfusion Chainer AMI Now Available

We’re excited to announce the newest version of our Bitfusion Ubuntu 14 Chainer AMI is now available in the AWS Marketplace.
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Latest Bitfusion Theano AMI Now Available

We’re excited to announce the newest version of our Bitfusion Ubuntu 14 Theano AMI is now available in the AWS Marketplace.
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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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Apposphere Uses Bitfusion Deep Learning AMIs and AWS to Quickly Deploy Intelligent Mobile Solutions

Leads and prospects generated from search or display ads can be very costly and challenging to obtain. That's why Apposphere, an Austin, Texas based company, saw a huge opportunity in mining the huge amount of information and activity happening on social media for lead generation.
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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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Training a Bird Classifier with Tensorflow and TFLearn

If you are new to our AMIs, head over to our Tensorflow README on how to get started, or check out our previous blog entry on getting started with TensorFlow Intro This entry is a walkthrough using the our latest Tenorflow AMI to train a model based on the example in Adam Geighty’s Medium article on Machine Learning . I am specifically using a g2.2xlarge EC2 instance to train the model to show the training benefits of using GPU instance over using a CPU instance. Adam Geighty’s article articulated a number of things really well - his code example split out the different steps needed to train the model and the steps matched with sections of the article itself, allowing you to get a good understanding of what he was explaining. The example he used is based on the Cifar-10 example code and uses a combination of datasets to train a bird classifier. You can read more about Cifar datasets here and the referenced TFLearn code example here.     Bitfusion Ubuntu 14 TensorFlow AMI Launch on AWS! Creating the Classifier Before we start, create a directory named bird_classifier in the ubuntu users home directory. We will carry out all operations in this directory as the ubuntu user. mkdir ~/bird_classifier cd ~/bird_classifier Next we need our dataset to work with. You can download the dataset referenced in the article from S3. It’s a combination of the the Cifar 10 dataset and Caltech-UCSD Birds-200–2011 data set. In total there are ~74K images. wget https://s3-us-west-2.amazonaws.com/ml-is-fun/data.zip unzip data.zip From this you will get the dataset: full_dataset.pkl Download the Training Code Next we need to get the code used in the article. I have provided a couple options to obtain it below: Option 1 - use wget: The code below will pull from a gist and save it as bird_classifier.py. wget -O bird_classifier.py https://gist.githubusercontent.com/sono-bfio/89a91da65a12175fb1169240cde3a87b/raw/b859d1673e0a81ebd42d7799d7c1df71517c175b/bird_classifier.py Option 2 - copy the code below to a file that is in the same directory as full_dataset.pkl. In my case, I copied it to a file called bird_classifier.py. from __future__ import division, print_function, absolute_import # Import tflearn and some helpers import tflearn from tflearn.data_utils import shuffle from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.estimator import regression from tflearn.data_preprocessing import ImagePreprocessing from tflearn.data_augmentation import ImageAugmentation import pickle # Load the data set X, Y, X_test, Y_test = pickle.load(open("full_dataset.pkl", "rb")) # Shuffle the data X, Y = shuffle(X, Y) # Make sure the data is normalized img_prep = ImagePreprocessing() img_prep.add_featurewise_zero_center() img_prep.add_featurewise_stdnorm() # Create extra synthetic training data by flipping, rotating and blurring the # images on our data set. img_aug = ImageAugmentation() img_aug.add_random_flip_leftright() img_aug.add_random_rotation(max_angle=25.) img_aug.add_random_blur(sigma_max=3.) # Define our network architecture: # Input is a 32x32 image with 3 color channels (red, green and blue) network = input_data(shape=[None, 32, 32, 3], data_preprocessing=img_prep, data_augmentation=img_aug) # Step 1: Convolution network = conv_2d(network, 32, 3, activation='relu') # Step 2: Max pooling network = max_pool_2d(network, 2) # Step 3: Convolution again network = conv_2d(network, 64, 3, activation='relu') # Step 4: Convolution yet again network = conv_2d(network, 64, 3, activation='relu') # Step 5: Max pooling again network = max_pool_2d(network, 2) # Step 6: Fully-connected 512 node neural network network = fully_connected(network, 512, activation='relu') # Step 7: Dropout - throw away some data randomly during training to prevent over-fitting network = dropout(network, 0.5) # Step 8: Fully-connected neural network with two outputs (0=isn't a bird, 1=is a bird) to make the final prediction network = fully_connected(network, 2, activation='softmax') # Tell tflearn how we want to train the network network = regression(network, optimizer='adam', loss='categorical_crossentropy', learning_rate=0.001) # Wrap the network in a model object model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path='bird-classifier.tfl.ckpt') # Train it! We'll do 100 training passes and monitor it as it goes. model.fit(X, Y, n_epoch=100, shuffle=True, validation_set=(X_test, Y_test), show_metric=True, batch_size=96, snapshot_epoch=True, run_id='bird-classifier') # Save model when training is complete to a file model.save("bird-classifier.tfl") print("Network trained and saved as bird-classifier.tfl!")   Train it! At this point, all we need to do is run our python script. The script carries out the following functions: Will run through our dataset 100 times (epoch=100) Takes roughly ~60 minutes (This is on a g2.2xlarge – EC2 Instance with a single GPU) Produce our model file: bird-classifier.tfl. $ python2 bird_classifier.py # OUTPUT BELOW I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so.7.5 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so.7.5 locally I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so locally .. ..... ........ Concatenated output ........... ..... .. I tensorflow/core/common_runtime/gpu/gpu_device.cc:806] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GRID K520, pci bus id: 0000:00:03.0) --------------------------------- Run id: bird-classifier Log directory: /tmp/tflearn_logs/ --------------------------------- Preprocessing... Calculating mean over all dataset (this may take long)... .. ..... ........ Concatenated output ........... ..... .. -- Training Step: 59200 | total loss: 0.16163 | Adam | epoch: 100 | loss: 0.16163 - acc: 0.9332 | val_loss: 0.24135 - val_acc: 0.9387 -- iter: 56780/56780 -- Network trained and saved as bird-classifier.tfl! Inference (Let’s test some images) The script above created out trained model bird-classifier.tfl. Next, we will download the inference script provided in the article and some images from the internet and test it. The code below will save the inference script as infer.py   wget -O infer.py https://gist.githubusercontent.com/ageitgey/a40dded08e82e59724c70da23786bbf0/raw/7c78536295f1ab8cce62d5c63ed57212cafd8950/r_u_a_bird.py Next, we will create a directory to store our test images and download some creative commons images from the net. The test set has a total of 6 images – three that are birds and three that are not.   mkdir -p test_images cd test_images wget -O bird_bullocks_oriole.jpg http://www.5ensesmag.com/wp-content/uploads/2013/03/800px-Bullocks_Oriole.jpg wget -O bird_mount_bluebird.jpg http://climate.audubon.org/sites/default/files/bird_images/Mountain_Bluebird_FlickrCC_1.jpg wget -O bird_african_fish_eagle.jpg http://www.nature.org/cs/groups/webcontent/@web/@africa/documents/media/african-fish-eagle-720x400.jpg wget -O not_a_bird_stop_sign.jpg https://upload.wikimedia.org/wikipedia/commons/f/fd/Free_creative_commons_Rural_Stop_Landscape,_Antelope_Island,_Utah_\(4594258122\).jpg wget -O not_a_bird_airplane.jpg http://blogs.voanews.com/student-union/files/2012/01/airplane-flickr-shyb.jpg wget -O not_a_bird_creativecommons_logo.jpg https://www.raspberrypi.org/wp-content/uploads/2014/03/creative_commons.j Let’s run our inference script against the images. I created a simple loop to go through and test each image below: # Make sure you are in the directory where you downloaded infer.py to for f in test_images/*.jpg; do echo "File: ${f}"; python2 infer.py ${f} 2>/dev/null; echo ""; done Here’s our output: File: test_images/bird_african_fish_eagle.jpg That's a bird! File: test_images/bird_bullocks_oriole.jpg That's a bird! File: test_images/bird_mount_bluebird.jpg That's a bird! File: test_images/not_a_bird_airplane.jpg That's not a bird! File: test_images/not_a_bird_creativecommons_logo.jpg That's not a bird! File: test_images/not_a_bird_stop_sign.jpg That's a bird! Pretty good, we got one false positive in the bunch (“not_a_bird_stop_sign.jpg”). I left this in here as it is reveals an interesting anomoly, Adam’s article has a section that speaks to this: “How accurate is 95% accurate?” Lastly, if you would like run the inference script with a single image rather than using the bash forloop I have above, run the following: python2 infer.py test_images/bird_african_fish_eagle.jpg GPU vs. CPU Performance To see the value of a GPU, I ran the training overnight with a CPU instance (C4.4xl). The results are below: c4.4xl - $0.838 per Hour 16 cores (hyper threaded) maxed out ~ 123 Minutes g2.2xl - $0.65 per Hour single GPU ~ 65 Minutes   Conclusion There you have it - a trained bird classifier based on the Medium Article using Bitfusion’s Tensorflow AMI. If you are interested in scientific computing or deep learning, I encourage you to take a look our AMI offerings. They are sure to speed up your development, prototyping and GPU cluster creation. Additionally, if you have trained models and are looking for solid infrastructure to serve them, contact us here. Questions or comments? Please post them in the comment section below or join our community Bitfusion-AWS Slack Channel. Get Started!   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 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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