> For the complete documentation index, see [llms.txt](https://teslaai-1.gitbook.io/tesla-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://teslaai-1.gitbook.io/tesla-ai/tesla-ai-architecture/architectural-layers.md).

# 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


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