id stringlengths 14 15 | text stringlengths 112 2.01k | metadata dict |
|---|---|---|
f7c061c33796-0 | Quickstart
Installing MLflow
You install MLflow by running:
# Install MLflow
pip
install
mlflow
# Install MLflow with extra ML libraries and 3rd-party tools
pip
install
mlflow
[extras
# Install a lightweight version of MLflow
pip
install
mlflow-skinny
install.packages
"mlflow"
Note
MLflow works on MacOS... | {
"url": "https://mlflow.org/docs/latest/quickstart.html"
} |
f7c061c33796-1 | We avoid running directly from our clone of MLflow as doing so would cause the tutorial to
use MLflow from source, rather than your PyPi installation of MLflow.
Using the Tracking API
The MLflow Tracking API lets you log metrics and artifacts (files) from your data
science code and see a history of your runs. You can... | {
"url": "https://mlflow.org/docs/latest/quickstart.html"
} |
f7c061c33796-2 | mlflow_log_artifact
"output.txt"
Viewing the Tracking UI
By default, wherever you run your program, the tracking API writes data into files into a local
./mlruns directory. You can then run MLflow’s Tracking UI:
mlflow
ui
mlflow_ui
()
and view it at http://localhost:5000.
Note
If you see message [CRITICAL] WO... | {
"url": "https://mlflow.org/docs/latest/quickstart.html"
} |
f7c061c33796-3 | For more information, see MLflow Projects.
Saving and Serving Models
MLflow includes a generic MLmodel format for saving models from a variety of tools in diverse
flavors. For example, many models can be served as Python functions, so an MLmodel file can
declare how each model should be interpreted as a Python functi... | {
"url": "https://mlflow.org/docs/latest/quickstart.html"
} |
f7c061c33796-4 | Once you have started the server, you can pass it some sample data and see the
predictions.
The following example uses curl to send a JSON-serialized pandas DataFrame with the split
orientation to the model server. For more information about the input data formats accepted by
the pyfunc model server, see the MLflow de... | {
"url": "https://mlflow.org/docs/latest/quickstart.html"
} |
f7c061c33796-5 | mlflow_set_experiment
"/my-experiment"
Log to Databricks Community Edition
Alternatively, sign up for Databricks Community Edition,
a free service that includes a hosted tracking server. Note that
Community Edition is intended for quick experimentation rather than production use cases.
After signing up, run databric... | {
"url": "https://mlflow.org/docs/latest/quickstart.html"
} |
2eb3b7e954eb-0 | MLflow Tracking
The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files
when running your machine learning code and for later visualizing the results.
MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs.
Table of Conte... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-1 | Key-value input parameters of your choice. Both keys and values are strings.
Key-value metrics, where the value is numeric. Each metric can be updated throughout the
course of the run (for example, to track how your model’s loss function is converging), and
MLflow records and lets you visualize the metric’s full histo... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-2 | There are different kinds of remote tracking URIs:
Local file path (specified as file:/my/local/dir), where data is just directly stored locally.
Database encoded as <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>. MLflow supports the dialects mysql, mssql, sqlite, and postgresql. For more details... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-3 | Scenario 1: MLflow on localhost
Many developers run MLflow on their local machine, where both the backend and artifact store share a directory
on the local filesystem—./mlruns—as shown in the diagram. The MLflow client directly interfaces with an
instance of a FileStore and LocalArtifactRepository.
In this simple sce... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-4 | Command to run the tracking server in this configuration
mlflow
server
-backend-store-uri
file:///path/to/mlruns
-no-serve-artifacts
To store all runs’ MLflow entities, the MLflow client interacts with the tracking server via a series of REST requests:
Part 1a and b:
The MLflow client creates an instance of a R... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-5 | The Tracking Server creates an instance of an SQLAlchemyStore and connects to the remote host to
insert MLflow entities in the database
For artifact logging, the MLflow client interacts with the remote Tracking Server and artifact storage host:
Part 2a, b, and c:
The MLflow client uses RestStore to send a REST reque... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-6 | # or permissions.
-artifacts-destination
s3://bucket_name
-host
remote_host
Enabling the Tracking Server to perform proxied artifact access in order to route client artifact requests to an object store location:
Part 1a and b:
The MLflow client creates an instance of a RestStore and sends REST API requests to lo... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-7 | Warning
The MLflow artifact proxied access service enables users to have an assumed role of access to all artifacts that are accessible to the Tracking Server.
Administrators who are enabling this feature should ensure that the access level granted to the Tracking Server for artifact
operations meets all security requ... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-8 | Note
If migrating from Scenario 5 to Scenario 6 due to request volumes, it is important to perform two validations:
Ensure that the new tracking server that is operating in --artifacts-only mode has access permissions to the
location set by --artifacts-destination that the former multi-role tracking server had.
The f... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-9 | mlflow.set_experiment() sets an experiment as active. If the experiment does not exist,
creates a new experiment. If you do not specify an experiment in mlflow.start_run(), new
runs are launched under this experiment.
mlflow.start_run() returns the currently active run (if one exists), or starts a new run
and returns ... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-10 | mlflow.log_metric() logs a single key-value metric. The value must always be a number.
MLflow remembers the history of values for each metric. Use mlflow.log_metrics() to log
multiple metrics at once.
mlflow.set_tag() sets a single key-value tag in the currently active run. The key and
value are both strings. Use mlfl... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-11 | timestamp is an optional long value that represents the time that the metric was logged. timestamp defaults to the current time. step is an optional integer that represents any measurement of training progress (number of training iterations, number of epochs, and so on). step defaults to 0 and has the following require... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-12 | Use library-specific autolog calls for each library you use in your code. See below for examples.
The following libraries support autologging:
Scikit-learn
Keras
Gluon
XGBoost
LightGBM
Statsmodels
Spark
Fastai
Pytorch
For flavors that automatically save models as an artifact, additional files for dependency ... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-13 | containing the following data:
Parent
run
Child
run
Child
run
...
containing the following data:
Run type
Metrics
Parameters
Tags
Artifacts
Parent
Training score
Parameter search estimator’s parameters
Best parameter combination
Class name
Fully qualified class name
Fitted parameter search estimator
F... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-14 | Gluon
Call mlflow.gluon.autolog() before your training code to enable automatic logging of metrics and parameters.
See example usages with Gluon .
Autologging captures the following information:
Framework
Metrics
Parameters
Tags
Artifacts
Gluon
Training loss; validation loss; user-specified metrics
Number of ... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-15 | Note
Each model subclass that overrides fit expects and logs its own parameters.
Spark
Initialize a SparkSession with the mlflow-spark JAR attached (e.g.
SparkSession.builder.config("spark.jars.packages", "org.mlflow.mlflow-spark")) and then
call mlflow.spark.autolog() to enable automatic logging of Spark datasource... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-16 | Framework/module
Metrics
Parameters
Tags
Artifacts
pytorch_lightning.trainer.Trainer
Training loss; validation loss; average_test_accuracy;
user-defined-metrics.
fit() parameters; optimizer name; learning rate; epsilon.
Model summary on training start, MLflow Model (Pytorch model) on training end;
pytorch_ligh... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-17 | [name]
MLFLOW_EXPERIMENT_NAME
-experiment-id
MLFLOW_EXPERIMENT_ID
# Set the experiment via environment variables
export
MLFLOW_EXPERIMENT_NAME
=fraud-detection
mlflow
experiments
create
-experiment-name
fraud-detection
# Launch a run. The experiment is inferred from the MLFLOW_EXPERIMENT_NAME environment
... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-18 | run
info
run_id
"tag_key"
"tag_value"
Important
Do not use the prefix mlflow. (e.g. mlflow.note) for a tag. This prefix is reserved for use by MLflow. See System Tags for a list of reserved tag keys.
Tracking UI
The Tracking UI lets you visualize, search and compare runs, as well as download run artifacts or
m... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-19 | Run automated parameter search algorithms, where you query the metrics from various runs to submit new ones. For an example of running automated parameter search algorithms, see the MLflow Hyperparameter Tuning Example project.
MLflow Tracking Servers
In this section:
Storage
Backend Stores
Artifact Stores
File sto... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-20 | A file store backend as ./path_to_store or file:/path_to_store
A database-backed store as SQLAlchemy database URI.
The database URI typically takes the format <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>.
MLflow supports the database dialects mysql, mssql, sqlite, and postgresql.
Drivers are opt... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-21 | The MLflow client caches artifact location information on a per-run basis.
It is therefore not recommended to alter a run’s artifact location before it has terminated.
In addition to local file paths, MLflow supports the following storage systems as artifact
stores: Amazon S3, Azure Blob Storage, Google Cloud Storage,... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-22 | mlflow-artifacts:/mlartifacts
If the host or host:port declaration is absent in client artifact requests to the MLflow server, the client API
will assume that the host is the same as the MLflow Tracking uri.
Note
If an MLflow server is running with the --artifact-only flag, the client should interact with this serve... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-23 | Amazon S3 and S3-compatible storage
MinIO or
Digital Ocean Spaces), specify a URI of the form
s3://<bucket>/<path>
~/.aws/credentials
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
Set up AWS Credentials and Region for Development.
To add S3 file upload extra arguments, set MLFLOW_S3_UPLOAD_EXTRA_ARGS to a JSON object... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-24 | =my_region
Warning
-default-artifact-root
$MLFLOW_S3_ENDPOINT_URL
MLFLOW_S3_ENDPOINT_URL
-default-artifact-root
MLFLOW_S3_ENDPOINT_URL
MLFLOW_S3_ENDPOINT_URL
https://<bucketname>.s3.<region>.amazonaws.com/<key>/<bucketname>/<key>
s3://<bucketname>/<key>/<bucketname>/<key>
unset
Complete list of configurable ... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-25 | Google Cloud Storage
To store artifacts in Google Cloud Storage, specify a URI of the form gs://<bucket>/<path>.
You should configure credentials for accessing the GCS container on the client and server as described
in the GCS documentation.
Finally, you must run pip install google-cloud-storage (on both your client a... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-26 | SFTP Server
To store artifacts in an SFTP server, specify a URI of the form sftp://user@host/path/to/directory.
You should configure the client to be able to log in to the SFTP server without a password over SSH (e.g. public key, identity file in ssh_config, etc.).
The format sftp://user:pass@host/ is supported for l... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-27 | The used HDFS driver is libhdfs.
File store performance
MLflow will automatically try to use LibYAML bindings if they are already installed.
However if you notice any performance issues when using file store backend, it could mean LibYAML is not installed on your system.
On Linux or Mac you can easily install it usin... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-28 | Using the Tracking Server for proxied artifact access
To use an instance of the MLflow Tracking server for artifact operations ( Scenario 5: MLflow Tracking Server enabled with proxied artifact storage access ),
start a server with the optional parameters --serve-artifacts to enable proxied artifact access and set a
p... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-29 | When a tracking server is configured in --artifacts-only mode, any tasks apart from those concerned with artifact
handling (i.e., model logging, loading models, logging artifacts, listing artifacts, etc.) will return an HTTPError.
See the following example of a client REST call in Python attempting to list experiments ... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-30 | mlflow
start_run
():
mlflow
log_param
"a"
mlflow
log_metric
"b"
library
mlflow
install_mlflow
()
remote_server_uri
"..."
# set to your server URI
mlflow_set_tracking_uri
remote_server_uri
# Note: on Databricks, the experiment name passed to mlflow_set_experiment must be a
# valid path in the workspac... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-31 | Note
If the MLflow server is not configured with the --serve-artifacts option, the client directly pushes artifacts
to the artifact store. It does not proxy these through the tracking server by default.
For this reason, the client needs direct access to the artifact store. For instructions on setting up these credent... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
2eb3b7e954eb-32 | Commit hash of the executed code, if in a git repository.
mlflow.source.git.branch
Name of the branch of the executed code, if in a git repository.
mlflow.source.git.repoURL
URL that the executed code was cloned from.
mlflow.project.env
The runtime context used by the MLflow project.
Possible values: "docker" and... | {
"url": "https://mlflow.org/docs/latest/tracking.html#automatic-logging"
} |
72da79a93de6-0 | MLflow Tracking
The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files
when running your machine learning code and for later visualizing the results.
MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs.
Table of Conte... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-1 | Key-value input parameters of your choice. Both keys and values are strings.
Key-value metrics, where the value is numeric. Each metric can be updated throughout the
course of the run (for example, to track how your model’s loss function is converging), and
MLflow records and lets you visualize the metric’s full histo... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-2 | There are different kinds of remote tracking URIs:
Local file path (specified as file:/my/local/dir), where data is just directly stored locally.
Database encoded as <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>. MLflow supports the dialects mysql, mssql, sqlite, and postgresql. For more details... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-3 | Scenario 1: MLflow on localhost
Many developers run MLflow on their local machine, where both the backend and artifact store share a directory
on the local filesystem—./mlruns—as shown in the diagram. The MLflow client directly interfaces with an
instance of a FileStore and LocalArtifactRepository.
In this simple sce... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-4 | Command to run the tracking server in this configuration
mlflow
server
-backend-store-uri
file:///path/to/mlruns
-no-serve-artifacts
To store all runs’ MLflow entities, the MLflow client interacts with the tracking server via a series of REST requests:
Part 1a and b:
The MLflow client creates an instance of a R... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-5 | The Tracking Server creates an instance of an SQLAlchemyStore and connects to the remote host to
insert MLflow entities in the database
For artifact logging, the MLflow client interacts with the remote Tracking Server and artifact storage host:
Part 2a, b, and c:
The MLflow client uses RestStore to send a REST reque... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-6 | # or permissions.
-artifacts-destination
s3://bucket_name
-host
remote_host
Enabling the Tracking Server to perform proxied artifact access in order to route client artifact requests to an object store location:
Part 1a and b:
The MLflow client creates an instance of a RestStore and sends REST API requests to lo... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-7 | Warning
The MLflow artifact proxied access service enables users to have an assumed role of access to all artifacts that are accessible to the Tracking Server.
Administrators who are enabling this feature should ensure that the access level granted to the Tracking Server for artifact
operations meets all security requ... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-8 | Note
If migrating from Scenario 5 to Scenario 6 due to request volumes, it is important to perform two validations:
Ensure that the new tracking server that is operating in --artifacts-only mode has access permissions to the
location set by --artifacts-destination that the former multi-role tracking server had.
The f... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-9 | mlflow.set_experiment() sets an experiment as active. If the experiment does not exist,
creates a new experiment. If you do not specify an experiment in mlflow.start_run(), new
runs are launched under this experiment.
mlflow.start_run() returns the currently active run (if one exists), or starts a new run
and returns ... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-10 | mlflow.log_metric() logs a single key-value metric. The value must always be a number.
MLflow remembers the history of values for each metric. Use mlflow.log_metrics() to log
multiple metrics at once.
mlflow.set_tag() sets a single key-value tag in the currently active run. The key and
value are both strings. Use mlfl... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-11 | timestamp is an optional long value that represents the time that the metric was logged. timestamp defaults to the current time. step is an optional integer that represents any measurement of training progress (number of training iterations, number of epochs, and so on). step defaults to 0 and has the following require... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-12 | Use library-specific autolog calls for each library you use in your code. See below for examples.
The following libraries support autologging:
Scikit-learn
Keras
Gluon
XGBoost
LightGBM
Statsmodels
Spark
Fastai
Pytorch
For flavors that automatically save models as an artifact, additional files for dependency ... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-13 | containing the following data:
Parent
run
Child
run
Child
run
...
containing the following data:
Run type
Metrics
Parameters
Tags
Artifacts
Parent
Training score
Parameter search estimator’s parameters
Best parameter combination
Class name
Fully qualified class name
Fitted parameter search estimator
F... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-14 | Gluon
Call mlflow.gluon.autolog() before your training code to enable automatic logging of metrics and parameters.
See example usages with Gluon .
Autologging captures the following information:
Framework
Metrics
Parameters
Tags
Artifacts
Gluon
Training loss; validation loss; user-specified metrics
Number of ... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-15 | Note
Each model subclass that overrides fit expects and logs its own parameters.
Spark
Initialize a SparkSession with the mlflow-spark JAR attached (e.g.
SparkSession.builder.config("spark.jars.packages", "org.mlflow.mlflow-spark")) and then
call mlflow.spark.autolog() to enable automatic logging of Spark datasource... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-16 | Framework/module
Metrics
Parameters
Tags
Artifacts
pytorch_lightning.trainer.Trainer
Training loss; validation loss; average_test_accuracy;
user-defined-metrics.
fit() parameters; optimizer name; learning rate; epsilon.
Model summary on training start, MLflow Model (Pytorch model) on training end;
pytorch_ligh... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-17 | [name]
MLFLOW_EXPERIMENT_NAME
-experiment-id
MLFLOW_EXPERIMENT_ID
# Set the experiment via environment variables
export
MLFLOW_EXPERIMENT_NAME
=fraud-detection
mlflow
experiments
create
-experiment-name
fraud-detection
# Launch a run. The experiment is inferred from the MLFLOW_EXPERIMENT_NAME environment
... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-18 | run
info
run_id
"tag_key"
"tag_value"
Important
Do not use the prefix mlflow. (e.g. mlflow.note) for a tag. This prefix is reserved for use by MLflow. See System Tags for a list of reserved tag keys.
Tracking UI
The Tracking UI lets you visualize, search and compare runs, as well as download run artifacts or
m... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-19 | Run automated parameter search algorithms, where you query the metrics from various runs to submit new ones. For an example of running automated parameter search algorithms, see the MLflow Hyperparameter Tuning Example project.
MLflow Tracking Servers
In this section:
Storage
Backend Stores
Artifact Stores
File sto... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-20 | A file store backend as ./path_to_store or file:/path_to_store
A database-backed store as SQLAlchemy database URI.
The database URI typically takes the format <dialect>+<driver>://<username>:<password>@<host>:<port>/<database>.
MLflow supports the database dialects mysql, mssql, sqlite, and postgresql.
Drivers are opt... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-21 | The MLflow client caches artifact location information on a per-run basis.
It is therefore not recommended to alter a run’s artifact location before it has terminated.
In addition to local file paths, MLflow supports the following storage systems as artifact
stores: Amazon S3, Azure Blob Storage, Google Cloud Storage,... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-22 | mlflow-artifacts:/mlartifacts
If the host or host:port declaration is absent in client artifact requests to the MLflow server, the client API
will assume that the host is the same as the MLflow Tracking uri.
Note
If an MLflow server is running with the --artifact-only flag, the client should interact with this serve... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-23 | Amazon S3 and S3-compatible storage
MinIO or
Digital Ocean Spaces), specify a URI of the form
s3://<bucket>/<path>
~/.aws/credentials
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
Set up AWS Credentials and Region for Development.
To add S3 file upload extra arguments, set MLFLOW_S3_UPLOAD_EXTRA_ARGS to a JSON object... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-24 | =my_region
Warning
-default-artifact-root
$MLFLOW_S3_ENDPOINT_URL
MLFLOW_S3_ENDPOINT_URL
-default-artifact-root
MLFLOW_S3_ENDPOINT_URL
MLFLOW_S3_ENDPOINT_URL
https://<bucketname>.s3.<region>.amazonaws.com/<key>/<bucketname>/<key>
s3://<bucketname>/<key>/<bucketname>/<key>
unset
Complete list of configurable ... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-25 | Google Cloud Storage
To store artifacts in Google Cloud Storage, specify a URI of the form gs://<bucket>/<path>.
You should configure credentials for accessing the GCS container on the client and server as described
in the GCS documentation.
Finally, you must run pip install google-cloud-storage (on both your client a... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-26 | SFTP Server
To store artifacts in an SFTP server, specify a URI of the form sftp://user@host/path/to/directory.
You should configure the client to be able to log in to the SFTP server without a password over SSH (e.g. public key, identity file in ssh_config, etc.).
The format sftp://user:pass@host/ is supported for l... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-27 | The used HDFS driver is libhdfs.
File store performance
MLflow will automatically try to use LibYAML bindings if they are already installed.
However if you notice any performance issues when using file store backend, it could mean LibYAML is not installed on your system.
On Linux or Mac you can easily install it usin... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-28 | Using the Tracking Server for proxied artifact access
To use an instance of the MLflow Tracking server for artifact operations ( Scenario 5: MLflow Tracking Server enabled with proxied artifact storage access ),
start a server with the optional parameters --serve-artifacts to enable proxied artifact access and set a
p... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-29 | When a tracking server is configured in --artifacts-only mode, any tasks apart from those concerned with artifact
handling (i.e., model logging, loading models, logging artifacts, listing artifacts, etc.) will return an HTTPError.
See the following example of a client REST call in Python attempting to list experiments ... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-30 | mlflow
start_run
():
mlflow
log_param
"a"
mlflow
log_metric
"b"
library
mlflow
install_mlflow
()
remote_server_uri
"..."
# set to your server URI
mlflow_set_tracking_uri
remote_server_uri
# Note: on Databricks, the experiment name passed to mlflow_set_experiment must be a
# valid path in the workspac... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-31 | Note
If the MLflow server is not configured with the --serve-artifacts option, the client directly pushes artifacts
to the artifact store. It does not proxy these through the tracking server by default.
For this reason, the client needs direct access to the artifact store. For instructions on setting up these credent... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
72da79a93de6-32 | Commit hash of the executed code, if in a git repository.
mlflow.source.git.branch
Name of the branch of the executed code, if in a git repository.
mlflow.source.git.repoURL
URL that the executed code was cloned from.
mlflow.project.env
The runtime context used by the MLflow project.
Possible values: "docker" and... | {
"url": "https://mlflow.org/docs/latest/tracking.html"
} |
ced13a6bcc4c-0 | MLflow Projects
An MLflow Project is a format for packaging data science code in a reusable and reproducible way,
based primarily on conventions. In addition, the Projects component includes an API and command-line
tools for running projects, making it possible to chain together projects into workflows.
Table of Cont... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-1 | You can run any project from a Git URI or from a local directory using the mlflow run
command-line tool, or the mlflow.projects.run() Python API. These APIs also allow submitting the
project for remote execution on Databricks and
Kubernetes.
Important
By default, MLflow uses a new, temporary working directory for Git... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-2 | Docker containers allow you to capture
non-Python dependencies such as Java libraries.
When you run an MLflow project that specifies a Docker image, MLflow adds a new Docker layer
that copies the project’s contents into the /mlflow/projects/code directory. This step produces
a new image. MLflow then runs the new image ... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-3 | By default, MLflow uses the system path to find and run the conda binary. You can use a
different Conda installation by setting the MLFLOW_CONDA_HOME environment variable; in this
case, MLflow attempts to run the binary at $MLFLOW_CONDA_HOME/bin/conda.
You can specify a Conda environment for your MLflow project by incl... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-4 | Any .py and .sh file in the project can be an entry point. MLflow uses Python
to execute entry points with the .py extension, and it uses bash to execute entry points with
the .sh extension. For more information about specifying project entrypoints at runtime,
see Running Projects.
By default, entry points do not have... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-5 | Include a top-level python_env entry in the MLproject file.
The value of this entry must be a relative path to a python_env YAML file
within the MLflow project’s directory. The following is an example MLProject
file with a python_env definition:
python_env: files/config/python_env.yaml
python_env refers to an environ... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-6 | In this example, docker_env refers to the Docker image with name
mlflow-docker-example-environment and default tag latest. Because no registry path is
specified, Docker searches for this image on the system that runs the MLflow project. If the
image is not found, Docker attempts to pull it from DockerHub.
Example 2: Mo... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-7 | In this example, docker_env refers to the Docker image with name
mlflow-docker-example-environment and tag 7.0 in the Docker registry with path
012345678910.dkr.ecr.us-west-2.amazonaws.com, which corresponds to an
Amazon ECR registry.
When the MLflow project is run, Docker attempts to pull the image from the specified ... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-8 | # Short syntax
parameter_name
# Long syntax
type
data_type
default
value
MLflow supports four parameter types, some of which it treats specially (for example, downloading
data to local files). Any undeclared parameters are treated as string. The parameter types are:
A text string.
A real number. MLflow validat... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-9 | Key-value parameters. Any parameters with
declared types are validated and transformed if needed.
Both the command-line and API let you launch projects remotely
in a Databricks environment. This includes setting cluster
parameters such as a VM type. Of course, you can also run projects on any other computing
infrastru... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-10 | Run an MLflow Project on Kubernetes
You can run MLflow Projects with Docker environments
on Kubernetes. The following sections provide an overview of the feature, including a simple
Project execution guide with examples.
To see this feature in action, you can also refer to the
Docker example, which includes
the requi... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-11 | Create a backend configuration JSON file with the following entries:
kube-context
The Kubernetes context
where MLflow will run the job. If not provided, MLflow will use the current context.
If no context is available, MLflow will assume it is running in a Kubernetes cluster
and it will use the Kubernetes service accou... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-12 | where <project_uri> is a Git repository URI or a folder.
Job Templates
MLflow executes Projects on Kubernetes by creating Kubernetes Job resources.
MLflow creates a Kubernetes Job for an MLflow Project by reading a user-specified
Job Spec.
When MLflow reads a Job Spec, it formats the following fields:
metadata.name ... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-13 | MLFLOW_TRACKING_URI
MLFLOW_RUN_ID
MLFLOW_EXPERIMENT_ID
container.env
KUBE_MLFLOW_TRACKING_URI
MLFLOW_TRACKING_URI
Iterating Quickly
If you want to rapidly develop a project, we recommend creating an MLproject file with your
main program specified as the main entry point, and running it with mlflow run ..
To avoi... | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
ced13a6bcc4c-14 | For an example of how to construct such a multistep workflow, see the MLflow Multistep Workflow Example project. | {
"url": "https://mlflow.org/docs/latest/projects.html"
} |
fe0d5088bc70-0 | MLflow Models
An MLflow Model is a standard format for packaging machine learning models that can be used in a
variety of downstream tools—for example, real-time serving through a REST API or batch inference
on Apache Spark. The format defines a convention that lets you save a model in different “flavors”
that can be ... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-1 | # Directory written by mlflow.sklearn.save_model(model, "my_model")
my_model/
├── MLmodel
├── model.pkl
├── conda.yaml
├── python_env.yaml
└── requirements.txt
And its MLmodel file describes two flavors:
time_created
2018-05-25T17:28:53.35
flavors
sklearn
sklearn_version
0.19.1
pickled_model
model.pkl
python_... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-2 | conda.yaml
python_env.yaml
requirements.txt
conda
virtualenv
pip
Note
Anaconda Inc. updated their terms of service for anaconda.org channels. Based on the new terms of service you may require a commercial license if you rely on Anaconda’s packaging and distribution. See Anaconda Commercial Edition FAQ for more i... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-3 | The requirements file is created from the pip portion of the conda.yaml environment specification. Additional pip dependencies can be added to requirements.txt by including them as a pip dependency in a conda environment and logging the model with the environment or using the pip_requirements argument of the mlflow.<fl... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-4 | Model Signature And Input Example
When working with ML models you often need to know some basic functional properties of the model
at hand, such as “What inputs does it expect?” and “What output does it produce?”. MLflow models can
include the following additional metadata about model inputs and outputs that can be us... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-5 | signature
inputs
'[{"name":
"sepal
length
(cm)",
"type":
"double"},
{"name":
"sepal
width
(cm)",
"type":
"double"},
{"name":
"petal
length
(cm)",
"type":
"double"},
{"name":
"petal
width
(cm)",
"type":
"double"}]'
outputs
'[{"type":
"integer"}]'
Tensor-based Signature Example
Only DL flavo... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-6 | [-1,
10],
"dtype":
"float32"}]'
Signature Enforcement
Schema enforcement checks the provided input against the model’s signature
and raises an exception if the input is not compatible. This enforcement is applied in MLflow before
calling the underlying model implementation. Note that this enforcement only applies ... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-7 | Handling Integers With Missing Values
Integer data with missing values is typically represented as floats in Python. Therefore, data
types of integer columns in Python can vary depending on the data sample. This type variance can
cause schema enforcement errors at runtime since integer and float are not compatible typ... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-8 | How To Log Models With Signatures
To include a signature with your model, pass signature object as an argument to the appropriate log_model call, e.g.
sklearn.log_model(). The model signature object can be created
by hand or inferred from datasets with valid model inputs
(e.g. the training dataset with target column o... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-9 | signature
ModelSignature
inputs
input_schema
outputs
output_schema
Tensor-based Signature Example
The following example demonstrates how to store a model signature for a simple classifier trained
on the MNIST dataset:
from
keras.datasets
import
mnist
from
keras.utils
import
to_categorical
from
keras.mo... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-10 | testX
testY
))
signature
infer_signature
testX
model
predict
testX
))
mlflow
tensorflow
log_model
model
"mnist_cnn"
signature
signature
The same signature can be created explicitly as follows:
import
numpy
as
np
from
mlflow.models.signature
import
ModelSignature
from
mlflow.types.schema
impo... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-11 | 1.4
"petal width (cm)"
0.2
mlflow
sklearn
log_model
...
input_example
input_example
How To Log Model With Tensor-based Example
For models accepting tensor-based inputs, an example must be a batch of inputs. By default, the axis 0
is the batch axis unless specified otherwise in the model signature. The sample ... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
fe0d5088bc70-12 | load to load a model from a local directory or
from an artifact in a previous run.
Built-In Model Flavors
MLflow provides several standard flavors that might be useful in your applications. Specifically,
many of its deployment tools support these flavors, so you can export your own model in one of these
flavors to be... | {
"url": "https://mlflow.org/docs/latest/models.html"
} |
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