R Interface to Google CloudML

We are excited to announce the availability of the cloudml package, which provides an R interface to Google Cloud Machine Learning Engine. CloudML provides a number of services including on-demand access to training on GPUs and hyperparameter tuning to optmize key attributes of model architectures.

J.J. Allaire https://github.com/jjallaire (RStudio)https://www.rstudio.com
01-10-2018

Overview

We are excited to announce the availability of the cloudml package, which provides an R interface to Google Cloud Machine Learning Engine. CloudML provides a number of services including:

Training with CloudML

Once you’ve configured your system to publish to CloudML, training a model is as straightforward as calling the cloudml_train() function:


library(cloudml)
cloudml_train("train.R")

CloudML provides a variety of GPU configurations, which can be easily selected when calling cloudml_train(). For example, the following would train the same model as above but with a Tesla K80 GPU:


cloudml_train("train.R", master_type = "standard_gpu")

To train using a Tesla P100 GPU you would specify "standard_p100":


cloudml_train("train.R", master_type = "standard_p100")

When training completes the job is collected and a training run report is displayed:


Learning More

Check out the cloudml package documentation to get started with training and deploying models on CloudML.

You can also find out more about the various capabilities of CloudML in these articles:

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Citation

For attribution, please cite this work as

Allaire (2018, Jan. 10). TensorFlow for R: R Interface to Google CloudML. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2018-01-10-r-interface-to-cloudml/

BibTeX citation

@misc{allaire2018r,
  author = {Allaire, J.J.},
  title = {TensorFlow for R: R Interface to Google CloudML},
  url = {https://blogs.rstudio.com/tensorflow/posts/2018-01-10-r-interface-to-cloudml/},
  year = {2018}
}