+
+
+Perform data science on `data` that remains in `someone else's` server
+
+# Quickstart
+
+β
`Linux` β
`macOS` β
`Windows` β
`Docker` β
`Podman` β
`Kubernetes`
+
+## Install Client
+
+```bash
+$ pip install -U syft
+```
+
+## Launch Server
+
+```python
+# from Jupyter / Python
+import syft as sy
+sy.requires(">=0.8.1,<0.8.2")
+node = sy.orchestra.launch(name="my-domain", port=8080, dev_mode=True, reset=True)
+```
+
+```bash
+# or from the command line
+$ syft launch --name=my-domain --port=8080 --reset=True
+
+Starting syft-node server on 0.0.0.0:8080
+```
+
+## Launch Client
+
+```python
+import syft as sy
+sy.requires(">=0.8.1,<0.8.2")
+domain_client = sy.login(port=8080, email="info@openmined.org", password="changethis")
+```
+
+## PySyft in 10 minutes
+
+π API Example Notebooks
+
+- 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
+
+## Deploy Kubernetes Helm Chart
+
+**Note**: Assuming we have a Kubernetes cluster already setup.
+
+#### 1. Add and update Helm repo for Syft
+
+```sh
+$ helm repo add openmined https://openmined.github.io/PySyft/helm
+$ helm repo update openmined
+```
+
+#### 2. Search for available Syft versions
+
+```sh
+$ helm search repo openmined/syft --versions --devel
+```
+
+#### 3. Set your preferred Syft Chart version
+
+```sh
+SYFT_VERSION="
+
+`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](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), [Differential Privacy](https://en.wikipedia.org/wiki/Differential_privacy), and [Encrypted Computation](https://en.wikipedia.org/wiki/Homomorphic_encryption). 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`.
+
+### Why should I use Syft?
+
+`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.
+
+
+
+# Terminology
+
+|
+ π¨π»βπΌ Data Owners + |
+
+ π©π½βπ¬ Data Scientists + |
+
|---|---|
| + + +Provide `datasets` which they would like to make available for `study` by an `outside party` they may or may not `fully trust` has good intentions. + + | ++ + +Are end `users` who desire to perform `computations` or `answer` a specific `question` using one or more data owners' `datasets`. + + | +
|
+ π° Domain Server + |
+
+ π Gateway Server + |
+
| + + +Manages the `remote study` of the data by a `Data Scientist` and allows the `Data Owner` to manage the `data` and control the `privacy guarantees` of the subjects under study. It also acts as a `gatekeeper` for the `Data Scientist's` access to the data to compute and experiment with the results. + + | ++ + +Provides services to a group of `Data Owners` and `Data Scientists`, such as dataset `search` and bulk `project approval` (legal / technical) to participate in a project. A gateway server acts as a bridge between it's members (`Domains`) and their subscribers (`Data Scientists`) and can provide access to a collection of `domains` at once. | + +
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+π₯ PETs: Remote Data Science Unleashed - R gov 2021 |
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+# Supporters
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+ +
+
+# Disclaimer
+
+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](https://slack.openmined.org/) or email if you would like to ask a question or have a use case that you would like to discuss.
+
+# License
+
+[Apache License 2.0](LICENSE)+Person icons created by Freepik - Flaticon + + diff --git a/packages/syft/setup.cfg b/packages/syft/setup.cfg index 4b0f8fe459f..c267a7e302e 100644 --- a/packages/syft/setup.cfg +++ b/packages/syft/setup.cfg @@ -5,7 +5,7 @@ description = Perform numpy-like analysis on data that remains in someone elses author = OpenMined author_email = info@openmined.org license = Apache-2.0 -long_description = file: README.md +long_description = file: PYPI.md long_description_content_type = text/markdown; charset=UTF-8; variant=GFM url = https://openmined.github.io/PySyft/ project_urls = diff --git a/scripts/convert_to_pypi_readme.py b/scripts/convert_to_pypi_readme.py new file mode 100644 index 00000000000..70d53ad3ae5 --- /dev/null +++ b/scripts/convert_to_pypi_readme.py @@ -0,0 +1,90 @@ +# stdlib +import argparse +from pathlib import Path +import re + + +def convert_github_markdown_to_pypi(input_file, output_file, repo, version): + with open(input_file, encoding="utf-8") as file: + content = file.read() + + # Regular expression pattern to match