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pollfly
2023-01-25 13:25:29 +02:00
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docs/clearml_serving/clearml_serving_tutorial.md
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@@ -37,7 +37,7 @@ clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_mo
```
:::info Service ID
Make sure that you have executed `clearml-servings`'s
Make sure that you have executed `clearml-serving`'s
[initial setup](clearml_serving.md#initial-setup), in which you create a Serving Service.
The Serving Service's ID is required to register a model, and to execute `clearml-serving`'s `metrics` and `config` commands
:::
@@ -92,7 +92,7 @@ or with the `clearml-serving` CLI.
```
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 it to register a new endpoint.
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.
@@ -105,7 +105,7 @@ or with the `clearml-serving` CLI.
:::info Model Storage
You can also provide a different storage destination for the model, such as S3/GS/Azure, by passing
`--destination="s3://bucket/folder"`, `gs://bucket/folder`, `azure://bucket/folder`. There is no need to provide a unique
path tp the destination argument, the location of the model will be a unique path based on the serving service ID and the
path to the destination argument, the location of the model will be a unique path based on the serving service ID and the
model name
:::
@@ -116,7 +116,7 @@ model name
The ClearML Serving Service supports automatic model deployment and upgrades, which is connected with the model
repository and API. When the model auto-deploy is configured, new model versions will be automatically deployed when you
`publish` or `tag` a new model in the ClearML model repository. This automation interface allows for simpler CI/CD model
deployment process, as a single API automatically deploy (or remove) a model from the Serving Service.
deployment process, as a single API automatically deploys (or removes) a model from the Serving Service.
#### Automatic Model Deployment Example
@@ -142,7 +142,7 @@ deployment process, as a single API automatically deploy (or remove) a model fro
### Canary Endpoint Setup
Canary endpoint deployment add a new endpoint where the actual request is sent to a preconfigured set of endpoints with
Canary endpoint deployment adds a new endpoint where the actual request is sent to a preconfigured set of endpoints with
pre-provided distribution. For example, let's create a new endpoint "test_model_sklearn_canary", you can provide a list
of endpoints and probabilities (weights).
@@ -195,13 +195,13 @@ Example:
ClearML serving instances send serving statistics (count/latency) automatically to Prometheus and Grafana can be used
to visualize and create live dashboards.
The default docker-compose installation is preconfigured with Prometheus and Grafana, do notice that by default data/ate
The default docker-compose installation is preconfigured with Prometheus and Grafana. Notice that by default data/ate
of both containers is *not* persistent. To add persistence, we recommend adding a volume mount.
You can also add many custom metrics on the input/predictions of your models. Once a model endpoint is registered,
adding custom metric can be done using the CLI.
For example, assume the mock scikit-learn model is deployed on endpoint `test_model_sklearn`, you can log the requests
For example, assume the mock scikit-learn model is deployed on endpoint `test_model_sklearn`, you can log the requests
inputs and outputs (see examples/sklearn/preprocess.py example):
```bash