From d8963d2ea5ae643a59fd0affe26f952f6f9a8ebf Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Sun, 30 Jun 2024 16:49:19 +0300 Subject: [PATCH] Fix spacing (#863) --- .../clearml_serving_tutorial.md | 38 ++++++++++--------- 1 file changed, 21 insertions(+), 17 deletions(-) diff --git a/docs/clearml_serving/clearml_serving_tutorial.md b/docs/clearml_serving/clearml_serving_tutorial.md index 3f4941da..e6907c31 100644 --- a/docs/clearml_serving/clearml_serving_tutorial.md +++ b/docs/clearml_serving/clearml_serving_tutorial.md @@ -94,11 +94,11 @@ or with the `clearml-serving` CLI. clearml-serving --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 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 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 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 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 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 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