ClearML/clearml-serving
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@ -48,7 +48,7 @@ pip install clearml-serving
Clearml-Serving will automatically serve *published* models from your ClearML model repository, so the first step is getting a model into your ClearML model repository. Clearml-Serving will automatically serve *published* models from your ClearML model repository, so the first step is getting a model into your ClearML model repository.
Background: When using `clearml` in your training code, any model stored by your python code is automatically registered (and, optionally, uploaded) to the model repository. This auto-magic logging is key for continuous model deployment. Background: When using `clearml` in your training code, any model stored by your python code is automatically registered (and, optionally, uploaded) to the model repository. This auto-magic logging is key for continuous model deployment.
To learn more on training models and the ClearML model repository, see the [ClearML documentation](https://allegro.ai/clearml/docs) To learn more on training models and the ClearML model repository, see the [ClearML documentation](https://clear.ml/docs/latest/docs/)
### Training a toy model with Keras (about 2 minutes on a laptop) ### Training a toy model with Keras (about 2 minutes on a laptop)
@ -115,7 +115,7 @@ clearml-serving triton --endpoint "keras_mnist" --model-project "examples" --mo
3. Launch the serving service. 3. Launch the serving service.
The service will be launched on your "services" queue, which by default runs services on the ClearML server machine. The service will be launched on your "services" queue, which by default runs services on the ClearML server machine.
(Read more on services queue [here](https://allegro.ai/clearml/docs/docs/concepts_fundamentals/concepts_fundamentals_clearml_server.html#clearml-agent-services-container)) (Read more on services queue [here](https://clear.ml/docs/latest/docs/clearml_agent#services-mode))
We set our serving-engine to launch on the "default" queue, We set our serving-engine to launch on the "default" queue,
```bash ```bash
clearml-serving launch --queue default clearml-serving launch --queue default
@ -123,7 +123,7 @@ clearml-serving launch --queue default
4. Optional: If you do not have a machine connected to your ClearML cluster, either read more on our Kubernetes integration, or spin a bare-metal worker and connect it with your ClearML Server. 4. Optional: If you do not have a machine connected to your ClearML cluster, either read more on our Kubernetes integration, or spin a bare-metal worker and connect it with your ClearML Server.
`clearml-serving` is leveraging the orchestration capabilities of `ClearML` to launch the serving engine on the cluster. `clearml-serving` is leveraging the orchestration capabilities of `ClearML` to launch the serving engine on the cluster.
Read more on the [ClearML Agent](https://github.com/allegroai/clearml-agent) orchestration module [here](https://allegro.ai/clearml/docs/docs/concepts_fundamentals/concepts_fundamentals_clearml_agent.html) Read more on the [ClearML Agent](https://github.com/allegroai/clearml-agent) orchestration module [here](https://clear.ml/docs/latest/docs/clearml_agent)
If you have not yet setup a ClearML worker connected to your `clearml` account, you can do this now using: If you have not yet setup a ClearML worker connected to your `clearml` account, you can do this now using:
```bash ```bash
pip install clearml-agent pip install clearml-agent