# h2o-gbm-algorithm-resource > A Sagemaker Algorithm Resource for H2O Gradient Boosting Machines (GBM) as a Cloudformation stack. [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](contributing.md) [![CodeBuild](https://s3-us-west-2.amazonaws.com/codefactory-us-west-2-prod-default-build-badges/passing.svg)](https://s3-us-west-2.amazonaws.com/codefactory-us-west-2-prod-default-build-badges/passing.svg) Current version: **1.0.0** Lead Maintainer: [Anil Sener](mailto:senera@amazon.com) ## 📋 Table of content - [Installation](#-install) - [Metrics](#-metrics) - [Pre-requisites](#-pre-requisites) - [Description](#-description) - [Usage](#-usage) - [See also](#-see-also) ## 🚀 Install In order to add this block, head to your project directory in your terminal and add it using NPM. Execute the following command to create a [Sagemaker Algorithm Resource](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-create-algo.html): #### Linux/MacOs: ```sh npm run deploy --region= \ --account_id= \ --s3bucket= \ --environment=development \ --training_image_name=h2o-gbm-trainer \ --inference_image_name=h2o-gbm-predictor ``` ## Windows: ```sh npm run deploy-win ^ --region= ^ --account_id= ^ --s3bucket= ^ --environment=development ^ --training_image_name=h2o-gbm-trainer ^ --inference_image_name=h2o-gbm-predictor ``` > ⚠️ You need to have the [AWS SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html) installed on your deployment machine before installing this package. ## 📊 Metrics The below metrics displays approximate values associated with deploying and using this block. Metric | Value ------ | ------ **Type** | Resource **Installation Time** | Less than 2 minutes **Audience** | Developers, Solutions Architects, Data Scientists **Requirements** | [AWS SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html),[Node Package Manager](https://www.npmjs.com/get-npm) ## 🎒 Pre-requisites - Make sure that you have installed the [AWS SAM CLI](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html) on your deployment machine. - Make sure that you have created a custom Sagemaker Training Image on Amazon ECR as in [H2O GBM Trainer](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/h2o-gbm-trainer) project. - Make sure that you have created a custom Sagemaker Inference Image on Amazon ECR as in [H2O GBM Predictor](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/h2o-gbm-predictor) project. ## 🔰 Description This block is to create a [Sagemaker Algorithm Resource](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-create-algo.html) for [H2O Gradient Boosting Machines (GBM)](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm.html) Estimator. > This project supports H2O version 3.30. ## 🛠 Usage A Sagemaker Algorithm Resource is created to support the [Bring Your Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html) and/or [Bring Your Own Model](https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html) approaches for Amazon Sagemaker Model Training and Deployment process. You can use this block as a standalone [Sagemaker Algorithm Resource](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-create-algo.html) or an input for other projects such as [Sagemaker Model Tuner with Endpoint Deployment](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/sagemaker-model-tuner-with-endpoint-deployment) or [Sagemaker Model Tuner](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/sagemaker-model-tuner). ### Deployment Options The deployment options that you can pass to this solution are described below. Name | Default value | Description -------------- | ------------- | ----------- **region** | None | AWS Region to deploy the infrastructure for Sagemaker Algorithm Resource. **account_id** | None | AWS Account ID to deploy the infrastructure for Sagemaker Algorithm Resource. **s3bucket** | None | Please set the S3 bucket to deploy the stack packages. **environment** | `development` | Environment to tag the created resources. **AlgorithmName** | None | Unique name of [Sagemaker Algorithm Resource](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-create-algo.html). **TrainingImageURI** | None | URI of custom Training Image on Amazon ECR. **InferenceImageURI** | None | URI of custom Inference Image on Amazon ECR. ## 👀 See also In this section, you can list the projects and blocks on which you depend, or which are linked to your block. - The [Docker](https://docs.docker.com/) official documentation. - The [AWS Sagemaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) official documentation. - The [H2O GBM Trainer](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/h2o-gbm-trainer) project. - The [H2O GBM Predictor](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/h2o-gbm-predictor) project. - The [Sagemaker Model Tuner](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/sagemaker-model-tuner) project. - The [Sagemaker Model Tuner with Endpoint Deployment](https://github.com/aws-samples/amazon-sagemaker-h2o-blog/tree/master/sagemaker-model-tuner-with-endpoint-deployment) project.