Perform data science on data
that remains in someone else's
server
β
Linux
β
macOS
β
Windows
β
Docker
β
Podman
β
Kubernetes
$ pip install -U syft[data_science]
# from Jupyter / Python
import syft as sy
sy.requires(">=0.8.6,<0.8.7")
node = sy.orchestra.launch(name="my-domain", port=8080, dev_mode=True, reset=True)
# or from the command line
$ syft launch --name=my-domain --port=8080 --reset=True
Starting syft-node server on 0.0.0.0:8080
import syft as sy
sy.requires(">=0.8.6,<0.8.7")
domain_client = sy.login(port=8080, email="info@openmined.org", password="changethis")
- 00-load-data.ipynb
- 01-submit-code.ipynb
- 02-review-code-and-approve.ipynb
- 03-data-scientist-download-result.ipynb
- 04-jax-example.ipynb
- 05-custom-policy.ipynb
- 06-multiple-code-requests.ipynb
- 07-domain-register-control-flow.ipynb
- 08-code-version.ipynb
- 09-blob-storage.ipynb
- 10-container-images.ipynb
- 11-container-images-k8s.ipynb
Note: Assuming we have a Kubernetes cluster already setup.
helm repo add openmined https://openmined.github.io/PySyft/helm
helm repo update openmined
helm search repo openmined/syft --versions --devel
SYFT_VERSION="<paste the chart version number>"
helm install my-domain openmined/syft --version $SYFT_VERSION --namespace syft --create-namespace --set ingress.className="traefik"
For Azure AKS
helm install ... --set ingress.className="azure-application-gateway"
For AWS EKS
helm install ... --set ingress.className="alb"
For Google GKE we need the gce
annotation annotation.
helm install ... --set ingress.class="gce"
-
Install our handy π΅ cli tool which makes deploying a Domain or Gateway server to Docker or VM a one-liner:
pip install -U hagrid
-
Then run our interactive jupyter Install π§π½ββοΈ WizardBETA:
hagrid quickstart
-
In the tutorial you will learn how to install and deploy:
PySyft
= ournumpy
-like π Python library for computing onprivate data
in someone else'sDomain
PyGrid
= our π³docker
/ π§vm
Domain
&Gateway
Servers whereprivate data
lives
- HAGrid 0.3 Requires: π
python
πgit
- Run:pip install -U hagrid
- Interactive Install π§π½ββοΈ WizardBETA Requires π΅
hagrid
: - Run:hagrid quickstart
- PySyft 0.8.1 Requires: π
python 3.10 - 3.12
- Run:pip install -U syft
- PyGrid Requires: π³
docker
, π¦¦podman
or βΈοΈkubernetes
- Run:hagrid launch ...
0.9.0
- Coming soon...
0.8.7
(Beta) - dev
branch ππ½ API - Coming soon...
0.8.6
(Stable) - API
Deprecated:
0.8.5-post.2
- API0.8.4
- API0.8.3
- API0.8.2
- API0.8.1
- API0.8.0
- API0.7.0
- Course 3 Updated0.6.0
- Course 30.5.1
- Course 2 + M1 Hotfix0.2.0
-0.5.0
PySyft and PyGrid use the same version
and its best to match them up where possible. We release weekly betas which can be used in each context:
PySyft (Stable): pip install -U syft
PyGrid (Stable) hagrid launch ... tag=latest
PySyft (Beta): pip install -U syft --pre
PyGrid (Beta): hagrid launch ... tag=beta
HAGrid is a cli / deployment tool so the latest version of hagrid
is usually the best.
Syft
is OpenMined's open source
stack that provides secure
and private
Data Science in Python. Syft decouples private data
from model training, using techniques like Federated Learning, Differential Privacy, and Encrypted Computation. This is done with a numpy
-like interface and integration with Deep Learning
frameworks, so that you as a Data Scientist
can maintain your current workflow while using these new privacy-enhancing techniques
.
Syft
allows a Data Scientist
to ask questions
about a dataset
and, within privacy limits
set by the data owner
, get answers
to those questions
, all without obtaining a copy
of the data itself. We call this process Remote Data Science
. It means in a wide variety of domains
across society, the current risks
of sharing information (copying
data) with someone such as, privacy invasion, IP theft and blackmail will no longer prevent the vast benefits
such as innovation, insights and scientific discovery which secure access will provide.
No more cold calls to get access
to a dataset. No more weeks of wait times
to get a result
on your query
. It also means 1000x more data
in every domain. PySyft opens the doors to a streamlined Data Scientist workflow
, all with the individual's privacy
at its heart.
OpenMined and Syft appreciates all contributors, if you would like to fix a bug or suggest a new feature, please see our guidelines.
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OpenMined
is a fiscally sponsored 501(c)(3)
in the USA. We are funded by our generous supporters on Open Collective.
Syft is under active development and is not yet ready for pilots on private data without our assistance. As early access participants, please contact us via Slack or email if you would like to ask a question or have a use case that you would like to discuss.
Apache License 2.0
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