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failed
Apr 29, 2024 in 0s
2998 tests run, 1676 passed, 1319 skipped, 3 failed.
Annotations
Check failure on line 948 in deeplake/enterprise/test_pytorch.py
github-actions / JUnit Test Report
test_pytorch.test_pytorch_data_decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf6 in position 0: invalid start byte
Raw output
local_auth_ds = Dataset(path='./hub_pytest/test_pytorch/test_pytorch_data_decode', tensors=['generic', 'text', 'json', 'list', 'class_label', 'image'])
cat_path = '/home/runner/work/deeplake/deeplake/deeplake/tests/dummy_data/images/cat.jpeg'
@requires_libdeeplake
@requires_torch
@pytest.mark.flaky
@pytest.mark.slow
def test_pytorch_data_decode(local_auth_ds, cat_path):
with local_auth_ds as ds:
ds.create_tensor("generic")
for i in range(10):
ds.generic.append(i)
ds.create_tensor("text", htype="text")
for i in range(10):
ds.text.append(f"hello {i}")
ds.create_tensor("json", htype="json")
for i in range(10):
ds.json.append({"x": i})
ds.create_tensor("list", htype="list")
for i in range(10):
ds.list.append([i, i + 1])
ds.create_tensor("class_label", htype="class_label")
animals = [
"cat",
"dog",
"bird",
"fish",
"horse",
"cow",
"pig",
"sheep",
"goat",
"chicken",
]
ds.class_label.extend(animals)
ds.create_tensor("image", htype="image", sample_compression="jpeg")
for i in range(10):
ds.image.append(deeplake.read(cat_path))
decode_method = {tensor: "data" for tensor in list(ds.tensors.keys())}
ptds = (
ds.dataloader()
.transform(identity)
.pytorch(decode_method=decode_method, collate_fn=identity_collate)
)
> for i, batch in enumerate(ptds):
deeplake/enterprise/test_pytorch.py:948:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/enterprise/dataloader.py:881: in __next__
return next(self._iterator)
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/loader.py:156: in __next__
return next(self._iterator)
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/single_process_iterator.py:80: in __next__
return self.get_data()
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/single_process_iterator.py:117: in get_data
batch = self._next_data()
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/indra/pytorch/single_process_iterator.py:104: in _next_data
sample[tensor] = bytes_to_text(sample[tensor], "list")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
buffer = b'\xf6\x80k\xf6\x07\x00', htype = 'list'
def bytes_to_text(buffer, htype):
buffer = bytes(buffer)
if htype == "json":
arr = np.empty(1, dtype=object)
arr[0] = json.loads(bytes.decode(buffer), cls=HubJsonDecoder)
return arr
elif htype in ("list", "tag"):
> lst = json.loads(bytes.decode(buffer), cls=HubJsonDecoder)
E UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf6 in position 0: invalid start byte
deeplake/core/serialize.py:484: UnicodeDecodeError
Check failure on line 1 in deeplake/client/test_client.py
github-actions / JUnit Test Report
test_client.test_deepmemory_delete
failed on setup with "deeplake.util.exceptions.GatewayTimeoutException: Activeloop server took too long to respond."
Raw output
request = <SubRequest 'corpus_query_relevances_copy' for <Function test_deepmemory_delete>>
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'
@pytest.fixture
def corpus_query_relevances_copy(request, hub_cloud_dev_token):
if not is_opt_true(request, HUB_CLOUD_OPT):
pytest.skip(f"{HUB_CLOUD_OPT} flag not set")
return
corpus = _get_storage_path(request, HUB_CLOUD)
> query_vs = VectorStore(
path=f"hub://{HUB_CLOUD_DEV_USERNAME}/deepmemory_test_queries",
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
)
deeplake/tests/path_fixtures.py:482:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
deeplake/core/vectorstore/deeplake_vectorstore.py:120: 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:49: in create_or_load_dataset
return load_dataset(
deeplake/core/vectorstore/vector_search/dataset/dataset.py:99: in load_dataset
dataset = deeplake.load(
deeplake/util/spinner.py:153: in inner
return func(*args, **kwargs)
deeplake/api/dataset.py:639: in load
storage, cache_chain = get_storage_and_cache_chain(
deeplake/util/storage.py:242: in get_storage_and_cache_chain
storage = storage_provider_from_path(
deeplake/util/storage.py:66: in storage_provider_from_path
storage = storage_provider_from_hub_path(
deeplake/util/storage.py:159: in storage_provider_from_hub_path
client = DeepLakeBackendClient(token=token)
deeplake/client/client.py:67: in __init__
orgs = self.get_user_organizations()
deeplake/client/client.py:355: in get_user_organizations
response = self.request(
deeplake/client/client.py:148: in request
check_response_status(response)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
response = <Response [504]>
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
E deeplake.util.exceptions.GatewayTimeoutException: Activeloop server took too long to respond.
deeplake/client/utils.py:74: GatewayTimeoutException
Check failure on line 189 in deeplake/core/vectorstore/deep_memory/test_deepmemory.py
github-actions / JUnit Test Report
test_deepmemory.test_deepmemory_evaluate
AssertionError: assert {'recall@1': ...@3': 0.5, ...} == {'recall@1': ...@3': 0.6, ...}
Omitting 1 identical items, use -vv to show
Differing items:
{'recall@3': 0.5} != {'recall@3': 0.6}
{'recall@10': 0.5} != {'recall@10': 0.6}
{'recall@5': 0.5} != {'recall@5': 0.6}
{'recall@50': 0.6} != {'recall@50': 0.7}
{'recall@1': 0.3} != {'recall@1': 0.4}
Full diff:
{
- 'recall@1': 0.4,
? ^
+ 'recall@1': 0.3,
? ^
- 'recall@10': 0.6,
? ^
+ 'recall@10': 0.5,
? ^
'recall@100': 0.9,
- 'recall@3': 0.6,
? ^
+ 'recall@3': 0.5,
? ^
+ 'recall@5': 0.5,
- 'recall@5': 0.6,
+ 'recall@50': 0.6,
? +
- 'recall@50': 0.7,
}
Raw output
corpus_query_relevances_copy = ('hub://testingacc2/tmp7001_test_deepmemory_test_deepmemory_evaluate', ['0-dimensional biomaterials lack inductive pro...5107', 1]], [['32587939', 1]], ...], 'hub://testingacc2/tmp7001_test_deepmemory_test_deepmemory_evaluate_eval_queries')
questions_embeddings_and_relevances = (array([[-0.01518817, 0.02033963, -0.01228631, ..., -0.00286692,
-0.0079668 , -0.00414979],
[-0.003503...A treatment decreases endoplasmic reticulum stress in response to general endoplasmic reticulum stress markers.', ...])
hub_cloud_dev_token = 'eyJhbGciOiJub25lIiwidHlwIjoiSldUIn0.eyJpZCI6InRlc3RpbmdhY2MyIiwiYXBpX2tleSI6IjU4Y0tLb1p6UE1BbThPU2RpbTRiZ2tBekhWekt1VUE3MFJpNTNyZUpKRTJuaiJ9.'
@pytest.mark.slow
@pytest.mark.timeout(600)
@pytest.mark.skipif(sys.platform == "win32", reason="Does not run on Windows")
@requires_libdeeplake
def test_deepmemory_evaluate(
corpus_query_relevances_copy,
questions_embeddings_and_relevances,
hub_cloud_dev_token,
):
corpus, _, _, query_path = corpus_query_relevances_copy
(
questions_embeddings,
question_relevances,
queries,
) = questions_embeddings_and_relevances
db = VectorStore(
corpus,
runtime={"tensor_db": True},
token=hub_cloud_dev_token,
)
# when qvs_params is wrong:
with pytest.raises(ValueError):
db.deep_memory.evaluate(
queries=queries,
embedding=questions_embeddings,
relevance=question_relevances,
qvs_params={
"log_queries": True,
"branch_name": "wrong_branch",
},
)
# embedding_function is not provided in the constructor or in the eval method
with pytest.raises(ValueError):
db.deep_memory.evaluate(
queries=queries,
relevance=question_relevances,
qvs_params={
"log_queries": True,
"branch_name": "wrong_branch",
},
)
recall = db.deep_memory.evaluate(
queries=queries,
embedding=questions_embeddings,
relevance=question_relevances,
qvs_params={
"branch": "queries",
},
)
> assert recall["without model"] == {
"recall@1": 0.4,
"recall@3": 0.6,
"recall@5": 0.6,
"recall@10": 0.6,
"recall@50": 0.7,
"recall@100": 0.9,
}
E AssertionError: assert {'recall@1': ...@3': 0.5, ...} == {'recall@1': ...@3': 0.6, ...}
E Omitting 1 identical items, use -vv to show
E Differing items:
E {'recall@3': 0.5} != {'recall@3': 0.6}
E {'recall@10': 0.5} != {'recall@10': 0.6}
E {'recall@5': 0.5} != {'recall@5': 0.6}
E {'recall@50': 0.6} != {'recall@50': 0.7}
E {'recall@1': 0.3} != {'recall@1': 0.4}
E Full diff:
E {
E - 'recall@1': 0.4,
E ? ^
E + 'recall@1': 0.3,
E ? ^
E - 'recall@10': 0.6,
E ? ^
E + 'recall@10': 0.5,
E ? ^
E 'recall@100': 0.9,
E - 'recall@3': 0.6,
E ? ^
E + 'recall@3': 0.5,
E ? ^
E + 'recall@5': 0.5,
E - 'recall@5': 0.6,
E + 'recall@50': 0.6,
E ? +
E - 'recall@50': 0.7,
E }
deeplake/core/vectorstore/deep_memory/test_deepmemory.py:189: AssertionError
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