clearml-docs/docs/clearml_serving/clearml_serving_cli.md

title

title
ClearML Serving 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 running Serving Services
• create - Create a new Serving Service
• metrics - Configure inference metrics Service
• config - Configure a new Serving Service
• model - Configure model endpoints for a running Service

Global Parameters

clearml-serving [-h] [--debug] [--yes] [--id ID] {list,create,metrics,config,model} 

Name	Description	Mandatory
--id	Serving Service (Control plane) Task ID to configure (if not provided, automatically detect the running control plane Task)
--debug	Print debug messages
--yes	Always answer YES on interactive inputs

:::info Service ID The Serving Service's ID (--id) is required to execute the metrics, config, and model commands. :::

list

List running Serving Services.

clearml-serving list [-h]

create

Create a new Serving Service.

clearml-serving create [-h] [--name NAME] [--tags TAGS [TAGS ...]] [--project PROJECT]

Parameters

Name	Description	Mandatory
--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.

clearml-serving metrics [-h] {add,remove,list}

add

Add/modify metric for a specific endpoint.

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	Mandatory
--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
--variable-scalar	Add float (scalar) argument to the metric logger, <name>=<histogram>. 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". Notice: In cases where 1000s of requests per second reach the serving, it makes no sense to display every datapoint. So scalars can be divided in buckets, and for each minute for example. Then it's possible to calculate what % of the total traffic fell in bucket 1, bucket 2, bucket 3 etc. The Y axis represents the buckets, color is the value in % of traffic in that bucket, and X is time.
--variable-enum	Add enum (string) argument to the metric logger, <name>=<optional_values>. Example: "detect=cat,dog,sheep"
--variable-value	Add non-samples scalar argument to the metric logger, <name>. Example: "latency"

remove

Remove metric from a specific endpoint.

clearml-serving metrics remove [-h] [--endpoint ENDPOINT]
                               [--variable VARIABLE [VARIABLE ...]]

Parameters

Name	Description	Mandatory
--endpoint	Metric endpoint name including version (e.g. "model/1" or a prefix "model/*")
--variable	Remove (scalar/enum) argument from the metric logger, <name> example: "x1"

list

List metrics logged on all endpoints.

clearml-serving metrics list [-h]

config

Configure a new Serving Service.

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	Mandatory
--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.

clearml-serving model [-h] {list,remove,upload,canary,auto-update,add}

list

List current models.

clearml-serving model list [-h]

remove

Remove model by its endpoint name.

clearml-serving model remove [-h] [--endpoint ENDPOINT]

Parameter

Name	Description	Mandatory
--endpoint	Model endpoint name

upload

Upload and register model files/folder.

clearml-serving model upload [-h] --name NAME [--tags TAGS [TAGS ...]] --project PROJECT
                             [--framework {tensorflow,tensorflowjs,tensorflowlite,pytorch,torchscript,caffe,caffe2,onnx,keras,mknet,cntk,torch,darknet,paddlepaddle,scikitlearn,xgboost,lightgbm,parquet,megengine,catboost,tensorrt,openvino,custom}]
                             [--publish] [--path PATH] [--url URL]
                             [--destination DESTINATION]

Parameters

Name	Description	Mandatory
--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: 'tensorflow', 'tensorflowjs', 'tensorflowlite', 'pytorch', 'torchscript', 'caffe', 'caffe2', 'onnx', 'keras', 'mknet', 'cntk', 'torch', 'darknet', 'paddlepaddle', 'scikitlearn', 'xgboost', 'lightgbm', 'parquet', 'megengine', 'catboost', 'tensorrt', 'openvino', 'custom'
--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. For example: S3: s3://bucket/folder Non-AWS S3-like services (such as MinIO): s3://host_addr:port/bucket. Note that port specification is required. Google Cloud Storage: gs://bucket-name/folder Azure Storage: azure://<account name>.blob.core.windows.net/path/to/file

canary

Add model Canary/A/B endpoint.

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	Mandatory
--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 <int> (e.g. my_model/1, my_model/v1), where the first weight matches the last version.

auto-update

Add/Modify model auto-update service.

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	Mandatory
--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.

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	Mandatory
--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