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.
65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import time
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
|
|
__all__ = ['BenchmarkHook']
|
|
|
|
|
|
class BenchmarkHook(tf.train.SessionRunHook):
|
|
latencies = ['avg', 50, 90, 95, 99, 100]
|
|
|
|
def __init__(self, global_batch_size, warmup_steps=10):
|
|
self.warmup_steps = warmup_steps
|
|
self.global_batch_size = global_batch_size
|
|
self.iter_times = []
|
|
|
|
def before_run(self, run_context):
|
|
self.t0 = time.time()
|
|
|
|
def after_run(self, run_context, run_values):
|
|
batch_time = time.time() - self.t0
|
|
self.iter_times.append(batch_time)
|
|
|
|
def get_average_speed_and_latencies(self):
|
|
if len(self.iter_times) > self.warmup_steps + 5:
|
|
warmup_steps = self.warmup_steps
|
|
elif len(self.iter_times) > 15:
|
|
warmup_steps = 10
|
|
elif len(self.iter_times) > 10:
|
|
warmup_steps = 5
|
|
elif len(self.iter_times) > 4:
|
|
warmup_steps = 2
|
|
elif len(self.iter_times) > 1:
|
|
warmup_steps = 1
|
|
else:
|
|
warmup_steps = 0
|
|
|
|
times = self.iter_times[warmup_steps:]
|
|
avg_time = np.mean(times)
|
|
speed = self.global_batch_size / avg_time
|
|
|
|
latencies = {}
|
|
for lat in self.latencies:
|
|
if lat == 'avg':
|
|
val = avg_time
|
|
else:
|
|
val = np.percentile(times, lat)
|
|
latencies[str(lat)] = val
|
|
|
|
return speed, latencies
|