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- No due date•10/14 issues closed
- No due date•5/5 issues closed
Design and implement the Decentralized Federated Learning solution employing a FedAvg of a Linear Regression machine learning model that classifies hand-written images of numbers in the MNIST dataset using PyTorch and Ray Train. This DFL solution will be deployed on a Data Mesh of CATs’ Data Products: - Report a classification performance of Federated Averaged Linear Regression model (e.g. Accuracy, Precision, etc.) - Integration test the CAT Factory using the CAT Invoice produced as a result of this Milestone to execute a CAT with a deterministic process confirming the idempotency of CATs by reproducing the same dataset This deliverable involves the design and implementation of a Decentralized Federated Learning (DFL) solution employing a FedAvg of a Linear Regression machine learning model that classifies hand-written images of numbers in the MNIST dataset using PyTorch and Ray Train. This DFL solution will be deployed on a Data Mesh of CATs’ Data Products. 0. Design DFL Solution 1. Performance report of Federated Averaged Linear Regression machine learning model using Accuracy and Precision 2. Documentation: Provide installation and execution examples with descriptions
No due dateDeploy CATs’ Node as a scalable REST API using Ray with endpoints that process CATs’ input and output HTTP requests: - Complete the implementation of the CATs’ Factory and its Executor product to report input and output HTTP requests - Integration tests a CAT service by submitting the same Order with a deterministic Process twice to verify CATs are idempotency. This CAT integration test should produce the same Invoice CID as a result. This deliverable involves the deployment of CATs’ scalable REST API (Service Node) using Ray Service with endpoints that process CATs’ input and output HTTP requests. 1. Update the GitHub repository with the following: - CATs’ REST API (Service Node) implementation 2. Continuous Integration (CI) test(s) of DFL on CATs’ Data Mesh component with PyTest & GitHub Actions with a GitHub test coverage report 3. Documentation: Provide installation and execution examples with descriptions 4. Presentation with demo
No due date•19/42 issues closedDesign and develop the Function software component of CATs’ Order input using the integration test from Milestone 0 to execute on the Structure component completed in Milestone 0: - Implement Function component of Order input. The Order will be CID-ed and contain the following Function components: - **Function (FaaS API):** data process executed on the Structural framework - **InfraFunction (FaaS):** interface for configuring and executing **Processes (SaaS)** on **Structure (PaaS)** via distributed computing frameworks [or **Plant (SaaS)**] - **Process (FaaS):** interface for data processing / computation performed by distributed computing frameworks deployed on platform infrastructure [or the **Plant**] using DataFrames, SQL, Python, etc. - Partially implement CATs’ Factory and its Executor product to execute Functions on Structure that outputs a CIDed CAT Invoice. This deliverable involves the design and development of CATs’ input (Order). 1. Publish the following to a GitHub repository: a. Means of creating an Order with integrated Function b. Extend Executor deploys Structure and execute Function on it; The Executor outputs a CIDed Invoice containing output data and order c. Extend Factory to produce Executor that composes Function(s) as well as Structure(s) 2. Continuous Integration (CI) test(s) of deliverables 1b - 1c
No due date•10/20 issues closedDesign and develop the Structure software component of CATs’ Order input and use CoD and Ray deployments on Kubernetes for integration tests that output CATs’ Invoice. This Milestone will provide the Structure software component of the **Order** to serve as the Kubernetes execution paradigm of the Function software component of Milestone 1: - Integrate Structure into Order pending the Function component to be completed as a result of Milestone 1. The Order will be CID-ed and contain the following Structure components: - **Structure (PaaS IaC API):** Software component of the platform on which the Function executes (Kubernetes) - **InfraStructure (IaaS):** Software sub-component of IaaS implemented using Terraform Python CDK - **Plant (SaaS):** Software sub-component implemented for CAT deployment and configuration of interoperable distributed / parallelized computing frameworks on a computing cluster on **InfraStructure (IaaS)** using Ray with access to platform integrations such as Apache Spark, Dask, etc. - Partially implement CATs’ Factory and its Executor product to deploy a Structure that outputs a CIDed CAT Invoice containing an Order CID, Data CID containing Dataset(s) CIDs and the their Partition CIDs, and a Seed CID for deterministic / idempotent processes. Deliverable: This deliverable involves the design and development of CATs’ Structure software component of CATs’ input 1. Publish the following to a GitHub repository: a. Software design of CATs’ Architectural Quantum b. Means of creating an Order with integrated Structure c. Partially Implement an Executor that deploys a Structure that outputs a CIDed Invoice containing output data and order d. Partially Implement Factory to produce the partial Executor above given an Order 2. Continuous Integration (CI) test(s) of deliverables 1b - 1d 3. Report progress via changelog and the completion of GitHub Project issues
No due date•10/13 issues closed