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configs.yml
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precision:
AMP:
static_loss_scale: 128
amp: True
FP32:
amp: False
TF32:
amp: False
platform:
DGX1V-16G:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGX1V-32G:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
T4:
workers: 8
DGX1V:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGX2V:
workers: 8
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGXA100:
workers: 10
prefetch: 4
gpu_affinity: socket_unique_contiguous
DGXH100:
workers: 10
prefetch: 4
gpu_affinity: socket_unique_contiguous
mode:
benchmark_training: &benchmark_training
print_freq: 1
epochs: 3
training_only: True
evaluate: False
save_checkpoints: False
benchmark_training_short:
<<: *benchmark_training
epochs: 1
data_backend: synthetic
prof: 100
benchmark_inference: &benchmark_inference
print_freq: 1
epochs: 1
training_only: False
evaluate: True
save_checkpoints: False
convergence:
print_freq: 20
training_only: False
evaluate: False
save_checkpoints: True
evaluate:
print_freq: 20
training_only: False
evaluate: True
epochs: 1
save_checkpoints: False
anchors:
# ResNet_like params: {{{
resnet_params: &resnet_params
label_smoothing: 0.1
mixup: 0.2
lr_schedule: cosine
momentum: 0.875
warmup: 8
epochs: 250
data_backend: pytorch
num_classes: 1000
image_size: 224
interpolation: bilinear
resnet_params_896: &resnet_params_896
<<: *resnet_params
optimizer_batch_size: 896
lr: 0.896
weight_decay: 6.103515625e-05
resnet_params_1k: &resnet_params_1k
<<: *resnet_params
optimizer_batch_size: 1024
lr: 1.024
weight_decay: 6.103515625e-05
resnet_params_2k: &resnet_params_2k
<<: *resnet_params
optimizer_batch_size: 2048
lr: 2.048
weight_decay: 3.0517578125e-05
resnet_params_4k: &resnet_params_4k
<<: *resnet_params
optimizer_batch_size: 4096
lr: 4.096
weight_decay: 3.0517578125e-05
# }}}
# EfficienNet Params: {{{
efficientnet_params: &efficientnet_params
optimizer: rmsprop
rmsprop_alpha: 0.9
rmsprop_eps: 0.01
print_freq: 100
label_smoothing: 0.1
mixup: 0.2
lr_schedule: cosine
momentum: 0.9
warmup: 16
epochs: 400
data_backend: pytorch
augmentation: autoaugment
num_classes: 1000
interpolation: bicubic
efficientnet_b0_params_4k: &efficientnet_b0_params_4k
<<: *efficientnet_params
optimizer_batch_size: 4096
lr: 0.08
weight_decay: 1e-05
image_size: 224
efficientnet_b4_params_4k: &efficientnet_b4_params_4k
<<: *efficientnet_params
optimizer_batch_size: 4096
lr: 0.16
weight_decay: 5e-06
image_size: 380
# }}}
models:
resnet50: # {{{
DGX1V: &RN50_DGX1V
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_896
batch_size: 112
DGX1V-16G:
<<: *RN50_DGX1V
DGX1V-32G:
<<: *RN50_DGX1V
DGX2V:
AMP:
<<: *resnet_params_4k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_4k
arch: resnet50
batch_size: 256
DGXA100:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
TF32:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
T4:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_2k
batch_size: 128
DGXH100:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
TF32:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
T4:
AMP:
<<: *resnet_params_2k
arch: resnet50
batch_size: 256
memory_format: nhwc
FP32:
<<: *resnet_params_2k
batch_size: 128
# }}}
resnext101-32x4d: # {{{
DGX1V: &RNXT_DGX1V
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 64
DGX1V-16G:
<<: *RNXT_DGX1V
DGX1V-32G:
<<: *RNXT_DGX1V
DGXA100:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
T4:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 64
DGXH100:
AMP:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: resnext101-32x4d
batch_size: 128
# }}}
se-resnext101-32x4d: # {{{
DGX1V: &SERNXT_DGX1V
AMP:
<<: *resnet_params_896
arch: se-resnext101-32x4d
batch_size: 112
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 64
DGX1V-16G:
<<: *SERNXT_DGX1V
DGX1V-32G:
<<: *SERNXT_DGX1V
DGXA100:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
DGXH100:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
TF32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
T4:
AMP:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 128
memory_format: nhwc
FP32:
<<: *resnet_params_1k
arch: se-resnext101-32x4d
batch_size: 64
# }}}
efficientnet-widese-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
DGXH100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-widese-b0
batch_size: 256
# }}}
efficientnet-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
DGXH100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-b0
batch_size: 256
# }}}
efficientnet-quant-b0: # {{{
T4:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 64
DGX1V-16G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 64
DGX1V-32G:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
FP32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 128
DGXA100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
DGXH100:
AMP:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
memory_format: nhwc
TF32:
<<: *efficientnet_b0_params_4k
arch: efficientnet-quant-b0
batch_size: 256
# }}}
efficientnet-widese-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
DGXH100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-widese-b4
batch_size: 64
# }}}
efficientnet-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
DGXH100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-b4
batch_size: 64
# }}}
efficientnet-quant-b4: # {{{
T4:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 16
DGX1V-16G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 16
DGX1V-32G:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
memory_format: nhwc
FP32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 32
DGXA100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
DGXH100:
AMP:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 128
memory_format: nhwc
TF32:
<<: *efficientnet_b4_params_4k
arch: efficientnet-quant-b4
batch_size: 64
# }}}