The public Python SDK for building, publishing, and running with MEGA.
Documentation: https://mega.tensorplay.cn/docs/hub/sdk
Source: https://github.com/ohtensorplay/megatensors
Welcome to megatensors
megatensors connects Python applications to MEGA repositories, datasets,
Spaces, Jobs, inference, storage, and Xet-backed large files. It combines a
high-level Hub client with efficient tensor loading and framework adapters, so
the same package can move an artifact, inspect it, and load it for execution.
Key features
- Discover and manage models, datasets, Spaces, and versioned repositories.
- Download individual files or complete snapshots with a local cache.
- Upload files and folders with resumable large-file support.
- Run inference, remote Jobs, schedules, and Sandbox sessions.
- Load tensors into PyTorch and Paddle through a shared API.
- Sign and verify artifacts for reproducible distribution workflows.
Installation
Install from PyPI:
pip install megatensors
Install the latest public source:
pip install "megatensors @ git+https://github.com/ohtensorplay/megatensors.git@main"
For local development:
git clone https://github.com/ohtensorplay/megatensors.git
cd megatensors
pip install -e .
Quick start
Download a repository snapshot:
from megatensors import snapshot_download
local_path = snapshot_download("owner/model-name")
print(local_path)
Use the typed Hub client:
from megatensors import MegaApi
api = MegaApi()
models = api.list_models(search="embedding")
for model in models:
print(model.id)
Explore the CLI:
mega --help
Repository layout
megatensors/— SDK, CLI, Hub client, tensor loaders, and framework adapters.tests/— API, storage, CLI, and compatibility contracts.examples/— runnable loading, generation, and signing examples.
Contributing
Issues and pull requests are welcome. Run the focused tests for the area you change and keep credentials, deployment configuration, and generated artifacts out of the repository.
License
Apache-2.0
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