Types
MLFlowClient.MLFlow — TypeMLFlowBase type which defines location and version for MLFlow API service.
Fields
apiroot::String: API root URL, e.g.http://localhost:5000/apiapiversion::Union{Integer, AbstractFloat}: used API version, e.g.2.0headers::Dict: HTTP headers to be provided with the REST API requests.username::Union{Nothing, String}: username for basic authentication.password::Union{Nothing, String}: password for basic authentication.
You cannot provide an Authorization header when an username and password are provided. An error will be thrown in that case.
- If
MLFLOW_TRACKING_URIis set, the providedapirootwill be ignored. - If
MLFLOW_TRACKING_USERNAMEis set, the providedusernamewill be ignored. - If
MLFLOW_TRACKING_PASSWORDis set, the providedpasswordwill be ignored.
These indications will be displayed as warnings.
Examples
mlf = MLFlow()remote_url="https://<your-server>.cloud.databricks.com"; # address of your remote server
mlf = MLFlow(remote_url, headers=Dict("Authorization" => "Bearer <your-secret-token>"))MLFlowClient.Tag — TypeTag <: LoggingDataGeneric tag type for MLFlow entities.
Fields
key::String: The tag key.value::String: The tag value.
MLFlowClient.Dataset — TypeDatasetRepresents a reference to data used for training, testing, or evaluation during the model development process.
Fields
name::String: The name of the dataset.digest::String: The digest of the dataset.source_type::String: The type of the dataset source.source::String: Source information for the dataset.schema::String: The schema of the dataset. This field is optional.profile::String: The profile of the dataset. This field is optional.
MLFlowClient.DatasetInput — TypeDatasetInputRepresents a dataset and input tags.
Fields
tags::Array{Tag}: A list of tags for the dataset input.dataset::Dataset: The dataset being used as a run input.
MLFlowClient.FileInfo — TypeFileInfoFields
path::String: Path relative to the root artifact directory run.is_dir::Bool: Whether the path is a directory.file_size::Int64: Size in bytes. Unset for directories.
MLFlowClient.ModelVersion — TypeModelVersionFields
name::String: Unique name of the model.version::String: Model’s version number.creation_timestamp::Int64: Timestamp recorded when this model_version was created.last_updated_timestamp::Int64: Timestamp recorded when metadata for this model_version was last updated.user_id::Union{String, Nothing}: User that created this model_version.current_stage::String: Current stage for this model_version.description::String: Description of this model_version.source::String: URI indicating the location of the source model artifacts, used when creating model_version.run_id::String: MLflow run ID used when creating model_version, if source was generated by an experiment run stored in MLflow tracking server.status::ModelVersionStatusEnum: Current status of model_version.status_message::String: Details on current status, if it is pending or failed.tags::Array{Tag}: Additional metadata key-value pairs.run_link::String: Direct link to the run that generated this version. This field is set at model version creation time only for model versions whose source run is from a tracking server that is different from the registry server.aliases::Array{String}: Aliases pointing to this model_version.model_id::String: Optionalmodel_idforModelVersionthat is used to link theRegisteredModelto the source logged model.model_params::Array{ModelParam}: Optional parameters for the model.model_metrics::Array{ModelMetric}: Optional metrics for the model.deployment_job_state::ModelVersionDeploymentJobState: Deployment job state for thisModelVersion.
MLFlowClient.RegisteredModel — TypeRegisteredModelFields
name::String: Unique name for the model.creation_timestamp::Int64: Timestamp recorded when this RegisteredModel was created.last_updated_timestamp::Int64: Timestamp recorded when metadata for this RegisteredModel was last updated.user_id::Union{String, Nothing}: User that created this RegisteredModel.description::Union{String, Nothing}: Description of this RegisteredModel.latest_versions::Array{ModelVersion}: Collection of latest model versions for each stage. Only contains models with current READY status.tags::Array{Tag}: Additional metadata key-value pairs.aliases::Array{RegisteredModelAlias}: Aliases pointing to model versions associated with this RegisteredModel.deployment_job_id::String: Deployment job id for this model.deployment_job_state::State: Deployment job state for this model.
MLFlowClient.RegisteredModelAlias — TypeRegisteredModelAliasAlias for a registered model.
Fields
alias::String: The name of the alias.version::String: The model version number that the alias points to.
MLFlowClient.Experiment — TypeExperimentFields
experiment_id::Integer: Unique identifier for the experiment.name::String: Human readable name that identifies the experiment.artifact_location::String: Location where artifacts for the experiment are stored.lifecycle_stage::String: Current life cycle stage of the experiment: “active” or “deleted”. Deleted experiments are not returned by APIs.last_update_time::Int64: Last update time.creation_time::Int64: Creation time.tags::Array{Tag}: Additional metadata key-value pairs.
MLFlowClient.Run — TypeRunA single run.
Fields
info::RunInfo: Metadata of the run.data::RunData: Run data (metrics, params, and tags).inputs::RunInputs: Run inputs.outputs::RunOutputs: Run outputs.
MLFlowClient.Param — TypeParam <: LoggingDataParam associated with a run.
Fields
key::String: Key identifying this param.value::String: Value associated with this param.
MLFlowClient.Metric — TypeMetric <: LoggingDataMetric associated with a run, represented as a key-value pair.
Fields
key::String: Key identifying this metric.value::Float64: Value associated with this metric.timestamp::Int64: The timestamp at which this metric was recorded.step::Union{Int64, Nothing}: Step at which to log the metric.
MLFlowClient.RunData — TypeRunInputsRun data (metrics, params, and tags).
Fields
metrics::Array{Metric}: Run metrics.params::Array{Param}: Run parameters.tags::Array{Tag}: Additional metadata key-value pairs.
MLFlowClient.RunInfo — TypeRunInfoMetadata of a single run.
Fields
run_id::String: Unique identifier for the run.run_name::String: The name of the run.experiment_id::String: The experiment ID.status::RunStatus: Current status of the run.start_time::Int64: Unix timestamp of when the run started in milliseconds.end_time::Int64: Unix timestamp of when the run ended in milliseconds.artifact_uri::String: URI of the directory where artifacts should be uploaded. This can be a local path (starting with “/”), or a distributed file system (DFS) path, like s3://bucket/directory or dbfs:/my/directory. If not set, the local ./mlruns directory is chosen.lifecycle_stage::String: Current life cycle stage of the experiment: "active" or "deleted".
MLFlowClient.RunInputs — TypeRunInputsRun inputs.
Fields
dataset_inputs::Array{DatasetInput}: Dataset inputs to the Run.
MLFlowClient.User — TypeUserFields
id::String: User ID.username::String: Username.is_admin::Bool: Whether the user is an admin.experiment_permissions::Array{ExperimentPermission}: All experiment permissions explicitly granted to the user.registered_model_permissions::Array{RegisteredModelPermission}: All registered model explicitly granted to the user.
MLFlowClient.ExperimentPermission — TypeExperimentPermissionFields
experiment_id::String:Experimentid.user_id::String:Userid.permission::Permission.PermissionEnum: Permission granted.
MLFlowClient.RegisteredModelPermission — TypeRegisteredModelPermissionFields
name::String:RegisteredModelname.user_id::String:Userid.permission::Permission.PermissionEnum: Permission granted.