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Commit 5281cb3a authored by Hashim Sharif's avatar Hashim Sharif
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Updating install time computation script with times with 150 OpenTuner runs

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class TuningParameters:
def __init__(self):
self.iterations_measured = 150
self.total_iterations = 30000
tunerParams = TuningParameters()
class Benchmark:
def __init__(self):
......@@ -7,45 +15,44 @@ class Benchmark:
### All times are real profiled times on the Jetson Board
### Times are for 5000 Images
### Times are for 150 OpenTuner iterations on Jetson
ResNet50 = Benchmark()
ResNet50.binary_time = 3.85 * 100 # 50 images * 100 batches
ResNet18 = Benchmark()
ResNet18.binary_time = 12.9 # 12.9 measured for 1000 images
MobileNet = Benchmark()
MobileNet.binary_time = 21.6 # 50 images * 100 batches
ResNet50.tuner_time = 3.85 * 100 * 150 # 50 images * 100 batches
VGG16_ImageNet = Benchmark()
VGG16_ImageNet.binary_time = 4.55 * 100 # 50 images * 100 batches
VGG16_ImageNet.tuner_time = 4.55 * 100 * 150 # 50 images * 100 batches
AlexNet_ImageNet = Benchmark()
AlexNet_ImageNet.tuner_time = 0.7 * 100 * 150
VGG16_CIFAR10 = Benchmark()
VGG16_CIFAR10.binary_time = 18.9 # 50 images * 100 batches
VGG16_CIFAR10.tuner_time = 1.54 * 60 * 60 # 50 images * 100 batches
VGG16_CIFAR100 = Benchmark()
VGG16_CIFAR100.binary_time = 18.9 # 50 images * 100 batches
VGG16_CIFAR100.tuner_time = 1.57 * 60 * 60 # 50 images * 100 batches
AlexNet_ImageNet = Benchmark()
AlexNet_ImageNet.binary_time = 0.7 * 100
ResNet18 = Benchmark()
ResNet18.tuner_time = 0.52 * 60 * 60 # 12.9 measured for 1000 images
MobileNet = Benchmark()
MobileNet.tuner_time = 0.72 * 60 * 60 # 50 images * 100 batches
AlexNet_CIFAR10 = Benchmark()
AlexNet_CIFAR10.binary_time = 13.5
AlexNet_CIFAR10.tuner_time = 0.67 * 60 * 60 # Time in hours
AlexNet2_CIFAR10 = Benchmark()
AlexNet2_CIFAR10.binary_time = 5.56
AlexNet2_CIFAR10.tuner_time = 0.19 * 60 * 60
LeNet_CIFAR10 = Benchmark()
LeNet_CIFAR10.binary_time = 1.22
LeNet_CIFAR10.tuner_time = 0.11 * 60 * 60
......@@ -54,18 +61,12 @@ LeNet_CIFAR10.binary_time = 1.22
def getInstallTime(Bench):
## We limit pareto configs to 50 after iterations of tuning complete
pareto_configs = 50
promise_runs = 30
iterations = 10000
thresholds = 3
total_runs = iterations * thresholds
total_iteration_time = total_runs * Bench.binary_time
total_validation_time = pareto_configs * promise_runs * Bench.binary_time
total_time = total_iteration_time + total_validation_time
time_hours = total_time / (60 * 60)
tuner_invocations = tunerParams.total_iterations / tunerParams.iterations_measured
extrapolated_time = tuner_invocations * Bench.tuner_time
time_hours = extrapolated_time / (60 * 60)
return time_hours
......
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