Bitfusion Ubuntu 14 TensorFlow

Starting from $0.00 to $5.34/hr for software + AWS usage fees

Ubuntu 14 AMI pre-installed with Nvidia Drivers, Cuda 7.5 Toolkit, cuDNN 5.1, TensorFlow 1.1.0, TFLearn, TensorBoard, Keras, Magenta, scikit-learn, Python 2 & 3 support, Hyperas, PyCuda, Pandas, NumPy, SciPy, Matplotlib, h5py, Enum34, SymPy, OpenCV and Jupyter to leverage Nvidia GPU as well as CPU instances. Designed for developers as well as those eager to get started with the TensorFlow Deep Learning Framework. Develop interactive TensorFlow scripts via python directly from your browser via the pre-installed Jupyter Notebook application. Get started with TensorFlow without worrying abo... See more

Customer Reviews

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tensorflow serving is not compiled

  • By misterf
  • on 10/24/2017

don't claim to have tensorflow serving "pre-installed" if you only have the repo downloaded. the whole benefit of using custom amis is to not have to compile everything myself. waste of time.

tensorflow_model_server not installed

  • By Marco
  • on 10/18/2017

We needed a server to serve our models, but tensorflow_model_server is not installed.
There is a folder "serving" where everything seems to be copied, but nothing is executable.

not working on G2 & P2 with latest aws updates

  • By Obins
  • on 08/09/2017

Peer access is not working on GPUs. Tensorflow backend is not able to distribute computing on multiple GPUs . Possibly requires updated cuda libraries.

Not working for Other type of instances.

  • By manoj
  • on 07/29/2017

It is not working for G2 and P2 instances. I tried with t2Medium. it works fine with only tthat instance type.

Life is so much easier with Bitfusion

  • By Hessam's review of Bitfusion
  • on 01/30/2017

Before trying Bitfusion I was spending so much time to configure my remote server for installing machine learning libraries and python modules. I am pleasantly surprised to see that every pre-installation and configuration for my deep learning codes is already done in Bitfusion Tensorflow, with a reasonable price.

Works out of the box as advertised.

  • By Tylar
  • on 12/28/2016

Tested working ML application running python, CUDA, and tensorflow. Minimal setup was needed to get my code running. Works great with a p2 instance.

Easy to use and out of the box solution

  • By Rakesh
  • on 12/09/2016

I spent less time on setting up an ami and more time on the model. Easy to use and out of the box solution to using Tensorflow on GPU :)

Works really well - saves a lot of hassle

  • By nikhil
  • on 11/28/2016

Tried putting tensorflow + gpu support all by myself on Ubuntu 12.04. Turned out to be a mess. This AMI works seamlessly and saved a lot of time. Proved to be a really useful because I was towards the fag end of my semester and was struggling with the dependency installs.

Productivity multiplier for my DeepLearning project

  • By Nishant Agrawal
  • on 09/04/2016

After wasting long hours on compiling TensorFlow on GPU instance, I switched to using BitFusion AMI on a trial. This saved me lot of trouble, and also saved me the cost of keeping a once compiled machine for long time. I can launch a new instance with working TensorFlow out-of-the-box. This has helped me immensely for Udacity Machine Learning NanoDegree.

Works like a charm!

  • By Arun Rajagopalan
  • on 08/02/2016

I used Bitfusion's g2.2x Ubuntu 14 TensorFlow image and it worked great out of the box... I also found the trial period very useful, since I had a problem similar to cifar10 and during the trial period I was able to make sure that this image pulls off cifar10 easily. All I had to run was a single line of command and it started training a cifar10 model, using the GPU. I was able to witness 8x speedup when compared to my weak CPU. Yesterday I came back for my actual problem with my dataset size being nearly 8 times greater than that of cifar10. With some minor changes to cifar10 codebase, I was able to train a model for my problem successfully. I think I can wait for some more time before buying myself a physical GPU. Infrastructure-as-Service rocks!

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