This release is a pre-release and may not be stable for production use.
Manta SDK
Beta. The SDK is in beta and every published release is a pre-release (
pip install --pre mantatech-sdk). New to the platform? Start with the beta quickstart: install, token, list your clusters, deploy a minimal module.
The Manta SDK is a unified Python library for distributed computing and federated learning on the Manta platform. It combines the functionality of both the high-level API client and the lightweight task execution runtime into a single, flexible package.
Features
- 🚀 Unified API: Single package for both client operations and task execution
- 📦 Modular Installation: Install only what you need with optional dependencies
- 🐳 Container Optimized: Lightweight
[light]mode for minimal container footprint - 🔄 Async/Sync APIs: Full async support with sync wrappers for convenience
- 🖥️ CLI Tools: Command-line interface for cluster and swarm management
- 🔒 Secure: JWT authentication with optional mTLS support
Documentation
📚 Full documentation: docs.manta-tech.io
- Getting Started
- SDK Usage
- Node Guide
- Tutorials
- Examples — four runnable scripts: federated learning and its non-federated control, on MNIST and CIFAR-10
Source: docs/source/ (Sphinx + Furo). Build locally with cd docs && make html.
Installation Options
For Task Execution (Lightweight - Recommended for Containers)
pip install mantatech-sdk[light]
This installs only the minimal dependencies needed for task execution within containers (~50MB smaller).
For API Client Development
pip install mantatech-sdk[api]
This installs the full client SDK for deploying and managing swarms from your application.
For CLI Usage
pip install mantatech-sdk[cli]
This installs the command-line tools for interactive cluster and swarm management.
Full Installation
pip install mantatech-sdk[full]
This installs all features: API client, task execution runtime, and CLI tools.
Development Installation
pip install mantatech-sdk[all]
This installs all features plus testing, documentation, and code analysis tools.
Quick Start
API Client Usage
import manta
from manta.apis import AsyncUserAPI
import asyncio
async def main():
# Initialize API client
api = AsyncUserAPI(
token="your_jwt_token",
host="localhost",
port=50052
)
# Check service availability
available = await api.is_available()
print(f"Service available: {available}")
# Get cluster API for specific cluster
cluster_api = api.get_async_cluster_api("cluster_id")
# Deploy a swarm
swarm_overview = await cluster_api.deploy_swarm(swarm_definition)
print(f"Deployed swarm: {swarm_overview.swarm_id}")
# Stream results in real-time
async for result in cluster_api.stream_results(swarm_id, tag="metrics"):
print(f"Result: {result.data}")
if __name__ == "__main__":
asyncio.run(main())
Task Execution Usage (Inside Containers)
from manta.light import Local, World, Results, Task
import numpy as np
# Initialize task runtime
task = Task()
local = Local()
world = World()
results = Results()
# Load data from cluster
data = local.load_data("training_data")
# Get global parameters
global_model = world.get("model_weights")
# Perform computation
model = train_model(data, global_model)
accuracy = evaluate_model(model, data)
# Save results
results.save({"accuracy": accuracy}, tag="metrics")
world.set("model_weights", model.state_dict())
CLI Usage
# Configure connection
manta config set --host localhost --port 50052 --token your_jwt_token
# List available clusters
manta cluster list
# Deploy a swarm
manta simulation deploy --swarm-file swarm.py --cluster-id cluster_123
# Monitor swarm execution
manta simulation logs --swarm-id swarm_456
# Stop running swarm
manta simulation stop --swarm-id swarm_456
API Modules
manta.apis - High-Level Client SDK
Access via: from manta.apis import AsyncUserAPI or import manta; api = manta.api
AsyncUserAPI/UserAPI: User operations and swarm managementAsyncClusterAPI/ClusterAPI: Cluster-specific operationsSwarm,Task,Module: High-level swarm definition classes
manta.light - Task Execution Runtime
Access via: from manta.light import Local or import manta; light = manta.light
Local: Access to cluster data and local resourcesWorld: Global state management across tasksResults: Result saving and sharingTask: Task runtime information and utilities
Migration from Previous Packages
From manta-core
# Old import (still works - backwards compatible)
from manta import AsyncUserAPI, Swarm, Task
# New recommended import pattern
import manta
from manta.apis import AsyncUserAPI, Swarm, Task
# Or access via: api_module = manta.api
From manta-light
# Old import
from manta_light import Local, World, Results
# New import (same functionality)
from manta.light import Local, World, Results
Container Optimization
The SDK is designed for optimal container usage:
Light Mode (Recommended for Tasks):
- Install:
pip install mantatech-sdk[light] - Size: ~50MB smaller than full installation
- Contains: Task execution runtime only
- Use case: Inside task containers
Full Mode (For Development/CLI):
- Install:
pip install mantatech-sdk[full] - Contains: Complete client SDK + task runtime
- Use case: Development machines, CI/CD pipelines
Advanced Usage
Environment Variables
Configure the SDK using environment variables:
export MANTA_HOST=localhost
export MANTA_PORT=50052
export MANTA_TOKEN=your_jwt_token
export MANTA_CERT_FOLDER=/path/to/certs # For mTLS
Secure Connections
For production environments with mTLS:
from manta.apis import AsyncUserAPI
api = AsyncUserAPI(
token="your_jwt_token",
host="prod-manager.example.com",
port=50052,
cert_folder="/etc/manta/certs" # Contains ca.crt, client.crt, client.key
)
Swarm Definition
Create complex swarms with task dependencies:
from manta.apis import Swarm, Task, Module
# Define algorithm module
module = Module(
name="federated_learning",
python_program="fl_trainer.py",
image="ghcr.io/mantatech/manta-light:pytorch"
)
# Define tasks with dependencies
aggregator = Task(
name="aggregator",
module=module,
command="python fl_trainer.py --role aggregator",
replicas=1
)
workers = Task(
name="worker",
module=module,
command="python fl_trainer.py --role worker",
replicas=5
)
# Create swarm
swarm = Swarm(
name="federated_mnist",
tasks=[aggregator, workers],
iteration=10,
circular=True
)
Further reading
- User docs — getting started, SDK usage, node guide, tutorials
- SDK Architecture — modular design, async/sync patterns, configuration system (internal)
- SDK Development — developer setup, testing, code quality (internal)
- Examples — four runnable scripts: federated learning and its non-federated control, on MNIST and CIFAR-10
Contributing
- Install development dependencies:
pip install mantatech-sdk[all] - Run tests:
python -m pytest tests/ - Check code style:
ruff check manta/ - Format code:
ruff format manta/
License
AGPL-3.0 with a linking exception for manta.light (ADR-0027) — see LICENSE file for details.
Importing manta.light from your own code does not make your program a derivative work; the copyleft terms of the AGPL continue to apply to manta.apis and manta.cli.
1 maintainer
Metadata
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