testing DCO app (#1146)

Signed-off-by: Wenqi Li <[email protected]>

part of #1143

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Avoid including large-size files in the PR.
- [x] Clean up long text outputs from code cells in the notebook.
- [x] Remove private info such as user name and private key from the
notebook and script files.
- [x] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [x] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

Signed-off-by: Wenqi Li <[email protected]>

Author: wyli

.github/dco.yml

@@ -0,0 +1,2 @@
+allowRemediationCommits:
+  individual: true

.github/pull_request_template.md

@@ -7,6 +7,6 @@ A few sentences describing the changes proposed in this pull request.
 <!--- Put an `x` in all the boxes that apply, and remove the not applicable items -->
 - [ ] Avoid including large-size files in the PR.
 - [ ] Clean up long text outputs from code cells in the notebook.
-- [ ] Remove private info such as user name and private key from the notebook and script files.
+- [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key.
 - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder
 - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

README.md

@@ -135,7 +135,7 @@ inference on a pretrained DeepEdit model.
 
 #### <ins>**Deployment**</ins>
 ##### [BentoML](./deployment/bentoml)
-This is a simple example of training and deploying a MONAI network with [BentoML](https://www.bentoml.ai/) as a web server, either locally using the BentoML respository or as a containerized service.
+This is a simple example of training and deploying a MONAI network with [BentoML](https://www.bentoml.ai/) as a web server, either locally using the BentoML repository or as a containerized service.
 ##### [Ray](./deployment/ray)
 This uses the previous notebook's trained network to demonstrate deployment a web server using [Ray](https://docs.ray.io/en/master/serve/index.html#rayserve).
 ##### [Triton](./deployment/Triton/)
@@ -290,4 +290,4 @@ This tutorial demonstrates a transfer learning pipeline from a pretrained model
 This tutorial shows several visualization approaches for 3D image during transform augmentation.
 
 #### [Auto3DSeg](./auto3dseg/)
-This folder shows how to run the comprehensive Auto3Dseg pipeline with minimal inputs and customize the Auto3Dseg modules to meet different user requirements.
+This folder shows how to run the comprehensive Auto3DSeg pipeline with minimal inputs and customize the Auto3Dseg modules to meet different user requirements.