Updated version for next release #1457
10844 tests run, 9084 passed, 1706 skipped, 54 failed.
Annotations
Check failure on line 770 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_evaluate_with_embedding_function_specified_in_constructor_should_not_throw_any_exception
deeplake.util.exceptions.DatasetHandlerError: A dataset already exists at the given path (hub://testingacc2/deepmemory_test_corpus_managed_2_eval_queries). If you want to create a new empty dataset, either specify another path or use overwrite=True. If you want to load the dataset that exists at this path, use deeplake.load() instead.
Raw output
corpus_query_pair_path = ('hub://testingacc2/deepmemory_test_corpus_managed_2', 'hub://testingacc2/deepmemory_test_corpus_managed_2_eval_queries')
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzcxMywiZXhwIjoxNzA1NjUzNzEzfQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.W82UH88qd7vmfZ8EEYEkw8yM87quWoMzP0AVZ3NlZj0nUH2mx5U4YDMn40yFvPdperNF75r_eOABRjngiDH8Sw'
@pytest.mark.slow
@pytest.mark.flaky(reruns=3)
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_evaluate_with_embedding_function_specified_in_constructor_should_not_throw_any_exception(
corpus_query_pair_path,
hub_cloud_dev_token,
):
corpus, queries = corpus_query_pair_path
db = VectorStore(
path=corpus,
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
embedding_function=embedding_fn,
)
> queries_vs = VectorStore(
path=queries,
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
embedding_function=embedding_fn,
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:770:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:114: in __init__
self.dataset_handler = get_dataset_handler(
deeplake/core/vectorstore/dataset_handlers/dataset_handler.py:13: in get_dataset_handler
return ClientSideDH(*args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:66: in __init__
self.dataset = dataset or dataset_utils.create_or_load_dataset(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:69: in create_or_load_dataset
return create_dataset(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:189: in create_dataset
dataset = deeplake.empty(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
path = 'hub://testingacc2/deepmemory_test_corpus_managed_2_eval_queries'
runtime = {'tensor_db': True}, overwrite = False, public = False
memory_cache_size = 2000, local_cache_size = 0, creds = {}
token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzcxMywiZXhwIjoxNzA1NjUzNzEzfQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.W82UH88qd7vmfZ8EEYEkw8yM87quWoMzP0AVZ3NlZj0nUH2mx5U4YDMn40yFvPdperNF75r_eOABRjngiDH8Sw'
org_id = None, lock_enabled = True, lock_timeout = 0, verbose = False
index_params = {'additional_params': {'M': 32, 'efConstruction': 600}, 'distance_metric': 'COS', 'threshold': -1}
@staticmethod
def empty(
path: Union[str, pathlib.Path],
runtime: Optional[dict] = None,
overwrite: bool = False,
public: bool = False,
memory_cache_size: int = DEFAULT_MEMORY_CACHE_SIZE,
local_cache_size: int = DEFAULT_LOCAL_CACHE_SIZE,
creds: Optional[Union[Dict, str]] = None,
token: Optional[str] = None,
org_id: Optional[str] = None,
lock_enabled: Optional[bool] = True,
lock_timeout: Optional[int] = 0,
verbose: bool = True,
index_params: Optional[Dict[str, Union[int, str]]] = None,
) -> Dataset:
"""Creates an empty dataset
Args:
path (str, pathlib.Path): - The full path to the dataset. It can be:
- a Deep Lake cloud path of the form ``hub://org_id/dataset_name``. Requires registration with Deep Lake.
- an s3 path of the form ``s3://bucketname/path/to/dataset``. Credentials are required in either the environment or passed to the creds argument.
- a local file system path of the form ``./path/to/dataset`` or ``~/path/to/dataset`` or ``path/to/dataset``.
- a memory path of the form ``mem://path/to/dataset`` which doesn't save the dataset but keeps it in memory instead. Should be used only for testing as it does not persist.
runtime (dict): Parameters for creating a dataset in the Deep Lake Tensor Database. Only applicable for paths of the form ``hub://org_id/dataset_name`` and runtime must be ``{"tensor_db": True}``.
overwrite (bool): If set to ``True`` this overwrites the dataset if it already exists. Defaults to ``False``.
public (bool): Defines if the dataset will have public access. Applicable only if Deep Lake cloud storage is used and a new Dataset is being created. Defaults to ``False``.
memory_cache_size (int): The size of the memory cache to be used in MB.
local_cache_size (int): The size of the local filesystem cache to be used in MB.
creds (dict, str, optional): The string ``ENV`` or a dictionary containing credentials used to access the dataset at the path.
- If 'aws_access_key_id', 'aws_secret_access_key', 'aws_session_token' are present, these take precedence over credentials present in the environment or in credentials file. Currently only works with s3 paths.
- It supports 'aws_access_key_id', 'aws_secret_access_key', 'aws_session_token', 'endpoint_url', 'aws_region', 'profile_name' as keys.
- If 'ENV' is passed, credentials are fetched from the environment variables. This is also the case when creds is not passed for cloud datasets. For datasets connected to hub cloud, specifying 'ENV' will override the credentials fetched from Activeloop and use local ones.
token (str, optional): Activeloop token, used for fetching credentials to the dataset at path if it is a Deep Lake dataset. This is optional, tokens are normally autogenerated.
org_id (str, Optional): Organization id to be used for enabling high-performance features. Only applicable for local datasets.
verbose (bool): If True, logs will be printed. Defaults to True.
lock_timeout (int): Number of seconds to wait before throwing a LockException. If None, wait indefinitely
lock_enabled (bool): If true, the dataset manages a write lock. NOTE: Only set to False if you are managing concurrent access externally.
index_params: Optional[Dict[str, Union[int, str]]]: Index parameters used while creating vector store, passed down to dataset.
Returns:
Dataset: Dataset created using the arguments provided.
Raises:
DatasetHandlerError: If a Dataset already exists at the given path and overwrite is False.
UserNotLoggedInException: When user is not logged in
InvalidTokenException: If the specified toke is invalid
TokenPermissionError: When there are permission or other errors related to token
ValueError: If version is specified in the path
Danger:
Setting ``overwrite`` to ``True`` will delete all of your data if it exists! Be very careful when setting this parameter.
"""
path, address = process_dataset_path(path)
if org_id is not None and get_path_type(path) != "local":
raise ValueError("org_id parameter can only be used with local datasets")
db_engine = parse_runtime_parameters(path, runtime)["tensor_db"]
if address:
raise ValueError(
"deeplake.empty does not accept version address in the dataset path."
)
verify_dataset_name(path)
if creds is None:
creds = {}
try:
storage, cache_chain = get_storage_and_cache_chain(
path=path,
db_engine=db_engine,
read_only=False,
creds=creds,
token=token,
memory_cache_size=memory_cache_size,
local_cache_size=local_cache_size,
)
feature_report_path(
path,
"empty",
{
"runtime": runtime,
"overwrite": overwrite,
"lock_enabled": lock_enabled,
"lock_timeout": lock_timeout,
"index_params": index_params,
},
token=token,
)
except Exception as e:
if isinstance(e, UserNotLoggedInException):
raise UserNotLoggedInException from None
raise
if overwrite and dataset_exists(cache_chain):
cache_chain.clear()
elif dataset_exists(cache_chain):
> raise DatasetHandlerError(
f"A dataset already exists at the given path ({path}). If you want to create"
f" a new empty dataset, either specify another path or use overwrite=True. "
f"If you want to load the dataset that exists at this path, use deeplake.load() instead."
)
E deeplake.util.exceptions.DatasetHandlerError: A dataset already exists at the given path (hub://testingacc2/deepmemory_test_corpus_managed_2_eval_queries). If you want to create a new empty dataset, either specify another path or use overwrite=True. If you want to load the dataset that exists at this path, use deeplake.load() instead.
deeplake/api/dataset.py:457: DatasetHandlerError
Check failure on line 20 in deeplake/cli/test_cli.py
github-actions / JUnit Test Report
test_cli.test_cli_auth[creds]
AssertionError: assert 'Encountered ...gain later.\n' == 'Successfully...Activeloop.\n'
- Successfully logged in to Activeloop.
+ Encountered an error You are over the allowed limits for this operation. Please try again later.
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzMyMiwiZXhwIjoxNzA1NjUzMzIyfQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.VdKsxFo2FRAgKSnGyeM1qsKrb0KqNolo8LrHdp8mof8bcNnR7zMcEnX7Yrce0nhNmgAt4nw_7OMnybJ_wXkN1Q'
method = 'creds'
@pytest.mark.parametrize("method", ["creds", "token"])
def test_cli_auth(hub_cloud_dev_credentials, hub_cloud_dev_token, method):
username, password = hub_cloud_dev_credentials
runner = CliRunner()
if method == "creds":
result = runner.invoke(login, f"-u {username} -p {password}")
elif method == "token":
result = runner.invoke(login, f"-t {hub_cloud_dev_token}")
assert result.exit_code == 0
> assert result.output == "Successfully logged in to Activeloop.\n"
E AssertionError: assert 'Encountered ...gain later.\n' == 'Successfully...Activeloop.\n'
E - Successfully logged in to Activeloop.
E + Encountered an error You are over the allowed limits for this operation. Please try again later.
deeplake/cli/test_cli.py:20: AssertionError
Check failure on line 719 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_search_managed
deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
Raw output
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzcxOCwiZXhwIjoxNzA1NjUzNzE4fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.Ufu9a-ypZm-FyrOyvYxxHlUg7QLTbH56yYuOmk_YUatP1las5Xu9zQv-3Ah5CDDpepyUIFQYWpnvnxh2Sgq9hg'
@requires_libdeeplake
@pytest.mark.slow
def test_search_managed(hub_cloud_dev_token):
"""Test whether managed TQL and client-side TQL return the same results"""
# initialize vector store object:
vector_store = DeepLakeVectorStore(
path="hub://testingacc2/vectorstore_test_managed",
read_only=True,
token=hub_cloud_dev_token,
)
# use indra implementation to search the data
data_ce = vector_store.search(
embedding=query_embedding,
exec_option="compute_engine",
)
> data_db = vector_store.search(
embedding=query_embedding,
exec_option="tensor_db",
)
deeplake/core/vectorstore/test_deeplake_vectorstore.py:719:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [502]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
> raise BadGatewayException
E deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
deeplake/client/utils.py:101: BadGatewayException
Check failure on line 548 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_search
deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp8001_test_deepmemory_test_deepmemory_search', ['0-dimensional biomaterials lack inductive prope...265107', 1]], [['32587939', 1]], ...], 'hub://testingacc2/tmp8001_test_deepmemory_test_deepmemory_search_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NTg2NywiZXhwIjoxNzA1NjU1ODY3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.BgUMKjjrg0Re8JqIahEBnLr6KIiPEPVOLbwAmC4An9PR33ENKPAJWbJXHQQ4qMtL7IM37ECCDjnUynWuPJ2eig'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_search(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
)
> output = db.search(
embedding=query_embedding, deep_memory=True, return_tensors=["id"]
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:548:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [502]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
> raise BadGatewayException
E deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
deeplake/client/utils.py:101: BadGatewayException
Check failure on line 706 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_search_should_contain_correct_answer
deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp8001_test_deepmemory_test_deepmemory_search_should_contain_correct_answer', ['0-dimensional bio...], ...], 'hub://testingacc2/tmp8001_test_deepmemory_test_deepmemory_search_should_contain_correct_answer_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NTg2NywiZXhwIjoxNzA1NjU1ODY3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.BgUMKjjrg0Re8JqIahEBnLr6KIiPEPVOLbwAmC4An9PR33ENKPAJWbJXHQQ4qMtL7IM37ECCDjnUynWuPJ2eig'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_search_should_contain_correct_answer(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
token=hub_cloud_dev_token,
)
> output = db.search(
embedding=query_embedding, deep_memory=True, return_tensors=["id"]
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:706:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [502]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
> raise BadGatewayException
E deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
deeplake/client/utils.py:101: BadGatewayException
Check failure on line 727 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deeplake_search_should_not_contain_correct_answer
deeplake.util.exceptions.ServerException: Server under maintenance, try again later.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp8001_test_deepmemory_test_deeplake_search_should_not_contain_correct_answer', ['0-dimensional b... ...], 'hub://testingacc2/tmp8001_test_deepmemory_test_deeplake_search_should_not_contain_correct_answer_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NTg2NywiZXhwIjoxNzA1NjU1ODY3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.BgUMKjjrg0Re8JqIahEBnLr6KIiPEPVOLbwAmC4An9PR33ENKPAJWbJXHQQ4qMtL7IM37ECCDjnUynWuPJ2eig'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deeplake_search_should_not_contain_correct_answer(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
token=hub_cloud_dev_token,
)
> output = db.search(embedding=query_embedding)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:727:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [503]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
raise BadGatewayException
elif response.status_code == 504:
raise GatewayTimeoutException
elif 500 <= response.status_code < 600:
> raise ServerException("Server under maintenance, try again later.")
E deeplake.util.exceptions.ServerException: Server under maintenance, try again later.
deeplake/client/utils.py:105: ServerException
Check failure on line 1310 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding_row_ids_and_ids_specified_should_throw_exception
ImportError: High performance features require the libdeeplake package which is not available in Windows OS
Raw output
local_path = './hub_pytest/test_deeplake_vectorstore/test_update_embedding_row_ids_and_ids_specified_should_throw_exception'
vector_store_hash_ids = ['0', '1', '2', '3', '4']
vector_store_row_ids = [0, 1, 2, 3, 4]
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1Mzk5NSwiZXhwIjoxNzA1NjUzOTk1fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.qwwJNEd0ptOQQyxAV35cJvJwmrIK_KKZ3GuxJ_MoZ-_0fHDYyt_oUdaoHTawpblKGCfW-PS8n6yUKZ3eYR4PvQ'
def test_update_embedding_row_ids_and_ids_specified_should_throw_exception(
local_path,
vector_store_hash_ids,
vector_store_row_ids,
hub_cloud_dev_token,
):
# specifying both row_ids and ids during update embedding should throw an exception
# initializing vectorstore and populating it:
> vector_store = create_and_populate_vs(
local_path,
token=hub_cloud_dev_token,
)
deeplake\core\vectorstore\test_deeplake_vectorstore.py:1336:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\test_deeplake_vectorstore.py:1310: in create_and_populate_vs
vector_store = DeepLakeVectorStore(
deeplake\core\vectorstore\deeplake_vectorstore.py:114: in __init__
self.dataset_handler = get_dataset_handler(
deeplake\core\vectorstore\dataset_handlers\dataset_handler.py:13: in get_dataset_handler
return ClientSideDH(*args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:66: in __init__
self.dataset = dataset or dataset_utils.create_or_load_dataset(
deeplake\core\vectorstore\vector_search\dataset\dataset.py:48: in create_or_load_dataset
utils.check_indra_installation(
deeplake\core\vectorstore\vector_search\utils.py:143: in check_indra_installation
raise raise_indra_installation_error(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
indra_import_error = False
def raise_indra_installation_error(indra_import_error: Optional[Exception] = None):
if not indra_import_error:
if os.name == "nt":
> raise ImportError(
"High performance features require the libdeeplake package which is not available in Windows OS"
)
E ImportError: High performance features require the libdeeplake package which is not available in Windows OS
deeplake\enterprise\util.py:13: ImportError
Check failure on line 1310 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding_row_ids_and_filter_specified_should_throw_exception
ImportError: High performance features require the libdeeplake package which is not available in Windows OS
Raw output
local_path = './hub_pytest/test_deeplake_vectorstore/test_update_embedding_row_ids_and_filter_specified_should_throw_exception'
vector_store_filters = {'metadata': {'a': 1}}
vector_store_row_ids = [0, 1, 2, 3, 4]
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1Mzk5NSwiZXhwIjoxNzA1NjUzOTk1fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.qwwJNEd0ptOQQyxAV35cJvJwmrIK_KKZ3GuxJ_MoZ-_0fHDYyt_oUdaoHTawpblKGCfW-PS8n6yUKZ3eYR4PvQ'
def test_update_embedding_row_ids_and_filter_specified_should_throw_exception(
local_path,
vector_store_filters,
vector_store_row_ids,
hub_cloud_dev_token,
):
# specifying both row_ids and filter during update embedding should throw an exception
# initializing vectorstore and populating it:
> vector_store = create_and_populate_vs(
local_path,
token=hub_cloud_dev_token,
)
deeplake\core\vectorstore\test_deeplake_vectorstore.py:1359:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\test_deeplake_vectorstore.py:1310: in create_and_populate_vs
vector_store = DeepLakeVectorStore(
deeplake\core\vectorstore\deeplake_vectorstore.py:114: in __init__
self.dataset_handler = get_dataset_handler(
deeplake\core\vectorstore\dataset_handlers\dataset_handler.py:13: in get_dataset_handler
return ClientSideDH(*args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:66: in __init__
self.dataset = dataset or dataset_utils.create_or_load_dataset(
deeplake\core\vectorstore\vector_search\dataset\dataset.py:48: in create_or_load_dataset
utils.check_indra_installation(
deeplake\core\vectorstore\vector_search\utils.py:143: in check_indra_installation
raise raise_indra_installation_error(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
indra_import_error = False
def raise_indra_installation_error(indra_import_error: Optional[Exception] = None):
if not indra_import_error:
if os.name == "nt":
> raise ImportError(
"High performance features require the libdeeplake package which is not available in Windows OS"
)
E ImportError: High performance features require the libdeeplake package which is not available in Windows OS
deeplake\enterprise\util.py:13: ImportError
Check failure on line 1310 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_vs_commit
ImportError: High performance features require the libdeeplake package which is not available in Windows OS
Raw output
local_path = './hub_pytest/test_deeplake_vectorstore/test_vs_commit'
def test_vs_commit(local_path):
# TODO: add index params, when index will support commit
> db = create_and_populate_vs(
local_path, number_of_data=NUMBER_OF_DATA, index_params=None
)
deeplake\core\vectorstore\test_deeplake_vectorstore.py:2847:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\test_deeplake_vectorstore.py:1310: in create_and_populate_vs
vector_store = DeepLakeVectorStore(
deeplake\core\vectorstore\deeplake_vectorstore.py:114: in __init__
self.dataset_handler = get_dataset_handler(
deeplake\core\vectorstore\dataset_handlers\dataset_handler.py:13: in get_dataset_handler
return ClientSideDH(*args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:66: in __init__
self.dataset = dataset or dataset_utils.create_or_load_dataset(
deeplake\core\vectorstore\vector_search\dataset\dataset.py:48: in create_or_load_dataset
utils.check_indra_installation(
deeplake\core\vectorstore\vector_search\utils.py:143: in check_indra_installation
raise raise_indra_installation_error(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
indra_import_error = False
def raise_indra_installation_error(indra_import_error: Optional[Exception] = None):
if not indra_import_error:
if os.name == "nt":
> raise ImportError(
"High performance features require the libdeeplake package which is not available in Windows OS"
)
E ImportError: High performance features require the libdeeplake package which is not available in Windows OS
deeplake\enterprise\util.py:13: ImportError
Check failure on line 2992 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql
NotImplementedError: return_tql is not supported for exec_option=python
Raw output
local_path = './hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql'
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1Mzk5NSwiZXhwIjoxNzA1NjUzOTk1fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.qwwJNEd0ptOQQyxAV35cJvJwmrIK_KKZ3GuxJ_MoZ-_0fHDYyt_oUdaoHTawpblKGCfW-PS8n6yUKZ3eYR4PvQ'
def test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql(
local_path,
hub_cloud_dev_token,
):
db = VectorStore(
path=local_path,
token=hub_cloud_dev_token,
)
texts, embeddings, ids, metadatas, _ = utils.create_data(
number_of_data=10, embedding_dim=3
)
db.add(text=texts, embedding=embeddings, id=ids, metadata=metadatas)
query_embedding = np.zeros(3, dtype=np.float32)
> output = db.search(embedding=query_embedding, return_tql=True)
deeplake\core\vectorstore\test_deeplake_vectorstore.py:2992:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake\core\vectorstore\deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake\core\vectorstore\deep_memory\deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake\core\vectorstore\dataset_handlers\client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake\core\vectorstore\vector_search\vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
query = None, query_emb = array([0., 0., 0.], dtype=float32)
exec_option = 'python'
dataset = Dataset(path='./hub_pytest/test_deeplake_vectorstore/test_returning_tql_for_exec_option_compute_engine_should_return_correct_tql', tensors=['text', 'metadata', 'embedding', 'id'])
logger = <Logger deeplake.core.vectorstore.deeplake_vectorstore (INFO)>
filter = None, embedding_tensor = 'embedding', distance_metric = 'COS', k = 4
return_tensors = ['text', 'metadata', 'id'], return_view = False
token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1Mzk5NSwiZXhwIjoxNzA1NjUzOTk1fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.qwwJNEd0ptOQQyxAV35cJvJwmrIK_KKZ3GuxJ_MoZ-_0fHDYyt_oUdaoHTawpblKGCfW-PS8n6yUKZ3eYR4PvQ'
org_id = None, return_tql = True
def vector_search(
query,
query_emb,
exec_option,
dataset,
logger,
filter,
embedding_tensor,
distance_metric,
k,
return_tensors,
return_view,
token,
org_id,
return_tql,
) -> Union[Dict, DeepLakeDataset]:
if query is not None:
raise NotImplementedError(
f"User-specified TQL queries are not supported for exec_option={exec_option} "
)
if return_tql:
> raise NotImplementedError(
f"return_tql is not supported for exec_option={exec_option}"
)
E NotImplementedError: return_tql is not supported for exec_option=python
deeplake\core\vectorstore\vector_search\python\vector_search.py:31: NotImplementedError
Check failure on line 58 in deeplake/client/test_client.py
github-actions / JUnit Test Report
test_client.test_client_workspace_organizations[creds]
AssertionError: assert 'testingacc2' in ['public']
+ where ['public'] = <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7fc3c32d9650>>()
+ where <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7fc3c32d9650>> = <deeplake.client.client.DeepLakeBackendClient object at 0x7fc3c32d9650>.get_user_organizations
Raw output
method = 'creds'
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzYwNSwiZXhwIjoxNzA1NjUzNjA1fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.9sIr6pQgxcU1XquEwYUuUU7baXqe8Qgmt0Gew57xa3xBDM2amkoCAgYuFlaxqMHjsHp3AUApqarBKnNe3ixUqw'
@pytest.mark.slow
@pytest.mark.parametrize("method", ["creds", "token"])
def test_client_workspace_organizations(
method, hub_cloud_dev_credentials, hub_cloud_dev_token
):
username, password = hub_cloud_dev_credentials
deeplake_client = DeepLakeBackendClient()
runner = CliRunner()
result = runner.invoke(logout)
assert result.exit_code == 0
assert deeplake_client.get_user_organizations() == ["public"]
if method == "creds":
runner.invoke(login, f"-u {username} -p {password}")
elif method == "token":
runner.invoke(login, f"-t {hub_cloud_dev_token}")
deeplake_client = DeepLakeBackendClient()
> assert username in deeplake_client.get_user_organizations()
E AssertionError: assert 'testingacc2' in ['public']
E + where ['public'] = <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7fc3c32d9650>>()
E + where <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7fc3c32d9650>> = <deeplake.client.client.DeepLakeBackendClient object at 0x7fc3c32d9650>.get_user_organizations
deeplake/client/test_client.py:58: AssertionError
Check failure on line 1 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding[embedding_fn3-hub_cloud_ds-None-None-None-None-vector_store_query-hub_cloud_dev_token]
failed on setup with "deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation."
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.fixture(scope="session")
def hub_cloud_dev_token(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
client = DeepLakeBackendClient()
> token = client.request_auth_token(username, password)
deeplake/tests/client_fixtures.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 1 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding[None-local_auth_ds-vector_store_hash_ids-None-None-None-None-hub_cloud_dev_token]
failed on setup with "deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation."
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.fixture(scope="session")
def hub_cloud_dev_token(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
client = DeepLakeBackendClient()
> token = client.request_auth_token(username, password)
deeplake/tests/client_fixtures.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 1 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding[None-local_auth_ds-None-vector_store_row_ids-None-None-None-hub_cloud_dev_token]
failed on setup with "deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation."
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.fixture(scope="session")
def hub_cloud_dev_token(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
client = DeepLakeBackendClient()
> token = client.request_auth_token(username, password)
deeplake/tests/client_fixtures.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 1 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding[None-local_auth_ds-None-None-None-vector_store_filter_udf-None-hub_cloud_dev_token]
failed on setup with "deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation."
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.fixture(scope="session")
def hub_cloud_dev_token(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
client = DeepLakeBackendClient()
> token = client.request_auth_token(username, password)
deeplake/tests/client_fixtures.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 1 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding[None-local_auth_ds-None-None-vector_store_filters-None-None-hub_cloud_dev_token]
failed on setup with "deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation."
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.fixture(scope="session")
def hub_cloud_dev_token(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
client = DeepLakeBackendClient()
> token = client.request_auth_token(username, password)
deeplake/tests/client_fixtures.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 1 in deeplake/core/vectorstore/test_deeplake_vectorstore.py
github-actions / JUnit Test Report
test_deeplake_vectorstore.test_update_embedding[None-hub_cloud_ds-None-None-None-None-vector_store_query-hub_cloud_dev_token]
failed on setup with "deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation."
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.fixture(scope="session")
def hub_cloud_dev_token(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
client = DeepLakeBackendClient()
> token = client.request_auth_token(username, password)
deeplake/tests/client_fixtures.py:51:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 548 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_search
deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp71a9_test_deepmemory_test_deepmemory_search', ['0-dimensional biomaterials lack inductive prope...265107', 1]], [['32587939', 1]], ...], 'hub://testingacc2/tmp71a9_test_deepmemory_test_deepmemory_search_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NDk0NywiZXhwIjoxNzA1NjU0OTQ3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.ZWQ-8eRq5iglFN3ZkYv1cOmCQIsalT_EJYj-Nw7J-sYAbk91czjibnSTPUJPMVsbmfH-WAjnuXDFHkWH1DVa8w'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_search(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
)
> output = db.search(
embedding=query_embedding, deep_memory=True, return_tensors=["id"]
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:548:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [502]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
> raise BadGatewayException
E deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
deeplake/client/utils.py:101: BadGatewayException
Check failure on line 706 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_search_should_contain_correct_answer
deeplake.util.exceptions.ServerException: Server under maintenance, try again later.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp71a9_test_deepmemory_test_deepmemory_search_should_contain_correct_answer', ['0-dimensional bio...], ...], 'hub://testingacc2/tmp71a9_test_deepmemory_test_deepmemory_search_should_contain_correct_answer_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NDk0NywiZXhwIjoxNzA1NjU0OTQ3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.ZWQ-8eRq5iglFN3ZkYv1cOmCQIsalT_EJYj-Nw7J-sYAbk91czjibnSTPUJPMVsbmfH-WAjnuXDFHkWH1DVa8w'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_search_should_contain_correct_answer(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
token=hub_cloud_dev_token,
)
> output = db.search(
embedding=query_embedding, deep_memory=True, return_tensors=["id"]
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:706:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [503]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
raise BadGatewayException
elif response.status_code == 504:
raise GatewayTimeoutException
elif 500 <= response.status_code < 600:
> raise ServerException("Server under maintenance, try again later.")
E deeplake.util.exceptions.ServerException: Server under maintenance, try again later.
deeplake/client/utils.py:105: ServerException
Check failure on line 727 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deeplake_search_should_not_contain_correct_answer
deeplake.util.exceptions.ServerException: Server under maintenance, try again later.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp71a9_test_deepmemory_test_deeplake_search_should_not_contain_correct_answer', ['0-dimensional b... ...], 'hub://testingacc2/tmp71a9_test_deepmemory_test_deeplake_search_should_not_contain_correct_answer_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NDk0NywiZXhwIjoxNzA1NjU0OTQ3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.ZWQ-8eRq5iglFN3ZkYv1cOmCQIsalT_EJYj-Nw7J-sYAbk91czjibnSTPUJPMVsbmfH-WAjnuXDFHkWH1DVa8w'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deeplake_search_should_not_contain_correct_answer(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
token=hub_cloud_dev_token,
)
> output = db.search(embedding=query_embedding)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:727:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [503]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
raise BadGatewayException
elif response.status_code == 504:
raise GatewayTimeoutException
elif 500 <= response.status_code < 600:
> raise ServerException("Server under maintenance, try again later.")
E deeplake.util.exceptions.ServerException: Server under maintenance, try again later.
deeplake/client/utils.py:105: ServerException
Check failure on line 20 in deeplake/cli/test_cli.py
github-actions / JUnit Test Report
test_cli.test_cli_auth[creds]
AssertionError: assert 'Encountered ...gain later.\n' == 'Successfully...Activeloop.\n'
- Successfully logged in to Activeloop.
+ Encountered an error You are over the allowed limits for this operation. Please try again later.
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzMyNywiZXhwIjoxNzA1NjUzMzI3fQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.rm_VYuJZCXubPhSaLX8YgouprLzq00xRbUQmVF6l5igM0WP0mnDhTT48kIbcyhsJUN_aFr1jd-hwWHSdpLiFhw'
method = 'creds'
@pytest.mark.parametrize("method", ["creds", "token"])
def test_cli_auth(hub_cloud_dev_credentials, hub_cloud_dev_token, method):
username, password = hub_cloud_dev_credentials
runner = CliRunner()
if method == "creds":
result = runner.invoke(login, f"-u {username} -p {password}")
elif method == "token":
result = runner.invoke(login, f"-t {hub_cloud_dev_token}")
assert result.exit_code == 0
> assert result.output == "Successfully logged in to Activeloop.\n"
E AssertionError: assert 'Encountered ...gain later.\n' == 'Successfully...Activeloop.\n'
E - Successfully logged in to Activeloop.
E + Encountered an error You are over the allowed limits for this operation. Please try again later.
deeplake/cli/test_cli.py:20: AssertionError
Check failure on line 23 in deeplake/client/test_client.py
github-actions / JUnit Test Report
test_client.test_client_requests
deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
Raw output
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
@pytest.mark.slow
def test_client_requests(hub_cloud_dev_credentials):
username, password = hub_cloud_dev_credentials
deeplake_client = DeepLakeBackendClient()
> deeplake_client.request_auth_token(username, password)
deeplake/client/test_client.py:23:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/client/client.py:192: in request_auth_token
response = self.request("POST", GET_TOKEN_SUFFIX, json=json)
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [429]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
> raise OverLimitException
E deeplake.util.exceptions.OverLimitException: You are over the allowed limits for this operation.
deeplake/client/utils.py:99: OverLimitException
Check failure on line 58 in deeplake/client/test_client.py
github-actions / JUnit Test Report
test_client.test_client_workspace_organizations[creds]
AssertionError: assert 'testingacc2' in ['public']
+ where ['public'] = <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7f9d256e6ee0>>()
+ where <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7f9d256e6ee0>> = <deeplake.client.client.DeepLakeBackendClient object at 0x7f9d256e6ee0>.get_user_organizations
Raw output
method = 'creds'
hub_cloud_dev_credentials = ('testingacc2', '63Fj@u#wHdxptRDn')
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1MzY5MiwiZXhwIjoxNzA1NjUzNjkyfQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.KqxD9tv0ywoyjbQtf20Rmb55qdd0K1LF7QyrDvJYTDdxBXzOAnRDIkfW5riA06cZJcyKvJQg7eoW8VPvtRKsEQ'
@pytest.mark.slow
@pytest.mark.parametrize("method", ["creds", "token"])
def test_client_workspace_organizations(
method, hub_cloud_dev_credentials, hub_cloud_dev_token
):
username, password = hub_cloud_dev_credentials
deeplake_client = DeepLakeBackendClient()
runner = CliRunner()
result = runner.invoke(logout)
assert result.exit_code == 0
assert deeplake_client.get_user_organizations() == ["public"]
if method == "creds":
runner.invoke(login, f"-u {username} -p {password}")
elif method == "token":
runner.invoke(login, f"-t {hub_cloud_dev_token}")
deeplake_client = DeepLakeBackendClient()
> assert username in deeplake_client.get_user_organizations()
E AssertionError: assert 'testingacc2' in ['public']
E + where ['public'] = <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7f9d256e6ee0>>()
E + where <bound method DeepLakeBackendClient.get_user_organizations of <deeplake.client.client.DeepLakeBackendClient object at 0x7f9d256e6ee0>> = <deeplake.client.client.DeepLakeBackendClient object at 0x7f9d256e6ee0>.get_user_organizations
deeplake/client/test_client.py:58: AssertionError
Check failure on line 548 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_search
deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmpfa35_test_deepmemory_test_deepmemory_search', ['0-dimensional biomaterials lack inductive prope...265107', 1]], [['32587939', 1]], ...], 'hub://testingacc2/tmpfa35_test_deepmemory_test_deepmemory_search_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NTcyMCwiZXhwIjoxNzA1NjU1NzIwfQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.sy4l6kH5Yi6yawAZ2JWT0UNffsIYa0P2AobTG2vf4qHSBSGHogi67b3R1GUvZseOJpmK-jFCwcc2oxVgk7dVfg'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_search(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
)
> output = db.search(
embedding=query_embedding, deep_memory=True, return_tensors=["id"]
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:548:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [502]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
> raise BadGatewayException
E deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
deeplake/client/utils.py:101: BadGatewayException
Check failure on line 706 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_search_should_contain_correct_answer
deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmpfa35_test_deepmemory_test_deepmemory_search_should_contain_correct_answer', ['0-dimensional bio...], ...], 'hub://testingacc2/tmpfa35_test_deepmemory_test_deepmemory_search_should_contain_correct_answer_eval_queries')
testing_relevance_query_deepmemory = ('31715818', [-0.015188165009021759, 0.02033962868154049, -0.012286307290196419, 0.009264647960662842, -0.00939110480248928, 0.00015578352031297982, ...])
hub_cloud_dev_token = 'eyJhbGciOiJIUzUxMiIsImlhdCI6MTcwMjA1NTcyMCwiZXhwIjoxNzA1NjU1NzIwfQ.eyJpZCI6InRlc3RpbmdhY2MyIn0.sy4l6kH5Yi6yawAZ2JWT0UNffsIYa0P2AobTG2vf4qHSBSGHogi67b3R1GUvZseOJpmK-jFCwcc2oxVgk7dVfg'
@pytest.mark.slow
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
def test_deepmemory_search_should_contain_correct_answer(
corpus_query_relevances_copy,
testing_relevance_query_deepmemory,
hub_cloud_dev_token,
):
corpus, _, _, _ = corpus_query_relevances_copy
relevance, query_embedding = testing_relevance_query_deepmemory
db = VectorStore(
path=corpus,
token=hub_cloud_dev_token,
)
> output = db.search(
embedding=query_embedding, deep_memory=True, return_tensors=["id"]
)
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:706:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:313: in search
return self.dataset_handler.search(
deeplake/core/vectorstore/deep_memory/deep_memory.py:53: in wrapper
return func(self, *args, **kwargs)
deeplake/core/vectorstore/dataset_handlers/client_side_dataset_handler.py:235: in search
return vector_search.search(
deeplake/core/vectorstore/vector_search/vector_search.py:57: in search
return EXEC_OPTION_TO_SEARCH_TYPE[exec_option](
deeplake/core/vectorstore/vector_search/indra/vector_search.py:47: in vector_search
return vectorstore.indra_search_algorithm(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:209: in search
return searcher.run(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:57: in run
view = self._get_view(
deeplake/core/vectorstore/vector_search/indra/search_algorithm.py:151: in _get_view
view, data = self.deeplake_dataset.query(
deeplake/core/dataset/dataset.py:2338: in query
response = client.remote_query(org_id, ds_name, query_string)
deeplake/client/client.py:507: in remote_query
response = self.request(
deeplake/client/client.py:163: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [502]>
def check_response_status(response: requests.Response):
"""Check response status and throw corresponding exception on failure."""
code = response.status_code
if code >= 200 and code < 300:
return
try:
message = response.json()["description"]
except Exception:
message = " "
if code == 400:
raise BadRequestException(message)
elif response.status_code == 401:
raise AuthenticationException
elif response.status_code == 403:
raise AuthorizationException(message, response=response)
elif response.status_code == 404:
if message != " ":
raise ResourceNotFoundException(message)
raise ResourceNotFoundException
elif response.status_code == 422:
raise UnprocessableEntityException(message)
elif response.status_code == 423:
raise LockedException
elif response.status_code == 429:
raise OverLimitException
elif response.status_code == 502:
> raise BadGatewayException
E deeplake.util.exceptions.BadGatewayException: Invalid response from Activeloop server.
deeplake/client/utils.py:101: BadGatewayException