--- 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) | No | |`--debug` | Print debug messages | Yes |
:::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`| No | |`--project`|Serving service's project. Default: `DevOps`| No | |`--tags` |Serving service's user tags. The serving service can be labeled, which can be useful for organizing | Yes|
## 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| No| |`--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)| Yes| |`--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"` | Yes| |`--variable-enum`|Add enum (string) argument to the metric logger, `=`. Example: `"detect=cat,dog,sheep"` |Yes| |`--variable-value`|Add non-samples scalar argument to the metric logger, ``. Example: `"latency"` |Yes|
### 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/*"`) |No| |`--variable`| Remove (scalar/enum) argument from the metric logger, `` example: `"x1"` |Yes|
### 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`|Yes| |`--triton-grpc-server`|External ClearML-Triton serving container gRPC address. Example: `127.0.0.1:9001`|Yes| |`--kafka-metric-server`|External Kafka service url. Example: `127.0.0.1:9092`|Yes| |`--metric-log-freq`|Set default metric logging frequency between 0.0 to 1.0. 1.0 means that 100% of all requests are logged|Yes|

## 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 | No|
### 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| No| |`--tags`| Add tags to the newly created model| Yes| |`--project`| Specify the project for the model to be registered in| No| |`--framework`| Specify the model framework. Options are: "scikit-learn", "xgboost", "lightgbm", "tensorflow", "pytorch" | Yes| |`--publish`| Publish the newly created model (change model state to "published" (i.e. locked and ready to deploy)|Yes| |`--path`|Specify a model file/folder to be uploaded and registered| Yes| |`--url`| Specify an already uploaded model url (e.g. `s3://bucket/model.bin`, `gs://bucket/model.bin`)|Yes| |`--destination`|Specify the target destination for the model to be uploaded (e.g. `s3://bucket/folder/`, `gs://bucket/folder/`)|Yes|
### 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`)| Yes| |`--weights`| Model canary weights (order matching model ep), (e.g. 0.2 0.8) |Yes| |`--input-endpoints`|Model endpoint prefixes, can also include version (e.g. `my_model`, `my_model/v1`)| Yes| |`--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.|Yes|
### 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)| No| |`--engine`| Model endpoint serving engine (triton, sklearn, xgboost, lightgbm)| No| |`--max-versions`|Max versions to store (and create endpoints) for the model. Highest number is the latest version | Yes| |`--name`| Specify model name to be selected and auto-updated (notice regexp selection use `"$name^"` for exact match) | Yes| |`--tags`|Specify tags to be selected and auto-updated |Yes| |`--project`|Specify model project to be selected and auto-updated | Yes| |`--published`| Only select published model for auto-update |Yes| |`--preprocess` |Specify Pre/Post processing code to be used with the model (point to local file / folder) - this should hold for all the models |Yes| |`--input-size`| Specify the model matrix input size [Rows x Columns X Channels etc ...] | Yes| |`--input-type`| Specify the model matrix input type. Examples: uint8, float32, int16, float16 etc. |Yes| |`--input-name`|Specify the model layer pushing input into. Example: layer_0 | Yes| |`--output-size`|Specify the model matrix output size [Rows x Columns X Channels etc ...]|Yes| |`--output_type`| Specify the model matrix output type. Examples: uint8, float32, int16, float16 etc. | Yes| |`--output-name`|Specify the model layer pulling results from. Examples: layer_99| Yes| |`--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")|Yes|
### 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)| No| |`--endpoint`| Base model endpoint (must be unique)| No| |`--version`|Model endpoint version (default: None) | Yes| |`model-id`|Specify a model ID to be served|No| |`--preprocess` |Specify Pre/Post processing code to be used with the model (point to local file / folder) - this should hold for all the models |Yes| |`--input-size`| Specify the model matrix input size [Rows x Columns X Channels etc ...] | Yes| |`--input-type`| Specify the model matrix input type. Examples: uint8, float32, int16, float16 etc. |Yes| |`--input-name`|Specify the model layer pushing input into. Example: layer_0 | Yes| |`--output-size`|Specify the model matrix output size [Rows x Columns X Channels etc ...]|Yes| |`--output_type`| Specify the model matrix output type. Examples: uint8, float32, int16, float16 etc. | Yes| |`--output-name`|Specify the model layer pulling results from. Examples: layer_99| Yes| |`--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")|Yes| |`--name`| Instead of specifying `model-id` select based on model name | Yes| |`--tags`|Specify tags to be selected and auto-updated |Yes| |`--project`|Instead of specifying `model-id` select based on model project | Yes| |`--published`| Instead of specifying `model-id` select based on model published |Yes|