Bitfusion Ubuntu 14 TensorFlow
Bitfusion.io | 2017.04Linux/Unix, Ubuntu 14.04 - 64-bit Amazon Machine Image (AMI)
Life is so much easier with Bitfusion
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.
- Leave a Comment |
- Mark review as helpful
Works out of the box as advertised.
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
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
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
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!
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!