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Task/prepare 12.0.0 pip packages (#278)
* Code sync with DSS 12.0.0 * Prepare 12.0.0 bump * Relax requests requirement. Remove nose * Update date of release
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Diff for: ‎HISTORY.txt

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Changelog
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==========
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12.0.0 (2023-06-13)
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---------------------
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* Initial release for DSS 12.0.0
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11.4.1.1 (2023-04-06)
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---------------------
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Diff for: ‎dataikuapi/apinode_admin_client.py

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@@ -17,27 +17,57 @@ def __init__(self, uri, api_key):
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########################################################
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def create_service(self, service_id):
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"""
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Creates a new API service
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:param service_id: id of the created API service
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"""
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self._perform_empty("POST", "services/", body = {
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"serviceId" : service_id
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})
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def list_services(self):
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"""
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Lists the currently declared services and their enabled/disabled state
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:return: a dict of services containing their id and state, as a JSON object
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:rtype: dict
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"""
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return self._perform_json("GET", "services")
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def service(self, service_id):
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"""
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Gets a handle to interact with a service
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:param service_id: id of requested service
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:rtype: :class: `dataikuapi.apinode_admin.service.APINodeService`
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"""
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return APINodeService(self, service_id)
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def auth(self):
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"""Returns a handle to interact with authentication"""
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"""
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Returns a handle to interact with authentication
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:rtype: :class: `dataikuapi.apinode_admin.auth.APINodeAuth`
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"""
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return APINodeAuth(self)
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def get_metrics(self):
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"""
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Get the metrics for this API Node
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:return: the metrics, as a JSON object
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:rtype: dict
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"""
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return self._perform_json("GET", "metrics")
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def import_code_env_in_cache(self, file_dir, language):
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"""
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Import a code env in global cache from an exported code env base folder
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:param file_dir: path of an exported code env base folder
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:param language: language of the code env (`python` or `R`)
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"""
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self._perform_empty("POST", "cached-code-envs", params={
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"fileDir": file_dir,
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"language": language

Diff for: ‎dataikuapi/apinode_client.py

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@@ -98,27 +98,49 @@ def forecast(self, endpoint_id, records, forced_generation=None, dispatch_key=No
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Forecast using a time series forecasting model on a DSS API node endpoint
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:param str endpoint_id: Identifier of the endpoint to query
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:param records: List of time series data records to be used as an input for the
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time series forecasting model. Each record should be a dict where
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keys are feature names, and values feature values.
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Example: records = [
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{'date': '2015-01-04T00:00:00.000Z', 'timeseries_id': 'A', 'target': 10.0},
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{'date': '2015-01-04T00:00:00.000Z', 'timeseries_id': 'B', 'target': 4.5},
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{'date': '2015-01-05T00:00:00.000Z', 'timeseries_id': 'A', 'target': 2.0},
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...,
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{'date': '2015-03-20T00:00:00.000Z', 'timeseries_id': 'B', 'target': 1.3}
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]
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:param array records: List of time series data records to be used as an input for the
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time series forecasting model. Each record should be a dict where
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keys are feature names, and values feature values.
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Example:
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.. code-block:: python
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records = [
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{'date': '2015-01-04T00:00:00.000Z',
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'timeseries_id': 'A', 'target': 10.0},
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{'date': '2015-01-04T00:00:00.000Z',
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'timeseries_id': 'B', 'target': 4.5},
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{'date': '2015-01-05T00:00:00.000Z',
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'timeseries_id': 'A', 'target': 2.0},
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...
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{'date': '2015-03-20T00:00:00.000Z',
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'timeseries_id': 'B', 'target': 1.3}
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]
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:param forced_generation: See documentation about multi-version prediction
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:param dispatch_key: See documentation about multi-version prediction
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:return: a Python dict of the API answer. The answer contains a "results" key
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(which is an array of result objects, corresponding to the forecast records)
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Example: {'results': [
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{'forecast': 12.57, 'ignored': False, 'quantiles': [0.0001, 0.5, 0.9999], 'quantilesValues': [3.0, 16.0, 16.0],
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'time': '2015-03-21T00:00:00.000000Z', 'timeseriesIdentifier': {'timeseries_id': 'A'}},
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{'forecast': 15.57, 'ignored': False, 'quantiles': [0.0001, 0.5, 0.9999], 'quantilesValues': [3.0, 18.0, 19.0],
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'time': '2015-03-21T00:00:00.000000Z', 'timeseriesIdentifier': {'timeseries_id': 'B'}},
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...], ...}
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(which is an array of result objects, corresponding to the forecast records)
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Example:
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.. code-block:: python
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{'results': [
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{'forecast': 12.57, 'ignored': False,
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'quantiles': [0.0001, 0.5, 0.9999],
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'quantilesValues': [3.0, 16.0, 16.0],
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'time': '2015-03-21T00:00:00.000000Z',
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'timeseriesIdentifier': {'timeseries_id': 'A'}},
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{'forecast': 15.57, 'ignored': False,
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'quantiles': [0.0001, 0.5, 0.9999],
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'quantilesValues': [3.0, 18.0, 19.0],
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'time': '2015-03-21T00:00:00.000000Z',
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'timeseriesIdentifier': {'timeseries_id': 'B'}},
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...],
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...}
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"""
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obj = {"items": records}

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