We’re excited to announce the provision of the cloudml package deal, which supplies an R interface to Google Cloud Machine Studying Engine. CloudML supplies quite a lot of companies 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 provision of the cloudml package deal, which supplies an R interface to Google Cloud Machine Studying Engine. CloudML supplies quite a lot of companies 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 with the intention to maximize predictive accuracy.

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

Coaching with CloudML

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

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

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

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

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

cloudml_train("prepare.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 too can discover out extra in regards to the varied capabilities of CloudML in these articles:

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

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

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

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

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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,
  writer = {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}
}