Custom - BASNET - CoreWeave

Search…
Custom - BASNET
Introduction
This example demonstrates deploying an auto-scaling Inference service from a pre-existing docker image. This can be useful when deploying off-the-shelf models that aren't available as ie. Tensorflow SavedModels. One example of this is the IBM COCO Based Object Detector. An example InferenceService for that also exists in this repository. The rest of this example will focus on a public wrapped version of the BASNet object detection model. This example and the test client is based on work by Cyril Diagne.
To follow along, clone the manifests from GitHub.
Input
​
Output
​
Getting Started
After installing kubectl and adding your CoreWeave Cloud access credentials, the following steps will deploy the Inference Service. Clone all the files in this repository to follow along.
1.Apply the resources. This can be used to both create and update existing manifests
 $ kubectl apply -f basnet-inferenceservice.yaml
 inferenceservice.serving.kubeflow.org/basnet configured
2.List pods to see that the Transformer and Predictor have launched successfully
$ kubectl get pods
NAME                                                           READY   STATUS    RESTARTS   AGE
basnet-predictor-default-sj9kr-deployment-76b67d669-4gjrp      2/2     Running   0          34s
If the predictor fails to init, look in the logs for clues kubectl logs basnet-predictor-default-sj9kr-deployment-76b67d669-4gjrp kfserving-container.
3.Once all the Pods are running, we can get the API endpoint for our model. Since this model doesn't adhere to the Tensorflow V1 HTTP API, we can't use the API endpoint provided by kubectl get inferenceservices. We have to hit up the predictor directly.
$ kubectl get ksvc
NAME                         URL                                                                       LATESTCREATED                      LATESTREADY                        READY   REASON
basnet-predictor-default     https://basnet-predictor-default.tenant-test.knative.chi.coreweave.com    basnet-predictor-default-sj9kr     basnet-predictor-default-sj9kr     True
The URL in the output is the public API URL for your newly deployed model.
4.Enter the client directory. You can either run the test client locally or in docker. The output will be in images/output.png.
 $ cd client/
 $ export SERVICE_URL=https://basnet-predictor-default.tenant-test.knative.chi.coreweave.com
 $ docker build -t test .; docker run --rm -it -v $(pwd)/images:/app/images test --basnet_service_host $SERVICE_URL
 INFO:root: > sending to BASNet...
 INFO:root:200
 INFO:root: > saving results...
 INFO:root: > opening mask...
 INFO:root: > compositing final image...
 INFO:root: > saving final image...
 $ open images/output.png
5.Remove the inference service
$ kubectl delete inferenceservices basnet
inferenceservice.serving.kubeflow.org "basnet" deleted
PyTorch - FastAI Sentiment
Next - Inference
Finetuning Machine Learning Models
Last modified 5mo ago
Copy link
Outline
Introduction
Getting Started