Introduction

This repo holds the Dockerfile image used for the MinIO application currently available in the Edge Orchestration Marketplace. MinIO is a high-performance, distributed object storage system that is compatible with Amazon S3. It is designed to handle large-scale data workloads such as machine learning, analytics, and cloud-native applications.

Features

• High Performance: MinIO is optimized for high throughput and low latency, making it ideal for demanding applications.
• Scalability: Easily scale MinIO across multiple nodes and data centers to handle growing data needs.
• Erasure Coding: Protects data with per-object inline erasure coding for high reliability.
• Bitrot Protection: Ensures data integrity by detecting and correcting bitrot.
• Encryption: Provides end-to-end encryption to secure data at rest and in transit.
• Identity Management: Supports integration with various identity providers for access control.
• Continuous Replication: Enables real-time data replication across multiple locations.
• Global Federation: Allows for the federation of multiple MinIO clusters across different geographies.
• Multi-Cloud Gateway: Facilitates seamless integration with multiple cloud providers.

Uses

• Machine Learning: Store and manage large datasets for training and inference.
• Analytics: Efficiently handle big data analytics workloads.
• Cloud-Native Applications: Serve as the backend storage for modern, containerized applications.
• Disaster Recovery: Implement robust disaster recovery solutions with continuous replication.
• Archiving: Archive large volumes of data with high reliability and security.

For more information, visit the MinIO website.

Prerequisites

1. You must have MinIO installed from the marketplace.

Use Cases

Currently, MinIO Support working with the following apps available on the marketplace as well:

1. MLFlow:
   • Description: An open-source platform designed to manage the end-to-end machine learning lifecycle
   • Use Case: Store MLflow tracking data and artifacts.
   • Integration: MLFlow will ask for a Minio bucket and a pair of access and secret access keys for use.
   • Emerson Github Link:EmersonDeltaV/mlflow.
2. Jupyter:
   • Description: An open-source web application for creating and sharing documents that contain live code, equations, visualizations, and narrative text.
   • Use Case: Interactive data analysis and model development.
   • Integration: When MLFlow is using Minio as the artifact URI, Jupyter will need the pair of access keys and bucket as well to be referenced usually in the form of s3://<bucketname>.
   • Emerson Github Link:EmersonDeltaV/jupyter-labs-for-edge

Minio Setup

1. Launch the MLFlow Web Interface: http://{edge_ip}:9001.
2. Log in using the default credentials; minioadmin for both username and password.
3. To create a bucket, navigate to 'Object Browser' on the left navigation pane and add a bucket.
4. To generate access keys, navigate to 'Access Keys' on the left navigation pane to create a key. Ensure that a copy of the secret key is kept elsewhere as it will only be shown once, and the access key is the only one kept.

Changelist

• 03/26/2025 - First version.