@precise-finance/sagemaker NPM

SageMaker Training and Deployment Utilities

This package provides utilities for training and deploying machine learning models on AWS SageMaker. It supports multiple ML frameworks including PyTorch, TensorFlow, XGBoost, Scikit-learn, and HuggingFace.

Installation

To install the package, run:

npm install @aws-sdk/client-sagemaker @aws-sdk/client-s3 @aws-sdk/lib-storage archiver

Usage

Training

To train a model using SageMaker, you can use the provided classes for each framework. Below are examples for PyTorch and TensorFlow.

PyTorch Training

import { PyTorchTraining } from './sagemaker-framework-extensions';
import { Logger } from './interfaces';

// Configuration for AWS and SageMaker
const config = {
  region: 'us-west-2',
  credentials: {
    accessKeyId: 'your-access-key-id',
    secretAccessKey: 'your-secret-access-key',
  },
  bucket: 'your-s3-bucket',
  role: 'your-sagemaker-role',
  service: 'your-service',
  model: 'your-model',
};

// Logger for logging messages
const logger: Logger = console;

// Directory containing your training script and other source files
const sourceDir = './path-to-your-source-code';

// Create an instance of PyTorchTraining
const pytorchTraining = new PyTorchTraining(config, sourceDir, logger);

// Framework-specific configuration
const frameworkConfig = {
  frameworkVersion: '2.1',
  pythonVersion: 'py310',
  imageUri: 'your-custom-image-uri', // Optional
};

// Resource configuration for the training job
const resourceConfig = {
  instanceCount: 1,
  instanceType: 'ml.p3.2xlarge',
  volumeSizeGB: 50,
};

// Hyperparameters for the training job
const hyperParameters = {
  learningRate: 0.001,
  batchSize: 32,
  epochs: 10,
};

// Example 1: Using S3 path for input data
const inputDataS3 = {
  data: 's3://your-bucket/path-to-your-data',
  format: 'application/json',
};

async function trainModel() {
  try {
    // Start the training job
    const metadata = await pytorchTraining.train(
      frameworkConfig,
      resourceConfig,
      hyperParameters,
      inputDataS3,
      [],
      true
    );
    console.log('Training completed:', metadata);

    // Get the training job name from the metadata
    const trainingJobName = metadata.trainingJobName;
    console.log('Training job name:', trainingJobName);
  } catch (error) {
    console.error('Training failed:', error);
  }
}

trainModel();

TensorFlow Training

import { TensorFlowTraining } from './sagemaker-framework-extensions';
import { Logger } from './interfaces';

// Configuration for AWS and SageMaker
const config = {
  region: 'us-west-2',
  credentials: {
    accessKeyId: 'your-access-key-id',
    secretAccessKey: 'your-secret-access-key',
  },
  bucket: 'your-s3-bucket',
  role: 'your-sagemaker-role',
  service: 'your-service',
  model: 'your-model',
};

// Logger for logging messages
const logger: Logger = console;

// Directory containing your training script and other source files
const sourceDir = './path-to-your-source-code';

// Create an instance of TensorFlowTraining
const tensorflowTraining = new TensorFlowTraining(config, sourceDir, logger);

// Framework-specific configuration
const frameworkConfig = {
  frameworkVersion: '2.12',
  pythonVersion: 'py310',
  imageUri: 'your-custom-image-uri', // Optional
};

// Resource configuration for the training job
const resourceConfig = {
  instanceCount: 1,
  instanceType: 'ml.p3.2xlarge',
  volumeSizeGB: 50,
};

// Hyperparameters for the training job
const hyperParameters = {
  learningRate: 0.001,
  batchSize: 32,
  epochs: 10,
};

// Example 1: Using S3 path for input data
const inputDataS3 = {
  data: 's3://your-bucket/path-to-your-data',
  format: 'application/json',
};

async function trainModel() {
  try {
    // Start the training job
    const metadata = await tensorflowTraining.train(
      frameworkConfig,
      resourceConfig,
      hyperParameters,
      inputDataS3,
      [],
      true
    );
    console.log('Training completed:', metadata);

    // Get the training job name from the metadata
    const trainingJobName = metadata.trainingJobName;
    console.log('Training job name:', trainingJobName);
  } catch (error) {
    console.error('Training failed:', error);
  }
}

trainModel();

Deployment

To deploy a trained model using SageMaker, you can use the provided classes for each framework. Below are examples for PyTorch and TensorFlow.

PyTorch Deployment

import { PyTorchDeployment } from './deploy';
import { Logger } from './interfaces';

// Configuration for AWS and SageMaker
const config = {
  region: 'us-west-2',
  credentials: {
    accessKeyId: 'your-access-key-id',
    secretAccessKey: 'your-secret-access-key',
  },
  bucket: 'your-s3-bucket',
  role: 'your-sagemaker-role',
  environmentVariables: {},
};

// Logger for logging messages
const logger: Logger = console;

// Service and model names
const service = 'your-service';
const model = 'your-model';

// Create an instance of PyTorchDeployment
const pytorchDeployment = new PyTorchDeployment(config, logger, service, model);

// Deployment input configuration
const deployInput = {
  frameworkVersion: '2.1',
  pythonVersion: 'py310',
  entryPoint: 'inference.py',
  trainingJobName: 'your-training-job-name',
  useGpu: true,
};

// Serverless configuration for the deployment
const serverlessConfig = {
  memorySizeInMb: 2048,
  maxConcurrency: 10,
};

async function deployModel() {
  try {
    // Deploy the model
    const result = await pytorchDeployment.deploy(deployInput, serverlessConfig);
    console.log('Deployment completed:', result);
  } catch (error) {
    console.error('Deployment failed:', error);
  }
}

deployModel();

TensorFlow Deployment

import { TensorFlowDeployment } from './deploy';
import { Logger } from './interfaces';

// Configuration for AWS and SageMaker
const config = {
  region: 'us-west-2',
  credentials: {
    accessKeyId: 'your-access-key-id',
    secretAccessKey: 'your-secret-access-key',
  },
  bucket: 'your-s3-bucket',
  role: 'your-sagemaker-role',
  environmentVariables: {},
};

// Logger for logging messages
const logger: Logger = console;

// Service and model names
const service = 'your-service';
const model = 'your-model';

// Create an instance of TensorFlowDeployment
const tensorflowDeployment = new TensorFlowDeployment(config, logger, service, model);

// Deployment input configuration
const deployInput = {
  frameworkVersion: '2.12',
  pythonVersion: 'py310',
  entryPoint: 'inference.py',
  trainingJobName: 'your-training-job-name',
  useGpu: true,
};

// Serverless configuration for the deployment
const serverlessConfig = {
  memorySizeInMb: 2048,
  maxConcurrency: 10,
};

async function deployModel() {
  try {
    // Deploy the model
    const result = await tensorflowDeployment.deploy(deployInput, serverlessConfig);
    console.log('Deployment completed:', result);
  } catch (error) {
    console.error('Deployment failed:', error);
  }
}

deployModel();