MLServer CLI¶

The MLServer package includes a mlserver CLI designed to help with some of the common tasks involved with a model’s lifecycle. Below, you can find the full list of supported subcommands. Note that you can also get a similar high-level outline at any time by running:

mlserver --help

Commands¶

mlserver¶

Command-line interface to manage MLServer models.

mlserver [OPTIONS] COMMAND [ARGS]...

Options

--version¶: Show the version and exit.

build¶

Build a Docker image for a custom MLServer runtime.

mlserver build [OPTIONS] FOLDER

Options

-t, --tag <tag>¶

--no-cache¶

Arguments

FOLDER¶: Required argument

dockerfile¶

Generate a Dockerfile

mlserver dockerfile [OPTIONS] FOLDER

Options

-i, --include-dockerignore¶

Arguments

FOLDER¶: Required argument

infer¶

Deprecated: This experimental feature will be removed in future work. Execute batch inference requests against V2 inference server.

mlserver infer [OPTIONS]

Options

-u, --url <url>¶: URL of the MLServer to send inference requests to. Should not contain http or https.

-m, --model-name <model_name>¶: Required Name of the model to send inference requests to.

-i, --input-data-path <input_data_path>¶: Required Local path to the input file containing inference requests to be processed.

-o, --output-data-path <output_data_path>¶: Required Local path to the output file for the inference responses to be written to.

-w, --workers <workers>¶

-r, --retries <retries>¶

-s, --batch-size <batch_size>¶: Send inference requests grouped together as micro-batches.

-b, --binary-data¶: Send inference requests as binary data (not fully supported).

-v, --verbose¶: Verbose mode.

-vv, --extra-verbose¶: Extra verbose mode (shows detailed requests and responses).

-t, --transport <transport>¶

Transport type to use to send inference requests. Can be ‘rest’ or ‘grpc’ (not yet supported).

Options:: rest | grpc

-H, --request-headers <request_headers>¶: Headers to be set on each inference request send to the server. Multiple options are allowed as: -H ‘Header1: Val1’ -H ‘Header2: Val2’. When setting up as environmental provide as ‘Header1:Val1 Header2:Val2’.

--timeout <timeout>¶: Connection timeout to be passed to tritonclient.

--batch-interval <batch_interval>¶: Minimum time interval (in seconds) between requests made by each worker.

--batch-jitter <batch_jitter>¶: Maximum random jitter (in seconds) added to batch interval between requests.

--use-ssl¶: Use SSL in communications with inference server.

--insecure¶: Disable SSL verification in communications. Use with caution.

Environment variables

MLSERVER_INFER_URL: Provide a default for --url

MLSERVER_INFER_MODEL_NAME: Provide a default for --model-name

MLSERVER_INFER_INPUT_DATA_PATH: Provide a default for --input-data-path

MLSERVER_INFER_OUTPUT_DATA_PATH: Provide a default for --output-data-path

MLSERVER_INFER_WORKERS: Provide a default for --workers

MLSERVER_INFER_RETRIES: Provide a default for --retries

MLSERVER_INFER_BATCH_SIZE: Provide a default for --batch-size

MLSERVER_INFER_BINARY_DATA: Provide a default for --binary-data

MLSERVER_INFER_VERBOSE: Provide a default for --verbose

MLSERVER_INFER_EXTRA_VERBOSE: Provide a default for --extra-verbose

MLSERVER_INFER_TRANSPORT: Provide a default for --transport

MLSERVER_INFER_REQUEST_HEADERS: Provide a default for --request-headers

MLSERVER_INFER_CONNECTION_TIMEOUT: Provide a default for --timeout

MLSERVER_INFER_BATCH_INTERVAL: Provide a default for --batch-interval

MLSERVER_INFER_BATCH_JITTER: Provide a default for --batch-jitter

MLSERVER_INFER_USE_SSL: Provide a default for --use-ssl

MLSERVER_INFER_INSECURE: Provide a default for --insecure

init¶

Generate a base project template

mlserver init [OPTIONS]

Options

-t, --template <template>¶

start¶

Start serving a machine learning model with MLServer.

mlserver start [OPTIONS] FOLDER

Arguments

FOLDER¶: Required argument