16. Release Notes¶
The Clara Deploy SDK 0.2.0 provides the following capability:
A new pipeline orchestration engine.
A pipeline definition specification.
New platform gRPC based API to create pipelines, trigger jobs and upload/download payloads.
The DICOM Adapter has been updated to this new platform.
The Render Server has been updated to this new platform.
The Clara Deploy SDK is not backward compatible and therefore the pipelines created with the previous versions have to be migrated to the new pipeline definition.
16.2. 0.1.7 Deprecated¶
The Render Server is now enabled in the dashboard.
16.3. 0.1.6 Deprecated¶
Installation of pre-requisites no longer deletes the current Docker configuration.
Deployment of Clara Deploy SDK via Helm Charts and Kubernetes.
Pipeline Client API provides integration for containers that need to be part of a pipeline. The Pipeline Client API supports:
Send to TRTIS
DICOM Adapter provides an integration point for a service such as a PACS server. It reads and writes DICOM data and pushes it to Clara Core to get a pipeline started.
Clara Core provides handling of the pipeline. Clara Core supports running a single pipeline at a time. New pipelines require a new deployment. Clara Core supports:
TRTIS as a service
Reference application is available to describe how to run in Clara Deploy SDK.
16.4. Pre-alpha Deprecated¶
The Clara Deploy SDK no longer support running pipelines with docker-compose.
The Clara Deploy SDK no longer support the Clara Inference Client API.
16.5. Known Issues¶
The following are known issues in this release of Clara Deploy SDK
Currently only abdominal segmentation and liver segmentation are supported.
In cases where the reconstructed volume covers more regions than just the abdomen. Settings can be used to specify the location of abdomen. See the readme file for settings descriptions.
Cases that contain regions smaller than abdomen are not tested.
Cases that are not an abdominal scan may contain incorrect or incomplete results. The current model is trained for abdominal scans.
AI model in the container is not trained across a range of patient scans and is subject to incorrect organ labels.
Input volume to AI container is first downsampled to a fixed size. Labels are generated on downsampled volume. Nearest neighbor approach is used to upsample the segmented mask (to match original reconstructed volume dimensions). This upsampling process results in extreme staircase artifacts in the segmented masks.
AI segmentation container outputs masks in MetaHeader format only.
220.127.116.11. Installation error with message
[ERROR Port-XXXXX]: Port XXXXX is in use¶
It is possible that your machine have
microk8s installed. Please remove those k8s distributions and install the prerequisites (
sudo ./install-prereqs.sh) again.
In case of
microk8s being installed by
snap, you can find if it is installed or not by executing
$ snap list Name Version Rev Tracking Publisher Notes ... microk8s v1.15.0 671 stable canonical✓ classic
And, can remove it by executing the following commands
microk8s.reset sudo snap remove microk8s
18.104.22.168. Installation error with message
/var/lib/etcd is not empty¶
If during the installation of the prerequisites, a failure occurs with the message
/var/lib/etcd is not empty, try removing this folder, uninstall the prerequisites(
sudo ./uninstall-prereqs.sh) and re-run the prerequisites installation(
22.214.171.124. Installation error with coreDNS pod failures¶
If after the installation, an error occurs and the coredns pods of Kubernetes are in
CrashLoopBackOff state, the workaround is documented here https://stackoverflow.com/questions/52645473/coredns-fails-to-run-in-kubernetes-cluster.
126.96.36.199. Installation error with space in the path¶
If the installer is downloaded to a directory that contains a space, the installation will fail. Move the installer to a directory that does not contain space in the path.
16.5.3. Installation error due insufficient Disk Space to Deploy Clara Container Images¶
Kubelet(‘node agent’ that runs on each node of k8s cluster) will perform garbage collection for containers every minute and garbage collection for images every five minutes.
Once disk usage exceeds the threshold (default: 85%), Kubelet will free (remove) container images until usage is below the threshold (default: 80%).
The user needs to make sure that the percent of disk usage in the VM is lower than 85% so that the necessary images for Clara Deploy SDK won’t be deleted locally.
16.5.4. Render Service¶
188.8.131.52. White empty viewport¶
After selecting another datasets or after going to
<IP of the machine>:8080, the viewport could be empty. Refreshing the browser usually solves the rendering problem.
184.108.40.206. Session management¶
The Render service only supports one single session. The last connected user will grab the session.
220.127.116.11. Changing datasets results in “Unable to reach RenderServer”¶
After changing the dataset, the user may probably see the error message
Unable to reach RenderServer! Please restart your container.
If the issue persists after refreshing the browser, the workaround for this issue is to restart the Render Service by running the following command:
clara render stop clara render start
We are interested in your feedback and any bugs found while using Clara Deploy SDK.
Post questions, feedback and bugs in the member-only forums: https://devtalk.nvidia.com/default/board/362/clara-sdk/ Note: New forum accounts may take one business day to reflect new memberships.
For any problems related to this developer program please use the general contact form: https://developer.nvidia.com/contact.
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