add new model resnet50v1.5

efficientnet/inference/AMP/DGXA100_efficientnet-b4_AMP.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/inference/AMP/DGXA100_efficientnet-widese-b0_AMP.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/inference/AMP/DGXA100_efficientnet-widese-b4_AMP.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/inference/FP32/DGXA100_efficientnet-b0_FP32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json

efficientnet/inference/FP32/DGXA100_efficientnet-b4_FP32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json

efficientnet/inference/FP32/DGXA100_efficientnet-widese-b0_FP32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json

efficientnet/inference/FP32/DGXA100_efficientnet-widese-b4_FP32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision FP32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json

efficientnet/inference/TF32/DGXA100_efficientnet-b0_TF32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/inference/TF32/DGXA100_efficientnet-b4_TF32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/inference/TF32/DGXA100_efficientnet-widese-b0_TF32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/inference/TF32/DGXA100_efficientnet-widese-b4_TF32.sh

+
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 1 --workspace ${1:-./} --raport-file raport_1.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 2 --workspace ${1:-./} --raport-file raport_2.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 4 --workspace ${1:-./} --raport-file raport_4.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 8 --workspace ${1:-./} --raport-file raport_8.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 16 --workspace ${1:-./} --raport-file raport_16.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 32 --workspace ${1:-./} --raport-file raport_32.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 64 --workspace ${1:-./} --raport-file raport_64.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 128 --workspace ${1:-./} --raport-file raport_128.json
+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision TF32 --mode benchmark_inference --platform DGXA100 /imagenet -b 256 --workspace ${1:-./} --raport-file raport_256.json

efficientnet/quantization/DGX1V-32G_efficientnet-quant-b0_FP32.sh

+python ./multiproc.py  \
+    --nproc_per_node 8 \
+        ./quant_main.py  /imagenet \
+            --arch efficientnet-quant-b0 \
+            --epochs 10 \
+            -j5 -p 500  \
+            --data-backend pytorch  \
+            --optimizer sgd \
+            -b 128 \
+            --lr 0.0125 \
+            --momentum 0.89 \
+            --weight-decay 4.50e-05 \
+            --lr-schedule cosine \
+            --pretrained-from-file "${1}"
\ No newline at end of file

efficientnet/quantization/DGX1V-32G_efficientnet-quant-b4_FP32.sh

+python ./multiproc.py  \
+    --nproc_per_node 8 \
+        ./quant_main.py  /imagenet \
+            --arch efficientnet-quant-b4 \
+            --epochs 2 \
+            -j5 -p 500  \
+            --data-backend pytorch  \
+            --optimizer rmsprop \
+            -b 32 \
+            --lr 4.09e-06 \
+            --momentum 0.9 \
+            --weight-decay 9.714e-04 \
+            --lr-schedule linear \
+            --rmsprop-alpha 0.853 \
+            --rmsprop-eps 0.00422 \
+            --pretrained-from-file "${1}"

efficientnet/training/AMP/DGX1V-16G_efficientnet-b0_AMP.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode convergence --platform DGX1V-16G /imagenet --workspace ${1:-./} --raport-file raport.json

efficientnet/training/AMP/DGX1V-16G_efficientnet-widese-b0_AMP.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode convergence --platform DGX1V-16G /imagenet --workspace ${1:-./} --raport-file raport.json

efficientnet/training/AMP/DGXA100_efficientnet-b0_AMP.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision AMP --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json

efficientnet/training/AMP/DGXA100_efficientnet-b4_AMP.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b4 --precision AMP --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json

efficientnet/training/AMP/DGXA100_efficientnet-widese-b0_AMP.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b0 --precision AMP --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json

efficientnet/training/AMP/DGXA100_efficientnet-widese-b4_AMP.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-widese-b4 --precision AMP --mode convergence --platform DGXA100 /imagenet --workspace ${1:-./} --raport-file raport.json

efficientnet/training/FP32/DGX1V-16G_efficientnet-b0_FP32.sh

+python ./multiproc.py --nproc_per_node 8 ./launch.py --model efficientnet-b0 --precision FP32 --mode convergence --platform DGX1V-16G /imagenet --workspace ${1:-./} --raport-file raport.json