Dataset Module

Module with all classes and methods to manage a MLOps Dataset

MLOpsDataset

class mlops_codex.dataset.MLOpsDataset(login: str, password: str, base_url: str, hash: str, dataset_name: str, group: str)

Bases: object

Dataset class to represent mlops dataset.

Parameters:

• login (str) – Login for authenticating with the client.

• password (str) – Password for authenticating with the client.

• base_url (str) – URL to MLOps Server. Default value is https://neomaril.datarisk.net, use it to test your deployment first before changing to production. You can also use the env variable MLOPS_URL to set this

• hash (str) – Dataset hash to download.

• dataset_name (str) – Name of the dataset.

• group (str) – Name of the group where we will search the dataset

• origin (str) – Origin of the dataset. It can be “Training”, “Preprocessing”, “Datasource” or “Model”

base_url: str

dataset_name: str

download(path: str | None = './', filename: str | None = 'dataset') → None

Download a dataset from mlops. The dataset will be a csv or parquet file.

Parameters:

• path (str, optional) – Path to the downloaded dataset. Defaults to ‘./’.

• filename (str, optional) – Name of the downloaded dataset. Defaults to ‘dataset.zip’.

Raises:

• AuthenticationError – Raised if there is an authentication issue.

• DatasetNotFoundError – Raised if there is no dataset with the given name.

• ServerError – Raised if the server encounters an issue.

group: str

hash: str

login: str

password: str

MLOpsDatasetClient

class mlops_codex.dataset.MLOpsDatasetClient(login: str | None = None, password: str | None = None, url: str | None = None)

Bases: BaseMLOpsClient

Class to operate actions in a dataset.

Parameters:

• login (str) – Login for authenticating with the client. You can also use the env variable MLOPS_USER to set this

• password (str) – Password for authenticating with the client. You can also use the env variable MLOPS_PASSWORD to set this

• url (str) – URL to MLOps Server. Default value is https://neomaril.datarisk.net, use it to test your deployment first before changing to production. You can also use the env variable MLOPS_URL to set this

delete(group: str, dataset_hash: str) → None

Delete the dataset on mlops. Pay attention when doing this action, it is irreversible!

Parameters:

• group (str) – Group to delete.

• dataset_hash (str) – Dataset hash to delete.

Example

>>> dataset.delete()

list_datasets(*, origin: str | None = None, origin_id: int | None = None, datasource_name: str | None = None, group: str | None = None) → None

List datasets from datasources.

Parameters:

• origin (Optional[str]) – Origin of a dataset. It can be “Training”, “Preprocessing”, “Datasource” or “Model”

• origin_id (Optional[str]) – Integer that represents the id of a dataset, given an origin

• datasource_name (Optional[str]) – Name of the datasource

• group (Optional[str]) – Name of the group where we will search the dataset

Example

>>> dataset.list_datasets()

load_dataset(dataset_hash: str)