〽️Architectural Layers

The TES AI architecture is a multi-layered, cohesive structure that provides a seamless, secure, and efficient user experience. Each layer has a distinct role, working in tandem to ensure the system's optimal performance. The architecture is built upon modern technologies, ensuring scalability, reliability, and robustness.

User Interface

This layer is the visual gateway for users. It comprises the Public website, Customers area, and GPU providers area (Workers). The design is intuitive and user-centric, ensuring easy navigation and interaction.

Tech Stack: ReactJS, Tailwind, web3.js, zustand.

Security Layer

A pivotal layer ensuring the system's integrity and safety. It encompasses a Firewall for network protection, an Authentication Service for user validation, and a Logging Service for tracking activities.

Tech Stack: Firewall (pfSense, iptables), Authentication (OAuth, JWT), Logging Service (ELK Stack, Graylog).

API Layer

Serving as the communication bridge, this layer has multiple facets: Public API for the website, Private APIs for Workers/GPU Providers and Customers, and Internal APIs for Cluster Management, Analytics, and Monitoring/Reporting.

Tech Stack: FastAPI, Python, GraphQL, RESTful services, gunicorn, solana.

Backend Layer

The system's powerhouse. It manages Providers (Workers), Cluster/GPU operations, Customer interactions, Fault Monitoring, Analytics, Billing/Usage Monitoring, and Autoscaling.

Tech Stack: FastAPI, Python, Node.js, Flask, solana, IO-SDK (a fork of Ray 2.3.0), Pandas.

Database Layer

The data repository of the system. It uses Main storage for structured data and Caching for temporary, frequently accessed data.

Tech Stack: Postgres (Main storage), Redis (Caching).

Message Broker/Task Layer

This layer orchestrates asynchronous communications and task management, ensuring smooth data flow and efficient task execution.

Tech Stack: RabbitMQ (Message Broker), Celery (Task Management).

Infrastructure Layer

The foundational layer. It houses the GPU Pool with hardware from our verified partners. Orchestration tools manage deployments, while Execution/ML Tasks handle computations and machine learning operations. Additionally, it provides Data Storage solutions. GPU performance is monitored using Nvidia-smi or NVIDIA DCGM.

Tech Stack:

• GPU/CPU Pool

• Orchestration: Kubernetes, Prefect, Apache Airflow

• Execution/ML Tasks: Ray, Ludwig, Pytorch, Keras, TensorFlow, Pandas

• Data Storage: Amazon S3, Hadoop HDFS

• Containerization: Docker

• Monitoring: Grafana, Datadog, Prometheus, NVIDIA DCGM