---
title: CLI
---
The `clearml-serving` utility is a CLI tool for model deployment and orchestration.
The following page provides a reference for `clearml-serving`'s CLI commands:
* [list](#list) - List running Serving Services
* [create](#create) - Create a new Serving Service
* [metrics](#metrics) - Configure inference metrics Service
* [config](#config) - Configure a new Serving Service
* [model](#model) - Configure model endpoints for a running Service
## Global Parameters
```bash
clearml-serving [-h] [--debug] [--id ID] {list,create,metrics,config,model}
```
|Name|Description|Optional|
|---|---|---|
|`--id`|Serving Service (Control plane) Task ID to configure (if not provided automatically detect the running control plane Task) |
|
|`--debug` | Print debug messages |
|
:::info Service ID
The Serving Service's ID (`--id`) is required to execute the `metrics`, `config`, and `model` commands.
:::
## list
List running Serving Services.
```bash
clearml-serving list [-h]
```
## create
Create a new Serving Service.
```bash
clearml-serving create [-h] [--name NAME] [--tags TAGS [TAGS ...]] [--project PROJECT]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--name` |Serving service's name. Default: `Serving-Service`|
|
|`--project`|Serving service's project. Default: `DevOps`|
|
|`--tags` |Serving service's user tags. The serving service can be labeled, which can be useful for organizing |
|
## metrics
Configure inference metrics Service.
```bash
clearml-serving metrics [-h] {add,remove,list}
```
### add
Add/modify metric for a specific endpoint.
```bash
clearml-serving metrics add [-h] --endpoint ENDPOINT [--log-freq LOG_FREQ]
[--variable-scalar VARIABLE_SCALAR [VARIABLE_SCALAR ...]]
[--variable-enum VARIABLE_ENUM [VARIABLE_ENUM ...]]
[--variable-value VARIABLE_VALUE [VARIABLE_VALUE ...]]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--endpoint`|Metric endpoint name including version (e.g. `"model/1"` or a prefix `"model/*"`). Notice: it will override any previous endpoint logged metrics|
|
|`--log-freq`|Logging request frequency, between 0.0 to 1.0. Example: 1.0 means all requests are logged, 0.5 means half of the requests are logged if not specified. To use global logging frequency, see [`config --metric-log-freq`](#config)|
|
|`--variable-scalar`|Add float (scalar) argument to the metric logger, `
=`. Example: with specific buckets: `"x1=0,0.2,0.4,0.6,0.8,1"` or with min/max/num_buckets `"x1=0.0/1.0/5"` | |
|`--variable-enum`|Add enum (string) argument to the metric logger, `=`. Example: `"detect=cat,dog,sheep"` ||
|`--variable-value`|Add non-samples scalar argument to the metric logger, ``. Example: `"latency"` ||
### remove
Remove metric from a specific endpoint.
```bash
clearml-serving metrics remove [-h] [--endpoint ENDPOINT]
[--variable VARIABLE [VARIABLE ...]]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--endpoint`| Metric endpoint name including version (e.g. `"model/1"` or a prefix `"model/*"`) |
|
|`--variable`| Remove (scalar/enum) argument from the metric logger, `
` example: `"x1"` ||
### list
List metrics logged on all endpoints.
```bash
clearml-serving metrics list [-h]
```
## config
Configure a new Serving Service.
```bash
clearml-serving config [-h] [--base-serving-url BASE_SERVING_URL]
[--triton-grpc-server TRITON_GRPC_SERVER]
[--kafka-metric-server KAFKA_METRIC_SERVER]
[--metric-log-freq METRIC_LOG_FREQ]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--base-serving-url`|External base serving service url. Example: `http://127.0.0.1:8080/serve`|
|
|`--triton-grpc-server`|External ClearML-Triton serving container gRPC address. Example: `127.0.0.1:9001`|
|
|`--kafka-metric-server`|External Kafka service url. Example: `127.0.0.1:9092`|
|
|`--metric-log-freq`|Set default metric logging frequency between 0.0 to 1.0. 1.0 means that 100% of all requests are logged|
|
## model
Configure model endpoints for an already running Service.
```bash
clearml-serving model [-h] {list,remove,upload,canary,auto-update,add}
```
### list
List current models.
```bash
clearml-serving model list [-h]
```
### remove
Remove model by its endpoint name.
```bash
clearml-serving model remove [-h] [--endpoint ENDPOINT]
```
**Parameter**
|Name|Description|Optional|
|---|---|---|
|`--endpoint` | Model endpoint name |
|
### upload
Upload and register model files/folder.
```bash
clearml-serving model upload [-h] --name NAME [--tags TAGS [TAGS ...]] --project PROJECT
[--framework {scikit-learn,xgboost,lightgbm,tensorflow,pytorch}]
[--publish] [--path PATH] [--url URL]
[--destination DESTINATION]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--name`|Specifying the model name to be registered in|
|
|`--tags`| Add tags to the newly created model|
|
|`--project`| Specify the project for the model to be registered in|
|
|`--framework`| Specify the model framework. Options are: "scikit-learn", "xgboost", "lightgbm", "tensorflow", "pytorch" |
|
|`--publish`| Publish the newly created model (change model state to "published" (i.e. locked and ready to deploy)|
|
|`--path`|Specify a model file/folder to be uploaded and registered|
|
|`--url`| Specify an already uploaded model url (e.g. `s3://bucket/model.bin`, `gs://bucket/model.bin`)|
|
|`--destination`|Specify the target destination for the model to be uploaded (e.g. `s3://bucket/folder/`, `gs://bucket/folder/`)|
|
### canary
Add model Canary/A/B endpoint.
```bash
clearml-serving model canary [-h] [--endpoint ENDPOINT] [--weights WEIGHTS [WEIGHTS ...]]
[--input-endpoints INPUT_ENDPOINTS [INPUT_ENDPOINTS ...]]
[--input-endpoint-prefix INPUT_ENDPOINT_PREFIX]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--endpoint`| Model canary serving endpoint name (e.g. `my_model/latest`)|
|
|`--weights`| Model canary weights (order matching model ep), (e.g. 0.2 0.8) |
|
|`--input-endpoints`|Model endpoint prefixes, can also include version (e.g. `my_model`, `my_model/v1`)|
|
|`--input-endpoint-prefix`| Model endpoint prefix, lexicographic order or by version `
` (e.g. `my_model/1`, `my_model/v1`), where the first weight matches the last version.||
### auto-update
Add/Modify model auto-update service.
```bash
clearml-serving model auto-update [-h] [--endpoint ENDPOINT] --engine ENGINE
[--max-versions MAX_VERSIONS] [--name NAME]
[--tags TAGS [TAGS ...]] [--project PROJECT]
[--published] [--preprocess PREPROCESS]
[--input-size INPUT_SIZE [INPUT_SIZE ...]]
[--input-type INPUT_TYPE] [--input-name INPUT_NAME]
[--output-size OUTPUT_SIZE [OUTPUT_SIZE ...]]
[--output_type OUTPUT_TYPE] [--output-name OUTPUT_NAME]
[--aux-config AUX_CONFIG [AUX_CONFIG ...]]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--endpoint`| Base model endpoint (must be unique)|
|
|`--engine`| Model endpoint serving engine (triton, sklearn, xgboost, lightgbm)|
|
|`--max-versions`|Max versions to store (and create endpoints) for the model. Highest number is the latest version |
|
|`--name`| Specify model name to be selected and auto-updated (notice regexp selection use `"$name^"` for exact match) |
|
|`--tags`|Specify tags to be selected and auto-updated |
|
|`--project`|Specify model project to be selected and auto-updated |
|
|`--published`| Only select published model for auto-update |
|
|`--preprocess` |Specify Pre/Post processing code to be used with the model (point to local file / folder) - this should hold for all the models |
|
|`--input-size`| Specify the model matrix input size [Rows x Columns X Channels etc ...] |
|
|`--input-type`| Specify the model matrix input type. Examples: uint8, float32, int16, float16 etc. |
|
|`--input-name`|Specify the model layer pushing input into. Example: layer_0 |
|
|`--output-size`|Specify the model matrix output size [Rows x Columns X Channels etc ...]|
|
|`--output_type`| Specify the model matrix output type. Examples: uint8, float32, int16, float16 etc. |
|
|`--output-name`|Specify the model layer pulling results from. Examples: layer_99|
|
|`--aux-config`| Specify additional engine specific auxiliary configuration in the form of key=value. Example: `platform=onnxruntime_onnx response_cache.enable=true max_batch_size=8`. Notice: you can also pass a full configuration file (e.g. Triton "config.pbtxt")|
|
### add
Add/Update model.
```bash
clearml-serving model add [-h] --engine ENGINE --endpoint ENDPOINT [--version VERSION]
[--model-id MODEL_ID] [--preprocess PREPROCESS]
[--input-size INPUT_SIZE [INPUT_SIZE ...]]
[--input-type INPUT_TYPE] [--input-name INPUT_NAME]
[--output-size OUTPUT_SIZE [OUTPUT_SIZE ...]]
[--output-type OUTPUT_TYPE] [--output-name OUTPUT_NAME]
[--aux-config AUX_CONFIG [AUX_CONFIG ...]] [--name NAME]
[--tags TAGS [TAGS ...]] [--project PROJECT] [--published]
```
**Parameters**
|Name|Description|Optional|
|---|---|---|
|`--engine`| Model endpoint serving engine (triton, sklearn, xgboost, lightgbm)|
|
|`--endpoint`| Base model endpoint (must be unique)|
|
|`--version`|Model endpoint version (default: None) |
|
|`model-id`|Specify a model ID to be served|
|
|`--preprocess` |Specify Pre/Post processing code to be used with the model (point to local file / folder) - this should hold for all the models |
|
|`--input-size`| Specify the model matrix input size [Rows x Columns X Channels etc ...] |
|
|`--input-type`| Specify the model matrix input type. Examples: uint8, float32, int16, float16 etc. |
|
|`--input-name`|Specify the model layer pushing input into. Example: layer_0 |
|
|`--output-size`|Specify the model matrix output size [Rows x Columns X Channels etc ...]|
|
|`--output_type`| Specify the model matrix output type. Examples: uint8, float32, int16, float16 etc. |
|
|`--output-name`|Specify the model layer pulling results from. Examples: layer_99|
|
|`--aux-config`| Specify additional engine specific auxiliary configuration in the form of key=value. Example: `platform=onnxruntime_onnx response_cache.enable=true max_batch_size=8`. Notice: you can also pass a full configuration file (e.g. Triton "config.pbtxt")|
|
|`--name`| Instead of specifying `model-id` select based on model name |
|
|`--tags`|Specify tags to be selected and auto-updated |
|
|`--project`|Instead of specifying `model-id` select based on model project |
|
|`--published`| Instead of specifying `model-id` select based on model published |
|