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Commit d7f9fed

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Merge pull request #108 from oslabs-beta/kyler/finalDocker
feat: Modified README.md and CONTRIBUTING.md
2 parents 8bb325a + 23f6a65 commit d7f9fed

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+196
-117
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‎CONTRIBUTING.md

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@@ -9,26 +9,27 @@ We are always open to accepting any potential contributions. Here is how you can
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git clone https://github.com/your-username/repository-name.git
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```
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3. Install dependencies for both mlflow and mlflow-site directories
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3. Create your feature branch
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```bash
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cd mlflow && npm install
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cd ../mlflow-site && npm install
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git checkout -b feature/AmazingFeature
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```
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4. Create your feature branch
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4. Install dependencies for both mlflow and mlflow-site directories
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```bash
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git checkout -b feature/AmazingFeature
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cd /mlflow && npm install
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cd ../mlflow-site && npm install
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```
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5. Run MLflow Tracking Server container with Docker
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5. Start the MLflow Tracking Server
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```bash
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cd mlflow
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npm run docker
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cd ../mlflow && npm run docker
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5001`.
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6. Make your changes
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7. Run ESLint to check code style
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(Make sure you have mlflow UI server running on port 5002. We set 5002 as our default port for testing.)
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```bash
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mlflow ui --port 5002 # Run this in a separate terminal
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cd /mlflow && npm run dockerTest # Run this in a separate terminal
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npm run test
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```
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9. Commit your changes
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This will launch the MLflow UI on your local machine at `http://localhost:5002`, and run the Jest tests.
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9. Add and commit your changes
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If the tests all pass:
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```bash
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git add .
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git commit -m 'Add AmazingFeature'
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```
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‎README.md

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@@ -60,10 +60,23 @@ pip install mlflow
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To start the MLflow tracking server locally, use the following command:
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```bash
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mlflow ui --port 5000
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mlflow ui --port 5001
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5000`.
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This will launch the MLflow UI on your local machine at `http://localhost:5001`.
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### Alternative Docker approach to the MLflow Tracking Server
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Install [Docker Desktop](https://www.docker.com/).
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To start the MLflow tracking server locally, use the following commands:
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```bash
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docker pull ghcr.io/mlflow/mlflow
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docker run -p 5001:5001 ghcr.io/mlflow/mlflow:latest mlflow server --host 0.0.0.0 --port 5001
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```
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This will launch the MLflow UI on your local machine at `http://localhost:5001`.
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### Development Setup
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‎mlflow/Dockerfile

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@@ -7,9 +7,6 @@ RUN apt-get update && apt-get install -y \
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curl \
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git \
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&& rm -rf /var/lib/apt/lists/*
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# RUN apt-get update
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# RUN apt-get install -y gcc
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# RUN apt-get install -y curl
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# Copy package files
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# Uses package-lock.json for exact versions
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COPY package*.json ./

‎mlflow/docker-compose-test.yml

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# docker-compose.yml
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name: mlflowtest
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services:
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# MLflow server for testing
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mlflowtest:
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# The latest mlflow docker image
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image: ghcr.io/mlflow/mlflow
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ports:
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- "5002:5002"
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command: mlflow server --host 0.0.0.0 --port 5002
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:5002/health')"]
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interval: 30s
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timeout: 10s
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retries: 3
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# Development environment to ensure consistency across contributors
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devtest:
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build:
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context: .
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dockerfile: Dockerfile
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volumes:
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- .:/mlflow # Mount code for live development
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- /mlflow/node_modules # Keep node_modules in container
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command: /bin/sh -c "while sleep 1000; do :; done" # Keep container running
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depends_on:
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mlflowtest:
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condition: service_healthy

‎mlflow/lib/tracking/ExperimentClient.d.ts

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* Mark an experiment for deletion.
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*
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* @param {string} experiment_id ID of the associated experiment. (required)
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* @returns {void}
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* @returns {Promise<void>}
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* @throws {ApiError} If the API request fails
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*/
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deleteExperiment(experiment_id: string): Promise<void>;
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/**
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* Restore an experiment marked for deletion.
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*
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* @param {string} experiment_id ID of the associated experiment. (required)
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* @returns {void}
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* @returns {Promise<void>}
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* @throws {ApiError} If the API request fails
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*/
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restoreExperiment(experiment_id: string): Promise<void>;
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*
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* @param {string} experiment_id ID of the associated experiment. (required)
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* @param {string} new_name The experiment’s name is changed to the new name. The new name must be unique. (required)
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* @returns {void}
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* @returns {Promise<void>}
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* @throws {ApiError} If the API request fails
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*/
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updateExperiment(experiment_id: string, new_name: string): Promise<void>;
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* @param {string} experiment_id ID of the experiment under which to log the tag. (required)
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* @param {string} key Name of the tag. (required)
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* @param {string} value String value of the tag being logged. (required)
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* @returns {void}
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* @returns {Promise<void>}
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* @throws {ApiError} If the API request fails
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*/
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setExperimentTag(experiment_id: string, key: string, value: string): Promise<void>;

‎mlflow/lib/tracking/ExperimentClient.js

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‎mlflow/lib/tracking/RunClient.d.ts

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import { MetricHistoryResponse } from '../utils/interface.js';
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declare class RunClient {
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private baseUrl;
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constructor(trackingUri: string);
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* @returns {Promise<MetricHistory>} A promise that resolves with the values for the specified metric.
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* @throws {ApiError} If the API request fails.
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*/
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getMetricHistory(run_id: string, metric_key: string, page_token?: string, max_results?: number): Promise<object>;
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getMetricHistory(run_id: string, metric_key: string, page_token?: string, max_results?: number): Promise<MetricHistoryResponse>;
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/**
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* Search for runs that satisfy expressions. Search expressions can use Metric and Param keys.
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*
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* @returns {Promise<SearchedRuns>} A promise that resovles with the runs that match the search criteria.
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* @throws {ApiError} If the API request fails.
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*/
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searchRuns(experiment_ids: Array<string>, filter: string, run_view_type?: 'ACTIVE_ONLY' | 'DELETED_ONLY' | 'ALL', max_results?: number, order_by?: Array<string>, page_token?: string): Promise<object>;
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searchRuns(experiment_ids: Array<string>, filter?: string, run_view_type?: 'ACTIVE_ONLY' | 'DELETED_ONLY' | 'ALL', max_results?: number, order_by?: Array<string>, page_token?: string): Promise<object>;
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/**
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* List artifacts for a run. Takes an optional artifact_path prefix which if specified, the response contains only
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* artifacts with the specified prefix.

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