fiftyone.zoo.models

• fiftyone.zoo.models.torch

Module contents

The FiftyOne Model Zoo.

Copyright 2017-2025, Voxel51, Inc.

voxel51.com

Functions:

list_zoo_models([tags, source, license])	Returns the list of available models in the FiftyOne Model Zoo.
list_downloaded_zoo_models()	Returns information about the zoo models that have been downloaded.
is_zoo_model_downloaded(name)	Determines whether the zoo model of the given name is downloaded.
download_zoo_model(name_or_url[, …])	Downloads the specified model from the FiftyOne Model Zoo.
install_zoo_model_requirements(name[, …])	Installs any package requirements for the specified zoo model.
ensure_zoo_model_requirements(name[, …])	Ensures that the package requirements for the specified zoo model are satisfied.
load_zoo_model(name_or_url[, model_name, …])	Loads the specified model from the FiftyOne Model Zoo.
find_zoo_model(name)	Returns the path to the zoo model on disk.
get_zoo_model(name)	Returns the ZooModel instance for the specified zoo model.
delete_zoo_model(name)	Deletes the zoo model from local disk, if necessary.
list_zoo_model_sources()	Returns the list of remote model sources that are registered locally.
register_zoo_model_source(url_or_gh_repo[, …])	Registers a remote source of models, if necessary.
delete_zoo_model_source(url_or_gh_repo)	Deletes the specified remote source and all downloaded models associated with it.

Classes:

HasZooModel()	Mixin class for Config classes of fiftyone.core.models.Model instances whose models are stored in the FiftyOne Model Zoo.
ZooModel(base_name[, base_filename, subdir, …])	Class describing a model in the FiftyOne Model Zoo.
RemoteZooModel(*args, **kwargs)
RemoteModelManagerConfig(d)
RemoteModelManager(config)
ZooModelsManifest([models, name, url])	Class that describes the collection of models in the FiftyOne Model Zoo.
RemoteZooModelsManifest([models, name, url])	Class that describes the collection of remotely-sourced models in the FiftyOne Model Zoo.

fiftyone.zoo.models.list_zoo_models(tags=None, source=None, license=None)

Returns the list of available models in the FiftyOne Model Zoo.

Also includes models from any remote sources that you’ve registered.

Example usage:

import fiftyone as fo
import fiftyone.zoo as foz

#
# List all zoo models
#

names = foz.list_zoo_models()
print(names)

#
# List all zoo models with the specified tag(s)
#

names = foz.list_zoo_models(tags="torch")
print(names)

Parameters

• tags (None) – only include models that have the specified tag or list of tags

• source (None) – only include models available via the given remote source

• license (None) – only include models that are distributed under the specified license or any of the specified list of licenses. Run fiftyone zoo models list to see the available licenses

Returns

a list of model names

fiftyone.zoo.models.list_downloaded_zoo_models()

Returns information about the zoo models that have been downloaded.

Returns

a dict mapping model names to (model path, ZooModel) tuples

fiftyone.zoo.models.is_zoo_model_downloaded(name)

Determines whether the zoo model of the given name is downloaded.

Parameters

name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

Returns

True/False

fiftyone.zoo.models.download_zoo_model(name_or_url, model_name=None, overwrite=False)

Downloads the specified model from the FiftyOne Model Zoo.

If the model is already downloaded, it is not re-downloaded unless overwrite == True is specified.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters

• name_or_url –

  the name of the zoo model to download, which can have @<ver> appended to refer to a specific version of the model, or the remote source to download it from, which can be:

  • a GitHub repo URL like https://github.com/<user>/<repo>

  • a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>

  • a GitHub ref string like <user>/<repo>[/<ref>]

  • a publicly accessible URL of an archive (eg zip or tar) file

• model_name (None) – the specific model to download, if name_or_url is a remote source

• overwrite (False) – whether to overwrite any existing files

Returns

tuple of

• model: the ZooModel for the model

• model_path: the path to the downloaded model on disk

fiftyone.zoo.models.install_zoo_model_requirements(name, error_level=None)

Installs any package requirements for the specified zoo model.

Parameters

• name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

• error_level (None) –

  the error level to use, defined as:

  • 0: raise error if a requirement install fails

  • 1: log warning if a requirement install fails

  • 2: ignore install fails requirements

  By default, fo.config.requirement_error_level is used

fiftyone.zoo.models.ensure_zoo_model_requirements(name, error_level=None, log_success=True)

Ensures that the package requirements for the specified zoo model are satisfied.

Parameters

• name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

• error_level (None) –

  the error level to use when installing/ensuring requirements, defined as:

  • 0: raise error if a requirement is not satisfied

  • 1: log warning if a requirement is not satisfied

  • 2: ignore unsatisfied requirements

  By default, fo.config.requirement_error_level is used

• log_success (True) – whether to generate a log message when a requirement is satisfied

fiftyone.zoo.models.load_zoo_model(name_or_url, model_name=None, download_if_necessary=True, ensure_requirements=True, install_requirements=False, error_level=None, cache=True, **kwargs)

Loads the specified model from the FiftyOne Model Zoo.

By default, the model will be downloaded if necessary, and any documented package requirements will be checked to ensure that they are installed.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters

• name_or_url –

  the name of the zoo model to load, which can have @<ver> appended to refer to a specific version of the model, or the remote source to load it from, which can be:

  • a GitHub repo URL like https://github.com/<user>/<repo>

  • a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>

  • a GitHub ref string like <user>/<repo>[/<ref>]

  • a publicly accessible URL of an archive (eg zip or tar) file

• model_name (None) – the specific model to load, if name_or_url is a remote source

• download_if_necessary (True) – whether to download the model if necessary

• ensure_requirements (True) – whether to ensure any requirements are installed before loading the model

• install_requirements (False) – whether to install any requirements before loading the model

• error_level (None) –

  the error level to use when installing/ensuring requirements, defined as:

  • 0: raise error if a requirement is not satisfied

  • 1: log warning if a requirement is not satisfied

  • 2: ignore unsatisfied requirements

  By default, fo.config.requirement_error_level is used

• cache (True) – whether to store a weak reference to the model so that running this method again will return the same instance while the model is still in use

• **kwargs – keyword arguments to inject into the model’s Config instance

Returns

a fiftyone.core.models.Model

fiftyone.zoo.models.find_zoo_model(name)

Returns the path to the zoo model on disk.

The model must be downloaded. Use download_zoo_model() to download models.

Parameters

name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

Returns

the path to the model on disk

Raises

ValueError – if the model does not exist or has not been downloaded

fiftyone.zoo.models.get_zoo_model(name)

Returns the ZooModel instance for the specified zoo model.

Parameters

name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

Returns

a ZooModel

fiftyone.zoo.models.delete_zoo_model(name)

Deletes the zoo model from local disk, if necessary.

Parameters

name – the name of the zoo model, which can have @<ver> appended to refer to a specific version of the model

fiftyone.zoo.models.list_zoo_model_sources()

Returns the list of remote model sources that are registered locally.

Returns

the list of remote sources

fiftyone.zoo.models.register_zoo_model_source(url_or_gh_repo, overwrite=False)

Registers a remote source of models, if necessary.

Note

To download from a private GitHub repository that you have access to, provide your GitHub personal access token by setting the GITHUB_TOKEN environment variable.

Parameters

• url_or_gh_repo –

  the remote source to register, which can be:

  • a GitHub repo URL like https://github.com/<user>/<repo>

  • a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>

  • a GitHub ref string like <user>/<repo>[/<ref>]

  • a publicly accessible URL of an archive (eg zip or tar) file

• overwrite (False) – whether to overwrite any existing files

fiftyone.zoo.models.delete_zoo_model_source(url_or_gh_repo)

Deletes the specified remote source and all downloaded models associated with it.

Parameters

url_or_gh_repo –

the remote source to delete, which can be:

• a GitHub repo URL like https://github.com/<user>/<repo>

• a GitHub ref like https://github.com/<user>/<repo>/tree/<branch> or https://github.com/<user>/<repo>/commit/<commit>

• a GitHub ref string like <user>/<repo>[/<ref>]

• a publicly accessible URL of an archive (eg zip or tar) file

class fiftyone.zoo.models.HasZooModel

Bases: eta.core.learning.HasPublishedModel

Mixin class for Config classes of fiftyone.core.models.Model instances whose models are stored in the FiftyOne Model Zoo.

This class provides the following functionality:

• The model to load can be specified either by:

  1. providing a model_name, which specifies the zoo model to load. The model will be downloaded, if necessary

  2. providing a model_path, which directly specifies the path to the model to load

• fiftyone.core.models.ModelConfig definitions that use zoo models with default deployments will have default values for any unspecified parameters loaded and applied at runtime

Parameters

• model_name – the name of the zoo model to load. If this value is provided, model_path does not need to be

• model_path – the path to an already downloaded zoo model on disk to load. If this value is provided, model_name does not need to be

Methods:

download_model_if_necessary()	Downloads the published model specified by the config, if necessary.
init(d)	Initializes the published model config.

download_model_if_necessary()

Downloads the published model specified by the config, if necessary.

After this method is called, the model_path attribute will always contain the path to the model on disk.

init(d)

Initializes the published model config.

This method should be called by ModelConfig.__init__(), and it performs the following tasks:

• Parses the model_name and model_path parameters

• Populates any default parameters in the provided ModelConfig dict

Parameters

d – a ModelConfig dict

Returns

a ModelConfig dict with any default parameters populated

class fiftyone.zoo.models.ZooModel(base_name, base_filename=None, subdir=None, manager=None, author=None, version=None, url=None, source=None, license=None, description=None, size_bytes=None, default_deployment_config_dict=None, requirements=None, tags=None, date_added=None)

Bases: eta.core.models.Model

Class describing a model in the FiftyOne Model Zoo.

Parameters

• base_name – the base name of the model (no version info)

• base_filename (None) – the base filename or directory of the model (no version info), if applicable

• author (None) – the author of the model

• version (None) – the version of the model

• url (None) – the URL at which the model is hosted

• license (None) – the license under which the model is distributed

• source (None) – the source of the model

• description (None) – the description of the model

• tags (None) – a list of tags for the model

• size_bytes (None) – the size of the model on disk

• date_added (None) – the datetime that the model was added to the zoo

• requirements (None) – the eta.core.models.ModelRequirements for the model

• manager (None) – the fiftyone.core.models.ModelManager instance that describes the remote storage location of the model, if applicable

• default_deployment_config_dict (None) – a fiftyone.core.models.ModelConfig dict describing the recommended settings for deploying the model

Methods:

attributes()	Returns a list of class attributes to be serialized.
copy()	Returns a deep copy of the object.
custom_attributes([dynamic, private])	Returns a customizable list of class attributes.
download_model(model_path[, force])	Downloads the model to the given local path.
ensure_requirements([error_level, log_success])	Ensures that any requirement(s) for this model are satisfied.
flush_model(model_path)	Flushes the copy of the model at the given local path, if necessary.
flush_model_from_dir(models_dir)	Flushes the copy of the model in the given models directory, if necessary.
from_dict(d[, subdir])	Constructs a Model from a JSON dictionary.
from_json(path, *args, **kwargs)	Constructs a Serializable object from a JSON file.
from_str(s, *args, **kwargs)	Constructs a Serializable object from a JSON string.
get_class_name()	Returns the fully-qualified class name string of this object.
get_path_in_dir(models_dir)	Gets the model path for the model in the given models directory.
has_tag(tag)	Whether this model has the given tag.
has_version_str(name)	Determines whether the given model name has a version string.
install_requirements([error_level])	Installs any necessary requirement(s) for this model.
is_in_dir(models_dir)	Determines whether a copy of the model exists in the given models directory.
is_model_downloaded(model_path)	Determines whether the model is downloaded to the given location.
parse_name(name)	Parses the model name, returning the base name and the version, if any.
serialize([reflective])	Serializes the object into a dictionary.
to_str([pretty_print])	Returns a string representation of this object.
write_json(path[, pretty_print])	Serializes the object and writes it to disk.

Attributes:

comp_version	The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.
filename	The version-aware filename of the model.
has_manager	Whether this model has a manager instance.
has_requirements	Whether this model has requirements in order to be used.
has_tags	Whether this model has tags.
has_version	Determines whether the model has a version.
name	The version-aware name of the model.
supports_cpu	Whether the model supports CPU (True), or not (False), or unknown (None).
supports_gpu	Whether the model supports GPU (True), or not (False), or unknown (None).

attributes()

Returns a list of class attributes to be serialized.

Returns

a list of class attributes

property comp_version

The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.

Models with no version are given a version of 0.0.0.

copy()

Returns a deep copy of the object.

Returns

a Serializable instance

custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters

• dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

• private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns

a list of class attributes

download_model(model_path, force=False)

Downloads the model to the given local path.

If the download is forced, any existing model is overwritten. If the download is not forced, the model will only be downloaded if it does not already exist locally.

If the model has no manager, nothing is downloaded.

Parameters

• model_path – the path to which to download the model

• force – whether to force download the model. If True, the model is always downloaded. If False, the model is only downloaded if necessary. The default is False

Raises

ModelError – if model downloading is not currently allowed

ensure_requirements(error_level=0, log_success=False)

Ensures that any requirement(s) for this model are satisfied.

Parameters

• error_level –

  the error level to use, defined as:

  0: raise error if a requirement is not satisfied 1: log warning if a requirement is not satisifed 2: ignore unsatisifed requirements

• log_success – whether to generate a log message when a requirement is satisifed

property filename

The version-aware filename of the model.

flush_model(model_path)

Flushes the copy of the model at the given local path, if necessary.

Parameters

model_path – the path on disk for the model

flush_model_from_dir(models_dir)

Flushes the copy of the model in the given models directory, if necessary.

Parameters

models_dir – the models directory

classmethod from_dict(d, subdir=None)

Constructs a Model from a JSON dictionary.

Parameters

• d – a JSON dictionary

• subdir (optional) – a subdirectory for the model

Returns

a Model instance

classmethod from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters

• path – the path to the JSON file on disk

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters

• s – a JSON string representation of a Serializable object

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod get_class_name()

Returns the fully-qualified class name string of this object.

get_path_in_dir(models_dir)

Gets the model path for the model in the given models directory.

Parameters

models_dir – the models directory

Returns

the model path, or None if the model has no manager

property has_manager

Whether this model has a manager instance.

property has_requirements

Whether this model has requirements in order to be used.

has_tag(tag)

Whether this model has the given tag.

Parameters

tag – a tag

Returns

True/False

property has_tags

Whether this model has tags.

property has_version

Determines whether the model has a version.

static has_version_str(name)

Determines whether the given model name has a version string.

Parameters

name – the model name

Returns

True/False

install_requirements(error_level=0)

Installs any necessary requirement(s) for this model.

Parameters

error_level –

the error level to use, defined as:

0: raise error if an install fails 1: log warning if an install fails 2: ignore install fails

is_in_dir(models_dir)

Determines whether a copy of the model exists in the given models directory.

Parameters

models_dir – the models directory

Returns

True/False, or None if the model has no manager

is_model_downloaded(model_path)

Determines whether the model is downloaded to the given location.

If model_path is an archive, this method will also return True if a directory with the same basename as model_path exists.

Parameters

model_path – the path on disk for the model

Returns

True/False, or None if the model has no manager

property name

The version-aware name of the model.

static parse_name(name)

Parses the model name, returning the base name and the version, if any.

Parameters

name – the name of the model, which can have “@<ver>” appended to refer to a specific version of the model

Returns

the base name of the model version: the version of the model, or None if no version was found

Return type

base_name

serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns

a JSON dictionary representation of the object

property supports_cpu

Whether the model supports CPU (True), or not (False), or unknown (None).

property supports_gpu

Whether the model supports GPU (True), or not (False), or unknown (None).

to_str(pretty_print=True, **kwargs)

Returns a string representation of this object.

Parameters

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

• **kwargs – optional keyword arguments for self.serialize()

Returns

a string representation of the object

write_json(path, pretty_print=False, **kwargs)

Serializes the object and writes it to disk.

Parameters

• path – the output path

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

• **kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.models.RemoteZooModel(*args, **kwargs)

Bases: fiftyone.zoo.models.ZooModel

Methods:

load_model(**kwargs)
resolve_input(ctx)
parse_parameters(ctx, params)
attributes()	Returns a list of class attributes to be serialized.
copy()	Returns a deep copy of the object.
custom_attributes([dynamic, private])	Returns a customizable list of class attributes.
download_model(model_path[, force])	Downloads the model to the given local path.
ensure_requirements([error_level, log_success])	Ensures that any requirement(s) for this model are satisfied.
flush_model(model_path)	Flushes the copy of the model at the given local path, if necessary.
flush_model_from_dir(models_dir)	Flushes the copy of the model in the given models directory, if necessary.
from_dict(d[, subdir])	Constructs a Model from a JSON dictionary.
from_json(path, *args, **kwargs)	Constructs a Serializable object from a JSON file.
from_str(s, *args, **kwargs)	Constructs a Serializable object from a JSON string.
get_class_name()	Returns the fully-qualified class name string of this object.
get_path_in_dir(models_dir)	Gets the model path for the model in the given models directory.
has_tag(tag)	Whether this model has the given tag.
has_version_str(name)	Determines whether the given model name has a version string.
install_requirements([error_level])	Installs any necessary requirement(s) for this model.
is_in_dir(models_dir)	Determines whether a copy of the model exists in the given models directory.
is_model_downloaded(model_path)	Determines whether the model is downloaded to the given location.
parse_name(name)	Parses the model name, returning the base name and the version, if any.
serialize([reflective])	Serializes the object into a dictionary.
to_str([pretty_print])	Returns a string representation of this object.
write_json(path[, pretty_print])	Serializes the object and writes it to disk.

Attributes:

comp_version	The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.
filename	The version-aware filename of the model.
has_manager	Whether this model has a manager instance.
has_requirements	Whether this model has requirements in order to be used.
has_tags	Whether this model has tags.
has_version	Determines whether the model has a version.
name	The version-aware name of the model.
supports_cpu	Whether the model supports CPU (True), or not (False), or unknown (None).
supports_gpu	Whether the model supports GPU (True), or not (False), or unknown (None).

load_model(**kwargs)

resolve_input(ctx)

parse_parameters(ctx, params)

attributes()

Returns a list of class attributes to be serialized.

Returns

a list of class attributes

property comp_version

The version of this model expressed as a distutils.version.LooseVersion intended for comparison operations.

Models with no version are given a version of 0.0.0.

copy()

Returns a deep copy of the object.

Returns

a Serializable instance

custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters

• dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

• private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns

a list of class attributes

download_model(model_path, force=False)

Downloads the model to the given local path.

If the download is forced, any existing model is overwritten. If the download is not forced, the model will only be downloaded if it does not already exist locally.

If the model has no manager, nothing is downloaded.

Parameters

• model_path – the path to which to download the model

• force – whether to force download the model. If True, the model is always downloaded. If False, the model is only downloaded if necessary. The default is False

Raises

ModelError – if model downloading is not currently allowed

ensure_requirements(error_level=0, log_success=False)

Ensures that any requirement(s) for this model are satisfied.

Parameters

• error_level –

  the error level to use, defined as:

  0: raise error if a requirement is not satisfied 1: log warning if a requirement is not satisifed 2: ignore unsatisifed requirements

• log_success – whether to generate a log message when a requirement is satisifed

property filename

The version-aware filename of the model.

flush_model(model_path)

Flushes the copy of the model at the given local path, if necessary.

Parameters

model_path – the path on disk for the model

flush_model_from_dir(models_dir)

Flushes the copy of the model in the given models directory, if necessary.

Parameters

models_dir – the models directory

classmethod from_dict(d, subdir=None)

Constructs a Model from a JSON dictionary.

Parameters

• d – a JSON dictionary

• subdir (optional) – a subdirectory for the model

Returns

a Model instance

classmethod from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters

• path – the path to the JSON file on disk

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters

• s – a JSON string representation of a Serializable object

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod get_class_name()

Returns the fully-qualified class name string of this object.

get_path_in_dir(models_dir)

Gets the model path for the model in the given models directory.

Parameters

models_dir – the models directory

Returns

the model path, or None if the model has no manager

property has_manager

Whether this model has a manager instance.

property has_requirements

Whether this model has requirements in order to be used.

has_tag(tag)

Whether this model has the given tag.

Parameters

tag – a tag

Returns

True/False

property has_tags

Whether this model has tags.

property has_version

Determines whether the model has a version.

static has_version_str(name)

Determines whether the given model name has a version string.

Parameters

name – the model name

Returns

True/False

install_requirements(error_level=0)

Installs any necessary requirement(s) for this model.

Parameters

error_level –

the error level to use, defined as:

0: raise error if an install fails 1: log warning if an install fails 2: ignore install fails

is_in_dir(models_dir)

Determines whether a copy of the model exists in the given models directory.

Parameters

models_dir – the models directory

Returns

True/False, or None if the model has no manager

is_model_downloaded(model_path)

Determines whether the model is downloaded to the given location.

If model_path is an archive, this method will also return True if a directory with the same basename as model_path exists.

Parameters

model_path – the path on disk for the model

Returns

True/False, or None if the model has no manager

property name

The version-aware name of the model.

static parse_name(name)

Parses the model name, returning the base name and the version, if any.

Parameters

name – the name of the model, which can have “@<ver>” appended to refer to a specific version of the model

Returns

the base name of the model version: the version of the model, or None if no version was found

Return type

base_name

serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns

a JSON dictionary representation of the object

property supports_cpu

Whether the model supports CPU (True), or not (False), or unknown (None).

property supports_gpu

Whether the model supports GPU (True), or not (False), or unknown (None).

to_str(pretty_print=True, **kwargs)

Returns a string representation of this object.

Parameters

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

• **kwargs – optional keyword arguments for self.serialize()

Returns

a string representation of the object

write_json(path, pretty_print=False, **kwargs)

Serializes the object and writes it to disk.

Parameters

• path – the output path

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

• **kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.models.RemoteModelManagerConfig(d)

Bases: eta.core.models.ModelManagerConfig

Methods:

attributes()	Returns a list of attributes to be serialized.
builder()	Returns a ConfigBuilder instance for this class.
copy()	Returns a deep copy of the object.
custom_attributes([dynamic, private])	Returns a customizable list of class attributes.
default()	Returns the default config instance.
from_dict(d)	Constructs a Config object from a JSON dictionary.
from_json(path, *args, **kwargs)	Constructs a Serializable object from a JSON file.
from_kwargs(**kwargs)	Constructs a Config object from keyword arguments.
from_str(s, *args, **kwargs)	Constructs a Serializable object from a JSON string.
get_class_name()	Returns the fully-qualified class name string of this object.
load_default()	Loads the default config instance from file.
parse_array(d, key[, default])	Parses a raw array attribute.
parse_bool(d, key[, default])	Parses a boolean value.
parse_categorical(d, key, choices[, default])	Parses a categorical JSON field, which must take a value from among the given choices.
parse_dict(d, key[, default])	Parses a dictionary attribute.
parse_int(d, key[, default])	Parses an integer attribute.
parse_mutually_exclusive_fields(fields)	Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.
parse_number(d, key[, default])	Parses a number attribute.
parse_object(d, key, cls[, default])	Parses an object attribute.
parse_object_array(d, key, cls[, default])	Parses an array of objects.
parse_object_dict(d, key, cls[, default])	Parses a dictionary whose values are objects.
parse_path(d, key[, default])	Parses a path attribute.
parse_raw(d, key[, default])	Parses a raw (arbitrary) JSON field.
parse_string(d, key[, default])	Parses a string attribute.
serialize([reflective])	Serializes the object into a dictionary.
to_str([pretty_print])	Returns a string representation of this object.
validate_all_or_nothing_fields(fields)	Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.
write_json(path[, pretty_print])	Serializes the object and writes it to disk.

attributes()

Returns a list of attributes to be serialized.

Returns

a list of attributes

classmethod builder()

Returns a ConfigBuilder instance for this class.

copy()

Returns a deep copy of the object.

Returns

a Serializable instance

custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters

• dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

• private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns

a list of class attributes

classmethod default()

Returns the default config instance.

By default, this method instantiates the class from an empty dictionary, which will only succeed if all attributes are optional. Otherwise, subclasses should override this method to provide the desired default configuration.

classmethod from_dict(d)

Constructs a Config object from a JSON dictionary.

Config subclass constructors accept JSON dictionaries, so this method simply passes the dictionary to cls().

Parameters

d – a dict of fields expected by cls

Returns

an instance of cls

classmethod from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters

• path – the path to the JSON file on disk

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod from_kwargs(**kwargs)

Constructs a Config object from keyword arguments.

Parameters

**kwargs – keyword arguments that define the fields expected by cls

Returns

an instance of cls

classmethod from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters

• s – a JSON string representation of a Serializable object

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod get_class_name()

Returns the fully-qualified class name string of this object.

classmethod load_default()

Loads the default config instance from file.

Subclasses must implement this method if they intend to support default instances.

static parse_array(d, key, default=<eta.core.config.NoDefault object>)

Parses a raw array attribute.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default list to return if key is not present

Returns

a list of raw (untouched) values

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_bool(d, key, default=<eta.core.config.NoDefault object>)

Parses a boolean value.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default bool to return if key is not present

Returns

True/False

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_categorical(d, key, choices, default=<eta.core.config.NoDefault object>)

Parses a categorical JSON field, which must take a value from among the given choices.

Parameters

• d – a JSON dictionary

• key – the key to parse

• choices – either an iterable of possible values or an enum-like class whose attributes define the possible values

• default – a default value to return if key is not present

Returns

the raw (untouched) value of the given field, which is equal to a value from choices

Raises

ConfigError – if the key was present in the dictionary but its value was not an allowed choice, or if no default value was provided and the key was not found in the dictionary

static parse_dict(d, key, default=<eta.core.config.NoDefault object>)

Parses a dictionary attribute.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default dict to return if key is not present

Returns

a dictionary

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_int(d, key, default=<eta.core.config.NoDefault object>)

Parses an integer attribute.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default integer value to return if key is not present

Returns

an int

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_mutually_exclusive_fields(fields)

Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.

Parameters

fields – a dictionary of pre-parsed fields

Returns

the (field, value) that was set

Raises

ConfigError – if zero or more than one truthy value was found

static parse_number(d, key, default=<eta.core.config.NoDefault object>)

Parses a number attribute.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default numeric value to return if key is not present

Returns

a number (e.g. int, float)

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object(d, key, cls, default=<eta.core.config.NoDefault object>)

Parses an object attribute.

The value of d[key] can be either an instance of cls or a serialized dict from an instance of cls.

Parameters

• d – a JSON dictionary

• key – the key to parse

• cls – the class of d[key]

• default – a default cls instance to return if key is not present

Returns

an instance of cls

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_array(d, key, cls, default=<eta.core.config.NoDefault object>)

Parses an array of objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters

• d – a JSON dictionary

• key – the key to parse

• cls – the class of the elements of list d[key]

• default – the default list to return if key is not present

Returns

a list of cls instances

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_dict(d, key, cls, default=<eta.core.config.NoDefault object>)

Parses a dictionary whose values are objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters

• d – a JSON dictionary

• key – the key to parse

• cls – the class of the values of dictionary d[key]

• default – the default dict of cls instances to return if key is not present

Returns

a dictionary whose values are cls instances

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_path(d, key, default=<eta.core.config.NoDefault object>)

Parses a path attribute.

The path is converted to an absolute path if necessary via os.path.abspath(os.path.expanduser(value)).

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default string to return if key is not present

Returns

a path string

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_raw(d, key, default=<eta.core.config.NoDefault object>)

Parses a raw (arbitrary) JSON field.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default value to return if key is not present

Returns

the raw (untouched) value of the given field

Raises

ConfigError – if no default value was provided and the key was not found in the dictionary

static parse_string(d, key, default=<eta.core.config.NoDefault object>)

Parses a string attribute.

Parameters

• d – a JSON dictionary

• key – the key to parse

• default – a default string to return if key is not present

Returns

a string

Raises

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns

a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)

Returns a string representation of this object.

Parameters

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

• **kwargs – optional keyword arguments for self.serialize()

Returns

a string representation of the object

static validate_all_or_nothing_fields(fields)

Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.

Parameters

fields – a dictionary of pre-parsed fields

Raises

ConfigError – if some values are truth and some are not

write_json(path, pretty_print=False, **kwargs)

Serializes the object and writes it to disk.

Parameters

• path – the output path

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

• **kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.models.RemoteModelManager(config)

Bases: eta.core.models.ModelManager

Methods:

attributes()	Returns a list of attributes to be serialized.
copy()	Returns a deep copy of the object.
custom_attributes([dynamic, private])	Returns a customizable list of class attributes.
delete_model()	Deletes the model from remote storage.
download_model(model_path[, force])	Downloads the model to the given local path.
flush_model(model_path)	Flushes the copy of the model at the given local path, if necessary.
from_config(config)	Instantiates a Configurable class from a <cls>Config instance.
from_dict(d)	Builds the ModelManager subclass from a JSON dictionary.
from_json(json_path)	Instantiates a Configurable class from a <cls>Config JSON file.
from_kwargs(**kwargs)	Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.
from_str(s, *args, **kwargs)	Constructs a Serializable object from a JSON string.
get_class_name()	Returns the fully-qualified class name string of this object.
is_model_downloaded(model_path)	Determines whether the model is downloaded to the given location.
parse(class_name[, module_name])	Parses a Configurable subclass name string.
serialize([reflective])	Serializes the object into a dictionary.
to_str([pretty_print])	Returns a string representation of this object.
upload_model(model_path, *args, **kwargs)
validate(config)	Validates that the given config is an instance of <cls>Config.
write_json(path[, pretty_print])	Serializes the object and writes it to disk.

attributes()

Returns a list of attributes to be serialized.

Returns

a list of attributes

copy()

Returns a deep copy of the object.

Returns

a Serializable instance

custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters

• dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

• private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns

a list of class attributes

delete_model()

Deletes the model from remote storage.

download_model(model_path, force=False)

Downloads the model to the given local path.

If the download is forced, any existing model is overwritten. If the download is not forced, the model will only be downloaded if it does not already exist locally.

Parameters

• model_path – the path to which to download the model

• force – whether to force download the model. If True, the model is always downloaded. If False, the model is only downloaded if necessary. The default is False

Raises

ModelError – if model downloading is not currently allowed

flush_model(model_path)

Flushes the copy of the model at the given local path, if necessary.

Parameters

model_path – the path on disk for the model

classmethod from_config(config)

Instantiates a Configurable class from a <cls>Config instance.

classmethod from_dict(d)

Builds the ModelManager subclass from a JSON dictionary.

Parameters

d – a JSON dictionary

Returns

a ModelManager instance

classmethod from_json(json_path)

Instantiates a Configurable class from a <cls>Config JSON file.

Parameters

json_path – path to a JSON file for type <cls>Config

Returns

an instance of cls

classmethod from_kwargs(**kwargs)

Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.

Parameters

**kwargs – keyword arguments that define the fields of a <cls>Config dict

Returns

an instance of cls

classmethod from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters

• s – a JSON string representation of a Serializable object

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod get_class_name()

Returns the fully-qualified class name string of this object.

is_model_downloaded(model_path)

Determines whether the model is downloaded to the given location.

If model_path is an archive, this method will also return True if a directory with the same basename as model_path exists.

Parameters

model_path – the path on disk for the model

Returns

True/False

static parse(class_name, module_name=None)

Parses a Configurable subclass name string.

Assumes both the Configurable class and the Config class are defined in the same module. The module containing the classes will be loaded if necessary.

Parameters

• class_name – a string containing the name of the Configurable class, e.g. “ClassName”, or a fully-qualified class name, e.g. “eta.core.config.ClassName”

• module_name – a string containing the fully-qualified module name, e.g. “eta.core.config”, or None if class_name includes the module name. Set module_name = __name__ to load a class from the calling module

Returns

the Configurable class config_cls: the Config class associated with cls

Return type

cls

serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns

a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)

Returns a string representation of this object.

Parameters

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

• **kwargs – optional keyword arguments for self.serialize()

Returns

a string representation of the object

static upload_model(model_path, *args, **kwargs)

classmethod validate(config)

Validates that the given config is an instance of <cls>Config.

Raises

ConfigurableError – if config is not an instance of <cls>Config

write_json(path, pretty_print=False, **kwargs)

Serializes the object and writes it to disk.

Parameters

• path – the output path

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

• **kwargs – optional keyword arguments for self.serialize()

class fiftyone.zoo.models.ZooModelsManifest(models=None, name=None, url=None)

Bases: eta.core.models.ModelsManifest

Class that describes the collection of models in the FiftyOne Model Zoo.

Parameters

models – a list of ZooModel instances

Methods:

add_model(model[, error_level])	Adds the given model to the manifest.
attributes()	Returns a list of class attributes to be serialized.
copy()	Returns a deep copy of the object.
custom_attributes([dynamic, private])	Returns a customizable list of class attributes.
dir_has_manifest(models_dir)	Determines whether the given directory has a models manifest.
from_dict(d)	Constructs a ModelsManifest from a JSON dictionary.
from_dir(models_dir)	Loads the ModelsManifest from the given models directory.
from_json(path, *args, **kwargs)	Constructs a Serializable object from a JSON file.
from_str(s, *args, **kwargs)	Constructs a Serializable object from a JSON string.
get_class_name()	Returns the fully-qualified class name string of this object.
get_latest_model_with_base_name(base_name)	Gets the Model instance for the latest version of the model with the given base name.
get_model_with_name(name)	Gets the model with the given name.
has_model_with_filename(model)	Determines whether this manifest contains a model with a conflicting filename.
has_model_with_name(name)	Determines whether this manifest contains the model with the given name.
make_manifest_path(models_dir)	Makes the manifest path for the given models directory.
merge(models_manifest[, error_level])	Merges the models manifest into this one.
remove_model(name[, error_level])	Removes the model with the given name from the ModelsManifest.
serialize([reflective])	Serializes the object into a dictionary.
to_str([pretty_print])	Returns a string representation of this object.
write_json(path[, pretty_print])	Serializes the object and writes it to disk.
write_to_dir(models_dir)	Writes the ModelsManifest to the given models directory.

Attributes:

subdir

add_model(model, error_level=0)

Adds the given model to the manifest.

Parameters

• model – a Model instance

• error_level –

  the error level to use, defined as:

  0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

attributes()

Returns a list of class attributes to be serialized.

This method is called internally by serialize() to determine the class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in vars(self) are returned, minus private attributes, i.e., those starting with “_”. The order of the attributes in this list is preserved when serializing objects, so a common pattern is for subclasses to override this method if they want their JSON files to be organized in a particular way.

Returns

a list of class attributes to be serialized

copy()

Returns a deep copy of the object.

Returns

a Serializable instance

custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters

• dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

• private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns

a list of class attributes

static dir_has_manifest(models_dir)

Determines whether the given directory has a models manifest.

Parameters

models_dir – the models directory

Returns

True/False

classmethod from_dict(d)

Constructs a ModelsManifest from a JSON dictionary.

Parameters

d – a JSON dictionary

Returns

a ModelsManifest

classmethod from_dir(models_dir)

Loads the ModelsManifest from the given models directory.

Parameters

models_dir – the models directory

Returns

a ModelsManifest

classmethod from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters

• path – the path to the JSON file on disk

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters

• s – a JSON string representation of a Serializable object

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod get_class_name()

Returns the fully-qualified class name string of this object.

get_latest_model_with_base_name(base_name)

Gets the Model instance for the latest version of the model with the given base name.

Parameters

base_name – the base name of the model

Returns

the Model instance

Raises

ModelError – if the model was not found

get_model_with_name(name)

Gets the model with the given name.

Parameters

name – the name of the model

Returns

the Model instance

Raises

ModelError – if the model was not found

has_model_with_filename(model)

Determines whether this manifest contains a model with a conflicting filename.

Parameters

model – a Model instance

Returns

True/False

has_model_with_name(name)

Determines whether this manifest contains the model with the given name.

Parameters

name – the model name

Returns

True/False

static make_manifest_path(models_dir)

Makes the manifest path for the given models directory.

Parameters

models_dir – the models directory

Returns

the manifest path

merge(models_manifest, error_level=0)

Merges the models manifest into this one.

Parameters

• models_manifest – a ModelsManifest

• error_level –

  the error level to use, defined as:

  0: raise error if a model cannot be added 1: log warning if a model cannot be added 2: ignore models that cannot be added

remove_model(name, error_level=0)

Removes the model with the given name from the ModelsManifest.

Parameters

• name – the name of the model

• error_level –

  the error level to use, defined as:

  0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns

a JSON dictionary representation of the object

property subdir

to_str(pretty_print=True, **kwargs)

Returns a string representation of this object.

Parameters

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

• **kwargs – optional keyword arguments for self.serialize()

Returns

a string representation of the object

write_json(path, pretty_print=False, **kwargs)

Serializes the object and writes it to disk.

Parameters

• path – the output path

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

• **kwargs – optional keyword arguments for self.serialize()

write_to_dir(models_dir)

Writes the ModelsManifest to the given models directory.

Parameters

models_dir – the models directory

class fiftyone.zoo.models.RemoteZooModelsManifest(models=None, name=None, url=None)

Bases: fiftyone.zoo.models.ZooModelsManifest

Class that describes the collection of remotely-sourced models in the FiftyOne Model Zoo.

Parameters

models – a list of RemoteZooModel instances

Methods:

add_model(model[, error_level])	Adds the given model to the manifest.
attributes()	Returns a list of class attributes to be serialized.
copy()	Returns a deep copy of the object.
custom_attributes([dynamic, private])	Returns a customizable list of class attributes.
dir_has_manifest(models_dir)	Determines whether the given directory has a models manifest.
from_dict(d)	Constructs a ModelsManifest from a JSON dictionary.
from_dir(models_dir)	Loads the ModelsManifest from the given models directory.
from_json(path, *args, **kwargs)	Constructs a Serializable object from a JSON file.
from_str(s, *args, **kwargs)	Constructs a Serializable object from a JSON string.
get_class_name()	Returns the fully-qualified class name string of this object.
get_latest_model_with_base_name(base_name)	Gets the Model instance for the latest version of the model with the given base name.
get_model_with_name(name)	Gets the model with the given name.
has_model_with_filename(model)	Determines whether this manifest contains a model with a conflicting filename.
has_model_with_name(name)	Determines whether this manifest contains the model with the given name.
make_manifest_path(models_dir)	Makes the manifest path for the given models directory.
merge(models_manifest[, error_level])	Merges the models manifest into this one.
remove_model(name[, error_level])	Removes the model with the given name from the ModelsManifest.
serialize([reflective])	Serializes the object into a dictionary.
to_str([pretty_print])	Returns a string representation of this object.
write_json(path[, pretty_print])	Serializes the object and writes it to disk.
write_to_dir(models_dir)	Writes the ModelsManifest to the given models directory.

Attributes:

subdir

add_model(model, error_level=0)

Adds the given model to the manifest.

Parameters

• model – a Model instance

• error_level –

  the error level to use, defined as:

  0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

attributes()

Returns a list of class attributes to be serialized.

This method is called internally by serialize() to determine the class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in vars(self) are returned, minus private attributes, i.e., those starting with “_”. The order of the attributes in this list is preserved when serializing objects, so a common pattern is for subclasses to override this method if they want their JSON files to be organized in a particular way.

Returns

a list of class attributes to be serialized

copy()

Returns a deep copy of the object.

Returns

a Serializable instance

custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters

• dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False

• private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns

a list of class attributes

static dir_has_manifest(models_dir)

Determines whether the given directory has a models manifest.

Parameters

models_dir – the models directory

Returns

True/False

classmethod from_dict(d)

Constructs a ModelsManifest from a JSON dictionary.

Parameters

d – a JSON dictionary

Returns

a ModelsManifest

classmethod from_dir(models_dir)

Loads the ModelsManifest from the given models directory.

Parameters

models_dir – the models directory

Returns

a ModelsManifest

classmethod from_json(path, *args, **kwargs)

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters

• path – the path to the JSON file on disk

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod from_str(s, *args, **kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters

• s – a JSON string representation of a Serializable object

• *args – optional positional arguments for self.from_dict()

• **kwargs – optional keyword arguments for self.from_dict()

Returns

an instance of the Serializable class

classmethod get_class_name()

Returns the fully-qualified class name string of this object.

get_latest_model_with_base_name(base_name)

Gets the Model instance for the latest version of the model with the given base name.

Parameters

base_name – the base name of the model

Returns

the Model instance

Raises

ModelError – if the model was not found

get_model_with_name(name)

Gets the model with the given name.

Parameters

name – the name of the model

Returns

the Model instance

Raises

ModelError – if the model was not found

has_model_with_filename(model)

Determines whether this manifest contains a model with a conflicting filename.

Parameters

model – a Model instance

Returns

True/False

has_model_with_name(name)

Determines whether this manifest contains the model with the given name.

Parameters

name – the model name

Returns

True/False

static make_manifest_path(models_dir)

Makes the manifest path for the given models directory.

Parameters

models_dir – the models directory

Returns

the manifest path

merge(models_manifest, error_level=0)

Merges the models manifest into this one.

Parameters

• models_manifest – a ModelsManifest

• error_level –

  the error level to use, defined as:

  0: raise error if a model cannot be added 1: log warning if a model cannot be added 2: ignore models that cannot be added

remove_model(name, error_level=0)

Removes the model with the given name from the ModelsManifest.

Parameters

• name – the name of the model

• error_level –

  the error level to use, defined as:

  0: raise error if the model cannot be added 1: log warning if the model cannot be added 2: ignore models that cannot be added

serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters

reflective – whether to include reflective attributes when serializing the object. By default, this is False

Returns

a JSON dictionary representation of the object

property subdir

to_str(pretty_print=True, **kwargs)

Returns a string representation of this object.

Parameters

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True

• **kwargs – optional keyword arguments for self.serialize()

Returns

a string representation of the object

write_json(path, pretty_print=False, **kwargs)

Serializes the object and writes it to disk.

Parameters

• path – the output path

• pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False

• **kwargs – optional keyword arguments for self.serialize()

write_to_dir(models_dir)

Writes the ModelsManifest to the given models directory.

Parameters

models_dir – the models directory