Mlflow Restore Experiment
Oswald Lemus
The MLflow command-line interface (CLI) provides a simple interface to various functionality in MLflow. You can use the CLI to run projects, start the tracking UI, create and list experiments, download run artifacts,serve MLflow Python Function and scikit-learn models, serve MLflow Python Function and scikit-learn models, and serve models onMicrosoft Azure Machine Learningand Amazon SageMaker.
Path of the local filesystem destination directory to which to download the specified artifacts. If the directory does not exist, it is created. If unspecified the artifacts are downloaded to a new uniquely-named directory on the local filesystem, unless the artifacts already exist on the local filesystem, in which case their local path is returned directly
Log the files within a local directory as an artifact of a run, optionallywithin a run-specific artifact path. Run artifacts can be organized intodirectories, so you can place the artifact in a directory this way.
IMPORTANT: Schema migrations can be slow and are not guaranteed to be transactional -always take a backup of your database before running migrations. The migrations README,which is located at _migrations/README.md, describeslarge migrations and includes information about how to estimate their performance andrecover from failures.
Generate explanations of model predictions on the specified input forthe deployed model for the given input(s). Explanation output formats varyby deployment target, and can include details like feature importance forunderstanding/debugging predictions. Run mlflow deployments help orconsult the documentation for your plugin for details on explanation format.For information about the input data formats accepted by this function,see the following documentation: -in-deployment-tools
Base location for runs to store artifact results. Artifacts will be stored at $artifact_location/$run_id/artifacts. See -runs-are-recorded for more info on the properties of artifact location. If no location is provided, the tracking server will pick a default.
Specific implementation of deletion is dependent on backend stores. FileStore movesexperiments marked for deletion under a .trash folder under the main folder used toinstantiate FileStore. Experiments marked for deletion can be permanently deleted byclearing the .trash folder. It is recommended to use a cron job or an alternateworkflow mechanism to clear .trash folder.
Permanently delete runs in the deleted lifecycle stage from the specified backend store.This command deletes all artifacts and metadata associated with the specified runs.If the provided artifact URL is invalid, the artifact deletion will be bypassed,and the gc process will continue.
Optional comma separated list of experiments to be permanently deleted including all of their associated runs. If experiment ids are not specified, data is removed for all experiments in the deleted lifecycle stage.
Builds a Docker image whose default entrypoint serves an MLflow model at port 8080, using thepython_function flavor. The container serves the model referenced by --model-uri, ifspecified when build-docker is called. If --model-uri is not specified when build_dockeris called, an MLflow Model directory must be mounted as a volume into the /opt/ml/modeldirectory in the container.
Since MLflow 2.10.1, the Docker image built with --model-uri does not install Javafor improved performance, unless the model flavor is one of ["johnsnowlabs", "h2o","mleap", "spark"]. If you need to install Java for other flavors, e.g. custom Python modelthat uses SparkML, please specify the --install-java flag to enforce Java installation.
If specified and there is a conda or virtualenv environment to be activated mlflow will be installed into the environment after it has been activated. The version of installed mlflow will be the same as the one used to invoke this command.
Generates a directory with Dockerfile whose default entrypoint serves an MLflow model at port8080 using the python_function flavor. The generated Dockerfile is written to the specifiedoutput directory, along with the model (if specified). This Dockerfile defines an image thatis equivalent to the one produced by mlflow models build-docker.
Serve a model saved with MLflow by launching a webserver on the specified host and port.The command supports models with the python_function or crate (R Function) flavor.For information about the input data formats accepted by the webserver, see the followingdocumentation: -in-deployment-tools.
Models built using MLflow 1.x will require adjustments to the endpoint request payloadif executed in an environment that has MLflow 2.x installed. In 1.x, a request payloadwas in the format: 'columns': [str], 'data': [[...]]. 2.x models requirepayloads that are defined by the structural-defining keys of either dataframe_split,instances, inputs or dataframe_records. See the examples below fordemonstrations of the changes to the invocation API endpoint in 2.0.
Requests made in pandas DataFrame structures can be made in either split or recordsoriented formats.See _json.html fordetailed information on orientation formats for converting a pandas DataFrame to json.
Note that model registry URIs (i.e. URIs in the form models:/) are notsupported, as artifacts in the model registry are intended to be read-only.Editing requirements is read-only artifact repositories is also not supported.
Remove all recipe outputs from the cache, or remove the cached outputs of a particularrecipe step if specified. After cached outputs are cleaned for a particular step, the stepwill be re-executed in its entirety the next time it is run.
Required The name of the recipe profile to use. Profiles customize the configuration of one or more recipe steps, and recipe executions with different profiles often produce different results.
Path to a file containing a JSON-formatted VPC configuration. This configuration will be used when creating the new SageMaker model associated with this application. For more information, see _VpcConfig.html
If specified, this command will return immediately after starting the deployment process. It will not wait for the deployment process to complete. The caller is responsible for monitoring the deployment process via native SageMaker APIs or the AWS console.
If specified, this command will return immediately after starting the termination process. It will not wait for the termination process to complete. The caller is responsible for monitoring the termination process via native SageMaker APIs or the AWS console.
The server listens on :5000 by default and only accepts connectionsfrom the local machine. To let the server accept connections from other machines, you will needto pass --host 0.0.0.0 to listen on all network interfaces(or a specific interface address).
If specified, configures the mlflow server to be used only for proxied artifact serving. With this mode enabled, functionality of the mlflow tracking service (e.g. run creation, metric logging, and parameter logging) is disabled. The server will only expose endpoints for uploading, downloading, and listing artifacts. Default: False
The MLflow REST API allows you to create, list, and get experiments and runs, and log parameters, metrics, and artifacts.The API is hosted under the /api route on the MLflow tracking server. For example, to search forexperiments on a tracking server hosted at :5000, make a POST request to :5000/api/2.0/mlflow/experiments/search.
Create an experiment with a name. Returns the ID of the newly created experiment.Validates that another experiment with the same name does not already exist and failsif another experiment with the same name already exists.
A collection of tags to set on the experiment. Maximum tag size and number of tags per requestdepends on the storage backend. All storage backends are guaranteed to support tag keys upto 250 bytes in size and tag values up to 5000 bytes in size. All storage backends are alsoguaranteed to support up to 20 tags per request.
Maximum number of experiments desired.Servers may select a desired default max_results value. All servers areguaranteed to support a max_results threshold of at least 1,000 but maysupport more. Callers of this endpoint are encouraged to pass max_resultsexplicitly and leverage page_token to iterate through experiments.
A filter expression over experiment attributes and tags that allows returning a subset ofexperiments. The syntax is a subset of SQL that supports ANDing together binary operationsbetween an attribute or tag, and a constant.
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