142 lines
6.6 KiB
Python
142 lines
6.6 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import subprocess
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import sys
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import itertools
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import atexit
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import dllogger
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from dllogger import Backend, JSONStreamBackend, StdOutBackend
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import torch.distributed as dist
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from torch.utils.tensorboard import SummaryWriter
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class TensorBoardBackend(Backend):
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def __init__(self, verbosity, log_dir):
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super().__init__(verbosity=verbosity)
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self.summary_writer = SummaryWriter(log_dir=os.path.join(log_dir, 'TB_summary'),
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flush_secs=120,
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max_queue=200
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)
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self.hp_cache = None
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atexit.register(self.summary_writer.close)
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@property
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def log_level(self):
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return self._log_level
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def metadata(self, timestamp, elapsedtime, metric, metadata):
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pass
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def log(self, timestamp, elapsedtime, step, data):
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if step == 'HPARAMS':
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parameters = {k: v for k, v in data.items() if not isinstance(v, (list, tuple))}
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#Unpack list and tuples
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for d in [{k+f'_{i}':v for i,v in enumerate(l)} for k,l in data.items() if isinstance(l, (list, tuple))]:
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parameters.update(d)
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#Remove custom classes
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parameters = {k: v for k, v in data.items() if isinstance(v, (int, float, str, bool))}
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parameters.update({k:'None' for k, v in data.items() if v is None})
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self.hp_cache = parameters
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if step == ():
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if self.hp_cache is None:
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print('Warning: Cannot save HParameters. Please log HParameters with step=\'HPARAMS\'', file=sys.stderr)
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return
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self.summary_writer.add_hparams(self.hp_cache, data)
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if not isinstance(step, int):
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return
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for k, v in data.items():
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self.summary_writer.add_scalar(k, v, step)
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def flush(self):
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pass
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def setup_logger(args):
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os.makedirs(args.results, exist_ok=True)
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log_path = os.path.join(args.results, args.log_file)
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if os.path.exists(log_path):
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for i in itertools.count():
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s_fname = args.log_file.split('.')
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fname = '.'.join(s_fname[:-1]) + f'_{i}.' + s_fname[-1] if len(s_fname) > 1 else args.stat_file + f'.{i}'
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log_path = os.path.join(args.results, fname)
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if not os.path.exists(log_path):
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break
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def metric_format(metric, metadata, value):
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return "{}: {}".format(metric, f'{value:.5f}' if isinstance(value, float) else value)
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def step_format(step):
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if step == ():
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return "Finished |"
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elif isinstance(step, int):
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return "Step {0: <5} |".format(step)
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return "Step {} |".format(step)
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if not dist.is_initialized() or not args.distributed_world_size > 1 or args.distributed_rank == 0:
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dllogger.init(backends=[JSONStreamBackend(verbosity=1, filename=log_path),
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TensorBoardBackend(verbosity=1, log_dir=args.results),
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StdOutBackend(verbosity=2,
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step_format=step_format,
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prefix_format=lambda x: "")#,
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#metric_format=metric_format)
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])
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else:
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dllogger.init(backends=[])
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dllogger.log(step='PARAMETER', data=vars(args), verbosity=0)
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container_setup_info = {**get_framework_env_vars(), **get_system_info()}
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dllogger.log(step='ENVIRONMENT', data=container_setup_info, verbosity=0)
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dllogger.metadata('loss', {'GOAL': 'MINIMIZE', 'STAGE': 'TRAIN', 'format': ':5f'})
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dllogger.metadata('P10', {'GOAL': 'MINIMIZE', 'STAGE': 'TRAIN', 'format': ':5f'})
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dllogger.metadata('P50', {'GOAL': 'MINIMIZE', 'STAGE': 'TRAIN', 'format': ':5f'})
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dllogger.metadata('P90', {'GOAL': 'MINIMIZE', 'STAGE': 'TRAIN', 'format': ':5f'})
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dllogger.metadata('items/s', {'GOAL': 'MAXIMIZE', 'STAGE': 'TRAIN', 'format': ':1f'})
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dllogger.metadata('val_loss', {'GOAL': 'MINIMIZE', 'STAGE': 'VAL', 'format':':5f'})
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dllogger.metadata('val_P10', {'GOAL': 'MINIMIZE', 'STAGE': 'VAL', 'format': ':5f'})
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dllogger.metadata('val_P50', {'GOAL': 'MINIMIZE', 'STAGE': 'VAL', 'format': ':5f'})
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dllogger.metadata('val_P90', {'GOAL': 'MINIMIZE', 'STAGE': 'VAL', 'format': ':5f'})
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dllogger.metadata('val_items/s', {'GOAL': 'MAXIMIZE', 'STAGE': 'VAL', 'format': ':1f'})
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dllogger.metadata('test_P10', {'GOAL': 'MINIMIZE', 'STAGE': 'TEST', 'format': ':5f'})
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dllogger.metadata('test_P50', {'GOAL': 'MINIMIZE', 'STAGE': 'TEST', 'format': ':5f'})
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dllogger.metadata('test_P90', {'GOAL': 'MINIMIZE', 'STAGE': 'TEST', 'format': ':5f'})
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dllogger.metadata('throughput', {'GOAL': 'MAXIMIZE', 'STAGE': 'TEST', 'format': ':1f'})
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dllogger.metadata('latency_p90', {'GOAL': 'MIMIMIZE', 'STAGE': 'TEST', 'format': ':5f'})
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dllogger.metadata('latency_p95', {'GOAL': 'MIMIMIZE', 'STAGE': 'TEST', 'format': ':5f'})
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dllogger.metadata('latency_p99', {'GOAL': 'MIMIMIZE', 'STAGE': 'TEST', 'format': ':5f'})
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def get_framework_env_vars():
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return {
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'NVIDIA_PYTORCH_VERSION': os.environ.get('NVIDIA_PYTORCH_VERSION'),
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'PYTORCH_VERSION': os.environ.get('PYTORCH_VERSION'),
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'CUBLAS_VERSION': os.environ.get('CUBLAS_VERSION'),
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'NCCL_VERSION': os.environ.get('NCCL_VERSION'),
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'CUDA_DRIVER_VERSION': os.environ.get('CUDA_DRIVER_VERSION'),
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'CUDNN_VERSION': os.environ.get('CUDNN_VERSION'),
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'CUDA_VERSION': os.environ.get('CUDA_VERSION'),
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'NVIDIA_PIPELINE_ID': os.environ.get('NVIDIA_PIPELINE_ID'),
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'NVIDIA_BUILD_ID': os.environ.get('NVIDIA_BUILD_ID'),
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'NVIDIA_TF32_OVERRIDE': os.environ.get('NVIDIA_TF32_OVERRIDE'),
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}
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def get_system_info():
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system_info = subprocess.run('nvidia-smi --query-gpu=gpu_name,memory.total,enforced.power.limit --format=csv'.split(), capture_output=True).stdout
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system_info = [i.decode('utf-8') for i in system_info.split(b'\n')]
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system_info = [x for x in system_info if x]
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return {'system_info': system_info}
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