To help you get started, we've collected the most common ways that syft is being used within popular public projects.

OpenMined / PySyft / syft / frameworks / torch / tensors / interpreters / precision.py View on Github

new_self = other.child
                new_other = self
            elif cmd == "div":
                # TODO how to divide by AST?
                raise NotImplementedError(
                    "Division of a FixedPrecisionTensor by an AdditiveSharingTensor not implemented"
                )

        elif (
            cmd == "mul"
            and isinstance(self.child, (AdditiveSharingTensor, MultiPointerTensor))
            and isinstance(other.child, (AdditiveSharingTensor, MultiPointerTensor))
        ):
            # If we try to multiply a FPT>torch.tensor with a FPT>AST,
            # we swap operators so that we do the same operation as above
            new_self, new_other, _ = hook_args.unwrap_args_from_method("mul", self, other, None)

        else:
            # Replace all syft tensor with their child attribute
            new_self, new_other, _ = hook_args.unwrap_args_from_method(cmd, self, other, None)

            # To avoid problems with negative numbers
            # we take absolute value of the operands
            # The problems could be 1) bad truncation for multiplication
            # 2) overflow when scaling self in division

            # sgn_self is 1 when new_self is positive else it's 0
            # The comparison is different is new_self is a torch tensor or an AST
            sgn_self = (
                (new_self < self.field // 2).long()
                if isinstance(new_self, torch.Tensor)
                else new_self > 0

View more ways to use syft

Data Science on data you are not allowed to see

PySyft enables a new way to do data science, where you can use non-public information, without seeing nor obtaining a copy of the data itself. All you need is to connect to a Datasite!

Datasites are like websites, but for data. Designed with the principles of structured transparency, they enable data owners to control how their data is protected and data scientists to use data without obtaining a copy.

PySyft supports any statistical analysis or machine learning, offering support for directly running Python code - even using third-party Python libraries.

Supported on:

✅ Linux ✅ macOS ✅ Windows ✅ Docker ✅ Kubernetes

Quickstart

Try out your first query against a live demo Datasite!

Install Client

pip install -U "syft[data_science]"

More instructions are available here.

Launch Server

Launch a development server directly in your Jupyter Notebook:

import syft as sy

sy.requires(">=0.9.2,<0.9.3")

server = sy.orchestra.launch(
    name="my-datasite",
    port=8080,
    create_producer=True,
    n_consumers=1,
    dev_mode=False,
    reset=True, # resets database
)

or from the command line:

$ syft launch --name=my-datasite --port=8080 --reset=True

Starting syft-datasite server on 0.0.0.0:8080

Datasite servers can be deployed as a single container using Docker or directly in Kubernetes. Check out our deployment guide.

Launch Client

Main way to use a Datasite is via our Syft client, in a Jupyter Notebook. Check out our PySyft client guide:

import syft as sy

sy.requires(">=0.9.2,<0.9.3")

datasite_client = sy.login(
    port=8080,
    email="info@openmined.org",
    password="changethis"
)

PySyft - Getting started 📝

Learn about PySyft via our getting started guide:

• PySyft from the ground up
• Part 1: Datasets & Assets
• Part 2: Client and Datasite Access
• Part 3: Propose the research study
• Part 4: Review Code Requests
• Part 5: Retrieving Results

PySyft In-depth

📚 Check out our docs website.

Quick PySyft components links:

• DataSite Server

• Syft Client

• Datasets API (.datasets)

• Users API (.users)

• Request API (.requests)

• Code API (.code)

• Syft Policies API (.policy)

• Settings API (.settings)

• Notifications API (.notifications)

• Sync API (.sync)

Why use PySyft?

In a variety of domains across society, data owners have valid concerns about the risks associated with sharing their data, such as legal risks, privacy invasion (misuing the data), or intellectual property (copying and redistributing it).

Datasites enable data scientists to answer questions without even seeing or acquiring a copy of the data, within the data owners's definition of acceptable use. We call this process Remote Data Science.

This means that the current risks of sharing information with someone will no longer prevent the vast benefits such as innovation, insights and scientific discovery. With each Datasite, data owners are able to enable 1000x more accesible data in each scientific field and lead, together with data scientists, breakthrough innovation.

Learn more about our work on our website.

Support

For questions about PySyft, reach out via #support on Slack.

Syft Versions

❗ PySyft and Syft Server must use the same version.

Latest Stable

• 0.9.2 (Stable) - Docs
• Install PySyft (Stable): pip install -U syft

Latest Beta

• 0.9.3 (Beta) - dev branch 👈🏽
• Install PySyft (Beta): pip install -U syft --pre

Find more about previous releases here.

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.

Courses

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.

Supporters

License

Apache License 2.0
Person icons created by Freepik - Flaticon