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Add ClearML Serving docs (#219)
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docs/clearml_serving/clearml_serving.md
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---
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title: Introduction
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---
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`clearml-serving` is a command line utility for model deployment and orchestration.
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It enables model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based
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solution.
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## Features
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* Easy to deploy & configure
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* Support Machine Learning Models (Scikit Learn, XGBoost, LightGBM)
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* Support Deep Learning Models (Tensorflow, PyTorch, ONNX)
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* Customizable RestAPI for serving (i.e. allow per model pre/post-processing for easy integration)
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* Flexible
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* On-line model deployment
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* On-line endpoint model/version deployment (i.e. no need to take the service down)
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* Per model standalone preprocessing and postprocessing python code
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* Scalable
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* Multi model per container
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* Multi models per serving service
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* Multi-service support (fully seperated multiple serving service running independently)
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* Multi cluster support
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* Out-of-the-box node auto-scaling based on load/usage
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* Efficient
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* Multi-container resource utilization
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* Support for CPU & GPU nodes
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* Auto-batching for DL models
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* [Automatic deployment](clearml_serving_tutorial.md#automatic-model-deployment)
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* Automatic model upgrades w/ canary support
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* Programmable API for model deployment
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* [Canary A/B deployment](clearml_serving_tutorial.md#canary-endpoint-setup) - online Canary updates
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* [Model Monitoring](clearml_serving_tutorial.md#model-monitoring-and-performance-metrics)
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* Usage Metric reporting
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* Metric Dashboard
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* Model performance metric
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* Model performance Dashboard
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## Components
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* **CLI** - Secure configuration interface for on-line model upgrade/deployment on running Serving Services
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* **Serving Service Task** - Control plane object storing configuration on all the endpoints. Support multiple separated
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instance, deployed on multiple clusters.
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* **Inference Services** - Inference containers, performing model serving pre/post processing. Also supports CPU model
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inferencing.
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* **Serving Engine Services** - Inference engine containers (e.g. Nvidia Triton, TorchServe etc.) used by the Inference
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Services for heavier model inference.
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* **Statistics Service** - Single instance per Serving Service collecting and broadcasting model serving & performance
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statistics
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* **Time-series DB** - Statistics collection service used by the Statistics Service, e.g. Prometheus
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* **Dashboards** - Customizable dashboard solution on top of the collected statistics, e.g. Grafana
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## Installation
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### Prerequisites
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* ClearML-Server : Model repository, Service Health, Control plane
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* Kubernetes / Single-instance Machine : Deploying containers
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* CLI : Configuration & model deployment interface
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### Initial Setup
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1. Set up your [ClearML Server](../deploying_clearml/clearml_server.md) or use the
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[free hosted service](https://app.clear.ml)
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1. Connect `clearml` SDK to the server, see instructions [here](../getting_started/ds/ds_first_steps.md#install-clearml)
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1. Install clearml-serving CLI:
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```bash
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pip3 install clearml-serving
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```
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1. Create the Serving Service Controller:
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```bash
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clearml-serving create --name "serving example"
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```
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The new serving service UID should be printed
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```console
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New Serving Service created: id=aa11bb22aa11bb22
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```
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Write down the Serving Service UID
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1. Clone the `clearml-serving` repository:
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```bash
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git clone https://github.com/allegroai/clearml-serving.git
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```
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1. Edit the environment variables file (docker/example.env) with your clearml-server credentials and Serving Service UID.
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For example, you should have something like
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```bash
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cat docker/example.env
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```
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```console
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CLEARML_WEB_HOST="https://app.clear.ml"
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CLEARML_API_HOST="https://api.clear.ml"
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CLEARML_FILES_HOST="https://files.clear.ml"
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CLEARML_API_ACCESS_KEY="<access_key_here>"
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CLEARML_API_SECRET_KEY="<secret_key_here>"
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CLEARML_SERVING_TASK_ID="<serving_service_id_here>"
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```
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1. Spin up the `clearml-serving` containers with `docker-compose` (or if running on Kubernetes, use the helm chart)
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```bash
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cd docker && docker-compose --env-file example.env -f docker-compose.yml up
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```
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If you need Triton support (keras/pytorch/onnx etc.), use the triton docker-compose file
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```bash
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cd docker && docker-compose --env-file example.env -f docker-compose-triton.yml up
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```
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If running on a GPU instance w/ Triton support (keras/pytorch/onnx etc.), use the triton gpu docker-compose file:
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```bash
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cd docker && docker-compose --env-file example.env -f docker-compose-triton-gpu.yml up
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```
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:::note
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Any model that registers with Triton engine will run the pre/post processing code on the Inference service container,
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and the model inference itself will be executed on the Triton Engine container.
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:::
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### Advanced Setup - S3/GS/Azure Access (Optional)
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To add access credentials and allow the inference containers to download models from your S3/GS/Azure object-storage,
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add the respective environment variables to your env files (example.env). See further details on configuring the storage
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access [here](../integrations/storage.md#configuring-storage).
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```
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AWS_ACCESS_KEY_ID
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AWS_SECRET_ACCESS_KEY
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AWS_DEFAULT_REGION
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GOOGLE_APPLICATION_CREDENTIALS
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AZURE_STORAGE_ACCOUNT
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AZURE_STORAGE_KEY
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```
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## Tutorial
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For further details, see the ClearML Serving [Tutorial](clearml_serving_tutorial.md).
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129
docs/clearml_serving/clearml_serving_cli.md
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docs/clearml_serving/clearml_serving_cli.md
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---
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title: CLI
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---
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The `clearml-serving` utility is a CLI tool for model deployment and orchestration.
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The following page provides a reference for `clearml-serving`'s CLI commands:
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* [list](#list) - List running Serving Services
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* [create](#create) - Create a new Serving Service
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* [metrics](#metrics) - Configure inference metrics Service
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* [config](#config) - Configure a new Serving Service
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* [model](#model) - Configure Model endpoints for a running Service
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```bash
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clearml-serving [-h] [--debug] [--id ID] {list,create,metrics,config,model}
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```
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**Parameters**
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<div className="tbl-cmd">
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|Name|Description|Optional|
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|---|---|---|
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|`--id`|Serving Service (Control plane) Task ID to configure (if not provided automatically detect the running control plane Task) | <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
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|`--debug` | Print debug messages | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
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</div>
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:::info Service ID
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The Serving Service's ID (`--id`) is required to execute the `metrics`, `config`, and `model` commands.
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:::
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### list
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```bash
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clearml-serving list [-h]
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```
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List running Serving Services.
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### create
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```bash
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clearml-serving create [-h] [--name NAME] [--tags TAGS [TAGS ...]] [--project PROJECT]
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```
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Create a new Serving Service
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**Parameters**
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<div className="tbl-cmd">
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|Name|Description|Optional|
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|---|---|---|
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|`--name` |Serving service's name. Default: `Serving-Service`| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
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|`--project`|Serving service's project. Default: `DevOps`| <img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" /> |
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|`--tags` |Serving service's user tags. The serving service can be labeled, which can be useful for organizing | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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</div>
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### metrics
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Configure inference metrics Service
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```bash
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clearml-serving metrics [-h] {add,remove,list}
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```
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**Parameters**
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<div className="tbl-cmd">
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|Name|Description|Optional|
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|---|---|---|
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|`--add` | Add/modify metric for a specific endpoint| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
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|`--remove` | Remove metric from a specific endpoint| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
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|`--list` | list metrics logged on all endpoints | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
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</div>
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<br/>
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### config
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Configure a new Serving Service.
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```bash
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clearml-serving {base-serving-url, triton-grpc, kafka-metric-server, metric-log-freq}
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```
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**Parameters**
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<div className="tbl-cmd">
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|Name|Description|Optional|
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|---|---|---|
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|`--base-serving-url`|External base serving service url. Example: `http://127.0.0.1:8080/serve`|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--triton-grpc-server`|External ClearML-Triton serving container gRPC address. Example: `127.0.0.1:9001`|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--kafka-metric-server`|External Kafka service url. Example: `127.0.0.1:9092`|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--metric-log-freq`|Set default metric logging frequency. 1.0 is 100% of all requests are logged|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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</div>
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<br/>
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### model
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Configure Model endpoints for an already running Service
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```bash
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clearml-serving model [-h] {list,remove,upload,canary,auto-update,add}
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```
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**Parameters**
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<div className="tbl-cmd">
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|Name|Description|Optional|
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|---|---|---|
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|`--list`| List current models| <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
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|`--remove`| Remove model by its endpoint name | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" /> |
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|`--upload` | Upload and register model files/folder | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--canary` | Add model Canary/A/B endpoint | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--auto-update` | Add/Modify model auto update service | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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|`--add` | Add/Update model | <img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
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</div>
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<br/>
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238
docs/clearml_serving/clearml_serving_tutorial.md
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238
docs/clearml_serving/clearml_serving_tutorial.md
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---
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title: Tutorial
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---
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In this tutorial, we will go over the model lifecycle -- from training to serving -- in the following steps:
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* Training a model using the [sklearn example script](https://github.com/allegroai/clearml-serving/blob/main/examples/sklearn/train_model.py)
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* Serving the model using **ClearML Serving**
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* Spinning the inference container
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The tutorial will also go over these additional options that you can use with `clearml-serving`:
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* Automatic model deployment
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* Canary endpoints
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* Model performance monitoring
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## Prerequisite
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Before executing the steps below, make sure you have completed `clearml-serving`'s [initial setup](clearml_serving.md#initial-setup).
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## Steps
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### Step 1: Train Model
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Train a model. Work from your local `clearml-serving` repository's root.
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- Create a python virtual environment
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- Install the script requirements `pip3 install -r examples/sklearn/requirements.txt`
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- Execute the [training script](https://github.com/allegroai/clearml-serving/blob/main/examples/sklearn/train_model.py)
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`python3 examples/sklearn/train_model.py`.
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During execution, ClearML automatically registers the sklearn model and uploads it into the model repository.
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For Manual model registration see [here](#registering--deploying-new-models-manually)
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### Step 2: Register Model
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Register the new Model on the Serving Service.
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```bash
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clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --project "serving examples"
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```
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:::info Service ID
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Make sure that you have executed `clearml-servings`'s
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[initial setup](clearml_serving.md#initial-setup), in which you create a Serving Service.
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The Serving Service's ID is required to register a model, and to execute `clearml-serving`'s `metrics` and `config` commands
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:::
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:::note
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The preprocessing python code is packaged and uploaded to the Serving Service, to be used by any inference container,
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and downloaded in realtime when updated
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:::
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### Step 3: Spin Inference Container
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Spin the Inference Container
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- Customize container [Dockerfile](https://github.com/allegroai/clearml-serving/blob/main/clearml_serving/serving/Dockerfile) if needed
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- Build container `
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```bash
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docker build --tag clearml-serving-inference:latest -f clearml_serving/serving/Dockerfile .
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```
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- Spin the inference container:
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```bash
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docker run -v ~/clearml.conf:/root/clearml.conf -p 8080:8080 -e CLEARML_SERVING_TASK_ID=<service_id> -e CLEARML_SERVING_POLL_FREQ=5 clearml-serving-inference:latest
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```
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Now, test the new model inference endpoint:
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```bash
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curl -X POST "http://127.0.0.1:8080/serve/test_model_sklearn" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'
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```
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Now that you have an inference container running, you can add new model inference endpoints directly with the CLI. The
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inference container will automatically sync once every 5 minutes. On the first few requests the inference container
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needs to download the model file and preprocessing python code, this means the request might take a little longer, once
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everything is cached, it will return almost immediately.
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:::note
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Review the model repository in the ClearML web UI, under the "serving examples" Project on your ClearML
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account/server ([free hosted](https://app.clear.ml) or [self-deployed](https://github.com/allegroai/clearml-server)).
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Inference services status, console outputs and machine metrics are available in the ClearML UI in the Serving Service
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project (default: "DevOps" project)
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:::
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## Registering & Deploying New Models Manually
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Uploading an existing model file into the model repository can be done via the `clearml` RestAPI, the python interface,
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or with the `clearml-serving` CLI.
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1. Upload the model file to the `clearml-server` file storage and register it. The `--path` parameter is used to input
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the path to a local model file.
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```bash
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clearml-serving --id <service_id> model upload --name "manual sklearn model" --project "serving examples" --framework "scikit-learn" --path examples/sklearn/sklearn-model.pkl
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```
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You now have a new Model named `manual sklearn model` in the `serving examples` project. The CLI output prints
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the UID of the new model, which you will use it to register a new endpoint.
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In the [ClearML web UI](../webapp/webapp_overview.md), the new model is listed under the **Models** tab of its project.
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You can also download the model file itself directly from the web UI.
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1. Register a new endpoint with the new model
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```bash
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clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --model-id <newly_created_model_id_here>
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```
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:::info Model Storage
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You can also provide a different storage destination for the model, such as S3/GS/Azure, by passing
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`--destination="s3://bucket/folder"`, `gs://bucket/folder`, `azure://bucket/folder`. There is no need to provide a unique
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path tp the destination argument, the location of the model will be a unique path based on the serving service ID and the
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model name
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:::
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## Additional Options
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### Automatic Model Deployment
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The ClearML Serving Service supports automatic model deployment and upgrades, which is connected with the model
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repository and API. When the model auto-deploy is configured, new model versions will be automatically deployed when you
|
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`publish` or `tag` a new model in the ClearML model repository. This automation interface allows for simpler CI/CD model
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deployment process, as a single API automatically deploy (or remove) a model from the Serving Service.
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#### Automatic Model Deployment Example
|
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1. Configure the model auto-update on the Serving Service
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```bash
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clearml-serving --id <service_id> model auto-update --engine sklearn --endpoint "test_model_sklearn_auto" --preprocess "preprocess.py" --name "train sklearn model" --project "serving examples" --max-versions 2`
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```
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1. Deploy the Inference container (if not already deployed)
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1. Publish a new model the model repository in one of the following ways:
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- Go to the "serving examples" project in the ClearML web UI, click on the Models Tab, search for "train sklearn model" right click and select "Publish"
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- Use the RestAPI (see [details](https://clear.ml/docs/latest/docs/references/api/models#post-modelspublish_many))
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- Use Python interface:
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```python
|
||||
from clearml import Model
|
||||
Model(model_id="unique_model_id_here").publish()
|
||||
```
|
||||
1. The new model is available on a new endpoint version (1), test with:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8080/serve/test_model_sklearn_auto/1" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'
|
||||
```
|
||||
|
||||
### Canary Endpoint Setup
|
||||
|
||||
Canary endpoint deployment add 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).
|
||||
|
||||
```bash
|
||||
clearml-serving --id <service_id> model canary --endpoint "test_model_sklearn_canary" --weights 0.1 0.9 --input-endpoints test_model_sklearn/2 test_model_sklearn/1
|
||||
```
|
||||
This means that any request coming to `/test_model_sklearn_canary/` will be routed with probability of 90% to
|
||||
`/test_model_sklearn/1/` and with probability of 10% to `/test_model_sklearn/2/`.
|
||||
|
||||
:::note
|
||||
As with any other Serving Service configuration, you can configure the Canary endpoint while the Inference containers are
|
||||
already running and deployed, they will get updated in their next update cycle (default: once every 5 minutes)
|
||||
:::
|
||||
|
||||
You can also prepare a "fixed" canary endpoint, always splitting the load between the last two deployed models:
|
||||
|
||||
```bash
|
||||
clearml-serving --id <service_id> model canary --endpoint "test_model_sklearn_canary" --weights 0.1 0.9 --input-endpoints-prefix test_model_sklearn/
|
||||
```
|
||||
|
||||
This means that you have two model inference endpoints: `/test_model_sklearn/1/` and `/test_model_sklearn/2/`. The 10%
|
||||
probability (weight 0.1) will match the last (order by version number) endpoint, i.e. `/test_model_sklearn/2/`, and the
|
||||
90% will match `/test_model_sklearn/2/`. When you add a new model endpoint version, e.g. `/test_model_sklearn/3/`, the
|
||||
canary distribution will automatically match the 90% probability to `/test_model_sklearn/2/` and the 10% to the new
|
||||
endpoint `/test_model_sklearn/3/`.
|
||||
|
||||
Example:
|
||||
1. Add two endpoints:
|
||||
```bash
|
||||
clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --version 1 --project "serving examples"
|
||||
```
|
||||
```bash
|
||||
clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --version 2 --project "serving examples"
|
||||
```
|
||||
|
||||
1. Add Canary endpoint:
|
||||
```bash
|
||||
clearml-serving --id <service_id> model canary --endpoint "test_model_sklearn_canary" --weights 0.1 0.9 --input-endpoints test_model_sklearn/2 test_model_sklearn/1
|
||||
```
|
||||
|
||||
1. Test Canary endpoint:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8080/serve/test_model" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'`
|
||||
```
|
||||
|
||||
### Model Monitoring and Performance Metrics
|
||||
|
||||
|
||||
|
||||
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
|
||||
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
|
||||
inputs and outputs (see examples/sklearn/preprocess.py example):
|
||||
|
||||
```bash
|
||||
clearml-serving --id <serving_service_id_here> metrics add --endpoint test_model_sklearn --variable-scalar
|
||||
x0=0,0.1,0.5,1,10 x1=0,0.1,0.5,1,10 y=0,0.1,0.5,0.75,1
|
||||
```
|
||||
|
||||
This will create a distribution histogram (buckets specified via a list of less-equal values after `=` sign),
|
||||
that you will be able to visualize on Grafana.
|
||||
|
||||
:::info time-series values
|
||||
You can also log time-series values with `--variable-value x2` or discrete results (e.g. classifications strings) with
|
||||
`--variable-enum animal=cat,dog,sheep`. Additional custom variables can be added in the preprocess and postprocess with
|
||||
a call to `collect_custom_statistics_fn({'new_var': 1.337})`. See [`preprocess_template.py`](https://github.com/allegroai/clearml-serving/blob/main/clearml_serving/preprocess/preprocess_template.py).
|
||||
:::
|
||||
|
||||
With the new metrics logged, you can create a visualization dashboard over the latency of the calls, and the output distribution.
|
||||
|
||||
#### Grafana Model Performance Example
|
||||
|
||||
1. Browse to `http://localhost:3000`
|
||||
1. Login with: admin/admin
|
||||
1. Create a new dashboard
|
||||
1. Select Prometheus as data source
|
||||
1. Add a query: `100 * increase(test_model_sklearn:_latency_bucket[1m]) / increase(test_model_sklearn:_latency_sum[1m])`
|
||||
1. Change type to heatmap, and select on the right hand-side under "Data Format" select "Time series buckets". You now have
|
||||
the latency distribution, over time.
|
||||
1. Repeat the same process for x0, the query would be `100 * increase(test_model_sklearn:x0_bucket[1m]) / increase(test_model_sklearn:x0_sum[1m])`
|
||||
|
||||
:::note
|
||||
If not specified all serving requests will be logged, which can be changed with the `CLEARML_DEFAULT_METRIC_LOG_FREQ`
|
||||
environment variable. For example `CLEARML_DEFAULT_METRIC_LOG_FREQ=0.2` means only 20% of all requests will be logged.
|
||||
You can also specify per-endpoint log frequency with the `clearml-serving` CLI. See [`clearml-serving metrics`](clearml_serving_cli.md#metrics)
|
||||
:::
|
||||
@ -19,6 +19,7 @@ module.exports = {
|
||||
'clearml_agent',
|
||||
{'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']},
|
||||
{'CLI Tools': ['apps/clearml_session', 'apps/clearml_task']},
|
||||
'integrations/libraries',
|
||||
'integrations/storage',
|
||||
|
||||
Loading…
Reference in New Issue
Block a user