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Signed-off-by: Pablo Ribalta Lorenzo <pribalta@nvidia.com>
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Pablo Ribalta Lorenzo 2021-01-29 18:57:15 +01:00
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PyTorch/Segmentation/nnUNet/README.md
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@ -562,7 +562,7 @@ The following sections provide details on how to achieve the same performance an
#### Training accuracy results
##### Training accuracy: NVIDIA DGX A100 (8x A100 40GB)
##### Training accuracy: NVIDIA DGX A100 40G
Our results were obtained by running the `python scripts/train.py --gpus {1,8} --fold {0,1,2,3,4} --dim {2,3} --batch_size <bsize> [--amp]` training scripts and averaging results in the PyTorch 20.12 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
@ -586,9 +586,9 @@ Our results were obtained by running the `python scripts/train.py --gpus {1,8} -
#### Training performance results
##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
##### Training performance: NVIDIA DGX A100 40G
Our results were obtained by running the `python scripts/benchmark.py --mode train --gpus {1,8} --dim {2,3} --batch_size <bsize> [--amp]` training script in the NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in volumes per second) were averaged over an entire training epoch.
Our results were obtained by running the `python scripts/benchmark.py --mode train --gpus {1,8} --dim {2,3} --batch_size <bsize> [--amp]` training script in the NGC container on NVIDIA DGX A100 40G GPUs. Performance numbers (in volumes per second) were averaged over an entire training epoch.
| Dimension | GPUs | Batch size / GPU | Throughput - mixed precision [img/s] | Throughput - TF32 [img/s] | Throughput speedup (TF32 - mixed precision) | Weak scaling - mixed precision | Weak scaling - TF32 |
|:-:|:-:|:--:|:------:|:------:|:-----:|:-----:|:-----:|
@ -632,9 +632,9 @@ To achieve these same results, follow the steps in the [Quick Start Guide](#quic
#### Inference performance results
##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)
##### Inference performance: NVIDIA DGX A100 40G
Our results were obtained by running the `python scripts/benchmark.py --mode predict --dim {2,3} --batch_size <bsize> [--amp]` inferencing benchmarking script in the PyTorch 20.10 NGC container on NVIDIA DGX A100 (1x A100 80GB) GPU.
Our results were obtained by running the `python scripts/benchmark.py --mode predict --dim {2,3} --batch_size <bsize> [--amp]` inferencing benchmarking script in the PyTorch 20.10 NGC container on NVIDIA DGX A100 40G GPU.
FP16