Benchmark tool evaluating training and inference from sudoku-ml.
pip install https://github.com/cloudmercato/sudoku-game/archive/refs/heads/master.zip pip install https://github.com/cloudmercato/sudoku-ml/archive/refs/heads/master.zip pip install https://github.com/cloudmercato/sudoku-ml-benchmark/archive/refs/heads/master.zip
usage: sudoku-ml-bench [-h] [--batch-size BATCH_SIZE] [--epochs EPOCHS] [--train-dataset-size TRAIN_DATASET_SIZE] [--train-removed TRAIN_REMOVED] [--infer-dataset-size INFER_DATASET_SIZE] [--infer-removed INFER_REMOVED] [--generator-processes GENERATOR_PROCESSES] [--model-path MODEL_PATH] [--model-load-file MODEL_LOAD_FILE] [--model-save-file MODEL_SAVE_FILE] [--log-dir LOG_DIR] [--tf-log-device] [--tf-dump-debug-info] [--tf-profiler-port TF_PROFILER_PORT] [--verbose VERBOSE] [--tf-verbose TF_VERBOSE] optional arguments: -h, --help show this help message and exit --batch-size BATCH_SIZE --epochs EPOCHS --train-dataset-size TRAIN_DATASET_SIZE --train-removed TRAIN_REMOVED --infer-dataset-size INFER_DATASET_SIZE --infer-removed INFER_REMOVED --generator-processes GENERATOR_PROCESSES --model-path MODEL_PATH Python path to the model to compile --model-load-file MODEL_LOAD_FILE Model load file path (h5) --model-save-file MODEL_SAVE_FILE Model save file path (h5) --log-dir LOG_DIR Tensorboard log directory --tf-log-device Determines whether TF compute device info is displayed. --tf-dump-debug-info --tf-profiler-port TF_PROFILER_PORT --verbose VERBOSE, -v VERBOSE --tf-verbose TF_VERBOSE, -tfv TF_VERBOSE
Dockerfile for classic Tensorflow and the GPU version are available:
# For CPU docker build -f Dockerfile -t sudoku-ml-bench . docker run -it sudoku-ml-bench # For GPU docker build -f Dockerfile-gpu -t sudoku-ml-bench-gpu . docker run --gpus all --ipc=host -it sudoku-ml-bench-gpu # Add -e TF_CPP_MIN_LOG_LEVEL=3 to catch only the JSON output
The commands above will run a training, then save an inference. You can mount a volume on /models/ to keep it. In the same idea you can mount a volume on /log_dir/, to retrive the Tensorboard data.