Indeed, if we inspect the source code, we see [GitHub]: def __new__(cls, *args, **kwargs): # We override this method in order to automatically create # `ListSerializer` classes instead when `many=True` is set. if kwargs.pop('many', False): return cls.many_init(*args, **kw...
RootLogger) -> None: """Format excetion traceback. Parameters: logger: The logger for logging exceptions. """ def _hook(exc_type, value, exc_tb) -> None: nest_dir = os.path.dirname(os.path.abspath(__file__)) traceback_str = '' idx = 0 for file_name, line_number, func_name...
path.join(logger.get_dir(), str(mpi_rank) + '.' + str(rank)), allow_early_resets=True) if env_type == 'atari': return wrap_deepmind(env, **wrapper_kwargs) elif reward_scale != 1: return RewardScaler(env, reward_scale) else: return env return _thunk set_global_seeds(seed) ...
The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before. ...
Only the first step, [[*Create a table with packages][create a table with packages,]] is specific to python. @@ -85,11 +92,6 @@ Removing just standard library is not easily generalizeable to other programming Removing the biggest cluster as detected by clustering algorithm from [[http:...
runtime_rank = tensorrt_llm.mpi_rank() world_size = tensorrt_llm.mpi_world_size() if not args.serial_build: torch.cuda.set_device(runtime_rank) strongly_typed = args.strongly_typed if args.quantization is not None and "fp8" in args.quantization: ...
在介绍 python-mpi-logger 之前,我们先简要地介绍一下日志的概念和其作用以及 Python 标准库提供的日志纪录 logging 模块,因为 python-mpi-logger 也是处于 logging 模块框架之下的。 日志(log) 日志(log)是一种可以追踪某些软件运行时所发生事件的方法。软件开发人员可以向他们的代码中调用日志记录相关的方法来表明发...
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logger.set_level(args.log_level) if args.model in get_allowed_models(benchmark_type="gpt"): build_gpt(args) elif args.model in get_allowed_models(benchmark_type="bert"): build_bert(args) elif args.model in get_allowed_models(benchmark_type="enc_dec"): ...
def __init__(self, custom_parser=None): self.version = __version__ logger.info( "PyTorch Version {}, Furnace Version {}".format(torch.__version__, self.version)) self.state = State() self.devices = None self.distributed = False if custom_parser is None: self.parser = argparse.Arg...