CrazyAra, ClassicAra, MultiAra 0.9.5.post0

This release provides binaries for CrazyAra, ClassicAra and MultiAra with the new OpenVino CPU backend.

The OpenVino backend features a new UCI-option Threads_NN_Inference which defines how many threads to use for neural network inference. This no longer requires setting up an environment variable called OMP_NUM_THREADS (#35).

Current limitation for OpenVino backend

• No Int8 inference support enabled yet.
• Only tested on regular CPU and not with Intel GPUs or the Intel Neural Compute Stick.

Installation instructions

The binary packages include the required inference libraries for each platform.
The latest ClassicAra model is included within each release package.
However, the models for the CrazyAra and MultiAra should be downloaded separately and unzipped (see release 0.9.5).

• CrazyAra-rl-model-os-96.zip
• MultiAra-rl-models.zip (improved MultiAra models using reinforcement learning (rl) )
• MultiAra-sl-models.zip (initial MultiAra models using supervised learning)

Next, move the model files into the model/<engine-name>/<variant> folder.

Regression test

ClassicAra

The new OpenVino backend is about 100 - 150 nps faster on CPU and much easier to install than the MXNetMKL backend.

[TimeControl "7+0.1"]
Score of ClassicAra - 0.9.5.post0 OpenVino vs ClassicAra 0.9.5 - MXNetMKL: 82 - 17 - 55 [0.711]
Elo difference: 156.4 +/- 45.9, LOS: 100.0 %, DrawRatio: 35.7 %

154 of 1000 games finished.

Inference libraries

The following inference libraries are used in each package:

• CrazyAra_ClassicAra_MultiAra_0.9.5.post0_Linux_OpenVino.zip
  • OpenVino 2021 4 LTS
• CrazyAra_ClassicAra_MultiAra_0.9.5.post0_Mac_OpenVino.zip
  • OpenVino 2021 4 LTS
• CrazyAra_ClassicAra_MultiAra_0.9.5.post0_Win_OpenVino.zip
  • OpenVino 2021 4 LTS