69 lines
3 KiB
Python
69 lines
3 KiB
Python
# Copyright (c) 2019, 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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# Report JSON file structure:
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# - "model" : architecture of the model (e.g. "resnet50").
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# - "ngpus" : number of gpus on which training was performed.
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# - "total_duration" : total duration of training in seconds.
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# - "cmd" : list of application arguments.
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# - "metrics" : per epoch metrics for train and validation
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# (some of below metrics may not exist in the report,
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# depending on application arguments)
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# - "train.top1" : training top1 accuracy in epoch.
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# - "train.top5" : training top5 accuracy in epoch.
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# - "train.loss" : training loss in epoch.
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# - "train.total_ips" : training speed (data and compute time taken into account) for epoch in images/sec.
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# - "train.latency_avg" : average latency of one iteration in seconds.
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# - "train.latency_50" : median latency of one iteration in seconds.
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# - "train.latency_90" : 90th percentile latency of one iteration in seconds.
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# - "train.latency_95" : 95th percentile latency of one iteration in seconds.
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# - "train.latency_99" : 99th percentile latency of one iteration in seconds.
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# - "train.latency_100" : highest observed latency of one iteration in seconds.
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# - "val.top1", "val.top5", "val.time", "val.total_ips", "val.latency_avg", "val.latency_50",
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# "val.latency_90", "val.latency_95", "val.latency_99", "val.latency_100" : the same but for validation.
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import json
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from collections import OrderedDict
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class Report:
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def __init__(self, model_name, ngpus, cmd):
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self.model_name = model_name
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self.ngpus = ngpus
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self.cmd = cmd
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self.total_duration = 0
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self.metrics = OrderedDict()
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def add_value(self, metric, value):
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if metric not in self.metrics:
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self.metrics[metric] = []
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self.metrics[metric].append(value)
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def set_total_duration(self, duration):
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self.total_duration = duration
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def save(self, filename):
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with open(filename, 'w') as f:
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f.write(self.get_report())
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def get_report(self):
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report = OrderedDict([
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('model', self.model_name),
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('ngpus', self.ngpus),
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('total_duration', self.total_duration),
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('cmd', self.cmd),
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('metrics', self.metrics),
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])
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return json.dumps(report, indent=4)
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