substra.sdk¶

Client¶

Client(url: Optional[str] = None, token: Optional[str] = None, retry_timeout: int = 300, insecure: bool = False, debug: bool = False)

Create a client

Arguments:

url (str, optional): URL of the Substra platform. Mandatory to connect to a Substra platform. If no URL is given debug must be True and all assets must be created locally. Defaults to None.
token (str, optional): Token to authenticate to the Substra platform. If no token is given, use the ‘login’ function to authenticate. Defaults to None.
retry_timeout (int, optional): Number of seconds before attempting a retry call in case of timeout. Defaults to 5 minutes.
insecure (bool, optional): If True, the client can call a not-certified backend. This is for development purposes. Defaults to False.
debug (bool, optional): Whether to use the default or debug mode. In debug mode, new assets are created locally but can access assets from the deployed Substra platform. The platform is in read-only mode. Defaults to False. Additionally, you can set the environment variable DEBUG_SPAWNER to docker if you want the tasks to be executed in containers (default) or subprocess to execute them in Python subprocesses (faster, experimental: The Dockerfile commands are not executed, requires dependencies to be installed locally).

backend_mode¶

This is a property.
Get the backend mode: deployed, local and which type of local mode

    Returns:
        str: Backend mode

temp_directory¶

This is a property.
Temporary directory for storing assets in debug mode. Deleted when the client is deleted.

add_aggregatetuple¶

add_aggregatetuple(self, data: Union[dict, substra.sdk.schemas.AggregatetupleSpec]) -> str

Create a new aggregate tuple asset.

Arguments:

data (Union[dict, schemas.AggregatetupleSpec], required): If it is a dict, it must have the same keys as specified in schemas.AggregatetupleSpec.

Returns:

str: Key of the asset

add_algo¶

add_algo(self, data: Union[dict, substra.sdk.schemas.AlgoSpec]) -> str

Create new algo asset.

Arguments:

data (Union[dict, schemas.AlgoSpec], required): If it is a dict, it must have the same keys as specified in schemas.AlgoSpec.

Returns:

str: Key of the algo

add_composite_traintuple¶

add_composite_traintuple(self, data: Union[dict, substra.sdk.schemas.CompositeTraintupleSpec]) -> str

Create new composite traintuple asset. As specified in the data structure, output trunk models cannot be made public.

Arguments:

data (Union[dict, schemas.CompositeTraintupleSpec], required): If it is a dict, it must have the same keys as specified in schemas.CompositeTraintupleSpec.

Returns:

str: Key of the asset

add_compute_plan¶

add_compute_plan(self, data: Union[dict, substra.sdk.schemas.ComputePlanSpec], auto_batching: bool = True, batch_size: int = 500) -> substra.sdk.models.ComputePlan

Create new compute plan asset. As specified in the data dict structure, output trunk models of composite traintuples cannot be made public.

Arguments:

data (Union[dict, schemas.ComputePlanSpec], required): If it is a dict, it must have the same keys as specified in schemas.ComputePlanSpec.
auto_batching (bool, optional): Set ‘auto_batching’ to False to upload all the tuples of the compute plan at once. Defaults to True.
batch_size (int, optional): If ‘auto_batching’ is True, change batch_size to define the number of tuples uploaded in each batch (default 500).

Returns:

models.ComputePlan: Created compute plan

add_compute_plan_tuples¶

add_compute_plan_tuples(self, key: str, tuples: Union[dict, substra.sdk.schemas.UpdateComputePlanTuplesSpec], auto_batching: bool = True, batch_size: int = 500) -> substra.sdk.models.ComputePlan

Update compute plan. As specified in the tuples dict structure, output trunk models of composite traintuples cannot be made public.

Arguments:

key (str, required): Compute plan key
tuples (Union[dict, schemas.UpdateComputePlanTuplesSpec], required): If it is a dict, it must have the same keys as specified in schemas.UpdateComputePlanTuplesSpec.
auto_batching (bool, optional): Set ‘auto_batching’ to False to upload all the tuples of the compute plan at once. Defaults to True.
batch_size (int, optional): If ‘auto_batching’ is True, change batch_size to define the number of tuples uploaded in each batch (default 500).

Returns:

models.ComputePlan: updated compute plan, as described in the models.ComputePlan model

add_data_sample¶

add_data_sample(self, data: Union[dict, substra.sdk.schemas.DataSampleSpec], local: bool = True) -> str

Create a new data sample asset and return its key.

Arguments:

data (Union[dict, schemas.DataSampleSpec], required): data sample to add. If it is a dict, it must follow the DataSampleSpec schema.
local (bool, optional): If local is true, path must refer to a directory located on the local filesystem. The file content will be transferred to the server through an HTTP query, so this mode should be used for relatively small files (<10mo).

If local is false, path must refer to a directory located on the server filesystem. This directory must be accessible (readable) by the server. This mode is well suited for all kind of file sizes. Defaults to True.

Returns:

str: key of the data sample

add_data_samples¶

add_data_samples(self, data: Union[dict, substra.sdk.schemas.DataSampleSpec], local: bool = True) -> List[str]

Create many data sample assets and return a list of keys. Create multiple data samples through a single HTTP request. This method is well suited for adding multiple small files only. For adding a large amount of data it is recommended to add them one by one. It allows a better control in case of failures.

Arguments:

data (Union[dict, schemas.DataSampleSpec], required): data samples to add. If it is a dict, it must follow the DataSampleSpec schema. The paths in the data dictionary must be a list of paths where each path points to a directory representing one data sample.
local (bool, optional): Please refer to the method Client.add_data_sample. Defaults to True.

Returns:

List[str]: List of the data sample keys

add_dataset¶

add_dataset(self, data: Union[dict, substra.sdk.schemas.DatasetSpec])

Create new dataset asset and return its key.

Arguments:

data (Union[dict, schemas.DatasetSpec], required): If it is a dict, it must have the same keys as specified in schemas.DatasetSpec.

Returns:

str: Key of the dataset

add_predicttuple¶

add_predicttuple(self, data: Union[dict, substra.sdk.schemas.PredicttupleSpec]) -> str

Create new predicttuple asset.

Arguments:

data (Union[dict, schemas.PredicttupleSpec], required): If it is a dict, it must have the same keys as specified in schemas.PredicttupleSpec.

Returns:

str: Key of the asset

add_testtuple¶

add_testtuple(self, data: Union[dict, substra.sdk.schemas.TesttupleSpec]) -> str

Create new testtuple asset.

Arguments:

data (Union[dict, schemas.TesttupleSpec], required): If it is a dict, it must have the same keys as specified in schemas.TesttupleSpec.

Returns:

str: Key of the asset

add_traintuple¶

add_traintuple(self, data: Union[dict, substra.sdk.schemas.TraintupleSpec]) -> str

Create new traintuple asset.

Arguments:

data (Union[dict, schemas.TraintupleSpec], required): If it is a dict, it must have the same keys as specified in schemas.TraintupleSpec.

Returns:

str: Key of the asset

cancel_compute_plan¶

cancel_compute_plan(self, key: str) -> None

Cancel execution of compute plan. Nothing is returned by this method

describe_algo¶

describe_algo(self, key: str) -> str

Get algo description.

describe_dataset¶

describe_dataset(self, key: str) -> str

Get dataset description.

download_algo¶

download_algo(self, key: str, destination_folder: str) -> None

Download algo resource. Download algo package in destination folder.

Arguments:

key (str, required): Algo key to download
destination_folder (str, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded algo

download_dataset¶

download_dataset(self, key: str, destination_folder: str) -> None

Download data manager resource. Download opener script in destination folder.

Arguments:

key (str, required): Dataset key to download
destination_folder (str, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded dataset

download_head_model_from_composite_traintuple¶

download_head_model_from_composite_traintuple(self, tuple_key: str, folder) -> None

Download composite traintuple head model to destination file. This model was saved using the ‘save_model’ function of the algorithm. To load and use the model, please refer to the ‘load_model’ and ‘predict’ functions of the algorithm.

Arguments:

tuple_key (str, required): Tuple key to download
folder (_type_, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded model

download_logs¶

download_logs(self, tuple_key: str, folder: str) -> str

Download the execution logs of a failed tuple to a destination file. The logs are saved in the folder to a file named ‘tuple_logs_{tuple_key}.txt’.

Logs are only available for tuples that experienced an execution failure. Attempting to retrieve logs for tuples in any other states or for non-existing tuples will result in a NotFound error.

Arguments:

tuple_key : the key of the tuple that produced the logs
folder : the destination directory

Returns:

str: The logs as a str

download_model¶

download_model(self, key: str, destination_folder) -> None

Download model to destination file. This model was saved using the ‘save_model’ function of the algorithm. To load and use the model, please refer to the ‘load_model’ and ‘predict’ functions of the algorithm.

Arguments:

key (str, required): Model key to download
destination_folder (str, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded model

download_model_from_aggregatetuple¶

download_model_from_aggregatetuple(self, tuple_key: str, folder) -> None

Download aggregatetuple model to destination file. This model was saved using the ‘save_model’ function of the algorithm. To load and use the model, please refer to the ‘load_model’ and ‘predict’ functions of the algorithm.

Arguments:

tuple_key (str, required): Tuple key to download
folder (_type_, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded model

download_model_from_traintuple¶

download_model_from_traintuple(self, tuple_key: str, folder) -> None

Download traintuple model to destination file. This model was saved using the ‘save_model’ function of the algorithm. To load and use the model, please refer to the ‘load_model’ and ‘predict’ functions of the algorithm.

Arguments:

tuple_key (str, required): Tuple key to download
folder (_type_, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded model

download_trunk_model_from_composite_traintuple¶

download_trunk_model_from_composite_traintuple(self, tuple_key: str, folder) -> None

Download composite traintuple trunk model to destination file. This model was saved using the ‘save_model’ function of the algorithm. To load and use the model, please refer to the ‘load_model’ and ‘predict’ functions of the algorithm.

Arguments:

tuple_key (str, required): Tuple key to download
folder (_type_, required): Destination folder

Returns:

pathlib.Path: Path of the downloaded model

from_config_file¶

from_config_file(profile_name: str = 'default', config_path: Union[str, pathlib.Path] = '~/.substra', tokens_path: Union[str, pathlib.Path] = '~/.substra-tokens', token: Optional[str] = None, retry_timeout: int = 300, debug: bool = False)

Returns a new Client configured with profile data from configuration files.

Arguments:

profile_name (str, optional): Name of the profile to load. Defaults to ‘default’.
config_path (Union[str, pathlib.Path], optional): Path to the configuration file. Defaults to ‘~/.substra’.
tokens_path (Union[str, pathlib.Path], optional): Path to the tokens file. Defaults to ‘~/.substra-tokens’.
token (str, optional): Token to use for authentication (will be used instead of any token found at tokens_path). Defaults to None.
retry_timeout (int, optional): Number of seconds before attempting a retry call in case of timeout. Defaults to 5 minutes.
debug (bool, required): Whether to use the default or debug mode. In debug mode, new assets are created locally but can get remote assets. The deployed platform is in read-only mode. Defaults to False.

Returns:

Client: The new client.

get_aggregatetuple¶

get_aggregatetuple(self, key: str) -> substra.sdk.models.Aggregatetuple

Get aggregatetuple by key, the returned object is described in the models.Aggregatetuple model

get_algo¶

get_algo(self, key: str) -> substra.sdk.models.Algo

Get algo by key, the returned object is described in the models.Algo model

get_composite_traintuple¶

get_composite_traintuple(self, key: str) -> substra.sdk.models.CompositeTraintuple

Get composite traintuple by key, the returned object is described in the models.CompositeTraintuple model

get_compute_plan¶

get_compute_plan(self, key: str) -> substra.sdk.models.ComputePlan

Get compute plan by key, the returned object is described in the models.ComputePlan model

get_data_sample¶

get_data_sample(self, key: str) -> substra.sdk.models.DataSample

Get data sample by key, the returned object is described in the models.Datasample model

get_dataset¶

get_dataset(self, key: str) -> substra.sdk.models.Dataset

Get dataset by key, the returned object is described in the models.Dataset model

get_logs¶

get_logs(self, tuple_key: str) -> str

Get tuple logs by tuple key, the returned object is a string containing the logs.

Logs are only available for tuples that experienced an execution failure. Attempting to retrieve logs for tuples in any other states or for non-existing tuples will result in a NotFound error.

get_model¶

get_model(self, key: str) -> substra.sdk.models.OutModel

None

get_performances¶

get_performances(self, key: str) -> substra.sdk.models.Performances

Get the compute plan performances by key, the returned object is described in the models.Performances and easily convertible to pandas dataframe.

Examples:

perf = client.get_performances(cp_key)
df = pd.DataFrame(perf.dict())
print(df)

get_predicttuple¶

get_predicttuple(self, key: str) -> substra.sdk.models.Predicttuple

Get predicttuple by key, the returned object is described in the models.Predicttuple model

get_testtuple¶

get_testtuple(self, key: str) -> substra.sdk.models.Testtuple

Get testtuple by key, the returned object is described in the models.Testtuple model

get_traintuple¶

get_traintuple(self, key: str) -> substra.sdk.models.Traintuple

Get traintuple by key, the returned object is described in the models.Traintuple model

link_dataset_with_data_samples¶

link_dataset_with_data_samples(self, dataset_key: str, data_sample_keys: str) -> List[str]

Link dataset with data samples.

list_aggregatetuple¶

list_aggregatetuple(self, filters: dict = None, order_by: str = 'creation_date', ascending: bool = False) -> List[substra.sdk.models.Aggregatetuple]

List aggregatetuples. The filters argument is a dictionary, with those possible keys:

key (List[str]): list aggregatetuples with listed keys.

owner (List[str]): list aggregatetuples with listed owners.

worker (List[str]): list aggregatetuples which ran on listed workers. Remote mode only.

rank (List[int]): list aggregatetuples which are at given ranks.

status (List[str]): list aggregatetuples with given status.
    The possible values are the values of `substra.models.Status`
metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list aggregatetuples matching provided conditions in metadata. Remote mode only.
compute_plan_key (str): list aggregatetuples that are in the given compute plan. Remote mode only.
algo_key (str): list aggregatetuples that used the given algo. Remote mode only.
dataset_key (str): list aggregatetuples linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list aggregatetuples linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
order_by (str, optional): Field to sort results by. Possible values: creation_date, start_date, end_date. Default creation_date.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.Aggregatetuple: the returned object is described

list_algo¶

list_algo(self, filters: dict = None, ascending: bool = False) -> List[substra.sdk.models.Algo]

List algos. The filters argument is a dictionary, with those possible keys:

key (List[str]): list algo with given keys.

name (str): list algo with name partially matching given string. Remote mode only.

owner (List[str]): list algo with given owners.

metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list algo matching provided conditions in metadata.

permissions (List[str]): list algo which can be used by any of the listed nodes. Remote mode only.
compute_plan_key (str): list algo that are in the given compute plan. Remote mode only.
dataset_key (str): list algo linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list algo linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
ascending (bool, optional): Sorts results by oldest creation_date first. Default False (descending order).

Returns:

models.Algo: the returned object is described in the models.Algo model

list_composite_traintuple¶

list_composite_traintuple(self, filters: dict = None, order_by: str = 'creation_date', ascending: bool = False) -> List[substra.sdk.models.CompositeTraintuple]

List composite traintuples. The filters argument is a dictionary, with those possible keys:

key (List[str]): list composite traintuples with listed keys.

owner (List[str]): list composite traintuples with listed owners.

worker (List[str]): list composite traintuples which ran on listed workers. Remote mode only.

rank (List[int]): list composite traintuples which are at given ranks.

status (List[str]): list composite traintuples with given status.
    The possible values are the values of `substra.models.Status`
metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list composite traintuples matching provided conditions in metadata. Remote mode only.
compute_plan_key (str): list composite traintuples that are in the given compute plan. Remote mode only.
algo_key (str): list composite traintuples that used the given algo. Remote mode only.
dataset_key (str): list composite traintuples linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list composite traintuples linked or that used this data sample(s).
    Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
order_by (str, optional): Field to sort results by. Possible values: creation_date, start_date, end_date. Default creation_date.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.CompositeTraintuple: the returned object is described

list_compute_plan¶

list_compute_plan(self, filters: dict = None, order_by: str = 'creation_date', ascending: bool = False) -> List[substra.sdk.models.ComputePlan]

List compute plans. The filters argument is a dictionary, with those possible keys:

key (List[str]): list compute plans with listed keys.

name (str): list compute plans with name partially matching given string. Remote mode only.

owner (List[str]): list compute plans with listed owners.

worker (List[str]): list compute plans which ran on listed workers. Remote mode only.

status (List[str]): list compute plans with given status.
    The possible values are the values of `substra.models.ComputePlanStatus`
metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list compute plans matching provided conditions in metadata. Remote mode only.
algo_key (str): list compute plans that used the given algo. Remote mode only.
dataset_key (str): list compute plans linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list compute plans linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
order_by (str, optional): Field to sort results by. Possible values: creation_date, start_date, end_date. Default creation_date.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.ComputePlan: the returned object is described

list_data_sample¶

list_data_sample(self, filters: dict = None, ascending: bool = False) -> List[substra.sdk.models.DataSample]

List data samples. The filters argument is a dictionary, with those possible keys:

    key (List[str]): list data samples with listed keys.
    owner (List[str]): list data samples with listed owners.
    compute_plan_key (str): list data samples that are in the given compute plan. Remote mode only.
    algo_key (str): list data samples that used the given algo. Remote mode only.
    dataset_key (str): list data samples linked or using this dataset. Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.DataSample: the returned object is described in the models.DataSample model

list_dataset¶

list_dataset(self, filters: dict = None, ascending: bool = False) -> List[substra.sdk.models.Dataset]

List datasets. The filters argument is a dictionary, with those possible keys:

key (List[str]): list dataset with given keys.

name (str): list dataset with name partially matching given string. Remote mode only.

owner (List[str]): list dataset with given owners.

metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list dataset matching provided conditions in metadata.

permissions (List[str]) : list dataset which can be used by any of the listed nodes. Remote mode only.
compute_plan_key (str): list dataset that are in the given compute plan. Remote mode only.
algo_key (str): list dataset that used the given algo. Remote mode only.
data_sample_key (List[str]): list dataset linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
ascending (bool, optional): Sorts results by oldest creation_date first. Default False (descending order).

Returns:

models.Dataset: the returned object is described

list_model¶

list_model(self, filters: dict = None, ascending: bool = False) -> List[substra.sdk.models.OutModel]

List models. The filters argument is a dictionnary, with those possible keys:

key (list[str]): list model with given keys.

compute_task_key (list[str]): list model produced by this compute task.

owner (list[str]): list model with given owners.

permissions (list[str]): list models which can be used by any of the listed nodes. Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
ascending (bool, optional): Sorts results by oldest creation_date first. Default False (descending order).

Returns: models.OutModel the returned object is described in the models.OutModel model

list_organization¶

list_organization(self, *args, **kwargs) -> List[substra.sdk.models.Organization]

List organizations, the returned object is described in the models.Organization model

list_predicttuple¶

list_predicttuple(self, filters: dict = None, order_by: str = 'creation_date', ascending: bool = False) -> List[substra.sdk.models.Predicttuple]

List predicttuples. The filters argument is a dictionary, with those possible keys:

key (List[str]): list predicttuples with listed keys.

owner (List[str]): list predicttuples with listed owners.

worker (List[str]): list predicttuples which ran on listed workers. Remote mode only.

rank (List[int]): list predicttuples which are at given ranks.

status (List[str]): list predicttuples with given status.
    The possible values are the values of `substra.models.Status`
metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list predicttuples matching provided conditions in metadata. Remote mode only.
compute_plan_key (str): list predicttuples that are in the given compute plan. Remote mode only.
algo_key (str): list predicttuples that used the given algo. Remote mode only.
dataset_key (str): list predicttuples linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list predicttuples linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
order_by (str, optional): Field to sort results by. Possible values: creation_date, start_date, end_date. Default creation_date.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.Predicttuple: the returned object is described in the models.Predicttuple model

list_testtuple¶

list_testtuple(self, filters: dict = None, order_by: str = 'creation_date', ascending: bool = False) -> List[substra.sdk.models.Testtuple]

List testtuples. The filters argument is a dictionary, with those possible keys:

key (List[str]): list testtuples with listed keys.

owner (List[str]): list testtuples with listed owners.

worker (List[str]): list testtuples which ran on listed workers. Remote mode only.

rank (List[int]): list testtuples which are at given ranks.

status (List[str]): list testtuples with given status.
    The possible values are the values of `substra.models.Status`
metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list testtuples matching provided conditions in metadata. Remote mode only.
compute_plan_key (str): list testtuples that are in the given compute plan. Remote mode only.
algo_key (str): list testtuples that used the given algo. Remote mode only.
dataset_key (str): list testtuples linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list testtuples linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
order_by (str, optional): Field to sort results by. Possible values: creation_date, start_date, end_date. Default creation_date.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.Testtuple: the returned object is described

list_traintuple¶

list_traintuple(self, filters: dict = None, order_by: str = 'creation_date', ascending: bool = False) -> List[substra.sdk.models.Traintuple]

List traintuples. The filters argument is a dictionary, with those possible keys:

key (List[str]): list traintuples with listed keys.

owner (List[str]): list traintuples with listed owners.

worker (List[str]): list traintuples which ran on listed workers. Remote mode only.

rank (List[int]): list traintuples which are at given ranks.

status (List[str]): list traintuples with given status.
    The possible values are the values of `substra.models.Status`
metadata (dict)
    {
        "key": str # the key of the metadata to filter on
        "type": "is", "contains" or "exists" # the type of query that will be used
        "value": str # the value that the key must be (if type is "is") or contain (if type if "contains")
    }: list traintuples matching provided conditions in metadata. Remote mode only.
compute_plan_key (str): list traintuples that are in the given compute plan. Remote mode only.
algo_key (str): list traintuples that used the given algo. Remote mode only.
dataset_key (str): list traintuples linked or using this dataset. Remote mode only.
data_sample_key (List[str]): list traintuples linked or that used this data sample(s). Remote mode only.

Arguments:

filters (dict, optional): List of key values pair to filter on. Default None.
order_by (str, optional): Field to sort results by. Possible values: creation_date, start_date, end_date. Default creation_date.
ascending (bool, optional): Sorts results on order_by by ascending order. Default False (descending order).

Returns:

models.Traintuple: the returned object is described

organization_info¶

organization_info(self) -> substra.sdk.models.OrganizationInfo

Get organization information.

update_algo¶

update_algo(self, key: str, name: str)

None

update_compute_plan¶

update_compute_plan(self, key: str, name: str)

None

update_dataset¶

update_dataset(self, key: str, name: str)

None

retry_on_exception¶

retry_on_exception(exceptions, timeout=300)

Retry function in case of exception(s).

Arguments:

exceptions (list, required): list of exception types that trigger a retry
timeout (int, optional): timeout in seconds

Examples:

from substra.sdk import exceptions, retry_on_exception

def my_function(arg1, arg2):
    pass

retry = retry_on_exception(
            exceptions=(exceptions.RequestTimeout),
            timeout=300,
        )
retry(my_function)(arg1, arg2)

substra.sdk¶

Client¶

backend_mode¶

temp_directory¶

add_aggregatetuple¶

add_algo¶

add_composite_traintuple¶

add_compute_plan¶

add_compute_plan_tuples¶

add_data_sample¶

add_data_samples¶

add_dataset¶

add_predicttuple¶

add_testtuple¶

add_traintuple¶

cancel_compute_plan¶

describe_algo¶

describe_dataset¶

download_algo¶

download_dataset¶

download_head_model_from_composite_traintuple¶

download_logs¶

download_model¶

download_model_from_aggregatetuple¶

download_model_from_traintuple¶

download_trunk_model_from_composite_traintuple¶

from_config_file¶

get_aggregatetuple¶

get_algo¶

get_composite_traintuple¶

get_compute_plan¶

get_data_sample¶

get_dataset¶

get_logs¶

get_model¶

get_performances¶

get_predicttuple¶

get_testtuple¶

get_traintuple¶

link_dataset_with_data_samples¶

list_aggregatetuple¶

list_algo¶

list_composite_traintuple¶

list_compute_plan¶

list_data_sample¶

list_dataset¶

list_model¶

list_organization¶

list_predicttuple¶

list_testtuple¶

list_traintuple¶

login¶

organization_info¶

update_algo¶

update_compute_plan¶

update_dataset¶

retry_on_exception¶