Interact with the CLI
The Featureform CLI allows you to describe, list, and monitor your resources.

APPLY Command

The apply command submits resource definitions to the Featureform instance.
The argument can either be a path to a local file or the url of a hosted file. Multiple files can be included at a time.
featureform apply --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT <definitions.py>
Upon success, all definitions in the definitions.py (or whatever you choose to call it) file will be sent to the Featureform instance, logged in the metadata, and materialized with the registered providers.
After applying new resource definitions, you can use the GET command to see the status of the resources you applied. A resource with the status READY is available for serving.

DASH Command

featureform dash
The DASH command is used to access the featureform dashboard. It returns a URL to the locally hosted dashboard.
The dashboard can be viewed at http://localhost:3000 in your browser
The Featureform dashboard:
Featureform dashboard
Each button on the dashboard redirects you to a list of resources of that resource type.
List of registered features
Each resource can then be clicked on to learn more.

GET Command

The GET command displays status, variants, and other metadata on a resource.
featureform get RESOURCE_TYPE NAME [VARIANT] --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT
RESOURCE_TYPE (required) can be:
  • feature - machine learning features
  • label - machine learning labels
  • training-set - set of features and one label for training ML models
  • user - registered users in your instance
  • entity - identifier for a source of features or labels (akin to a primary key)
  • model - registered machine learning models which training sets and features are fed to
  • provider - registered 3rd party providers which store your data
  • source - files, tables, or transformations that features, labels and training sets source from
NAME is the name of the resource type to be queried.
VARIANT is optional, for when information on a specific variant is needed.

Example: Getting a User

The commands are both valid ways to retrieve information on the user "featureformer".
The first is with certification. The second without; the --insecure flag disables the need for the --cert flag
featureform get user featureformer --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT
featureform get user featureformer --insecure --host $FEATUREFORM_HOST
Either command returns the following output.
USER NAME: featureformer
NAME VARIANT TYPE
avg_transactions quickstart feature
fraudulent quickstart label
fraud_training quickstart training set
transactions kaggle source
average_user_transaction quickstart source
Listed below the user are all the resources registered to that user.

Example: Getting a Resource

The following command shows how to retrieve information on a specific resource, a feature named "avg_transactions".
featureform get feature avg_transactions --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT
NAME: avg_transactions
STATUS: NO_STATUS
VARIANTS:
quickstart default
v1
v2
prodThis would be the output:

Example: Getting a Resource Variant

The command below retrieves information on the specific variant of the feature "avg_transactions", "quickstart"
featureform get feature avg_transactions quickstart --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT
NAME: avg_transactions
VARIANT: quickstart
TYPE: float32
ENTITY: user
OWNER: featureformer
DESCRIPTION:
PROVIDER: redis-quickstart
STATUS: READY
SOURCE:
NAME VARIANT
average_user_transaction quickstart
TRAINING SETS:
NAME VARIANT
fraud_training quickstart
Listed below are the metadata on that variant, as well as its source and the training sets it belongs to.

LIST Command

The LIST command displays the name, variant and status of all the resources of that resource type.
featureform list RESOURCE_TYPE --host $FEATUREFORM_HOST –cert $FEATUREFORM_CERT
RESOURCE_TYPE (required) can be:
  • feature - machine learning features
  • label - machine learning labels
  • training-set - set of features and one label for training ML models
  • user - registered users in your instance
  • entity - identifier for a source of features or labels (akin to a primary key)
  • model - registered machine learning models which training sets and features are fed to
  • provider - registered 3rd party providers which store your data
  • source - files, tables, or transformations that features, labels and training sets source from
NOTE: The --cert $FEATUREFORM_CERT is only required for self-signed certs

Example: Getting the list of users

featureform list users --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT
featureform list users --insecure --host $FEATUREFORM_HOST
The commands are both valid ways to retrieve a list of users. The first is when the user uses a self-signed cert.
The following uses the local flag to access resources created and stored in localmode:
featureform list users –-local
The above commands return the following list of users which have been registered:
NAME STATUS
default_user ready
featureformer ready

Example: Getting the list of resources of a given type

featureform list features --host $FEATUREFORM_HOST --cert $FEATUREFORM_CERT
featureform list features --insecure --host $FEATUREFORM_HOST
In local mode:
featureform list features –-local
The given commands return the list of registered features and their variants
NAME VARIANT STATUS
avg_transactions quickstart(default) ready
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Outline
APPLY Command
DASH Command
GET Command
Example: Getting a User
Example: Getting a Resource
Example: Getting a Resource Variant
LIST Command
Example: Getting the list of users
Example: Getting the list of resources of a given type