From ccb26e0fffe75ea1c6da01a77be3e9b0c82a34c3 Mon Sep 17 00:00:00 2001
From: Xuan Gu <xuagu37@gmail.com>
Date: Mon, 31 Oct 2022 14:34:18 +0100
Subject: [PATCH] Update README.md
---
README.md | 32 +++-----------------------------
1 file changed, 3 insertions(+), 29 deletions(-)
diff --git a/README.md b/README.md
index 6f5a788..4b645c9 100644
--- a/README.md
+++ b/README.md
@@ -51,39 +51,13 @@ sbash benchmark_nnunet_pytorch_berzelius.sh
sbash benchmark_nnunet_pytorch_berzelius_multi_node.sh
```
-<!--
-#### For single node
-- Start an interactive session
-```
-interactive -N2 --reservation=nsc-testing -t 600
-```
-
-- Pull the image for Singularity and run
-```
-cd /proj/nsc/xuan/ngc/DeepLearningExamples/PyTorch/Segmentation/nnUNet
-singularity pull nvidia_nnu-net_for_pytorch.sif docker://xuagu37/nvidia_nnu-net_for_pytorch:21.11.0
-singularity shell -B ${PWD}/data:/data -B ${PWD}/results:/results --nv nvidia_nnu-net_for_pytorch.sif
-```
-- Run the benchmark script
-```
-bash benchmark_nnunet_pytorch_berzelius.sh
-```
-
-#### For multi-node
-- Run the benchmark script
-```
-cd /proj/nsc/xuan/ngc/DeepLearningExamples/PyTorch/Segmentation/nnUNet
-sbash benchmark_nnunet_pytorch_berzelius_multi_node.sh
-```
--->
-
### Results
We collect benchmark results of throughput (images/sec) for
- Precisions = TF32, AMP
- Dimention = 2
-- Nodes = 1, 2
-- GPUs = 1 - 8 (for 1 node), 16 (for 2 nodes)
-- Batch size = 1, 2, 4, 8, 16, 32, 64, 128
+- Nodes = 1, 2, 3, 4, 5, 6, 7, 8
+- GPUs = 1 - 8 (for 1 node), all gpus (for multi-node)
+- Batch size = 1, 2, 4, 8, 16, 32, 64, 128, 256
TF32 (TensorFloat32) mode is for accelerating FP32 convolutions and matrix multiplications. TF32 mode is the default option for AI training with 32-bit variables on Ampere GPU architecture.
--
GitLab