google course builder how to export

How easy is it to make games with Google Game Builder?

Mar 02, 2022 · There are several ways to export SavedModels from TensorFlow training code. The following list describes a few ways that work for various TensorFlow APIs: If you have used Keras for training, use...

How do I export a canvas course?

Feb 28, 2022 · Export a SavedModel from your estimator using tf.estimator.Estimator.export_saved_model, passing in the path to your model as the export_dir_base parameter, and the name of your serving input...

How do I set up Google Cloud AI platform training?

Modernize legacy services or build cloud-native applications. Build, deploy, and maintain applications on-premise, in the cloud, or across providers. Design and build data processing systems. Develop and manage APIs to build new applications and connected experiences using Apigee. Manage and scale your networks.

What can you do with Google Cloud training?

Mar 08, 2022 · You can train your model on AI Platform Training in three steps: Create your Python model file. Add code to download your data from Cloud Storage so that AI Platform Training can use it. Add code to export and save the model to Cloud Storage after AI Platform Training finishes training the model.

What is a saved model in TensorFlow?

A SavedModel is TensorFlow's recommended format for saving models , and it is the required format for deploying trained TensorFlow models on AI Platform Prediction. Exporting your trained model as a SavedModel saves your training graph with its assets, variables and metadata in a format that AI Platform Prediction can consume and restore for predictions.

What is the tradeoff between high precision and low precision?

Reducing the precision of variables and input data is a tradeoff that reduces your model size significantly with some cost of prediction accuracy. High-precision data is stored less efficiently than low-precision data. Although low-precision data is a source of noise, a neural network may "disregard" this noise and still produce fairly accurate predictions.

Can you get predictions without training?

If you have already trained your model, you can get predictions without retraining. This process is very similar to creating a serving graph during training. The main difference is that you create the serving graph in a separate Python script that you run after training is over. The basic idea is to construct the Estimator with the same model_dir used in training, then to call tf.estimator.Estimator.export_saved_model as described in the previous section.

Learning paths

Implement, deploy, migrate, and maintain applications on cloud infrastructure.

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Overview

Terraform is an open source tool that lets you provision Google Cloud resources with declarative configuration files—resources such as virtual machines, containers, storage, and networking.

Getting started

Use the following resources to help you get started with using Terraform with Google Cloud:

Tools for using Terraform with Google Cloud

There are a variety of tools you can use to optimize your Terraform experience:

Terraform support for Google Cloud

The Terraform provider for Google Cloud is jointly developed by HashiCorp and Google, with support for more than 250 Google Cloud resources. The core Terraform CLI is developed by HashiCorp.

What is AI platform training?

The AI Platform Training training service manages computing resources in the cloud to train your models. This page describes the process to train a scikit-learn model using AI Platform Training.

Can you use an existing bucket?

You can use an existing bucket, but it must be in the same region where you plan on running AI Platform jobs. Additionally, if it is not part of the project you are using to run AI Platform Training, you must explicitly grant access to the AI Platform Training service accounts. Specify a name for your new bucket.

Do you need a cloud storage bucket?

You'll need a Cloud Storage bucket to store your training code and dependencies. For the purposes of this tutorial, it is easiest to use a dedicated Cloud Storage bucket in the same project you're using for AI Platform Training.

How do I export a Canvas course?

You can export a Canvas course to give to someone in another Canvas account, to upload to another institution's account at a later date, or to create a copy as a backup on your local computer. You can import an export file into Canvas at any time.

Select Export Type

In the Export Type heading, click the Course radio button [1]. Click the Create Export button [2].

View Export Process

View the progress bar. Exporting a course in Canvas may take a few minutes, depending on its size. You will receive an email when the export is complete.

Overview

Getting Started

Tools For Using Terraform with Google Cloud

• There are a variety of tools you can use to optimize your Terraform experience: 1. Export a Google Cloud configuration to Terraform by running thegcloud beta resource-config bulk-export--resource-format=terraformcommand. The command outputs a project's, folder's, or organization'sentire configuration in Terraform. 2. Explore the Cloud Foundation To...

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Terraform Support For Google Cloud