GPU Workstations in the Cloud with Paperspace

We are very pleased to announce the availability of an RStudio TensorFlow template for the Paperspace cloud desktop service.

If you don’t have local access to a modern NVIDIA GPU, your best bet is typically to run GPU intensive training jobs in the cloud. Paperspace is a cloud service that provides access to a fully preconfigured Ubuntu 16.04 desktop environment equipped with a GPU. With the addition of the RStudio TensorFlow template you can now provision a ready to use RStudio TensorFlow w/ GPU workstation in just a few clicks. Preconfigured software includes:

• RStudio Desktop and RStudio Server

• NVIDIA GPU libraries (CUDA 8.0 and cuDNN 6.0)

• TensorFlow v1.4 w/ GPU

• The R keras, tfestimators, and tensorflow packages.

• The tidyverse suite of packages (ggplot2, dplyr, tidyr, readr, etc.)

Getting Started

To get started, first signup for a Paperspace account (you can use the RSTUDIO promo code when you sign up to receive a $5 account credit).

Then, create a new Paperspace instance using the RStudio template:

Then, choose one of the Paperspace GPU instances (as opposed to the CPU instances). For example, here we select the P4000 machine type which includes an NVIDIA Quadro P4000 GPU:

See the Cloud Desktop GPUs with Paperspace article on the TensorFlow for R website for full details on getting started.

Training a Convolutional MNIST Model

The performance gains for training convoluational and recurrent models on GPUs can be substantial. Let’s try training the Keras MNIST CNN example on our new Paperspace instance:

Training the model for 12 epochs takes about 1 minute (~ 5 seconds per epoch). On the other hand, training the same model on CPU on a high end Macbook Pro takes 15 minutes! (~ 75 seconds per epoch). Using a Paperspace GPU yields a 15x performance gain in model training.

This model was trained on an NVIDIA Quadro P4000, which costs $0.40 per hour. Paperspace instances can be configured to automatically shut down after a period of inactivity to prevent accruing cloud charges when you aren’t actually using the machine.

If you are training convolutional or recurrent models and don’t currently have access to a local NVIDIA GPU, using RStudio on Paperspace is a great way to accelerate training performance. You can use the RSTUDIO promo code when you sign up for Paperspace to receive a $5 account credit.

Citation

For attribution, please cite this work as

Allaire (2018, April 2). Posit AI Blog: GPU Workstations in the Cloud with Paperspace. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2018-04-02-rstudio-gpu-paperspace/

BibTeX citation

@misc{allaire2018gpu,
  author = {Allaire, J.J.},
  title = {Posit AI Blog: GPU Workstations in the Cloud with Paperspace},
  url = {https://blogs.rstudio.com/tensorflow/posts/2018-04-02-rstudio-gpu-paperspace/},
  year = {2018}
}