a644350589
Tacotron2+Waveglow/PyT * AMP support * Data preprocessing for Tacotron 2 training * Fixed dropouts on LSTMCells SSD/PyT * script and notebook for inference * AMP support * README update * updates to examples/* BERT/PyT * initial release GNMT/PyT * Default container updated to NGC PyTorch 19.05-py3 * Mixed precision training implemented using APEX AMP * Added inference throughput and latency results on NVIDIA Tesla V100 16G * Added option to run inference on user-provided raw input text from command line NCF/PyT * Updated performance tables. * Default container changed to PyTorch 19.06-py3. * Caching validation negatives between runs Transformer/PyT * new README * jit support added UNet Medical/TF * inference example scripts added * inference benchmark measuring latency added * TRT/TF-TRT support added * README updated GNMT/TF * Performance improvements Small updates (mostly README) for other models.
66 lines
1.8 KiB
Python
66 lines
1.8 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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import time
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import numpy as np
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import tensorflow as tf
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__all__ = ['BenchmarkHook']
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class BenchmarkHook(tf.train.SessionRunHook):
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latencies = ['avg', 50, 90, 95, 99, 100]
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def __init__(self, global_batch_size, warmup_steps=10):
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self.warmup_steps = warmup_steps
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self.global_batch_size = global_batch_size
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self.iter_times = []
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def before_run(self, run_context):
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self.t0 = time.time()
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def after_run(self, run_context, run_values):
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batch_time = time.time() - self.t0
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self.iter_times.append(batch_time)
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def get_average_speed_and_latencies(self):
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if len(self.iter_times) > self.warmup_steps + 5:
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warmup_steps = self.warmup_steps
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elif len(self.iter_times) > 15:
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warmup_steps = 10
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elif len(self.iter_times) > 10:
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warmup_steps = 5
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elif len(self.iter_times) > 4:
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warmup_steps = 2
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elif len(self.iter_times) > 1:
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warmup_steps = 1
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else:
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warmup_steps = 0
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times = self.iter_times[warmup_steps:]
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avg_time = np.mean(times)
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speed = self.global_batch_size / avg_time
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latencies = {}
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for lat in self.latencies:
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if lat == 'avg':
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val = avg_time
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else:
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val = np.percentile(times, lat)
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latencies[str(lat)] = val
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return speed, latencies
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