maxmustermann/DeepLearningExamples
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

Update A100 to 80G

Browse source 
Signed-off-by: Pablo Ribalta Lorenzo <pribalta@nvidia.com>
This commit is contained in:
Pablo Ribalta Lorenzo 2021-01-29 19:00:36 +01:00
parent f113e7f192
commit 43e943f883
1 changed files with 7 additions and 7 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

14
PyTorch/Segmentation/nnUNet/README.md
View file

@ -33,10 +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 40G](#training-performance-nvidia-dgx-a100-40g)
* [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)
* [Inference performance results](#inference-performance-results)
* [Inference performance: NVIDIA DGX A100 40G](#inference-performance-nvidia-dgx-a100-40g)
* [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)
@ -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 40G
##### 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.
@ -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 40G
##### 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 A100 40G 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 |
|:-:|:-:|:--:|:------:|:------:|:-----:|:-----:|:-----:|
@ -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 40G
##### 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 A100 40G 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.
FP16