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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.... 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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