Artificial Intelligence

R Interface to Google CloudML

August 26, 2024
roosho

We’re excited to announce the supply of the cloudml package deal, which supplies an R interface to Google Cloud Machine Studying Engine. CloudML supplies quite a few providers together with on-demand entry to coaching on GPUs and hyperparameter tuning to optimize key attributes of mannequin architectures.

Overview

We’re excited to announce the supply of the cloudml package deal, which supplies an R interface to Google Cloud Machine Studying Engine. CloudML supplies quite a few providers together with:

  • Scalable coaching of fashions constructed with the keras, tfestimators, and tensorflow R packages.

  • On-demand entry to coaching on GPUs, together with the brand new Tesla P100 GPUs from NVIDIA®.

  • Hyperparameter tuning to optmize key attributes of mannequin architectures in an effort to maximize predictive accuracy.

  • Deployment of educated fashions to the Google world prediction platform that may help 1000’s of customers and TBs of knowledge.

Coaching with CloudML

When you’ve configured your system to publish to CloudML, coaching a mannequin is as easy as calling the cloudml_train() perform:

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

CloudML supplies quite a lot of GPU configurations, which might be simply chosen when calling cloudml_train(). For instance, the next would practice the identical mannequin as above however with a Tesla K80 GPU:

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

To coach utilizing a Tesla P100 GPU you’d specify "standard_p100":

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

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

Studying Extra

Try the cloudml package deal documentation to get began with coaching and deploying fashions on CloudML.

You can even discover out extra concerning the numerous capabilities of CloudML in these articles:

  • Coaching with CloudML goes into further depth on managing coaching jobs and their output.

  • Hyperparameter Tuning explores how one can enhance the efficiency of your fashions by working many trials with distinct hyperparameters (e.g. quantity and measurement of layers) to find out their optimum values.

  • Google Cloud Storage supplies info on copying information between your native machine and Google Storage and likewise describes use information inside Google Storage throughout coaching.

  • Deploying Fashions describes deploy educated fashions and generate predictions from them.

Reuse

Textual content and figures are licensed underneath Artistic Commons Attribution CC BY 4.0. The figures which were reused from different sources do not fall underneath this license and might be acknowledged by a observe of their caption: “Determine from …”.

Quotation

For attribution, please cite this work as

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

BibTeX quotation

@misc{allaire2018r,
  creator = {Allaire, J.J.},
  title = {Posit AI Weblog: R Interface to Google CloudML},
  url = {https://blogs.rstudio.com/tensorflow/posts/2018-01-10-r-interface-to-cloudml/},
  yr = {2018}
}