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37
docs/clearml_serving/clearml_serving_setup.md
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@ -15,7 +15,7 @@ The following page goes over how to set up and upgrade `clearml-serving`.
[free hosted service](https://app.clear.ml)
1. Connect `clearml` SDK to the server, see instructions [here](../clearml_sdk/clearml_sdk_setup#install-clearml)
1. Install clearml-serving CLI:
1. Install the `clearml-serving` CLI:
```bash
pip3 install clearml-serving
@ -27,21 +27,22 @@ The following page goes over how to set up and upgrade `clearml-serving`.
clearml-serving create --name "serving example"
```
The new serving service UID should be printed
This command prints the Serving Service UID:
```console
New Serving Service created: id=aa11bb22aa11bb22
```
Write down the Serving Service UID
Copy the Serving Service UID (e.g., `aa11bb22aa11bb22`), as you will need it in the next steps.
1. Clone the `clearml-serving` repository:
```bash
git clone https://github.com/clearml/clearml-serving.git
```
1. Edit the environment variables file (docker/example.env) with your clearml-server credentials and Serving Service UID.
For example, you should have something like
1. Edit the environment variables file (`docker/example.env`) with your `clearml-server` API credentials and Serving Service UID.
For example:
```bash
cat docker/example.env
```
@ -55,31 +56,30 @@ The following page goes over how to set up and upgrade `clearml-serving`.
CLEARML_SERVING_TASK_ID="<serving_service_id_here>"
```
1. Spin up the `clearml-serving` containers with `docker-compose` (or if running on Kubernetes, use the helm chart)
1. Spin up the `clearml-serving` containers with `docker-compose` (or if running on Kubernetes, use the helm chart):
```bash
cd docker && docker-compose --env-file example.env -f docker-compose.yml up
```
If you need Triton support (keras/pytorch/onnx etc.), use the triton docker-compose file
If you need Triton support (Keras/PyTorch/ONNX etc.), use the triton `docker-compose` file:
```bash
cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up
```
If running on a GPU instance with Triton support (keras/pytorch/onnx etc.), use the triton gpu docker-compose file:
If running on a GPU instance with Triton support (Keras/PyTorch/ONNX etc.), use the triton gpu docker-compose file:
```bash
cd docker && docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up
```
:::note
Any model that registers with Triton engine will run the pre/post-processing code on the Inference service container,
Any model that registers with Triton engine will run the pre/post-processing code in the Inference service container,
and the model inference itself will be executed on the Triton Engine container.
:::
## Advanced Setup - S3/GS/Azure Access (Optional)
To add access credentials and allow the inference containers to download models from your S3/GS/Azure object-storage,
add the respective environment variables to your env files (example.env). For further details, see
[Configuring Storage](../integrations/storage.md#configuring-storage).
To enable inference containers to download models from S3, Google Cloud Storage (GS), or Azure,
add access credentials in the respective environment variables to your env files (`example.env`):
```
AWS_ACCESS_KEY_ID
@ -92,14 +92,21 @@ AZURE_STORAGE_ACCOUNT
AZURE_STORAGE_KEY
```
For further details, see [Configuring Storage](../integrations/storage.md#configuring-storage).
## Upgrading ClearML Serving
**Upgrading to v1.1**
1. Take down the serving containers (`docker-compose` or k8s)
1. Update the `clearml-serving` CLI `pip3 install -U clearml-serving`
1. Shut down the serving containers (`docker-compose` or k8s)
1. Update the `clearml-serving` CLI:
```
pip3 install -U clearml-serving
```
1. Re-add a single existing endpoint with `clearml-serving model add ...` (press yes when asked). It will upgrade the
`clearml-serving` session definitions
`clearml-serving` session definitions.
1. Pull the latest serving containers (`docker-compose pull ...` or k8s)
1. Re-spin serving containers (`docker-compose` or k8s)

4
docs/integrations/transformers.md
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@ -77,12 +77,12 @@ cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents:
and shuts down instances as needed, according to a resource budget that you set.
### Reproducing Tasks
### Reproducing Task Runs
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif#light-mode-only)
![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5_dark.gif#dark-mode-only)
Use ClearML's web interface to reproduce tasks and edit their details, like hyperparameters or input models, then execute the tasks
Use ClearML's web interface to reproduce task runs and edit their details, like hyperparameters or input models, then execute the tasks
with the new configuration on a remote machine.
When ClearML is integrated into a script, it captures and stores configurations, such as hyperparameters