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pollfly
2023-08-09 13:28:25 +03:00
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docs/clearml_serving/clearml_serving_tutorial.md
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@@ -26,7 +26,7 @@ Train a model. Work from your local `clearml-serving` repository's root.
`python3 examples/sklearn/train_model.py`.
During execution, ClearML automatically registers the sklearn model and uploads it into the model repository.
For Manual model registration see [here](#registering--deploying-new-models-manually)
For Manual model registration see [here](#registering-and-deploying-new-models-manually)
### Step 2: Register Model
@@ -79,7 +79,7 @@ Inference services status, console outputs and machine metrics are available in
project (default: "DevOps" project)
:::
## Registering & Deploying New Models Manually
## Registering and Deploying New Models Manually
Uploading an existing model file into the model repository can be done via the `clearml` RestAPI, the python interface,
or with the `clearml-serving` CLI.
@@ -196,7 +196,7 @@ ClearML serving instances send serving statistics (count/latency) automatically
to visualize and create live dashboards.
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.
of both containers is *not* persistent. To add persistence, adding a volume mount is recommended.
You can also add many custom metrics on the input/predictions of your models. Once a model endpoint is registered,
adding custom metrics can be done using the CLI.