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Merge pull request #819 from NVIDIA/pribalta_fixes_nnunet_nomenclature

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Fixes in nomenclature for DGX A100 in nnUNet/PyT
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nv-kkudrynski 2021-01-29 19:34:03 +01:00 committed by GitHub
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15
PyTorch/Segmentation/nnUNet/README.md
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@ -33,11 +33,10 @@ This repository provides a script and recipe to train the nnU-Net model to achie
* [Training accuracy results](#training-accuracy-results)
* [Training accuracy: NVIDIA DGX-1 (8x V100 16GB)](#training-accuracy-nvidia-dgx-1-8x-v100-16gb)
* [Training performance results](#training-performance-results)
* [Training performance: NVIDIA DGX A100 (8x A100 40GB)](#training-performance-nvidia-dgx-a100-8x-a100-40gb)
* [Training performance: NVIDIA DGX A100 80G](#training-performance-nvidia-dgx-a100-80G)
* [Training performance: NVIDIA DGX-1 (8x V100 16GB)](#training-performance-nvidia-dgx-1-8x-v100-16gb)
* [Training performance: NVIDIA DGX-2 (16x V100 32GB)](#training-performance-nvidia-dgx-2-16x-v100-32gb)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX A100 (1x A100 40GB)](#inference-performance-nvidia-dgx-a100-1x-a100-40gb)
* [Inference performance: NVIDIA DGX A100 80G](#inference-performance-nvidia-dgx-a100-80G)
* [Inference performance: NVIDIA DGX-1 (1x V100 16GB)](#inference-performance-nvidia-dgx-1-1x-v100-16gb)
- [Release notes](#release-notes)
* [Changelog](#changelog)
@ -563,7 +562,7 @@ The following sections provide details on how to achieve the same performance an
#### Training accuracy results
##### Training accuracy: NVIDIA DGX-1 (8x A100 16GB)
##### Training accuracy: NVIDIA DGX A100 80G
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.
@ -587,9 +586,9 @@ Our results were obtained by running the `python scripts/train.py --gpus {1,8} -
#### Training performance results
##### Training performance: NVIDIA DGX-2 A100 (8x A100 80GB)
##### Training performance: NVIDIA DGX A100 80G
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-2 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 80G 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 |
|:-:|:-:|:--:|:------:|:------:|:-----:|:-----:|:-----:|
@ -633,9 +632,9 @@ To achieve these same results, follow the steps in the [Quick Start Guide](#quic
#### Inference performance results
##### Inference performance: NVIDIA DGX-2 A100 (1x A100 80GB)
##### Inference performance: NVIDIA DGX A100 80G
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-2 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 80G GPU.
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