Reorganize ClearML Serving pages (#326)

docs/clearml_serving/clearml_serving.md

@@ -59,100 +59,7 @@ solution.
 
 * **Dashboards** - Customizable dashboard solution on top of the collected statistics, e.g. Grafana
 
-## Installation
+## Next Steps
 
-:::important Upgrading ClearML Serving
+See ClearML Serving setup instructions [here](clearml_serving_setup.md). For further details, see the ClearML Serving 
-To upgrade to ClearML Serving version 1.1, see instructions [here](../release_notes/ver_1_1.md#clearml-serving-110).
+[Tutorial](clearml_serving_tutorial.md).
-:::
-
-### Prerequisites
-
-* ClearML-Server : Model repository, Service Health, Control plane
-* Kubernetes / Single-instance Machine : Deploying containers
-* CLI : Configuration & model deployment interface
-
-### Initial Setup
-1. Set up your [ClearML Server](../deploying_clearml/clearml_server.md) or use the 
-  [free hosted service](https://app.clear.ml)
-1. Connect `clearml` SDK to the server, see instructions [here](../getting_started/ds/ds_first_steps.md#install-clearml)
-
-1. Install clearml-serving CLI:
-   
-   ```bash
-   pip3 install clearml-serving
-   ```
-
-1. Create the Serving Service Controller:
-   
-   ```bash
-   clearml-serving create --name "serving example"
-   ```
-   
-   The new serving service UID should be printed 
-   
-   ```console
-   New Serving Service created: id=aa11bb22aa11bb22
-   ```
-   
-   Write down the Serving Service UID
-
-1. Clone the `clearml-serving` repository:
-   ```bash
-   git clone https://github.com/allegroai/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
-   ```bash
-   cat docker/example.env
-   ```
-   
-   ```console 
-    CLEARML_WEB_HOST="https://app.clear.ml"
-    CLEARML_API_HOST="https://api.clear.ml"
-    CLEARML_FILES_HOST="https://files.clear.ml"
-    CLEARML_API_ACCESS_KEY="<access_key_here>"
-    CLEARML_API_SECRET_KEY="<secret_key_here>"
-    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)
-   
-   ```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
-   ```bash
-   cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up 
-   ```
-   
-   If running on a GPU instance w/ 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, 
-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). See further details on configuring the storage 
-access [here](../integrations/storage.md#configuring-storage).
-
-```
-AWS_ACCESS_KEY_ID
-AWS_SECRET_ACCESS_KEY
-AWS_DEFAULT_REGION
-
-GOOGLE_APPLICATION_CREDENTIALS
-
-AZURE_STORAGE_ACCOUNT
-AZURE_STORAGE_KEY
-```
-
-## Tutorial
-
-For further details, see the ClearML Serving [Tutorial](clearml_serving_tutorial.md).

docs/clearml_serving/clearml_serving_setup.md

@@ -0,0 +1,109 @@
+---
+title: Setup
+---
+
+The following page goes over how to set up and upgrade `clearml-serving`.
+
+## Prerequisites
+
+* ClearML-Server : Model repository, Service Health, Control plane
+* Kubernetes / Single-instance Machine : Deploying containers
+* CLI : Configuration & model deployment interface
+
+## Initial Setup
+1. Set up your [ClearML Server](../deploying_clearml/clearml_server.md) or use the 
+  [free hosted service](https://app.clear.ml)
+1. Connect `clearml` SDK to the server, see instructions [here](../getting_started/ds/ds_first_steps.md#install-clearml)
+
+1. Install clearml-serving CLI:
+   
+   ```bash
+   pip3 install clearml-serving
+   ```
+
+1. Create the Serving Service Controller:
+   
+   ```bash
+   clearml-serving create --name "serving example"
+   ```
+   
+   The new serving service UID should be printed 
+   
+   ```console
+   New Serving Service created: id=aa11bb22aa11bb22
+   ```
+   
+   Write down the Serving Service UID
+
+1. Clone the `clearml-serving` repository:
+   ```bash
+   git clone https://github.com/allegroai/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
+   ```bash
+   cat docker/example.env
+   ```
+   
+   ```console 
+    CLEARML_WEB_HOST="https://app.clear.ml"
+    CLEARML_API_HOST="https://api.clear.ml"
+    CLEARML_FILES_HOST="https://files.clear.ml"
+    CLEARML_API_ACCESS_KEY="<access_key_here>"
+    CLEARML_API_SECRET_KEY="<secret_key_here>"
+    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)
+   
+   ```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
+   ```bash
+   cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up 
+   ```
+   
+   If running on a GPU instance w/ 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, 
+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). See further details on configuring the storage 
+access [here](../integrations/storage.md#configuring-storage).
+
+```
+AWS_ACCESS_KEY_ID
+AWS_SECRET_ACCESS_KEY
+AWS_DEFAULT_REGION
+
+GOOGLE_APPLICATION_CREDENTIALS
+
+AZURE_STORAGE_ACCOUNT
+AZURE_STORAGE_KEY
+```
+
+## 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. Re-add a single existing endpoint with `clearml-serving model add ... ` (press yes when asked). It will upgrade the 
+   `clearml-serving` session definitions
+1. Pull the latest serving containers (`docker-compose pull ...` or k8s)
+1. Re-spin serving containers (`docker-compose` or k8s)
+
+
+## Tutorial
+
+For further details, see the ClearML Serving [Tutorial](clearml_serving_tutorial.md).

docs/clearml_serving/clearml_serving_tutorial.md

@@ -14,7 +14,7 @@ The tutorial will also go over these additional options that you can use with `c
 
 ## Prerequisite
 
-Before executing the steps below, make sure you have completed `clearml-serving`'s [initial setup](clearml_serving.md#initial-setup).
+Before executing the steps below, make sure you have completed `clearml-serving`'s [initial setup](clearml_serving_setup.md#initial-setup).
 
 ##  Steps
 ### Step 1: Train Model

docs/release_notes/ver_1_1.md

@@ -17,32 +17,8 @@ This release is not backwards compatible - see notes below on upgrading
 * Triton engine support for variable request (matrix) sizes
 * Triton support, fix `--aux-config` to support more configurations elements
 * Huggingface Transformer support
-* `Preprocess` class as module (see note below)
+* `Preprocess` class as module 
 
-:::important Preprocess Class
-To add a `Preprocess` class from a module (the entire module folder will be packaged)
-
-```
-preprocess_folder
-├── __init__.py  # from .sub.some_file import Preprocess
-└── sub
-    └── some_file.py
-```
-Pass the top folder as a path for --preprocess, for example:
-
-```bash
-clearml-serving module --id <serving_session_id> add --preprocess /path/to/preprocess_folder ...
-```
-:::
-
-**Upgrading from v1.0**
-
-1. Take 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 module add ... ` (press yes when asked). It will upgrade the 
-   `clearml-serving` session definitions
-1. Pull the latest serving containers (`docker-compose pull ...` or k8s)
-1. Re-spin serving containers (`docker-compose` or k8s)
 
 
 ### ClearML Agent 1.1.2

sidebars.js

@@ -37,7 +37,7 @@ module.exports = {
         },
         {'ClearML Data': ['clearml_data/clearml_data', 'clearml_data/clearml_data_cli', 'clearml_data/clearml_data_sdk', 'clearml_data/best_practices',
                 {'Workflows': ['clearml_data/data_management_examples/workflows', 'clearml_data/data_management_examples/data_man_simple', 'clearml_data/data_management_examples/data_man_folder_sync', 'clearml_data/data_management_examples/data_man_cifar_classification', 'clearml_data/data_management_examples/data_man_python']},]},
-        {'ClearML Serving':['clearml_serving/clearml_serving', 'clearml_serving/clearml_serving_cli', 'clearml_serving/clearml_serving_tutorial']},
+        {'ClearML Serving':['clearml_serving/clearml_serving', 'clearml_serving/clearml_serving_setup', 'clearml_serving/clearml_serving_cli', 'clearml_serving/clearml_serving_tutorial']},
         {'CLI Tools': ['apps/clearml_session', 'apps/clearml_task', 'apps/clearml_param_search']},
         'integrations/libraries',
         'integrations/storage',