- TRAIN CARET HOW TO
- TRAIN CARET CODE
model_prediction.R - This script downloads the trained model from GCS and loads (only once). TRAIN CARET CODE
The src/caret/serving directory includes the following code files: Then you containerize this Web API and deploy it in Cloud Run. In order to serve the trained CARET model as a Web API, you need to wrap it with a prediction function, as serve this prediction function as a REST API.
Submit an AI Platform Training job with the custom container. Push the Docker container image to Container Registry. Build a Docker container image with that runs the model_trainer.R. set your PROJECT_ID and BUCKET_NAME in training/model_trainer.R, and PROJECT_ID in training/Dockerfile so that the first line reads "FROM gcr.io//caret_base". To submit the training job with the custom container to AI Platform, you need to do the following steps: Dockerfile - This is the definition of the Docker container image to run the model_trainer.R script. model_trainer.R - This is the implementation of the CARET model training logic. The src/caret/training directory includes the following code files: In order to train your CARET model in at scale using AI Platform Training, you need to implement your training logic in an R script file, containerize it in a Docker image, and submit the Docker image to AI Platform Training. Submit a Training Job to AI Platform with Custom Containers Xgbtree <- readRDS ( file.path ( model_dir, model_name, "trained_model.rds" )) estimate_babyweights <- function ( instances_json ) ] ' estimate <- round ( estimate_babyweights ( instances_json ), digits = 2 ) print ( paste ( "Estimated weight(s):", estimate ))ģ. Learn about GCP pricing, use the Pricing Calculator to generate a cost estimate based on your projected usage. Invoke the deployed model API for predictions. 
Deploy the exported model to AI Platform prediction using the cloudml APIs.Train and export the Estimator on AI Platform Training using the cloudml APIs.Create a TensorFlow premade Estimator trainer using R interface.This tutorial uses billable components of Google Cloud Platform (GCP):
Use the AI Platform Notebooks to drive the workflow. Invoke the deployed Web API for predictions. Deploy the prediction Web API container image model on Cloud Run. Build Docker container image for the prediction Web API. Implement a Web API wrapper to the trained model using Plumber R package. Train the CARET model using on AI Platform Training with custom R container. In this notebook, we focus on Exploratory Data Analysis, while the goal is to predict the baby's weight given a number of factors about the pregnancy and the baby's mother. We use the data extracted from BigQuery and stored as CSV in Cloud Storage (GCS) in the Exploratory Data Analysis notebook. The dataset is available in BigQuery public dataset. The dataset used in this tutorial is natality data, which describes all United States births registered in the 50 States, the District of Columbia, and New York City from 1969 to 2008, with more than 137 million records. With over 10,000 packages in the open-source repository of CRAN, R caters to all statistical data analysis applications, ML, and visualisation. R is one of the most widely used programming languages for statistical modeling, which has a large and active community of data scientists and ML professional. Rhen use the Cloud Run to serve the trained model as a Web API for online predictions. We use AI Platform Training with Custom Containers to train the TensorFlow model at scale. TRAIN CARET HOW TO
This notebook illustrates how to use CARET R package to build an ML model to estimate the baby's weight given a number of factors, using the BigQuery natality dataset. Training and Serving CARET models using AI Platform Custom Containers and Cloud Run Overview
0 kommentar(er)
