The project's README from github.com/OpenMined/PySyft. As one markdown file: README.md.
syft0.10+ is the successor ofsyft-client.syftis the sync engine (import syft as sy); datasets and jobs live insyft-rds(from syft_rds import login_do, login_ds). If you depend on the legacy PySyft ≤0.9 API, pinsyft<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.
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)
Submit the job and retrieve results:
# Submit job
ds.submit_python_job(
user="[email protected]",
code_path="analysis.py",
)
ds.sync(); do.sync()
# Data owner Approves & runs job
do.jobs[0].approve()
do.process_approved_jobs(share_outputs_with_submitter=True)
do.sync(); ds.sync()
result = open(ds.jobs[-1].output_paths[0]).read()
| Package | Description |
|---|---|
syft-rds |
Remote Data Science client: login_do/login_ds, datasets, jobs |
syft-datasets |
Dataset management and sharing |
syft-job |
Job submission and execution |
syft-permissions |
Permission system for Syft datasites |
syft-perms |
User-facing permission API for Syft datasites |
syft-bg |
Background services TUI dashboard for SyftBox |
syft-notebook-ui |
Jupyter notebook display utilities |
# 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.
For questions about PySyft, reach out via #support on Slack.
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.
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!
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.
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Apache License 2.0
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