1.2-RC4

This is the fourth release candidate for RedisAI v1.2!

Headlines:

#482 DAG performance enhancement: Auto-batching support for MODELRUN commands inside a DAG Added DAG general timeout.
#489 Execute Redis commands in TorchScript. This capability enables, but not limited, to convert data residing in Redis (or modules) data structures into tensors to be fed to the model as well as store script results in Redis data structures. Scripts can be run in DAG's.
#787 Allow executing models from within TorchScript.
#775 Support for BOOL tensors.
#511, #537, #547, #556 Allow async model, script and DAG run via low-level API. This capability will allow other modules to call RedisAI. For example a module that hold time series data, can call RedisAI directly for anomaly detection.
#723 #682 #680, #792 - New API commands MODELSTORE MODELEXECUTE, SCRIPTSTORE, SCRIPTEXECUTE DAGEXECUTE replacing and deprecating MODELSET MODELRUN, SCRIPTSET, SCRIPTRUN DAGRUN for better enterprise cluster support.
#779 #797 Allow ONNXRuntime backend execution timeout

Details:

Minor enhancements:

#566 #619 TensorFlow 2.4, Pytorch 1.7, ONNXRuntime 1.7. Note: Current ONNXRuntime distribution is experimental under RedisAI build. Future version will return the original ONNXRuntime distribution.
#499 Allow setting number of inter/intra op threads in Torch backend.
#529 Expose model definitions for inputs and outputs in MODELGET command.
#530 Expose Redis/RedisAI main thread cpu usage.
#540 Reuse memory in TENSORSET command.
#580 Expose backend version on AI.INFO
#581 Cache tensor length.
#791 Allow AI.MODELGET and AI.SCRIPTGET without optional args

Bugfixes:

#488 Handle short reads during RDB load.
#538 Handle binary strings as tensor names.
#462, #553 Handle memory leaks.
#558 Erroneous replies for invalid MODELGET commands.
#748 Model RDB encoding missing properties
#754 AOF-Rewrite logic
#793 Fix DAG reply for AI.TENSORGET op
#794 #794 Fix MODELGET, SCRIPTGET , _MODELSCAN and _SCRIPTSCAN commands

Notes:
This is not the GA version of 1.2. The version inside Redis will be 10204 or 1.2.4 in semantic versioning. Since the version of a module in Redis is numeric, we could not add an RC4 flag.