syft 0.10+ is the successor of syft-client.syft is the sync engine (import syft as sy); datasets and jobs live in syft-rds (from syft_rds import login_do, login_ds). If you depend on the legacy PySyft ≤0.9 API, pin syft<0.10.
PySyft lets data scientists submit computations which are run by data owners on private data — all through cloud storage their organizations already use (Google Drive, Microsoft 365, etc.). No new infrastructure required.
Docs
Workflow — End-to-end privacy-preserving data analysis workflow
API Reference — All public client methods and properties
Double blind LLM evals in enclaves — a researcher evaluates a private model on a private benchmark inside an enclave, with neither side revealing its assets
Privacy-preserving — Private data never leaves the data owner's machine; only approved results are shared
Transport-agnostic — Works over Google Drive today, extensible to any file-based transport
Offline-first — Full functionality even when peers are offline; changes sync when connectivity resumes
Peer-to-peer with explicit auth — Data owners must approve each collaborator before any data flows
Isolated job execution — Jobs run in sandboxed Python virtual environments with controlled access to private data
Dataset sharing with mock/private separation — Data scientists explore mock data, then submit jobs that run on the real thing
Quick Start
We assume two parties here, a Data Owner (DO) and a Data Scientist (DS), the DS wants to do an analysis on private data of the DO. For brevity we use code blocks, but in practice these would be distributed: each party executes their code on their own machine.
# Data scientist
uv pip install "syft>=0.10.0" "syft-rds>=0.6.0"
# Data owner (adds the background services)
uv pip install "syft>=0.10.0" "syft-rds>=0.6.0" "syft-bg>=0.3.12"
import syft as sy # sync engine + helpers (resolve_dataset_file_path, bug_report, ...)
from syft_rds import login_do, login_ds # datasets + jobs (the Remote Data Science client)
# Login (colab auth, for non-colab pass token_path)
do = login_do(email="[email protected]") # use your own email
ds = login_ds(email="[email protected]") # use another email here
# Peer request & approve
ds.add_peer("[email protected]")
do.approve_peer_request("[email protected]")
# Create & sync dataset
do.create_dataset(
name="census",
mock_path="mock.txt", # create this and add some text
private_path="private.txt", # create this and add some text
users=["[email protected]"],
)
do.sync(); ds.sync()
datasets = ds.datasets.get_all()
Write an analysis.py that reads the dataset and produces a result in our case this is just the length of the data. Inside a job, resolve_dataset_file_path automatically resolves to the private data:
# analysis.py
import json
import syft as sy
data_path = sy.resolve_dataset_file_path("census")
with open(data_path, "r") as f:
data = f.read()
with open("outputs/result.json", "w") as f:
json.dump({"length": len(data)}, f)
# Install in development mode
uv pip install -e .
# Run tests
just test-unit # Unit tests (fast, mocked)
just test-integration # Integration tests (slow, real API)
Built by OpenMined — building open-source technology for privacy-preserving data science and AI.
Support
For questions about PySyft, reach out via #support on Slack.
Community
Supported by the OpenMined Foundation, the OpenMined Community is an online network of over 17,000 technologists, researchers, and industry professionals keen to unlock 1000x more data in every scientific field and industry.
Contributors
OpenMined and Syft appreciates all contributors, if you would like to fix a bug or suggest a new feature, please reach out via Github or Slack!
About OpenMined
OpenMined is a non-profit foundation creating technology infrastructure that helps researchers get answers from data without needing a copy or direct access. Our community of technologists is building Syft.
PySyft（即 syft 包）让你可以对留在别人服务器上的数据做数据科学分析，研究人员无需拿到数据副本或直接访问数据，也能从中得到答案。