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Commit 8bb325a

Browse files
Merge pull request #107 from oslabs-beta/yiqun/new
feat: add lib, revise gitignore, revise example
2 parents 09d2c64 + d525032 commit 8bb325a

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‎mlflow/.gitignore

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@@ -1,6 +1,12 @@
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# Dependencies
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node_modules/
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# Environment variables
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.env
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# IDE
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.vscode
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# Misc
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.DS_Store
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../.DS_Store
@@ -9,8 +15,12 @@ node_modules/
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.venv/
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mlruns/
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# Build output
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lib/
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# Temporary files
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temp/
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temp/
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# Test coverage
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coverage/
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=

‎mlflow/examples/LinearRegressionExample.js

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@@ -1,7 +1,7 @@
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/**
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* @fileoverview Example of using MLflow.js for machine learning projects
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* The example creates a synthetic dataset and trains a linear regression model using TensorFlow.js,
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* tracking various metricsand parameters throughout the training process with MLflow tracking server.
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* The example creates a synthetic dataset and trains a linear regression model using TensorFlow.js,
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* tracking various metrics, hyperparameters and other meta data throughout the training process with MLflow tracking server.
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* It showcases:
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* - Experiment and run management
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* - Hyperparameter logging
@@ -12,19 +12,28 @@
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* - Artifact storage
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*
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* @requires @tensorflow/tfjs-node
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* @requires @mlflow.js
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*
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* @note Ensure MLflow server is running at http://localhost:5001 before executing
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* @note Ensure MLflow server is running before executing
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*/
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import * as tf from '@tensorflow/tfjs-node';
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import MLflow from 'mlflow-js';
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import Mlflow from 'mlflow-js';
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import { fileURLToPath } from 'url';
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import { dirname } from 'path';
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import { dirname, join } from 'path';
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import dotenv from 'dotenv';
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const mlflow = new MLflow('http://localhost:5001');
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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dotenv.config({ path: join(__dirname, '../.env') });
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const MLFLOW_TRACKING_URI =
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process.env.MLFLOW_TRACKING_URI || 'http://localhost:5001';
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const mlflow = new Mlflow(MLFLOW_TRACKING_URI);
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console.log('MLflow server URL:', MLFLOW_TRACKING_URI);
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const HYPERPARAMETERS = {
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learningRate: 0.1,
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learningRate: 0.25,
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epochs: 10,
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batchSize: 32,
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validationSplit: 0.2,
@@ -168,13 +177,22 @@ async function main() {
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console.log(`MLflow Experiment ID: ${experimentId}`);
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// Create run
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const run = await mlflow.createRun(
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experimentId,
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'Simple Linear Regression'
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);
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const run = await mlflow.createRun(experimentId);
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const runId = run.info.run_id;
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console.log(`MLflow Run ID: ${runId}`);
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// Generate and prepare data
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console.log('Generating data...');
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const { xTrain, yTrain, xTest, yTest } = generateData();
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// Log dataset info
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const datasetInfo = {
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dataset: {
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name: 'synthetic_linear_regression_data',
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},
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};
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await mlflow.logInputs(runId, [datasetInfo]);
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// Log hyperparameters
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const params = [
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{ key: 'learning_rate', value: HYPERPARAMETERS.learningRate.toString() },
@@ -189,18 +207,6 @@ async function main() {
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];
190208
await mlflow.logBatch(runId, undefined, params);
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// Log dataset info
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const datasetInfo = {
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dataset: {
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name: 'synthetic_linear_regression_data',
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},
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};
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await mlflow.logInputs(runId, [datasetInfo]);
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// Generate and prepare data
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console.log('Generating data...');
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const { xTrain, yTrain, xTest, yTest } = generateData();
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// Train model
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console.log('Creating and training model...');
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const model = createModel();
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// Evaluate model
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const evaluation = evaluateModel(model, xTest, yTest);
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// Save model artifects
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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const artifactsPath = `${__dirname}/../mlruns/${experimentId}/${runId}/artifacts`;
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await model.save(`file://${artifactsPath}`);
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// Log evaluation metrics
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const finalMetrics = [
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{ key: 'test_mse', value: evaluation.metrics.mse, timestamp: Date.now() },
@@ -235,6 +235,12 @@ async function main() {
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];
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await mlflow.logBatch(runId, undefined, undefined, paramTags);
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// Save model artifects
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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const artifactsPath = `${__dirname}/../mlruns/${experimentId}/${runId}/artifacts`;
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await model.save(`file://${artifactsPath}`);
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// Register model if performance meets threshold
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if (evaluation.metrics.r2 > 0.9) {
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const modelName = 'LinearRegression';
@@ -259,7 +265,10 @@ async function main() {
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`runs:/${runId}/model`,
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runId,
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[
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{ key: 'r2', value: evaluation.metrics.r2.toString() },
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{
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key: 'learning_rate',
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value: HYPERPARAMETERS.learningRate.toString(),
271+
},
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{ key: 'rmse', value: evaluation.metrics.rmse.toString() },
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]
265274
);
@@ -277,13 +286,6 @@ async function main() {
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}
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}
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280-
// Log additional metadata
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const tags = [
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{ key: 'model_type', value: 'linear_regression' },
283-
{ key: 'data_source', value: 'synthetic' },
284-
];
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await mlflow.logBatch(runId, undefined, undefined, tags);
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// Finish run
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await mlflow.updateRun(runId, 'FINISHED');
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‎mlflow/examples/NeuralNetworkHyperparameterTuning.js

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const HYPERPARAMETER_SPACE = {
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networkArchitectures: [
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[16, 8],// Small network
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[32, 16],// Medium network
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[64, 32],// Larger network
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[16, 8],
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[32, 16],
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[64, 32],
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],
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learningRates: [0.001, 0.01],
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batchSizes: [32, 64],
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datasetSize: 2000,
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inputFeatures: 5,
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outputClasses: 3,
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minibatchSize: 128,// Added for faster training
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minibatchSize: 128,
2727
};
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2929
// Data generation
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252252
try {
253253
console.time('Total Execution Time');
254254

255-
const experimentName = 'Neural_Network_Hyperparameter_Tuning_Fast';
255+
const experimentName = 'Neural_Network_Hyperparameter_Tuning';
256256
let experimentId;
257257
try {
258258
const experiment = await mlflow.getExperimentByName(experimentName);

‎mlflow/lib/index.d.ts

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import Mlflow from './mlflow.js';
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export default Mlflow;

‎mlflow/lib/index.js

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‎mlflow/lib/index.js.map

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‎mlflow/lib/mlflow.d.ts

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import ExperimentClient from './tracking/ExperimentClient.js';
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import ExperimentManager from './workflows/ExperimentManager.js';
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import RunClient from './tracking/RunClient.js';
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import RunManager from './workflows/RunManager.js';
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import ModelRegistryClient from './model-registry/ModelRegistryClient.js';
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import ModelVersionClient from './model-registry/ModelVersionClient.js';
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import ModelManager from './workflows/ModelManager.js';
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declare class Mlflow {
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private components;
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constructor(trackingUri: string);
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private initializeMethods;
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getExperimentClient(): ExperimentClient;
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getRunClient(): RunClient;
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getModelRegistryClient(): ModelRegistryClient;
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getModelVersionClient(): ModelVersionClient;
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getExperimentManager(): ExperimentManager;
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getRunManager(): RunManager;
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getModelManager(): ModelManager;
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}
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export default Mlflow;

‎mlflow/lib/mlflow.js

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