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results = benchmark.run() print(results) ==================== INFERENCE - SPEED - RESULT ==================== Model Name Batch Size Seq Length Time in s google-bert/bert-base-uncased 8 8 0.006 google-bert/bert-base-uncased 8 32 0.006 google-bert/bert-base-uncased 8 128 0.018 google-bert/bert-base-uncased 8 512 0.088 ==================== INFERENCE - MEMORY - RESULT ==================== Model Name Batch Size Seq Length Memory in MB google-bert/bert-base-uncased 8 8 1227 google-bert/bert-base-uncased 8 32 1281 google-bert/bert-base-uncased 8 128 1307 google-bert/bert-base-uncased 8 512 1539 ==================== ENVIRONMENT INFORMATION ==================== transformers_version: 2.11.0 framework: PyTorch use_torchscript: False framework_version: 1.4.0 python_version: 3.6.10 system: Linux cpu: x86_64 architecture: 64bit date: 2020-06-29 time: 08:58:43.371351 fp16: False use_multiprocessing: True only_pretrain_model: False cpu_ram_mb: 32088 use_gpu: True num_gpus: 1 gpu: TITAN RTX gpu_ram_mb: 24217 gpu_power_watts: 280.0 gpu_performance_state: 2 use_tpu: False </pt> <tf>bash python examples/tensorflow/benchmarking/run_benchmark_tf.py --help An instantiated benchmark object can then simply be run by calling benchmark.run(). |