RNN-T CmdGen improvements

[psyhtest]

The RNN-T CmdGen is work-in-progress. We started it for the v0.7 submission round, but eventually did not submit due to a belatedly discovered postprocessing issue. Not surprisingly, it needs more love to get into shape.

Currently, the usage is:

$ ck run cmdgen:benchmark.speech-recognition-loadgen --model=rnnt \
--scenario=singlestream --mode=accuracy \
--sut=aws-g4dn.4xlarge

Future improvements:

• The --model parameter should be optional. (We only support one model after all.)
• The --sut parameter should allow any SUT name. At the moment, it is restricted to handful, and alternatives result in an error (CK error: [cmdgen] build_map[sut] is missing both 'aws-g4dn.4xsmall' and '###' values!.)
• The record name (e.g. mlperf-closed-aws-g4dn.4xlarge-pytorch-v1.15.1-rnnt-singlestream) must include the --mode to allow keeping both performance and accuracy experiment entries simultaneously. At the moment, a previously recorded experiment entry for one mode must be removed (ck rm local:experiment:mlperf-closed*rnnt* -f) to allow for the other mode.
• The record name should not include a bogus inference engine version (v1.15.1). The default inference engine name (pytorch) should be customizable according to the plugins used.

[psyhtest]

By the way, the above mentioned postprocessing issue also needs to be fixed but here's a quick workaround (installing the LoadGen Python module to userspace):

$ python -m pip install --user $(find $(ck locate env --tags=mlperf,inference,dividiti.rnnt) -name *.whl)
Processing ./dist/mlperf_loadgen-0.5a0-cp38-cp38-linux_x86_64.whl
Installing collected packages: mlperf-loadgen
Successfully installed mlperf-loadgen-0.5a0