![]() The method to split the transform job’s data files into smaller batches -a, -accept The compression type of the transform data -s, -split-type Required The S3 path to store the output results of the Sagemaker transform job -compression-type Required The multipurpose internet mail extension (MIME) type of the data -o, -output-path Required S3 key name prefix or manifest of the input data -content-type Required Input data type for the transform job -u, -input-uri For more information about supported remote URIs for model artifacts, see -input-data-type A local path, a ‘runs:/’ URI, or a remote storage URI (e.g., an ‘s3://’ URI). Required Transform job name -m, -model-uri DefaultĮnvironment variables MLFLOW_EXPERIMENT_NAME If specified, build a new Docker image that’s based on the image specified by the image field in the MLproject file, and contains files in the project directory. If not specified, the MLflow Run name is left unset. The name to give the MLflow Run associated with the project execution. Note: this argument is used internally by the MLflow project APIs and should not be specified. If specified, the given run ID will be used instead of creating a new run. MLflow downloads artifacts from distributed URIs passed to parameters of type ‘path’ to subdirectories of storage_dir. For example, if MLproject.yaml contains a python_env key, If unspecified, the appropriate environment manager is automatically selected based on If specified, create an environment for MLproject using the specifiedĮnvironment manager. The exact content which should be provided is different for each execution backend and is documented at. Path to JSON file (must end in ‘.json’) or JSON string which will be passed as config to the backend. See for more info on configuring a Databricks CLI profile. Otherwise, runs against the workspace specified by the default Databricks CLI profile. by setting the MLFLOW_TRACKING_URI environment variable), will run against the workspace specified by. If running against Databricks, will run against a Databricks workspace determined as follows: if a Databricks tracking URI of the form ‘databricks://profile’ has been set (e.g. Supported values: ‘local’, ‘databricks’, kubernetes (experimental). b, -backend Įxecution backend to use for run. ID of the experiment under which to launch the run. ![]() If not specified, ‘experiment-id’ option will be used to launch run. Name of the experiment under which to launch the run. The argument will then be passed as docker run –name value or docker run –name respectively. Provided parameters that are not in the list of parameters for an entry point will be passed to the corresponding entry point as command-line arguments in the form –name value -A, -docker-args Ī docker run argument or flag, of the form -A name=value (e.g. P, -param-list Ī parameter for the run, of the form -P name=value. Version of the project to run, as a Git commit reference for Git projects. py files and the default shell (specified by environment variable $SHELL) to run. If the entry point is not found, attempts to run the project file with the specified name as a script, using ‘python’ to run. For more information about supported remote URIs for model artifacts, see -f, -flavor Įntry point within project. This will be auto inferred if it’s not given For more information about supported remote URIs for model artifacts, see -f, -flavor See all supported deployment targets and installation Support is currently installed for deployment to: sagemaker ![]() More details on the supported URI format and config options Required Name of the deployment -t, -target See documentation/help for your deployment target for a list of supported config options. Extra target-specific config for the model deployment, of the form -C name=value.
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