Fix upstream CI tests by jsignell · Pull Request #6896 · dask/dask

jsignell · 2020-11-25T22:01:00Z

Tests added / passed
Passes black dask / flake8 dask

Closes: #6148, closes: #7051

jsignell · 2020-11-25T22:09:52Z

There are 3 failures when running tests with just the packages from environment-3.8-dev.yaml:

Details


============================================== ERRORS ==============================================
____________________________ ERROR at teardown of test_parquet[pyarrow] ____________________________
[gw1] linux -- Python 3.8.6 /home/julia/conda/envs/dask-upstream/bin/python

self = <s3fs.core.S3FileSystem object at 0x7f66e03df340>, path = 'test'

    def rmdir(self, path):
        path = self._strip_protocol(path).rstrip('/')
        if not self._parent(path):
            try:
>               self.s3.delete_bucket(Bucket=path)

../conda/envs/dask-upstream/lib/python3.8/site-packages/s3fs/core.py:440: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.S3 object at 0x7f66c9051280>, args = (), kwargs = {'Bucket': 'test'}

    def _api_call(self, *args, **kwargs):
        # We're accepting *args so that we can give a more helpful
        # error message than TypeError: _api_call takes exactly
        # 1 argument.
        if args:
            raise TypeError(
                "%s() only accepts keyword arguments." % py_operation_name)
        # The "self" in this scope is referring to the BaseClient.
>       return self._make_api_call(operation_name, kwargs)

../conda/envs/dask-upstream/lib/python3.8/site-packages/botocore/client.py:357: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.S3 object at 0x7f66c9051280>, operation_name = 'DeleteBucket'
api_params = {'Bucket': 'test'}

    def _make_api_call(self, operation_name, api_params):
        operation_model = self._service_model.operation_model(operation_name)
        service_name = self._service_model.service_name
        history_recorder.record('API_CALL', {
            'service': service_name,
            'operation': operation_name,
            'params': api_params,
        })
        if operation_model.deprecated:
            logger.debug('Warning: %s.%s() is deprecated',
                         service_name, operation_name)
        request_context = {
            'client_region': self.meta.region_name,
            'client_config': self.meta.config,
            'has_streaming_input': operation_model.has_streaming_input,
            'auth_type': operation_model.auth_type,
        }
        request_dict = self._convert_to_request_dict(
            api_params, operation_model, context=request_context)
    
        service_id = self._service_model.service_id.hyphenize()
        handler, event_response = self.meta.events.emit_until_response(
            'before-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            model=operation_model, params=request_dict,
            request_signer=self._request_signer, context=request_context)
    
        if event_response is not None:
            http, parsed_response = event_response
        else:
            http, parsed_response = self._make_request(
                operation_model, request_dict, request_context)
    
        self.meta.events.emit(
            'after-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            http_response=http, parsed=parsed_response,
            model=operation_model, context=request_context
        )
    
        if http.status_code >= 300:
            error_code = parsed_response.get("Error", {}).get("Code")
            error_class = self.exceptions.from_code(error_code)
>           raise error_class(parsed_response, operation_name)
E           botocore.exceptions.ClientError: An error occurred (BucketNotEmpty) when calling the DeleteBucket operation: The bucket you tried to delete is not empty

../conda/envs/dask-upstream/lib/python3.8/site-packages/botocore/client.py:676: ClientError

During handling of the above exception, another exception occurred:

s3_base = None

    @pytest.fixture
    def s3(s3_base):
        with s3_context() as fs:
>           yield fs

dask/bytes/tests/test_s3.py:119: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../conda/envs/dask-upstream/lib/python3.8/contextlib.py:120: in __exit__
    next(self.gen)
dask/bytes/tests/test_s3.py:136: in s3_context
    fs.rm(bucket, recursive=True)
../conda/envs/dask-upstream/lib/python3.8/site-packages/s3fs/core.py:997: in rm
    self.rmdir(bucket)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <s3fs.core.S3FileSystem object at 0x7f66e03df340>, path = 'test'

    def rmdir(self, path):
        path = self._strip_protocol(path).rstrip('/')
        if not self._parent(path):
            try:
                self.s3.delete_bucket(Bucket=path)
            except ClientError as e:
>               raise translate_boto_error(e)
E               OSError: [Errno 39] The bucket you tried to delete is not empty

../conda/envs/dask-upstream/lib/python3.8/site-packages/s3fs/core.py:442: OSError
____________________ ERROR at setup of test_read_bytes_blocksize_on_large_data _____________________
[gw7] linux -- Python 3.8.6 /home/julia/conda/envs/dask-upstream/bin/python

s3 = <s3fs.core.S3FileSystem object at 0x7fa94da38fa0>

    @pytest.fixture()
    @pytest.mark.slow
    def s3_with_yellow_tripdata(s3):
        """
        Fixture with sample yellowtrip CSVs loaded into S3.
    
        Provides the following CSVs:
    
        * s3://test/nyc-taxi/2015/yellow_tripdata_2015-01.csv
        * s3://test/nyc-taxi/2014/yellow_tripdata_2015-mm.csv
          for mm from 01 - 12.
        """
        np = pytest.importorskip("numpy")
        pd = pytest.importorskip("pandas")
    
        data = {
            "VendorID": {0: 2, 1: 1, 2: 1, 3: 1, 4: 1},
            "tpep_pickup_datetime": {
                0: "2015-01-15 19:05:39",
                1: "2015-01-10 20:33:38",
                2: "2015-01-10 20:33:38",
                3: "2015-01-10 20:33:39",
                4: "2015-01-10 20:33:39",
            },
            "tpep_dropoff_datetime": {
                0: "2015-01-15 19:23:42",
                1: "2015-01-10 20:53:28",
                2: "2015-01-10 20:43:41",
                3: "2015-01-10 20:35:31",
                4: "2015-01-10 20:52:58",
            },
            "passenger_count": {0: 1, 1: 1, 2: 1, 3: 1, 4: 1},
            "trip_distance": {0: 1.59, 1: 3.3, 2: 1.8, 3: 0.5, 4: 3.0},
            "pickup_longitude": {
                0: -73.993896484375,
                1: -74.00164794921875,
                2: -73.96334075927734,
                3: -74.00908660888672,
                4: -73.97117614746094,
            },
            "pickup_latitude": {
                0: 40.7501106262207,
                1: 40.7242431640625,
                2: 40.80278778076172,
                3: 40.71381759643555,
                4: 40.762428283691406,
            },
            "RateCodeID": {0: 1, 1: 1, 2: 1, 3: 1, 4: 1},
            "store_and_fwd_flag": {0: "N", 1: "N", 2: "N", 3: "N", 4: "N"},
            "dropoff_longitude": {
                0: -73.97478485107422,
                1: -73.99441528320312,
                2: -73.95182037353516,
                3: -74.00432586669923,
                4: -74.00418090820312,
            },
            "dropoff_latitude": {
                0: 40.75061798095703,
                1: 40.75910949707031,
                2: 40.82441329956055,
                3: 40.71998596191406,
                4: 40.742652893066406,
            },
            "payment_type": {0: 1, 1: 1, 2: 2, 3: 2, 4: 2},
            "fare_amount": {0: 12.0, 1: 14.5, 2: 9.5, 3: 3.5, 4: 15.0},
            "extra": {0: 1.0, 1: 0.5, 2: 0.5, 3: 0.5, 4: 0.5},
            "mta_tax": {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5, 4: 0.5},
            "tip_amount": {0: 3.25, 1: 2.0, 2: 0.0, 3: 0.0, 4: 0.0},
            "tolls_amount": {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
            "improvement_surcharge": {0: 0.3, 1: 0.3, 2: 0.3, 3: 0.3, 4: 0.3},
            "total_amount": {0: 17.05, 1: 17.8, 2: 10.8, 3: 4.8, 4: 16.3},
        }
        sample = pd.DataFrame(data)
        df = sample.take(np.arange(5).repeat(10000))
        file = io.BytesIO()
        sfile = io.TextIOWrapper(file)
        df.to_csv(sfile, index=False)
    
        key = "nyc-taxi/2015/yellow_tripdata_2015-01.csv"
        client = boto3.client("s3", endpoint_url="http://127.0.0.1:5555/")
        client.put_object(Bucket=test_bucket_name, Key=key, Body=file)
        key = "nyc-taxi/2014/yellow_tripdata_2014-{:0>2d}.csv"
    
        for i in range(1, 13):
            file.seek(0)
>           client.put_object(Bucket=test_bucket_name, Key=key.format(i), Body=file)

dask/bytes/tests/test_s3.py:224: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../conda/envs/dask-upstream/lib/python3.8/site-packages/botocore/client.py:357: in _api_call
    return self._make_api_call(operation_name, kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.S3 object at 0x7fa94d601160>, operation_name = 'PutObject'
api_params = {'Body': <_io.BytesIO object at 0x7fa94dae93b0>, 'Bucket': 'test', 'Key': 'nyc-taxi/2014/yellow_tripdata_2014-04.csv'}

    def _make_api_call(self, operation_name, api_params):
        operation_model = self._service_model.operation_model(operation_name)
        service_name = self._service_model.service_name
        history_recorder.record('API_CALL', {
            'service': service_name,
            'operation': operation_name,
            'params': api_params,
        })
        if operation_model.deprecated:
            logger.debug('Warning: %s.%s() is deprecated',
                         service_name, operation_name)
        request_context = {
            'client_region': self.meta.region_name,
            'client_config': self.meta.config,
            'has_streaming_input': operation_model.has_streaming_input,
            'auth_type': operation_model.auth_type,
        }
        request_dict = self._convert_to_request_dict(
            api_params, operation_model, context=request_context)
    
        service_id = self._service_model.service_id.hyphenize()
        handler, event_response = self.meta.events.emit_until_response(
            'before-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            model=operation_model, params=request_dict,
            request_signer=self._request_signer, context=request_context)
    
        if event_response is not None:
            http, parsed_response = event_response
        else:
            http, parsed_response = self._make_request(
                operation_model, request_dict, request_context)
    
        self.meta.events.emit(
            'after-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            http_response=http, parsed=parsed_response,
            model=operation_model, context=request_context
        )
    
        if http.status_code >= 300:
            error_code = parsed_response.get("Error", {}).get("Code")
            error_class = self.exceptions.from_code(error_code)
>           raise error_class(parsed_response, operation_name)
E           botocore.errorfactory.NoSuchBucket: An error occurred (NoSuchBucket) when calling the PutObject operation: The specified bucket does not exist

../conda/envs/dask-upstream/lib/python3.8/site-packages/botocore/client.py:676: NoSuchBucket
___________________ ERROR at teardown of test_read_bytes_blocksize_on_large_data ___________________
[gw7] linux -- Python 3.8.6 /home/julia/conda/envs/dask-upstream/bin/python

self = <s3fs.core.S3FileSystem object at 0x7fa94da38fa0>, path = 'test'

    def rmdir(self, path):
        path = self._strip_protocol(path).rstrip('/')
        if not self._parent(path):
            try:
>               self.s3.delete_bucket(Bucket=path)

../conda/envs/dask-upstream/lib/python3.8/site-packages/s3fs/core.py:440: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.S3 object at 0x7fa94d6431c0>, args = (), kwargs = {'Bucket': 'test'}

    def _api_call(self, *args, **kwargs):
        # We're accepting *args so that we can give a more helpful
        # error message than TypeError: _api_call takes exactly
        # 1 argument.
        if args:
            raise TypeError(
                "%s() only accepts keyword arguments." % py_operation_name)
        # The "self" in this scope is referring to the BaseClient.
>       return self._make_api_call(operation_name, kwargs)

../conda/envs/dask-upstream/lib/python3.8/site-packages/botocore/client.py:357: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <botocore.client.S3 object at 0x7fa94d6431c0>, operation_name = 'DeleteBucket'
api_params = {'Bucket': 'test'}

    def _make_api_call(self, operation_name, api_params):
        operation_model = self._service_model.operation_model(operation_name)
        service_name = self._service_model.service_name
        history_recorder.record('API_CALL', {
            'service': service_name,
            'operation': operation_name,
            'params': api_params,
        })
        if operation_model.deprecated:
            logger.debug('Warning: %s.%s() is deprecated',
                         service_name, operation_name)
        request_context = {
            'client_region': self.meta.region_name,
            'client_config': self.meta.config,
            'has_streaming_input': operation_model.has_streaming_input,
            'auth_type': operation_model.auth_type,
        }
        request_dict = self._convert_to_request_dict(
            api_params, operation_model, context=request_context)
    
        service_id = self._service_model.service_id.hyphenize()
        handler, event_response = self.meta.events.emit_until_response(
            'before-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            model=operation_model, params=request_dict,
            request_signer=self._request_signer, context=request_context)
    
        if event_response is not None:
            http, parsed_response = event_response
        else:
            http, parsed_response = self._make_request(
                operation_model, request_dict, request_context)
    
        self.meta.events.emit(
            'after-call.{service_id}.{operation_name}'.format(
                service_id=service_id,
                operation_name=operation_name),
            http_response=http, parsed=parsed_response,
            model=operation_model, context=request_context
        )
    
        if http.status_code >= 300:
            error_code = parsed_response.get("Error", {}).get("Code")
            error_class = self.exceptions.from_code(error_code)
>           raise error_class(parsed_response, operation_name)
E           botocore.errorfactory.NoSuchBucket: An error occurred (NoSuchBucket) when calling the DeleteBucket operation: The specified bucket does not exist

../conda/envs/dask-upstream/lib/python3.8/site-packages/botocore/client.py:676: NoSuchBucket

During handling of the above exception, another exception occurred:

s3_base = None

    @pytest.fixture
    def s3(s3_base):
        with s3_context() as fs:
>           yield fs

dask/bytes/tests/test_s3.py:119: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../conda/envs/dask-upstream/lib/python3.8/contextlib.py:120: in __exit__
    next(self.gen)
dask/bytes/tests/test_s3.py:136: in s3_context
    fs.rm(bucket, recursive=True)
../conda/envs/dask-upstream/lib/python3.8/site-packages/s3fs/core.py:997: in rm
    self.rmdir(bucket)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <s3fs.core.S3FileSystem object at 0x7fa94da38fa0>, path = 'test'

    def rmdir(self, path):
        path = self._strip_protocol(path).rstrip('/')
        if not self._parent(path):
            try:
                self.s3.delete_bucket(Bucket=path)
            except ClientError as e:
>               raise translate_boto_error(e)
E               FileNotFoundError: The specified bucket does not exist

../conda/envs/dask-upstream/lib/python3.8/site-packages/s3fs/core.py:442: FileNotFoundError
============================================= FAILURES =============================================
_____________________ test_partitioned_preserve_index[fastparquet-fastparquet] _____________________
[gw4] linux -- Python 3.8.6 /home/julia/conda/envs/dask-upstream/bin/python

tmpdir = local('/tmp/pytest-of-julia/pytest-27/popen-gw4/test_partitioned_preserve_inde0')
write_engine = 'fastparquet', read_engine = 'fastparquet'

    @write_read_engines_xfail
    def test_partitioned_preserve_index(tmpdir, write_engine, read_engine):
    
        if write_engine == "pyarrow" and pa.__version__ < LooseVersion("0.15.0"):
            pytest.skip("PyArrow>=0.15 Required.")
    
        tmp = str(tmpdir)
        size = 1_000
        npartitions = 4
        b = np.arange(npartitions).repeat(size // npartitions)
        data = pd.DataFrame(
            {
                "myindex": np.arange(size),
                "A": np.random.random(size=size),
                "B": pd.Categorical(b),
            }
        ).set_index("myindex")
        data.index.name = None
        df1 = dd.from_pandas(data, npartitions=npartitions)
        df1.to_parquet(tmp, partition_on="B", engine=write_engine)
    
        expect = data[data["B"] == 1]
        got = dd.read_parquet(tmp, engine=read_engine, filters=[("B", "==", 1)])
>       assert_eq(expect, got)

dask/dataframe/io/tests/test_parquet.py:2788: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a =             A  B
250  0.836370  1
251  0.029695  1
252  0.184864  1
253  0.639815  1
254  0.177313  1
..        ... ..
495  0.048179  1
496  0.719764  1
497  0.802599  1
498  0.798046  1
499  0.897758  1

[250 rows x 2 columns]
b = Empty DataFrame
Columns: [A, B]
Index: [], check_names = True, check_dtypes = True
check_divisions = True, check_index = True, kwargs = {}

    def assert_eq(
        a,
        b,
        check_names=True,
        check_dtypes=True,
        check_divisions=True,
        check_index=True,
        **kwargs,
    ):
        if check_divisions:
            assert_divisions(a)
            assert_divisions(b)
            if hasattr(a, "divisions") and hasattr(b, "divisions"):
                at = type(np.asarray(a.divisions).tolist()[0])  # numpy to python
                bt = type(np.asarray(b.divisions).tolist()[0])  # scalar conversion
                assert at == bt, (at, bt)
        assert_sane_keynames(a)
        assert_sane_keynames(b)
        a = _check_dask(a, check_names=check_names, check_dtypes=check_dtypes)
        b = _check_dask(b, check_names=check_names, check_dtypes=check_dtypes)
        if not check_index:
            a = a.reset_index(drop=True)
            b = b.reset_index(drop=True)
        if hasattr(a, "to_pandas"):
            a = a.to_pandas()
        if hasattr(b, "to_pandas"):
            b = b.to_pandas()
        if isinstance(a, pd.DataFrame):
            a = _maybe_sort(a)
            b = _maybe_sort(b)
>           tm.assert_frame_equal(a, b, **kwargs)
E           AssertionError: DataFrame are different
E           
E           DataFrame shape mismatch
E           [left]:  (250, 2)
E           [right]: (0, 2)

dask/dataframe/utils.py:828: AssertionError
_______________________ test_partitioned_preserve_index[fastparquet-pyarrow] _______________________
[gw4] linux -- Python 3.8.6 /home/julia/conda/envs/dask-upstream/bin/python

tmpdir = local('/tmp/pytest-of-julia/pytest-27/popen-gw4/test_partitioned_preserve_inde1')
write_engine = 'fastparquet', read_engine = 'pyarrow'

    @write_read_engines_xfail
    def test_partitioned_preserve_index(tmpdir, write_engine, read_engine):
    
        if write_engine == "pyarrow" and pa.__version__ < LooseVersion("0.15.0"):
            pytest.skip("PyArrow>=0.15 Required.")
    
        tmp = str(tmpdir)
        size = 1_000
        npartitions = 4
        b = np.arange(npartitions).repeat(size // npartitions)
        data = pd.DataFrame(
            {
                "myindex": np.arange(size),
                "A": np.random.random(size=size),
                "B": pd.Categorical(b),
            }
        ).set_index("myindex")
        data.index.name = None
        df1 = dd.from_pandas(data, npartitions=npartitions)
        df1.to_parquet(tmp, partition_on="B", engine=write_engine)
    
        expect = data[data["B"] == 1]
        got = dd.read_parquet(tmp, engine=read_engine, filters=[("B", "==", 1)])
>       assert_eq(expect, got)

dask/dataframe/io/tests/test_parquet.py:2788: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a =             A  B
250  0.986650  1
251  0.673856  1
252  0.881741  1
253  0.144109  1
254  0.916354  1
..        ... ..
495  0.468871  1
496  0.093040  1
497  0.007673  1
498  0.034938  1
499  0.503552  1

[250 rows x 2 columns]
b = Empty DataFrame
Columns: [A, B]
Index: [], check_names = True, check_dtypes = True
check_divisions = True, check_index = True, kwargs = {}

    def assert_eq(
        a,
        b,
        check_names=True,
        check_dtypes=True,
        check_divisions=True,
        check_index=True,
        **kwargs,
    ):
        if check_divisions:
            assert_divisions(a)
            assert_divisions(b)
            if hasattr(a, "divisions") and hasattr(b, "divisions"):
                at = type(np.asarray(a.divisions).tolist()[0])  # numpy to python
                bt = type(np.asarray(b.divisions).tolist()[0])  # scalar conversion
                assert at == bt, (at, bt)
        assert_sane_keynames(a)
        assert_sane_keynames(b)
        a = _check_dask(a, check_names=check_names, check_dtypes=check_dtypes)
        b = _check_dask(b, check_names=check_names, check_dtypes=check_dtypes)
        if not check_index:
            a = a.reset_index(drop=True)
            b = b.reset_index(drop=True)
        if hasattr(a, "to_pandas"):
            a = a.to_pandas()
        if hasattr(b, "to_pandas"):
            b = b.to_pandas()
        if isinstance(a, pd.DataFrame):
            a = _maybe_sort(a)
            b = _maybe_sort(b)
>           tm.assert_frame_equal(a, b, **kwargs)
E           AssertionError: DataFrame are different
E           
E           DataFrame shape mismatch
E           [left]:  (250, 2)
E           [right]: (0, 2)

dask/dataframe/utils.py:828: AssertionError

jsignell · 2020-11-25T22:16:28Z

After first commit -> https://github.com/jsignell/dask/runs/1455951088

jsignell · 2020-11-25T22:43:31Z

Strangely it looks like it used to work to set slices of np.arrays of integer type to np.nan they would become some very large negative integer. Now that is raising an error, so I think we should probably change those tests to use floats.

jsignell · 2021-01-25T22:43:26Z

The last issues should be with sparse, so hopefully I can fix those up and finally get this thing merged!

jsignell · 2021-02-04T15:43:46Z

I wrote up the sparse issue in #7169 and I think it should be tracked separately. I am 🤞 thinking that this might actually go green

continuous_integration/environment-3.8.yaml

jsignell · 2021-02-04T20:15:28Z

I'm planning to merge this when it's green.

This was referenced Nov 30, 2020

upstream failing #6902

Closed

Only run the cron CI on dask/dask not forks #6905

Merged

jsignell force-pushed the upstream branch from bb48166 to 7a825ef Compare December 22, 2020 14:48

jsignell mentioned this pull request Jan 6, 2021

CI: test pyarrow 2.0 and nightly #7030

Merged

jsignell force-pushed the upstream branch 2 times, most recently from 5d79f51 to 531b416 Compare January 6, 2021 18:48

jsignell mentioned this pull request Jan 11, 2021

sparse test_basic's mean fails on the upstream #7051

Closed

jsignell mentioned this pull request Jan 21, 2021

Test pandas 1.1.x / 1.2.0 releases and pandas nightly #6996

Merged

jsignell force-pushed the upstream branch from 531b416 to 5babbad Compare January 25, 2021 22:41

jsignell added 9 commits February 4, 2021 09:45

Fix failing sparse tests

f3757ba

Run the tests in parallel

e691d6a

Fix new sparse changes

f18d861

Don't make the pandas changes in rolling

6b02f83

Use a new numpy in one of the test envs

645b0da

Comment out logic

3dd04f9

Get rid of last sparse-383

1c684ff

Use numpy 1.20 in environment-3.8

f2640af

Fix dtype=np.float

7aa6126

jsignell force-pushed the upstream branch from 8943bd9 to 7aa6126 Compare February 4, 2021 15:06

Revert to same xfail as on master

f1ceddb

jsignell marked this pull request as ready for review February 4, 2021 15:42

jsignell mentioned this pull request Feb 4, 2021

2021.02.0 release dask/community#128

Closed

jsignell added 2 commits February 4, 2021 11:42

Revert changes in upstream ci config

d8aea80

Just use numpy 1.19 until 7170 is resolved

8839c19

jrbourbeau reviewed Feb 4, 2021

View reviewed changes

continuous_integration/environment-3.8.yaml Outdated Show resolved Hide resolved

Skip failing numpy 1.20 test

41a6169

jsignell merged commit 2632bbc into dask:master Feb 4, 2021

jsignell deleted the upstream branch February 4, 2021 23:10

bnavigator mentioned this pull request Feb 17, 2021

Numpy dtype fails to pickle "'_DTypeMeta' has no attribute '_abstract'" #7170

Closed

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Fix upstream CI tests#6896

Fix upstream CI tests#6896
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