R Interface to Google CloudML

We are excited to announce the supply of the cloudml package deal, which supplies an R interface to Google Cloud Machine Learning Engine. CloudML supplies numerous companies together with on-demand entry to coaching on GPUs and hyperparameter tuning to optimize key attributes of mannequin architectures.

Overview

We are excited to announce the supply of the cloudml package deal, which supplies an R interface to Google Cloud Machine Learning Engine. CloudML supplies numerous 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 a purpose to maximize predictive accuracy.

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

Training with CloudML

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

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

CloudML supplies quite a lot of GPU configurations, which will 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 practice 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:

Learning More

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

You also can discover out extra in regards to the varied capabilities of CloudML in these articles:

• Training 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 operating 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 knowledge between your native machine and Google Storage and in addition describes the way to use knowledge inside Google Storage throughout coaching.

• Deploying Models describes the way to deploy educated fashions and generate predictions from them.

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Citation

For attribution, please cite this work as

Allaire (2018, Jan. 10). Posit AI Blog: 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 Blog: R Interface to Google CloudML},
  url = {https://blogs.rstudio.com/tensorflow/posts/2018-01-10-r-interface-to-cloudml/},
  yr = {2018}
}