v0.2.6

PySyft 0.2.6 Release Notes

Breaking changes since v0.2.5:

• Message.contents field has now been fully removed after previous deprecation (#3477 by @karlhigley)
• TrainConfig has been fully removed (#3532 by @karlhigley)

New functionality:

• Add initial support for auto-scaling PyGrid workers and notebooks on Google Cloud Platform (#3437 and #3483 by @rimijoker)
• Add asynchronous send_command method to WebsocketClient (#3454 by @LaRiffle and @Syzygianinfern0)
• Build Protocol Roles from @func2protocol decorator (#3488 by @Jasopaum)
• Trace CommunicationActions into Protocol Roles (#3448 by @Prtfw)
• Add a reset() method on Role (#3495 by @Jasopaum)
• Add Plan argument type-checking along with support for lists and dicts as Plan arguments (#3386 by @tudorcebere)
• Support additional ML/DL frameworks during Plan tracing (#3461 by @gmuraru)
• Provide a way to trace module-level methods like torch.rand() into Plan/Protocol Roles (#3522 by @karlhigley)
• Allow Protocol Roles to have State for initial values (#3499 by @Jasopaum)
• Add ability to seed the Syft IdProvider and safely toggle verbose mode (#3457 by @Syzygianinfern0)
• Add support for WebRTC connections between peer Workers (#3548 and several more by @IonesioJunior)
• Add Protobuf serialization support for torch.dtype, syft.generic.String, and PointerDataset (#3584, #3594, #3600 by @tudorcebere)
• Print useful information on syft.grid.register() (#3589 by @IonesioJunior)
• Add RoleAssignments to Protocols (#3544 by @Jasopaum)
• Add ways to fetch Grid network peers to Network and WebRTCManager #3582 by @joaolcaas)
• Host private datasets locally with Grid (#3596 by @IonesioJunior)
• Add a method to return the supported ML/DL frameworks in BaseWorker (#3535 by @shubham3121)

Bug fixes:

• Fix the type of the allowed users parameter to PrivateTensors (#3446 by @gmuraru)
• Fix PointerTensor.move() when the source and destination workers are the same (#3442 by @Syzygianinfern0)
• Remove stray objects from FSS Worker/Plan initialization (#3525 by @karlhigley)
• Fix Batchnorm for Federated Learning (#3472 by @LaRiffle)
• Add scaffolding for Federated Learning client worker API (#3426 by @vvmnnnkv)
• Avoid registering Grid peer Workers multiple times (#3591 by @jefersonf)
• Remove test_spinup_time which has been flaky (#3603 by @robert-wagner)

Refactoring:

• Clean up Syft implementation of RNNs (#3441 by @dcato98)
• Store logged message in BaseWorker in an unserialized format for readability (#3473 by @karlhigley)
• Update Role.register_action() to allow registering CommunicationActions (#3465 by @karlhigley)
• Refactor BaseWorker.send_command() to accept explicit arguments (#3487 by @shubham3121)
• Add a SyftSerializable base class for Syft classes that need to be serialized to msgpack or Protobuf (#3386 by @tudorcebere)
• Convert ObjectStore to a stand-alone class instead of a base class for Workers (#3484 by @karlhigley)
• Extract @abstractmethods from BaseWorker to AbstractWorker (#3519 by @karlhigley)
• Create a VirtualWorker for each Plan/Protocol Role and use it for tracing (#3521 by @karlhigley)
• Improve the serde interface with lazy data loaders (#3530 by @tudorcebere)
• Split FrameworkHook into multiple parts (#3561 by @karlhigley)
• Clean up the code style of PySyft (#3535 by @gmuraru)

Examples and Tutorials:

• Add an experimental notebook for how to create a Plan that trains a NN on MNIST using Syft autograd (#3567 by @vvmnnnkv)

Translations:

• Updates to Bengali translation of Parts 4-5, 12-13 (#3415 by @jabertuhin, #3445, #3449, #3466, and #3500 by @znreza)

Documentation:

• Update the Pull Request templates (#3506 by @tkrsh, #3520 by @karlhigley, additional work by @cereallarceny)

Tests and Builds:

• Add a check for docstring coverage to the Github builds (#3462 and #3505 by @karlhigley)
• Remove verbose flag from code format check to highlight necessary changes (#3475 by @karlhigley)