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Tests fail with tensorflow 2.2.0 release candidates

See original GitHub issue

Describe the bug

In #1077, I pinned the version of tensorflow to the first 2.2.0 release candidate https://github.com/tensorflow/tensorflow/releases/tag/v2.2.0-rc0.

Some of our tests failed. https://buildkite.com/stellar/stellargraph-public/builds/2284

To Reproduce

Steps to reproduce the behavior:

Install the tensorflow release candidate pip install tensorflow==2.2.0rc0
Run test (e.g. pytest)

Observed behavior

4 tests fail:

FAILED tests/test_ensemble.py::test_compile - Failed: DID NOT RAISE <class 'V...
FAILED tests/reproducibility/test_graphsage.py::test_unsupervised[True] - Ass...
FAILED tests/reproducibility/test_graphsage.py::test_unsupervised[False] - As...
FAILED tests/reproducibility/test_graphsage.py::test_nai[True] - AssertionErr...

Expected behavior

No tests should fail.

Environment

Operating system: CI

Python version: 3.6.x and 3.7.x

Package versions:

absl-py==0.9.0
ansiwrap==0.8.4
appdirs==1.4.3
astunparse==1.6.3
attrs==19.3.0
backcall==0.1.0
black==19.10b0
bleach==3.1.1
boto==2.49.0
boto3==1.12.20
botocore==1.15.20
cachetools==4.0.0
certifi==2019.11.28
chardet==3.0.4
click==7.1.1
coverage==4.5.4
cycler==0.10.0
decorator==4.4.2
defusedxml==0.6.0
docopt==0.6.2
docutils==0.15.2
entrypoints==0.3
gast==0.3.3
gensim==3.8.1
google-auth==1.11.2
google-auth-oauthlib==0.4.1
google-pasta==0.1.8
grpcio==1.27.2
h5py==2.10.0
idna==2.9
importlib-metadata==1.5.0
ipykernel==5.1.4
ipython==7.13.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
isodate==0.6.0
jedi==0.16.0
Jinja2==2.11.1
jmespath==0.9.5
joblib==0.14.1
jsonschema==3.2.0
jupyter==1.0.0
jupyter-client==6.0.0
jupyter-console==6.1.0
jupyter-core==4.6.3
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
llvmlite==0.31.0
Markdown==3.2.1
MarkupSafe==1.1.1
matplotlib==3.2.0
mistune==0.8.4
more-itertools==8.2.0
mplleaflet==0.0.5
nbclient==0.1.0
nbconvert==5.6.1
nbformat==5.0.4
networkx==2.4
notebook==6.0.3
numba==0.48.0
numpy==1.18.1
oauthlib==3.1.0
opt-einsum==3.2.0
packaging==20.3
pandas==1.0.1
pandocfilters==1.4.2
papermill==2.0.0
parso==0.6.2
pathspec==0.7.0
pexpect==4.8.0
pickleshare==0.7.5
pluggy==0.13.1
prometheus-client==0.7.1
prompt-toolkit==3.0.4
protobuf==3.11.3
ptyprocess==0.6.0
py==1.8.1
py-cpuinfo==5.0.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
Pygments==2.6.1
pyparsing==2.4.6
pyrsistent==0.15.7
pytest==5.3.1
pytest-benchmark==3.2.3
pytest-cov==2.8.1
python-dateutil==2.8.1
pytz==2019.3
PyYAML==5.3
pyzmq==19.0.0
qtconsole==4.7.1
QtPy==1.9.0
rdflib==4.2.2
regex==2020.2.20
requests==2.23.0
requests-oauthlib==1.3.0
rsa==4.0
s3transfer==0.3.3
scikit-learn==0.22.2.post1
scipy==1.4.1
seaborn==0.10.0
Send2Trash==1.5.0
six==1.14.0
smart-open==1.9.0
tenacity==6.1.0
tensorboard==2.1.1
tensorflow==2.2.0rc0
tensorflow-estimator==2.1.0
termcolor==1.1.0
terminado==0.8.3
testpath==0.4.4
textwrap3==0.9.2
toml==0.10.0
tornado==6.0.4
tqdm==4.43.0
traitlets==4.3.3
treon==0.1.3
typed-ast==1.4.1
urllib3==1.25.8
wcwidth==0.1.8
webencodings==0.5.1
Werkzeug==1.0.0
widgetsnbextension==3.5.1
wrapt==1.12.1
zipp==3.1.0

Additional context

N/A

Issue Analytics

State:
Created 4 years ago
Comments:7 (7 by maintainers)

Top GitHub Comments

1reaction

kjun9commented, Mar 30, 2020

This seems to improve a lot (less failures) after upgrading from 2.2rc1 to 2.2rc2, I suspect it’s from this issue being resolved. There’s still some intermittent failures but it’s unclear how it compares to the flakiness prior to 2.2.

Running python -m pytest --count=300 tests/reproducibility/test_graphsage.py (using pytest-repeat)

Output:

============================= test session starts ==============================
platform darwin -- Python 3.7.5, pytest-5.3.1, py-1.8.1, pluggy-0.13.1
benchmark: 3.2.2 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /Users/jun021/work/stellar/stellargraph, inifile: pytest.ini
plugins: repeat-0.8.0, benchmark-3.2.2, cov-2.8.1
collected 1800 items

tests/reproducibility/test_graphsage.py ................................ [  1%]
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....................................................................F... [ 65%]
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=================================== FAILURES ===================================
_______________________ test_unsupervised[True-273-300] ________________________

petersen_graph = <stellargraph.core.graph.StellarGraph object at 0x140047bd0>
shuffle = True

    @pytest.mark.parametrize("shuffle", [True, False])
    def test_unsupervised(petersen_graph, shuffle):
        assert_reproducible(
>           lambda: unsup_gs(
                petersen_graph,
                [2, 2],
                tf.optimizers.Adam(1e-3),
                epochs=4,
                walk_length=2,
                batch_size=4,
                shuffle=shuffle,
            )
        )

tests/reproducibility/test_graphsage.py:212: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

func = <function test_unsupervised.<locals>.<lambda> at 0x141869c20>
num_iter = 10

    def assert_reproducible(func, num_iter=10):
        """
        Assert Keras models produced from calling ``func`` are reproducible.
    
        Args:
            func (callable): Function to check for reproducible model
            num_iter (int): Number of iterations to run through to validate reproducibility.
    
        """
        model = func()
        for i in range(num_iter):
            model_new = func()
            assert models_equals(model, model_new), (
                model.get_weights(),
>               model_new.get_weights(),
            )
E           AssertionError: ([array([ 0.00623061,  0.00806176,  0.00267832, -0.00561015,  0.00372594,
E                   0.00892242,  0.        , -0.00134451, -0.01095336,  0.        ,
E                   0.00657242,  0.00542241,  0.00895842,  0.00455641,  0.011158  ,
E                   0.0004458 , -0.00978916, -0.00199516,  0.00843709, -0.01030695,
E                   0.        ,  0.01186865,  0.01337261,  0.0118678 ,  0.00488744,
E                  -0.00986822,  0.        , -0.00795464, -0.00746133,  0.        ,
E                   0.        ,  0.00760732,  0.00342201, -0.00922432,  0.00494049,
E                  -0.00974373,  0.        ,  0.00423221, -0.00971612,  0.        ,
E                   0.00552816,  0.0026735 , -0.00945645,  0.00256908,  0.        ,
E                  -0.008481  ,  0.00913319,  0.00932978, -0.00565406,  0.        ],
E                 dtype=float32), array([[-1.64424449e-01, -2.36992612e-01,  3.19461003e-02,
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E                  ...,
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tests/reproducibility/fixtures.py:41: AssertionError
----------------------------- Captured stdout call -----------------------------
link_classification: using 'ip' method to combine node embeddings into edge embeddings
Epoch 1/4

1/5 [=====>........................] - ETA: 0s - loss: 0.5708
5/5 [==============================] - 0s 2ms/step - loss: 0.7991
Epoch 2/4

1/5 [=====>........................] - ETA: 0s - loss: 0.7869
5/5 [==============================] - 0s 2ms/step - loss: 0.7996
Epoch 3/4

1/5 [=====>........................] - ETA: 0s - loss: 0.8082
5/5 [==============================] - 0s 2ms/step - loss: 0.7939
Epoch 4/4

1/5 [=====>........................] - ETA: 0s - loss: 0.7747
5/5 [==============================] - 0s 2ms/step - loss: 0.7946
link_classification: using 'ip' method to combine node embeddings into edge embeddings
Epoch 1/4

1/5 [=====>........................] - ETA: 0s - loss: 0.5708
5/5 [==============================] - 0s 1ms/step - loss: 0.7991
Epoch 2/4

1/5 [=====>........................] - ETA: 0s - loss: 0.7869
5/5 [==============================] - 0s 1ms/step - loss: 0.7996
Epoch 3/4

1/5 [=====>........................] - ETA: 0s - loss: 0.8082
5/5 [==============================] - 0s 1ms/step - loss: 0.7939
Epoch 4/4

1/5 [=====>........................] - ETA: 0s - loss: 0.7747
5/5 [==============================] - 0s 1ms/step - loss: 0.7946
link_classification: using 'ip' method to combine node embeddings into edge embeddings
Epoch 1/4

1/5 [=====>........................] - ETA: 0s - loss: 0.5708
5/5 [==============================] - 0s 2ms/step - loss: 0.7991
Epoch 2/4

1/5 [=====>........................] - ETA: 0s - loss: 0.7869
2/5 [===========>..................] - ETA: 0s - loss: 0.9147
5/5 [==============================] - 0s 19ms/step - loss: 0.7996
Epoch 3/4

1/5 [=====>........................] - ETA: 0s - loss: 0.8007
5/5 [==============================] - 0s 2ms/step - loss: 0.8054
Epoch 4/4

1/5 [=====>........................] - ETA: 0s - loss: 0.3464
5/5 [==============================] - 0s 2ms/step - loss: 0.7944
___________________________ test_nai[False-281-300] ____________________________

petersen_graph = <stellargraph.core.graph.StellarGraph object at 0x13ff98810>
shuffle = False

    @pytest.mark.parametrize("shuffle", [True, False])
    def test_nai(petersen_graph, shuffle):
        target_size = 10
        targets = np.random.rand(len(petersen_graph.nodes()), target_size)
        assert_reproducible(
>           lambda: gs_nai(
                petersen_graph, targets, [2, 2], tf.optimizers.Adam(1e-3), shuffle=shuffle
            )
        )

tests/reproducibility/test_graphsage.py:229: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

func = <function test_nai.<locals>.<lambda> at 0x1428becb0>, num_iter = 10

    def assert_reproducible(func, num_iter=10):
        """
        Assert Keras models produced from calling ``func`` are reproducible.
    
        Args:
            func (callable): Function to check for reproducible model
            num_iter (int): Number of iterations to run through to validate reproducibility.
    
        """
        model = func()
        for i in range(num_iter):
            model_new = func()
            assert models_equals(model, model_new), (
                model.get_weights(),
>               model_new.get_weights(),
            )
E           AssertionError: ([array([ 0.00636744, -0.00966135,  0.0064503 ,  0.00792544,  0.00431308,
E                   0.01200567,  0.        , -0.0109778 , -0.01006021,  0.        ,
E                   0.01051244,  0.01077931, -0.00792994,  0.00329707,  0.00807588,
E                   0.01079916, -0.01196575, -0.00354123,  0.01017802, -0.01188231,
E                   0.        ,  0.01040664, -0.01051376,  0.00536426,  0.01049285,
E                  -0.00904355,  0.        ,  0.01161325, -0.01025783,  0.        ,
E                   0.        ,  0.00408577,  0.00499609,  0.01062843, -0.0080355 ,
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E                  [ 0.01737066, -0.11818796,  0.1424404 ,  0.05718692,  0.04514444,
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E                  [-0.05660693,  0.03811905,  0.16994037, -0.32374611, -0.07980082,
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tests/reproducibility/fixtures.py:41: AssertionError
----------------------------- Captured stdout call -----------------------------
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 1ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 1ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 2ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 1ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 1ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 2ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 1ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 1ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 1ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 1ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 936us/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 1ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 1ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 12.9175
3/3 [==============================] - 0s 1ms/step - loss: 12.2991
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 11.6822
3/3 [==============================] - 0s 1ms/step - loss: 12.6043
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1449
3/3 [==============================] - 0s 1ms/step - loss: 12.1539
Epoch 1/4

1/3 [=========>....................] - ETA: 0s - loss: 12.0645
3/3 [==============================] - 0s 1ms/step - loss: 12.5564
Epoch 2/4

1/3 [=========>....................] - ETA: 0s - loss: 11.7316
3/3 [==============================] - 0s 1ms/step - loss: 12.6142
Epoch 3/4

1/3 [=========>....................] - ETA: 0s - loss: 13.1749
3/3 [==============================] - 0s 1ms/step - loss: 12.1678
Epoch 4/4

1/3 [=========>....................] - ETA: 0s - loss: 12.5269
3/3 [==============================] - 0s 1ms/step - loss: 12.2119
=============================== warnings summary ===============================
/Users/jun021/miniconda3/envs/stellargraph-dev/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py:15
  /Users/jun021/miniconda3/envs/stellargraph-dev/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
    import imp

tests/reproducibility/test_graphsage.py::test_unsupervised[True-1-300]
  /Users/jun021/miniconda3/envs/stellargraph-dev/lib/python3.7/site-packages/tensorflow/python/framework/indexed_slices.py:349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working
    if not isinstance(values, collections.Sequence):

-- Docs: https://docs.pytest.org/en/latest/warnings.html
========== 2 failed, 1798 passed, 2 warnings in 37397.79s (10:23:17) ===========

The two failures out of 300 runs are for:

test_unsupervised[True] (https://github.com/stellargraph/stellargraph/issues/1115)
test_nai[False] (new)

Didn’t see any failures for #970 (I added test_link_prediction[True] back into the test set before running the above test)

1reaction

huonwcommented, Mar 13, 2020

I didn’t include them here, because they’re huge and they’re available in the public buildkite logs linked in the description.

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