v0.1.0

This is the first release of eProsima AML-IP (Algebraic Machine Learning - Integrating Platform).

This release includes the following User Interface features:

• C++ API
• Python API
• Add implementation of DDS entities.
• Add implementation of Multiservice protocol.
• Add implementation of Asynchronous Multiservice protocol.
• New internal package amlip_demo_nodes to include the demos packages.
• Dockerfile for creating a docker image with AML-IP.
• Add Custom RPC communication over DDS.

This release supports the following Deployment Scenarios:

• Monitor State
• Workload Distribution
• Collaborative Learning
• Distributed Inference

This release includes the following new AML-IP Nodes:

• Status: node that listens to other nodes status.
• Main: node that sends training data and collects the solution to that data.
• Computing: node that waits for training data and retrieves a solution.
• Edge: node that sends data and waits for the inferred solution.
• Inference: node that waits for data and retrieves an inference.
• Agent: node in charge of the communication with the network.
  • Client
  • Server
  • Repeater
• Model Manager Receiver: node that receives statistical data from models and sends requests to those models.
• Model Manager Sender: node that sends statistical data from models, receives requests and sends replies to those requests.

This release includes the following new AML-IP Data Types:

• Status: status messages sent by each node with its id, type and current state.
• Job: messages that represent training data.
• Job Solution: messages that represent the solution for a given set of training data.
• Inference: messages that represent a partial data-set.
• Inference Solution: messages that represent the inference of a data-set.
• Model Statistics: messages that represent statistical data from models.
• Model: messages that represent a problem model request.
• Model Solution: messages that represent a problem reply with the requested model.

This release includes the following Demos:

• Collaborative Learning
• TensorFlow Inference
• TensorFlow Inference using ROSbot 2R
• Workload Distribution

This release includes the following Documentation features:

• Online documentation publicly available hosted on ReadTheDocs.
• API Code Documentation

This release includes the following Continuous Integration features:

• Continuous Integration deployment in GitHub Actions.
• Compile with -Wall flag Clang job.
• Add Address Sanitizer check to all tests.
• Add Python Liner test to the Python API.
• Disable Data Sharing from test.