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==================================
Acumos Python Client Release Notes
==================================

v0.7.1
======

-  Authentication

   - Username and password authentication has been deprecated
   - Users are now interactively prompted for an onboarding token, as opposed to a username and password

v0.7.0
======

-  Requirements

   - Python script dependencies can now be specified using a Requirements object
   - Python script dependencies found during the introspection stage are now included with the model

v0.6.5
======

-  Bug fixes

   - Don't attempt to use an empty auth token (avoids blank strings to be set in environment)

v0.6.4
======

-  Bug fixes

   - The normalized path of the system base prefix is now used for identifying stdlib packages

v0.6.3
======

-  Bug fixes

   - Improved dependency inspection when using a virtualenv
   - Removed custom packages from model metadata, as it caused image build failures
   - Fixed Python 3.5.2 ordering bug in wrapped model usage

v0.6.2
======

-  TensorFlow

   - Fixed a serialization issue that occurred when using a frozen graph

v0.6.1
======

-  Model upload

   - The JWT is now cleared immediately after a failed upload
   - Additional HTTP information is now included in the error message

v0.6.0
======

-  Authentication token

   -  A new environment variable ``ACUMOS_TOKEN`` can be used to short-circuit
      the authentication process

-  Extra headers

   -  ``AcumosSession.push`` now accepts an optional ``extra_headers`` argument,
      which will allow users and systems to include additional information when
      pushing models to the onboarding server

v0.5.0
======

-  Modeling

   -  Python 3.6 NamedTuple syntax support now tested
   -  User documentation includes example of new NamedTuple syntax

-  Model wrapper

   -  Model wrapper now has APIs for consuming and producing Python
      dicts and JSON strings

-  Protobuf and protoc

   -  An explicit check for protoc is now made, which raises a more
      informative error message
   -  User documentation is more clear about dependence on protoc, and
      provides an easier way to install protoc via Anaconda

-  Keras

   -  The active keras backend is now included as a tracked module
   -  keras_contrib layers are now supported

v0.4.0
======

-  Replaced library-specific onboarding functions with “new-style”
   models

   -  Support for arbitrary Python functions using type hints
   -  Support for custom user-defined types
   -  Support for TensorFlow models
   -  Improved dependency introspection
   -  Improved object serialization mechanisms