update the TensorRT part in the README file

README.md

@@ -47,20 +47,28 @@ IoU was used for evaluating the performance of the model. This model achieves a
 ![A graph showing the validation mean IoU over 100 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_tool_segmentation_val_iou.png)
 
 #### TensorRT speedup
-The `endoscopic_tool_segmentation` bundle supports the TensorRT acceleration. The table below shows the speedup ratios benchmarked on an A100 80G GPU, in which the `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing and the `end2end` means run the bundle end to end with the TensorRT based model. The `torch_fp32` and `torch_amp` is for the pytorch model with or without `amp` mode. The `trt_fp32` and `trt_fp16` is for the TensorRT based model converted in corresponding precision. The `speedup amp`, `speedup fp32` and `speedup fp16` is the speedup ratio of corresponding models versus the pytorch float32 model, while the `amp vs fp16` is between the pytorch amp model and the TensorRT float16 based model.
+The `endoscopic_tool_segmentation` bundle supports the TensorRT acceleration. The table below shows the speedup ratios benchmarked on an A100 80G GPU.
 
 | method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
 | model computation | 12.00 | 14.06 | 6.59 | 5.20 | 0.85 | 1.82 | 2.31 | 2.70 |
 | end2end |170.04 | 172.20 | 155.26 | 155.57 | 0.99 | 1.10 | 1.09 | 1.11 |
 
+Where:
+- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
+- `end2end` means run the bundle end-to-end with the TensorRT based model.
+- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.
+- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.
+- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model
+- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.
+
 This result is benchmarked under:
  - TensorRT: 8.5.3+cuda11.8
  - Torch-TensorRT Version: 1.4.0
  - CPU Architecture: x86-64
  - OS: ubuntu 20.04
  - Python version:3.8.10
- - CUDA version: 11.8
+ - CUDA version: 12.0
  - GPU models and configuration: A100 80G
 
 ## MONAI Bundle Commands

configs/metadata.json

@@ -1,7 +1,8 @@
 {
     "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
-    "version": "0.4.7",
+    "version": "0.4.8",
     "changelog": {
+        "0.4.8": "update the TensorRT part in the README file",
         "0.4.7": "fix mgpu finalize issue",
         "0.4.6": "enable deterministic training",
         "0.4.5": "add the command of executing inference with TensorRT models",

docs/README.md

@@ -40,20 +40,28 @@ IoU was used for evaluating the performance of the model. This model achieves a
 ![A graph showing the validation mean IoU over 100 epochs.](https://developer.download.nvidia.com/assets/Clara/Images/monai_endoscopic_tool_segmentation_val_iou.png)
 
 #### TensorRT speedup
-The `endoscopic_tool_segmentation` bundle supports the TensorRT acceleration. The table below shows the speedup ratios benchmarked on an A100 80G GPU, in which the `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing and the `end2end` means run the bundle end to end with the TensorRT based model. The `torch_fp32` and `torch_amp` is for the pytorch model with or without `amp` mode. The `trt_fp32` and `trt_fp16` is for the TensorRT based model converted in corresponding precision. The `speedup amp`, `speedup fp32` and `speedup fp16` is the speedup ratio of corresponding models versus the pytorch float32 model, while the `amp vs fp16` is between the pytorch amp model and the TensorRT float16 based model.
+The `endoscopic_tool_segmentation` bundle supports the TensorRT acceleration. The table below shows the speedup ratios benchmarked on an A100 80G GPU.
 
 | method | torch_fp32(ms) | torch_amp(ms) | trt_fp32(ms) | trt_fp16(ms) | speedup amp | speedup fp32 | speedup fp16 | amp vs fp16|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
 | model computation | 12.00 | 14.06 | 6.59 | 5.20 | 0.85 | 1.82 | 2.31 | 2.70 |
 | end2end |170.04 | 172.20 | 155.26 | 155.57 | 0.99 | 1.10 | 1.09 | 1.11 |
 
+Where:
+- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
+- `end2end` means run the bundle end-to-end with the TensorRT based model.
+- `torch_fp32` and `torch_amp` are for the PyTorch models with or without `amp` mode.
+- `trt_fp32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.
+- `speedup amp`, `speedup fp32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model
+- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.
+
 This result is benchmarked under:
  - TensorRT: 8.5.3+cuda11.8
  - Torch-TensorRT Version: 1.4.0
  - CPU Architecture: x86-64
  - OS: ubuntu 20.04
  - Python version:3.8.10
- - CUDA version: 11.8
+ - CUDA version: 12.0
  - GPU models and configuration: A100 80G
 
 ## MONAI Bundle Commands