@precise-finance/sagemaker

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();

License

This project is licensed under the MIT License.