Model object

The Model object comes with three sets of methods: CRUD methods, I/O methods and kind specific methods.

CRUD methods

Crud methods are used to interact with the entity object in the backend or locally.

save(): Save or update the entity into the backend.
export(): Export the entity locally as yaml file.
refresh(): Refresh (read) the entity from the backend.

`save`

Save entity into backend.

Parameters:

Name	Type	Description	Default
`update`	`bool`	Flag to indicate update.	`False`

Returns:

Type	Description
`Model`	Entity saved.

`export`

Export object as a YAML file in the context folder.

Returns:

Type	Description
`str`	Exported filepath.

`refresh`

Refresh object from backend.

Returns:

Type	Description
`ContextEntity`	Entity refreshed.

I/O methods

I/O methods are used to handle objects as files.

as_file(): Dowloads the model into a local temporary destination
download(): Downloads the model into a specified path
upload(): Uploads the model to model spec path

`as_file`

Get object as file(s). It downloads the object from storage in a temporary folder and returns the list of downloaded files paths.

Returns:

Type	Description
`list[str]`	List of file paths.

`download`

This function downloads one or more file from storage on local machine from spec.path. The files are downloaded into a destination folder. If the destination is not specified, it will set by default under the context path as '/', e.g. './dataitem'. The overwrite flag allows to overwrite existing file(s) in the destination folder.

Parameters:

Name	Type	Description	Default
`destination`	`str`	Destination path as filename or directory.	`None`
`overwrite`	`bool`	Specify if overwrite existing file(s). If file(s) already exist and overwrite is False, it will raise an error.	`False`

Returns:

Type	Description
`str`	Download path.

Examples:

Download a single file:

>>> path = entity.download()
>>> print(path)
dataitem/data.csv

`upload`

Upload object from given local path to spec path destination. Source must be a local path. If the path is a folder, destination path (object's spec path) must be a folder or a partition ending with '/' (s3).

Parameters:

Name	Type	Description	Default
`source`	`str \| list[str]`	Local filepath, directory or list of filepaths.	required

Examples:

Upload a single file:

>>> entity.spec.path = "s3://bucket/data.csv"
>>> entity.upload("./data.csv")

Upload a folder:

>>> entity.spec.path = "s3://bucket/data/"
>>> entity.upload("./data")

Model specific methods

There are several generic model methods on the Model object.

log_metric: Log a metric in the model.
log_metrics: Log multiple metrics in the model.

`log_metric`

Log metric into entity status. A metric is named by a key and value (single number or list of numbers). The metric by default is put in a list or appended to an existing list. If single_value is True, the value will be a single number.

Parameters:

Name	Type	Description	Default
`key`	`str`	Key of the metric.	required
`value`	`MetricType`	Value of the metric.	required
`overwrite`	`bool`	If True, overwrite existing metric.	`False`
`single_value`	`bool`	If True, value is a single value.	`False`

Examples:

Log a new value in a list

>>> entity.log_metric("loss", 0.002)

Append a new value in a list

>>> entity.log_metric("loss", 0.0019)

Log a list of values and append them to existing metric:

>>> entity.log_metric(
...     "loss",
...     [
...         0.0018,
...         0.0015,
...     ],
... )

Log a single value (not represented as list):

>>> entity.log_metric(
...     "accuracy",
...     0.9,
...     single_value=True,
... )

Log a list of values and overwrite existing metric:

>>> entity.log_metric(
...     "accuracy",
...     [0.8, 0.9],
...     overwrite=True,
... )

`log_metrics`

Log metrics into entity status. If a metric is a list, it will be logged as a list. Otherwise, it will be logged as a single value.

Parameters:

Name	Type	Description	Default
`metrics`	`dict[str, MetricType]`	Dict of metrics to log.	required
`overwrite`	`bool`	If True, overwrite existing metrics.	`False`

Examples:

Log multiple metrics at once

>>> entity.log_metrics(
...     {
...         "loss": 0.002,
...         "accuracy": 0.95,
...     }
... )

Log metrics with lists and single values

>>> entity.log_metrics(
...     {
...         "loss": [
...             0.1,
...             0.05,
...         ],
...         "epoch": 10,
...     }
... )

Append to existing metrics (default behavior)

>>> entity.log_metrics(
...     {
...         "loss": 0.001,
...         "accuracy": 0.96,
...     }
... )  # Appends to existing

Overwrite existing metrics

>>> entity.log_metrics(
...     {
...         "loss": 0.0005,
...         "accuracy": 0.98,
...     },
...     overwrite=True,
... )

Kind specific methods

Kind specific methods are used to express potential behaviors of different object kinds. See the kinds section for more information.