radiant_mlhub.client package

Submodules

radiant_mlhub.client.catalog_downloader module

class radiant_mlhub.client.catalog_downloader.AssetRecord(*, rowid: int = None, asset_key: str = None, asset_save_path: str = None, asset_url: str = None, bbox_json: str = None, collection_id: str = None, common_asset: bool = False, single_datetime: datetime.datetime = None, start_datetime: datetime.datetime = None, end_datetime: datetime.datetime = None, filtered: bool = False, geometry_json: str = None, item_id: str = None)[source]

Bases: pydantic.main.BaseModel

A stac_assets db record.

class Config[source]

Bases: object

arbitrary_types_allowed = True
asset_key: Optional[str]
asset_save_path: Optional[str]
asset_url: Optional[str]
bbox_json: Optional[str]
collection_id: Optional[str]
common_asset: bool
end_datetime: Optional[datetime.datetime]
filtered: bool
geometry_json: Optional[str]
item_id: Optional[str]
rowid: Optional[int]
single_datetime: Optional[datetime.datetime]
start_datetime: Optional[datetime.datetime]
radiant_mlhub.client.catalog_downloader.COMMON_ASSET_NAMES = ['documentation', 'readme', 'test_split', 'train_split', 'validation_split']

Common assets will be put into _common and only downloaded once.

class radiant_mlhub.client.catalog_downloader.CatalogDownloader(config: radiant_mlhub.client.catalog_downloader.CatalogDownloaderConfig)[source]

Bases: object

catalog_file: pathlib.Path
config: radiant_mlhub.client.catalog_downloader.CatalogDownloaderConfig
db_conn: sqlite3.Connection
db_cur: sqlite3.Cursor
err_report: _io.TextIOWrapper
err_report_path: pathlib.Path
work_dir: pathlib.Path
class radiant_mlhub.client.catalog_downloader.CatalogDownloaderConfig(*, api_key: str = None, bbox: Optional[Union[Tuple[float], List[float]]] = None, catalog_only: bool = False, collection_filter: Dict[str, List[str]] = None, dataset_id: str, if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts = DownloadIfExistsOpts.resume, intersects: Dict[str, Any] = None, output_dir: pathlib.Path, profile: str = None, mlhub_api_session: radiant_mlhub.session.Session, temporal_query: Optional[Union[datetime.datetime, Tuple[datetime.datetime, datetime.datetime]]] = None)[source]

Bases: pydantic.main.BaseModel

Configuration model & validator for CatalogDownloader.

class Config[source]

Bases: object

arbitrary_types_allowed = True
api_key: Optional[str]
bbox: Optional[Union[Tuple[float], List[float]]]
catalog_only: bool
collection_filter: Optional[Dict[str, List[str]]]
dataset_id: str
if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts
intersects: Optional[Dict[str, Any]]
mlhub_api_session: radiant_mlhub.session.Session

Requests session for mlhub api calls.

output_dir: pathlib.Path
profile: Optional[str]
temporal_query: Optional[Union[datetime.datetime, Tuple[datetime.datetime, datetime.datetime]]]

radiant_mlhub.client.collections module

radiant_mlhub.client.collections.get_collection(collection_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /collections/{p1} endpoint.

See the MLHub API docs for details.

Parameters
  • collection_id (str) – The ID of the collection to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

collection

Return type

dict

Raises
radiant_mlhub.client.collections.get_collection_item(collection_id: str, item_id: str, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /collections/{p1}/items/{p2} endpoint.

Parameters
  • collection_id (str) – The ID of the Collection to which the Item belongs.

  • item_id (str) – The ID of the Item.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

item

Return type

dict

radiant_mlhub.client.collections.list_collection_items(collection_id: str, *, page_size: Optional[int] = None, extensions: Optional[List[str]] = None, limit: int = 10, api_key: Optional[str] = None, profile: Optional[str] = None) Iterator[Dict[str, Any]][source]

Yields JSON-like dictionaries representing STAC Item objects returned by the Radiant MLHub GET /collections/{collection_id}/items endpoint.

Note

Because some collections may contain hundreds of thousands of items, this function limits the total number of responses to 10 by default. You can change this value by increasing the value of the limit keyword argument, or setting it to None to list all items. Be aware that trying to list all items in a large collection may take a very long time.

Parameters
  • collection_id (str) – The ID of the collection from which to fetch items

  • page_size (int) – The number of items to return in each page. If set to None, then this parameter will not be passed to the API and the default API value will be used (currently 30).

  • extensions (list) – If provided, then only items that support all of the extensions listed will be returned.

  • limit (int) – The maximum total number of items to yield. Defaults to 10.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Yields

item (dict) – JSON-like dictionary representing a STAC Item associated with the given collection.

radiant_mlhub.client.collections.list_collections(*, api_key: Optional[str] = None, profile: Optional[str] = None) List[Dict[str, Any]][source]

Gets a list of JSON-like dictionaries representing STAC Collection objects returned by the Radiant MLHub GET /collections endpoint.

See the MLHub API docs for details.

Parameters
  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

collections – List of JSON-like dictionaries representing STAC Collection objects.

Return type

List[dict]

radiant_mlhub.client.datasets module

radiant_mlhub.client.datasets.download_collection_archive(archive_id: str, output_dir: Optional[Union[str, pathlib.Path]] = None, *, if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts = DownloadIfExistsOpts.resume, api_key: Optional[str] = None, profile: Optional[str] = None) pathlib.Path[source]

Downloads the archive with the given ID to an output location (current working directory by default).

The if_exists argument determines how to handle an existing archive file in the output directory. The default behavior (defined by if_exists="resume") is to resume the download by requesting a byte range starting at the size of the existing file. If the existing file is the same size as the file to be downloaded (as determined by the Content-Length header), then the download is skipped. You can automatically skip download using if_exists="skip" (this may be faster if you know the download was not interrupted, since no network request is made to get the archive size). You can also overwrite the existing file using if_exists="overwrite".

Parameters
  • archive_id (str) – The ID of the archive to download. This is the same as the Collection ID.

  • output_dir (Path) – Path to which the archive will be downloaded. Defaults to the current working directory.

  • if_exists (str, optional) – How to handle an existing archive at the same location. If "skip", the download will be skipped. If "overwrite", the existing file will be overwritten and the entire file will be re-downloaded. If "resume" (the default), the existing file size will be compared to the size of the download (using the Content-Length header). If the existing file is smaller, then only the remaining portion will be downloaded. Otherwise, the download will be skipped.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

output_path – The full path to the downloaded archive file.

Return type

Path

Raises

ValueError – If if_exists is not one of "skip", "overwrite", or "resume".

radiant_mlhub.client.datasets.get_catalog_info(dataset_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Gets info for the given archive from the /catalog/{dataset_id}/info endpoint as a JSON-like dictionary.

The JSON object returned by the API has the following properties:

  • dataset: ID of the dataset that this archive’s Collection belongs to.

  • stac_catalog_size: size of the dataset_id.tar.gz STAC archive (in bytes)

  • estimated_dataset_size: size in bytes of entire dataset (estimated)

Parameters
  • dataset_id (str) – The ID of the dataset

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

archive_info – JSON-like dictionary representing the API response.

Return type

dict

radiant_mlhub.client.datasets.get_collection_archive_info(archive_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Gets info for the given archive from the /archive/{archive_id}/info endpoint as a JSON-like dictionary.

The JSON object returned by the API has the following properties:

  • collection: The ID of the Collection that this archive is associated with.

  • dataset: The ID of the dataset that this archive’s Collection belongs to.

  • size: The size of the archive (in bytes)

  • types: The types associated with this archive’s Collection. Will be one of "source_imagery" or "label".

Parameters
  • archive_id (str) – The ID of the archive. This is the same as the Collection ID.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

archive_info – JSON-like dictionary representing the API response.

Return type

dict

radiant_mlhub.client.datasets.get_dataset(dataset_id_or_doi: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing a dataset by first trying to look up the dataset by ID, then falling back to finding the dataset by DOI.

See the MLHub API docs for details.

Parameters
  • dataset_id_or_doi (str) – The ID of the dataset to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

dataset

Return type

dict

radiant_mlhub.client.datasets.get_dataset_by_doi(dataset_doi: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /datasets/doi/{dataset_id} endpoint.

See the MLHub API docs for details.

Parameters
  • dataset_doi (str) – The DOI of the dataset to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

dataset

Return type

dict

radiant_mlhub.client.datasets.get_dataset_by_id(dataset_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /datasets/{dataset_id} endpoint.

See the MLHub API docs for details.

Parameters
  • dataset_id (str) – The ID of the dataset to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

dataset

Return type

dict

radiant_mlhub.client.datasets.list_datasets(*, tags: Optional[Union[str, Iterable[str]]] = None, text: Optional[Union[str, Iterable[str]]] = None, api_key: Optional[str] = None, profile: Optional[str] = None) List[Dict[str, Any]][source]

Gets a list of JSON-like dictionaries representing dataset objects returned by the Radiant MLHub GET /datasets endpoint.

See the MLHub API docs for details.

Parameters
  • tags (A tag or list of tags to filter datasets by. If not None, only datasets) – containing all provided tags will be returned.

  • text (A text phrase or list of text phrases to filter datasets by. If not None,) – only datasets containing all phrases will be returned.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

datasets

Return type

List[dict]

radiant_mlhub.client.ml_models module

radiant_mlhub.client.ml_models.get_model_by_id(model_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /models/{model_id} endpoint.

See the MLHub API docs for details.

Parameters
  • model_id (str) – The ID of the ML Model to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

model

Return type

dict

radiant_mlhub.client.ml_models.list_models(*, api_key: Optional[str] = None, profile: Optional[str] = None) List[Dict[str, Any]][source]

Gets a list of JSON-like dictionaries representing ML Model objects returned by the Radiant MLHub GET /models endpoint.

See the MLHub API docs for details.

Parameters
  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

models

Return type

List[dict]

radiant_mlhub.client.resumable_downloader module

class radiant_mlhub.client.resumable_downloader.ResumableDownloader(url: str, out_file: pathlib.Path, desc: Optional[str] = None, session: Optional[requests.sessions.Session] = None, if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts = DownloadIfExistsOpts.overwrite, disable_progress_bar: bool = True, chunk_size: int = 1024, chunk_unit: str = 'KB')[source]

Bases: object

Resumable downloader, for a single file.

  • Similar to datasets._download_collection_archive_chunked(), but this is not parallelized.

  • Supports DownloadIfExistsOpts.

  • Displays progress bar (optional).

chunk_size: int
chunk_unit: str
desc: Optional[str]
disable_progress_bar: bool
if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts
out_file: pathlib.Path
run() None[source]
session: requests.sessions.Session
url: str

Module contents

Low-level functions for making requests to MLHub API and Blob Storage endpoints.

class radiant_mlhub.client.CatalogDownloader(config: radiant_mlhub.client.catalog_downloader.CatalogDownloaderConfig)[source]

Bases: object

catalog_file: pathlib.Path
config: radiant_mlhub.client.catalog_downloader.CatalogDownloaderConfig
db_conn: sqlite3.Connection
db_cur: sqlite3.Cursor
err_report: _io.TextIOWrapper
err_report_path: pathlib.Path
work_dir: pathlib.Path
class radiant_mlhub.client.CatalogDownloaderConfig(*, api_key: str = None, bbox: Optional[Union[Tuple[float], List[float]]] = None, catalog_only: bool = False, collection_filter: Dict[str, List[str]] = None, dataset_id: str, if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts = DownloadIfExistsOpts.resume, intersects: Dict[str, Any] = None, output_dir: pathlib.Path, profile: str = None, mlhub_api_session: radiant_mlhub.session.Session, temporal_query: Optional[Union[datetime.datetime, Tuple[datetime.datetime, datetime.datetime]]] = None)[source]

Bases: pydantic.main.BaseModel

Configuration model & validator for CatalogDownloader.

class Config[source]

Bases: object

arbitrary_types_allowed = True
api_key: Optional[str]
bbox: Optional[Union[Tuple[float], List[float]]]
catalog_only: bool
collection_filter: Optional[Dict[str, List[str]]]
dataset_id: str
if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts
intersects: Optional[Dict[str, Any]]
mlhub_api_session: radiant_mlhub.session.Session

Requests session for mlhub api calls.

output_dir: pathlib.Path
profile: Optional[str]
temporal_query: Optional[Union[datetime.datetime, Tuple[datetime.datetime, datetime.datetime]]]
radiant_mlhub.client.download_collection_archive(archive_id: str, output_dir: Optional[Union[str, pathlib.Path]] = None, *, if_exists: radiant_mlhub.if_exists.DownloadIfExistsOpts = DownloadIfExistsOpts.resume, api_key: Optional[str] = None, profile: Optional[str] = None) pathlib.Path[source]

Downloads the archive with the given ID to an output location (current working directory by default).

The if_exists argument determines how to handle an existing archive file in the output directory. The default behavior (defined by if_exists="resume") is to resume the download by requesting a byte range starting at the size of the existing file. If the existing file is the same size as the file to be downloaded (as determined by the Content-Length header), then the download is skipped. You can automatically skip download using if_exists="skip" (this may be faster if you know the download was not interrupted, since no network request is made to get the archive size). You can also overwrite the existing file using if_exists="overwrite".

Parameters
  • archive_id (str) – The ID of the archive to download. This is the same as the Collection ID.

  • output_dir (Path) – Path to which the archive will be downloaded. Defaults to the current working directory.

  • if_exists (str, optional) – How to handle an existing archive at the same location. If "skip", the download will be skipped. If "overwrite", the existing file will be overwritten and the entire file will be re-downloaded. If "resume" (the default), the existing file size will be compared to the size of the download (using the Content-Length header). If the existing file is smaller, then only the remaining portion will be downloaded. Otherwise, the download will be skipped.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

output_path – The full path to the downloaded archive file.

Return type

Path

Raises

ValueError – If if_exists is not one of "skip", "overwrite", or "resume".

radiant_mlhub.client.get_catalog_info(dataset_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Gets info for the given archive from the /catalog/{dataset_id}/info endpoint as a JSON-like dictionary.

The JSON object returned by the API has the following properties:

  • dataset: ID of the dataset that this archive’s Collection belongs to.

  • stac_catalog_size: size of the dataset_id.tar.gz STAC archive (in bytes)

  • estimated_dataset_size: size in bytes of entire dataset (estimated)

Parameters
  • dataset_id (str) – The ID of the dataset

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

archive_info – JSON-like dictionary representing the API response.

Return type

dict

radiant_mlhub.client.get_collection(collection_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /collections/{p1} endpoint.

See the MLHub API docs for details.

Parameters
  • collection_id (str) – The ID of the collection to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

collection

Return type

dict

Raises
radiant_mlhub.client.get_collection_archive_info(archive_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Gets info for the given archive from the /archive/{archive_id}/info endpoint as a JSON-like dictionary.

The JSON object returned by the API has the following properties:

  • collection: The ID of the Collection that this archive is associated with.

  • dataset: The ID of the dataset that this archive’s Collection belongs to.

  • size: The size of the archive (in bytes)

  • types: The types associated with this archive’s Collection. Will be one of "source_imagery" or "label".

Parameters
  • archive_id (str) – The ID of the archive. This is the same as the Collection ID.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

archive_info – JSON-like dictionary representing the API response.

Return type

dict

radiant_mlhub.client.get_collection_item(collection_id: str, item_id: str, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /collections/{p1}/items/{p2} endpoint.

Parameters
  • collection_id (str) – The ID of the Collection to which the Item belongs.

  • item_id (str) – The ID of the Item.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

item

Return type

dict

radiant_mlhub.client.get_dataset(dataset_id_or_doi: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing a dataset by first trying to look up the dataset by ID, then falling back to finding the dataset by DOI.

See the MLHub API docs for details.

Parameters
  • dataset_id_or_doi (str) – The ID of the dataset to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

dataset

Return type

dict

radiant_mlhub.client.get_dataset_by_doi(dataset_doi: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /datasets/doi/{dataset_id} endpoint.

See the MLHub API docs for details.

Parameters
  • dataset_doi (str) – The DOI of the dataset to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

dataset

Return type

dict

radiant_mlhub.client.get_dataset_by_id(dataset_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /datasets/{dataset_id} endpoint.

See the MLHub API docs for details.

Parameters
  • dataset_id (str) – The ID of the dataset to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

dataset

Return type

dict

radiant_mlhub.client.get_model_by_id(model_id: str, *, api_key: Optional[str] = None, profile: Optional[str] = None) Dict[str, Any][source]

Returns a JSON-like dictionary representing the response from the Radiant MLHub GET /models/{model_id} endpoint.

See the MLHub API docs for details.

Parameters
  • model_id (str) – The ID of the ML Model to fetch

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

model

Return type

dict

radiant_mlhub.client.list_collection_items(collection_id: str, *, page_size: Optional[int] = None, extensions: Optional[List[str]] = None, limit: int = 10, api_key: Optional[str] = None, profile: Optional[str] = None) Iterator[Dict[str, Any]][source]

Yields JSON-like dictionaries representing STAC Item objects returned by the Radiant MLHub GET /collections/{collection_id}/items endpoint.

Note

Because some collections may contain hundreds of thousands of items, this function limits the total number of responses to 10 by default. You can change this value by increasing the value of the limit keyword argument, or setting it to None to list all items. Be aware that trying to list all items in a large collection may take a very long time.

Parameters
  • collection_id (str) – The ID of the collection from which to fetch items

  • page_size (int) – The number of items to return in each page. If set to None, then this parameter will not be passed to the API and the default API value will be used (currently 30).

  • extensions (list) – If provided, then only items that support all of the extensions listed will be returned.

  • limit (int) – The maximum total number of items to yield. Defaults to 10.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Yields

item (dict) – JSON-like dictionary representing a STAC Item associated with the given collection.

radiant_mlhub.client.list_collections(*, api_key: Optional[str] = None, profile: Optional[str] = None) List[Dict[str, Any]][source]

Gets a list of JSON-like dictionaries representing STAC Collection objects returned by the Radiant MLHub GET /collections endpoint.

See the MLHub API docs for details.

Parameters
  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

collections – List of JSON-like dictionaries representing STAC Collection objects.

Return type

List[dict]

radiant_mlhub.client.list_datasets(*, tags: Optional[Union[str, Iterable[str]]] = None, text: Optional[Union[str, Iterable[str]]] = None, api_key: Optional[str] = None, profile: Optional[str] = None) List[Dict[str, Any]][source]

Gets a list of JSON-like dictionaries representing dataset objects returned by the Radiant MLHub GET /datasets endpoint.

See the MLHub API docs for details.

Parameters
  • tags (A tag or list of tags to filter datasets by. If not None, only datasets) – containing all provided tags will be returned.

  • text (A text phrase or list of text phrases to filter datasets by. If not None,) – only datasets containing all phrases will be returned.

  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

datasets

Return type

List[dict]

radiant_mlhub.client.list_models(*, api_key: Optional[str] = None, profile: Optional[str] = None) List[Dict[str, Any]][source]

Gets a list of JSON-like dictionaries representing ML Model objects returned by the Radiant MLHub GET /models endpoint.

See the MLHub API docs for details.

Parameters
  • api_key (str) – An API key to use for this request. This will override an API key set in a profile on using an environment variable

  • profile (str) – A profile to use when making this request.

Returns

models

Return type

List[dict]