llama-distributed 0.1.0

Distributed task execution and coordination for LlamaAI workloads.

llama-distributed

Llama Distributed (llama-distributed) is a toolkit within the LlamaSearch AI ecosystem designed for distributing tasks or packages across multiple nodes or environments. It likely aids in packaging applications and managing their deployment or execution in a distributed setting.

Key Features

• Package Distribution: Core logic related to packaging and distributing Python applications or tasks (package.py).
• Deployment Management (Potential): May include tools for deploying packages to target environments.
• Task Execution (Potential): Could support running distributed tasks or parallel processing.
• Core Module: Manages the distribution process (core.py).
• Configurable: Allows specifying target environments, package details, and distribution methods (config.py).

Installation

pip install llama-distributed
# Or install directly from GitHub for the latest version:
# pip install git+https://github.com/llamasearchai/llama-distributed.git

Usage

(Usage examples for packaging and distributing applications or tasks will be added here.)

# Placeholder for Python client usage
# from llama_distributed import Distributor, PackageConfig

# config = PackageConfig.load("config.yaml")
# distributor = Distributor(config)

# # Define package or task
# package_path = "/path/to/my_app"
# target_nodes = ["node1.example.com", "node2.example.com"]

# # Distribute the package
# distribution_job = distributor.distribute(
#     package_path=package_path,
#     targets=target_nodes,
#     options={'run_command': 'python main.py'}
# )
# print(f"Distribution job started: {distribution_job.id}")

Architecture Overview

graph TD
    A[User / Build System] --> B{Core Distributor (core.py)};
    B --> C{Packaging Logic (package.py)};
    C --> D[Packaged Application / Task];
    B -- Uses --> E{Deployment / Execution Interface};
    E -- Deploys/Runs on --> F[Target Node 1];
    E -- Deploys/Runs on --> G[Target Node 2];
    E -- Deploys/Runs on --> H[...];

    I[Configuration (config.py)] -- Configures --> B;
    I -- Configures --> C;
    I -- Configures --> E;

    style B fill:#f9f,stroke:#333,stroke-width:2px
    style F fill:#ccf,stroke:#333,stroke-width:1px
    style G fill:#ccf,stroke:#333,stroke-width:1px
    style H fill:#ccf,stroke:#333,stroke-width:1px

1. Input: User or a build system triggers the distribution process.
2. Core Distributor: Manages the workflow based on configuration.
3. Packaging: The application or task is packaged for distribution.
4. Deployment/Execution: The packaged artifact is sent to target nodes and potentially executed.
5. Targets: Represents the remote machines or environments where the package is distributed.
6. Configuration: Defines the package source, target nodes, deployment methods, execution commands, etc.

Configuration

(Details on configuring source packages, target node addresses/credentials, distribution protocols (SSH, etc.), post-deployment commands, etc., will be added here.)

Development

Setup

# Clone the repository
git clone https://github.com/llamasearchai/llama-distributed.git
cd llama-distributed

# Install in editable mode with development dependencies
pip install -e ".[dev]"

Testing

pytest tests/

Contributing

Contributions are welcome! Please refer to CONTRIBUTING.md and submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.