mlrun.config

Configuration system.

Configuration can be in either a configuration file specified by MLRUN_CONFIG_FILE environment variable or by environment variables.

Environment variables are in the format “MLRUN_httpdb__port=8080”. This will be mapped to config.httpdb.port. Values should be in JSON format.

class mlrun.config.Config(cfg=None)

Bases: object

property dbpath

static decode_base64_config_and_load_to_object(attribute_path: str, expected_type=<class 'dict'>)

decodes and loads the config attribute to expected type :param attribute_path: the path in the default_config e.g. preemptible_nodes.node_selector :param expected_type: the object type valid values are : dict, list etc… :return: the expected type instance

dump_yaml(stream=None)

classmethod from_dict(dict_)

static get_build_args()

get_default_function_node_selector() → dict

static get_default_function_pod_requirement_resources(requirement: str, with_gpu: bool = True)

Parameters

• requirement – kubernetes requirement resource one of the following : requests, limits

• with_gpu – whether to return requirement resources with nvidia.com/gpu field (e.g. you cannot specify GPU requests without specifying GPU limits) https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/

Returns

a dict containing the defaults resources (cpu, memory, nvidia.com/gpu)

get_default_function_pod_resources(with_gpu_requests=False, with_gpu_limits=False)

get_default_function_security_context() → dict

static get_hub_url()

get_model_monitoring_file_target_path(project: str = '', kind: str = '', target: str = 'online', artifact_path: Optional[str] = None) → str

Get the full path from the configuration based on the provided project and kind.

Parameters

• project – Project name.

• kind – Kind of target path (e.g. events, log_stream, endpoints, etc.)

• target – Can be either online or offline. If the target is online, then we try to get a specific path for the provided kind. If it doesn’t exist, use the default path. If the target path is offline and the offline path is already a full path in the configuration, then the result will be that path as-is. If the offline path is a relative path, then the result will be based on the project artifact path and the offline relative path. If project artifact path wasn’t provided, then we use MLRun artifact path instead.

• artifact_path – Optional artifact path that will be used as a relative path. If not provided, the relative artifact path will be taken from the global MLRun artifact path.

Returns

Full configured path for the provided kind.

static get_parsed_igz_version() → Optional[semver.version.Version]

get_preemptible_node_selector() → dict

get_preemptible_tolerations() → list

static get_security_context_enrichment_group_id(user_unix_id: int) → int

static get_storage_auto_mount_params()

get_v3io_access_key()

static get_valid_function_priority_class_names()

property iguazio_api_url

we want to be able to run with old versions of the service who runs the API (which doesn’t configure this value) so we’re doing best effort to try and resolve it from other configurations TODO: Remove this hack when 0.6.x is old enough

is_api_running_on_k8s()

is_ce_mode() → bool

is_nuclio_detected()

static is_pip_ca_configured()

is_preemption_nodes_configured()

static is_running_on_iguazio() → bool

static reload()

resolve_chief_api_url() → str

resolve_kfp_url(namespace=None)

resolve_runs_monitoring_missing_runtime_resources_debouncing_interval()

static resolve_ui_url()

to_dict()

update(cfg, skip_errors=False)

use_nuclio_mock(force_mock=None)

verify_security_context_enrichment_mode_is_allowed()

property version

mlrun.config.is_running_as_api()

mlrun.config.read_env(env=None, prefix='MLRUN_')

Read configuration from environment