v0.2.4

Changes since 0.2.3

New functionality:

• Added the option to compute tanh using sigmoid or Chebyshev approximation (#3113 by @gmuraru)
• Added model parameters as a default optimizer parameter in FederatedClient (#3117 by @gconst02)
• Added an Optims class to track remote optimizers in federated learning (#3179 by @rimijoker)
• Added the ability to use a Plan input as an output (#3199 by @tudorcebere)
• Added the ability to send and get Datasets (#2960 by @abogaziah)

Refactoring:

• Extensive improvements Plan, Placeholder, and the Torch hook (#3080 by @LaRiffle)
• Split Operation class into separate message and action classes (#3132 and #3168 by @Jasopaum and @karlhigley)
• Added a new message for executing non-tensor operations on remote workers (#3048 by @midokura-silvia)
• Reworked BaseWorker's message router to use message classes in handler functions (#3175 by @Jasopaum and @karlhigley)
• Standardized the names of command message to TensorCommandMessage, PlanCommandMessage, and WorkerCommandMessage (#3198 by @karlhigley)
• Moved the Placeholder class to the execution package (#3211 by @karlhigley)
• Converted existing formatted strings to f-strings for consistency and performance (#3186 by @TTitcombe
• Replaced zstd compression library with zlib (#3150 and #3164 by @refactormyself)

Bug fixes:

• Fixed VirtualWorker message sending test (#3139 by @gmuraru)
• Fixed event loop error when starting a WebsocketServerWorker in a notebook (#3196 by @imskr and #3204 by @gmuraru)
• Stopped creating grad tensors for frozen layers (#3200 by @tudorcebere)

Documentation:

• Added homomorphic encryption to the Introduction section (#3135 by @LaRiffle)
• Updated supported PyTorch version to 1.4 (#3140 by @codeboy5)
• Added a note about support Python versions (#3154 by @J-Yash)
• Added a note about using Python 3.7 with conda (#3162 by @teddykoker)
• Added docstrings to callable pointer tests (#3130 by @steph-en-m)
• Added instructions for installing TF Encrypted (#3197 by @rimijoker)

Tutorials:

• Fixed FL training plan tutorials and added a PyGrid FL hosting example (#3185 by @vvmnnnkv)
• Cleaned up the tutorials directory structure (#3159 by @bicycleman15)
• Removed a stray cell from Part 5 - Welcome to the Sandbox (#3155 by @bicycleman15)
• Removed unnecessary requires_grad in Part 2 (#3216 by @bicycleman15)

Translations:

• Bengali:
  • Part 3 (#3133 by @adventuroussrv), Parts 4 and 5 (#3121 by @jabertuhin)
• German:
  • Part 1 (#3178 by @vineetjai)

Builds:

• Set up code owners to automatically request reviews from the relevant OpenMined teams for various parts of the PySyft codebase (#3192 and #3215 by @karlhigley, @LaRiffle, and @AlanAboudib)

v0.2.3

Changes since 0.2.2

New functionality:

• Migrates PySyft to PyTorch 1.4 (#2930 by @gmuraru)
• Implements tanh for FixedPrecisionTensors using Chebyshev approximation (#3004 by @gmuraru)
• Adds the ability to simulate latency with VirtualWorkers (#3070 by @jefersonf)
• Adds Protobuf serialization for Placeholders, Plans, and States (#2972 by @karlhigley)

Refactoring:

• Reworks Plans for smoother serialization to multiple formats (#2910 by @LaRiffle and @vvmnnnkv)
• Moves Plans, Protocols, and States from the messaging package to the execution package (#3078 by @karlhigley)
• Renames Operation class to OperationMessage (#3090 by @karlhigley)

Bug fixes:

• Fixes retrieval of the fit() result in WebsocketClientWorker (#2948 by @brandonhee)
• Fixes numeric issues in handcrafted Conv and Pool implementations (#2945 and #2964 by @arshjot)
• Removes an insecure eval in native tensor interpreter (#2951 by @karlhigley)
• Fixes PyTorch JIT tracing compatibility for Plans (#2988 by @karlhigley)
• Removes workarounds for previous versions of PyTorch (#2999 by @gmuraru)
• Pins requests dependency specification to version 2.22.0 (#2970 by @ADMoreau)
• Fixes interoperability of AutogradTensors with other tensors vis a vis requires_grad (#2998 by @gmuraru)
• Improves logging, typing, and documentation of PATE implementation (#3033 by @TTitcombe)
• Fixes a potential security issue with unsafe YAML loading (#3037 by @systemshift)
• Raises an error when attempting to additively share FloatTensors (#3094 by @pierrepocreau)
• Improves testing for Syft's RNN implementation (#3092 by @jimboH)
• Changes dependency specifications to require compatible versions (#3119 by @karlhigley)
• Fixes compatibility with msgpack 1.0 serialization library (#3067 by @IonesioJunior and #3073 by @hdodenhof)

Documentation:

• Adds Sphinx documentation (#3017 by @Benardi)
• Fixes notebook test badge (#3028 by @jefersonf)
• Adds a link to the Udacity Secure And Private AI course (#3016 by @AVJdataminer)
• Improves instructions for developing protocol changes with syft-proto (#2818 by @refactormyself)

Tutorials:

• Adds model evaluation to SplitNN tutorial (#2983 by @midokura-silvia)
• Adds a note to Part 10 FL with Secure Aggregation tutorial about hooking Numpy (#3022 by @fdroessler)

Translations:

• Bengali:
  • Parts 1, 2, 6, and 7 (#2938, #2942, #3052, and #3053 by @ucalyptus)
  • Parts 8, 9, 11, 12, and 12bis (#3096, #3050, #3041, #3039, and #3038 by @adventuroussrv)
• French:
  • Part 1 (#3107 by @r0cketr1kky)
• Hindi:
  • Parts 5, 6, 7, 13b, and 13c (#2909 and #3055 by @raheja)
  • Part 13a (#2958 by @Yugandhartripathi)
• Italian:
  • Part 1 (#3056 by @DanyEle)
• Portuguese:
  • Part 1 (#3035 by @MarcioPorto)
  • Parts 7, 8, and 8bis (#2977 by @joaolcaas)
  • Parts 9, 10 and 11 (#2980 by @jefersonf)
  • Parts 12 and 13a (#3015 by @marcusvlc)
  • Parts 12bis and 13b (#3020 by @hericlesme)
  • Part 13c (#3023 by @Izabellaaaq)
• Spanish:
  • Parts 6 (#2941 by @ricardopretelt)
  • Parts 7, 8, 9, 10, 11, and 13abc (#2944, #2962, #3031 and #3032 by @arturomf94)
  • Part 12 (#2822 by @socd06)
• Ukrainian:
  • Part 1 (#2854 by @KyloRen1)

Builds:

• Moves automated testing of PRs from Travis to Github Actions (#2936, #3012, and #3013 by @karlhigley and @systemshift)
• Adds a security scan for every PR (#3036 by @systemshift)
• Runs automated translation tests only on the notebooks that changed to speed up the builds (#3064 by @arturomf94)
• Automatically updates the pysyft-notebook Docker image when changes are merged to master (#3030 by @linamnt)
• Caches dependencies in Github Actions (#3124 by @imskr)

v0.2.3.a3

Third release of v0.2.3 to incorporate Syft protocol schema updates.

v0.2.3.a2

Second release of v0.2.3 to incorporate msgpack 1.0 compatibility and Syft protocol schema updates.

v0.2.3.a1

New functionality:

• Migrates PySyft to PyTorch 1.4 (#2930 by @gmuraru)
• Reworks Plans for smoother serialization to multiple formats (#2910 by @LaRiffle and @vvmnnnkv)

Bug fixes:

• Fixes numeric issues in handcrafted Conv and Pool implementations (#2945 and #2964 by @arshjot)
• Removes an insecure eval in native tensor interpreter (#2951 by @karlhigley)
• Fixes parameters to ObjectRequestMessage in websocket_client.py (#2948 by @brandonhee)

Tutorial updates:

• Bengali: Parts 1 and 2 (#2938 and #2942 by @ucalyptus)
• Hindi: Part 13a (#2958 by @Yugandhartripathi)
• Spanish: Parts 6, 7, and 8 (#2941, #2944, and #2962 by @ricardopretelt and @arturomf94)

v0.2.2.a1

Includes some noteworthy new functionality:

• CUDA processing enabled in PySyft (#2735 and #2772 by @midokura-silvia)
• A PrivateTensor type (#2709 by @IonesioJunior)
• Promise and PromiseTensor types (#2610 by @Jasopaum)
• A complete test suite for msgpack serialization and standardization of the serialization format (#2762 and #2812 by @vvmnnnkv)
• An implementation of Distributed Association Scan Hammer (DASH) algorithm (#2658 by @andrelmfarias)
• String and StringPointer types added to support NLP applications (#2684 by @AlanAboudib)
• The ability to nest Plans within other Plans (#2791 by @gmuraru)
• Serializability for the grad_fn in AutogradTensors (#2871 by @sukhadj)
• PyGrid module moved to PySyft (#2760 by @IonesioJunior)
• A NumpyTensor type (#2913 by @iamtrask)
• Python implementations of torch.nn.Conv2d and torch.nn.AvgPool2d (#2896 by @iamtrask)
• Approximate exp log inverse and sigmoid for SMPC (#2659 by @LaRiffle)

Also includes updates to the tutorials:

• A tutorial on Promises and PromiseTensors (#2786 by @Jasopaum)
• A split neural network tutorial (#2808 by @H4LL)
• Tutorial notebooks translated into Chinese, Spanish, Hindi, Indonesian, Korean, Portuguese, and Romanian (many PRs by @dljgs1, @Bingyy, @socd06, @ricardopretelt, @arturomf94, @darkmatter18, @Yugandhartripathi, @nggih, @wonderit, @jefersonf, and @gmuraru.)

And finally, includes:

• Many bug fixes, which are too numerous to list but nonetheless much appreciated!