src/transformers/benchmark/benchmark_utils.py/Benchmark/environment_info
class Benchmark:    def environment_info(self):
        if self._environment_info is None:
            info = {}
            info["transformers_version"] = version
            info["framework"] = self.framework
            if self.framework == "PyTorch":
                info["use_torchscript"] = self.args.torchscript
            if self.framework == "TensorFlow":
                info["eager_mode"] = self.args.eager_mode
                info["use_xla"] = self.args.use_xla
            info["framework_version"] = self.framework_version
            info["python_version"] = platform.python_version()
            info["system"] = platform.system()
            info["cpu"] = platform.processor()
            info["architecture"] = platform.architecture()[0]
            info["date"] = datetime.date(datetime.now())
            info["time"] = datetime.time(datetime.now())
            info["fp16"] = self.args.fp16
            info["use_multiprocessing"] = self.args.do_multi_processing
            info["only_pretrain_model"] = self.args.only_pretrain_model

            if is_psutil_available():
                info["cpu_ram_mb"] = bytes_to_mega_bytes(psutil.virtual_memory().total)
            else:
                logger.warning(
                    "Psutil not installed, we won't log available CPU memory. "
                    "Install psutil (pip install psutil) to log available CPU memory."
                )
                info["cpu_ram_mb"] = "N/A"

            info["use_gpu"] = self.args.is_gpu
            if self.args.is_gpu:
                info["num_gpus"] = 1  # TODO(PVP) Currently only single GPU is supported
                if is_py3nvml_available():
                    nvml.nvmlInit()
                    handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                    info["gpu"] = nvml.nvmlDeviceGetName(handle)
                    info["gpu_ram_mb"] = bytes_to_mega_bytes(nvml.nvmlDeviceGetMemoryInfo(handle).total)
                    info["gpu_power_watts"] = nvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000
                    info["gpu_performance_state"] = nvml.nvmlDeviceGetPerformanceState(handle)
                    nvml.nvmlShutdown()
                else:
                    logger.warning(
                        "py3nvml not installed, we won't log GPU memory usage. "
                        "Install py3nvml (pip install py3nvml) to log information about GPU."
                    )
                    info["gpu"] = "N/A"
                    info["gpu_ram_mb"] = "N/A"
                    info["gpu_power_watts"] = "N/A"
                    info["gpu_performance_state"] = "N/A"

            info["use_tpu"] = self.args.is_tpu
            # TODO(PVP): See if we can add more information about TPU
            # see: https://github.com/pytorch/xla/issues/2180

            self._environment_info = info
        return self._environment_info