ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-01-30 22:18:02 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Fix spacing (#863)

Browse Source
This commit is contained in:
pollfly 2024-06-30 16:49:19 +03:00 committed by GitHub
parent a7371aebee
commit d8963d2ea5
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 21 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
docs/clearml_serving/clearml_serving_tutorial.md
View File

@ -94,11 +94,11 @@ or with the `clearml-serving` CLI.
clearml-serving --id <service_id> model upload --name "manual sklearn model" --project "serving examples" --framework "scikitlearn" --path ./sklearn-model.pkl
```
You now have a new Model named `manual sklearn model` in the `serving examples` project. The CLI output prints
the UID of the new model, which you will use to register a new endpoint.
You now have a new Model named `manual sklearn model` in the `serving examples` project. The CLI output prints
the UID of the new model, which you will use to register a new endpoint.
In the [ClearML web UI](../webapp/webapp_overview.md), the new model is listed under the **Models** tab of its project.
You can also download the model file itself directly from the web UI.
In the [ClearML web UI](../webapp/webapp_overview.md), the new model is listed under the **Models** tab of its project.
You can also download the model file itself directly from the web UI.
1. Register a new endpoint with the new model:
@ -177,22 +177,26 @@ endpoint `/test_model_sklearn/3/`.
Example:
1. Add two endpoints:
```bash
clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --version 1 --project "serving examples"
```
```bash
clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --version 2 --project "serving examples"
```
```bash
clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --version 1 --project "serving examples"
```
```bash
clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --version 2 --project "serving examples"
```
1. Add Canary endpoint:
```bash
clearml-serving --id <service_id> model canary --endpoint "test_model_sklearn_canary" --weights 0.1 0.9 --input-endpoints test_model_sklearn/2 test_model_sklearn/1
```
1. Add Canary endpoint:
```bash
clearml-serving --id <service_id> model canary --endpoint "test_model_sklearn_canary" --weights 0.1 0.9 --input-endpoints test_model_sklearn/2 test_model_sklearn/1
```
1. Test Canary endpoint:
```bash
curl -X POST "http://127.0.0.1:8080/serve/test_model" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'`
```
```bash
curl -X POST "http://127.0.0.1:8080/serve/test_model" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'`
```
### Model Monitoring and Performance Metrics