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[UNet medical/TF2] minor fixes in paths
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‎TensorFlow2/Segmentation/UNet_Medical/README.md‎

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**Learning curves**
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The following image show the training loss as a function of iteration for training using DGX A100 (TF32 and TF-AMP) and DGX-1 V100 (FP32 and TF-AMP).
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![LearningCurves](images/UNetMed_TF2_conv.png)
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![LearningCurves](images/U-NetMed_TF2_conv.png)
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#### Training performance results

‎TensorFlow2/Segmentation/UNet_Medical/examples/unet_TRAIN_1GPU.sh‎

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@@ -21,4 +21,4 @@ horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --m
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horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 2 --augment --xla > $2/log_FP32_1GPU_fold2.txt
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horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 3 --augment --xla > $2/log_FP32_1GPU_fold3.txt
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horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 4 --augment --xla > $2/log_FP32_1GPU_fold4.txt
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python utils/parse_results.py --model_dir $2 --exec_mode convergence --env FP32_1GPU
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python runtime/parse_results.py --model_dir $2 --exec_mode convergence --env FP32_1GPU

‎TensorFlow2/Segmentation/UNet_Medical/examples/unet_TRAIN_8GPU.sh‎

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horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 2 --augment --xla > $2/log_FP32_8GPU_fold2.txt
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horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 3 --augment --xla > $2/log_FP32_8GPU_fold3.txt
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horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 4 --augment --xla > $2/log_FP32_8GPU_fold4.txt
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python utils/parse_results.py --model_dir $2 --exec_mode convergence --env FP32_8GPU
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python runtime/parse_results.py --model_dir $2 --exec_mode convergence --env FP32_8GPU

‎TensorFlow2/Segmentation/UNet_Medical/examples/unet_TRAIN_TF-AMP_1GPU.sh‎

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horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 2 --augment --xla --amp > $2/log_TF-AMP_1GPU_fold2.txt
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horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 3 --augment --xla --amp > $2/log_TF-AMP_1GPU_fold3.txt
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horovodrun -np 1 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 4 --augment --xla --amp > $2/log_TF-AMP_1GPU_fold4.txt
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python utils/parse_results.py --model_dir $2 --exec_mode convergence --env TF-AMP_1GPU
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python runtime/parse_results.py --model_dir $2 --exec_mode convergence --env TF-AMP_1GPU

‎TensorFlow2/Segmentation/UNet_Medical/examples/unet_TRAIN_TF-AMP_8GPU.sh‎

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horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 2 --augment --xla --amp > $2/log_TF-AMP_8GPU_fold2.txt
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horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 3 --augment --xla --amp > $2/log_TF-AMP_8GPU_fold3.txt
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horovodrun -np 8 python main.py --data_dir $1 --model_dir $2 --log_every 100 --max_steps 6400 --batch_size $3 --exec_mode train_and_evaluate --fold 4 --augment --xla --amp > $2/log_TF-AMP_8GPU_fold4.txt
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python utils/parse_results.py --model_dir $2 --exec_mode convergence --env TF-AMP_8GPU
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python runtime/parse_results.py --model_dir $2 --exec_mode convergence --env TF-AMP_8GPU

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