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
1 maintainer
Release files for mantatech-sdk 0.6b0.dev625
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mantatech_sdk-0.6b0.dev625-py3-none-any.whl | Python 3 | none | any | Details |
Release files / mantatech_sdk-0.6b0.dev625-py3-none-any.whl
| Download URL | mantatech_sdk-0.6b0.dev625-py3-none-any.whl |
|---|---|
| Size | 314.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0e388bef3d637d1f331ae2b2acec1e916444e53f9177b5383c46a8337c541094
|
|
BLAKE2b-256 checksum How to use checksums |
9e3f2e8b07763b2dccf1e3980c5c7444037b06d4b0bcab279f0e82f944202226
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 15, 2026.
Transparency log
