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uv-metrics's Issues

`make build` fails on workstation

On workstation, the command make build fails when trying to create an VirtualEnv with python3.6 installation. The full output is as follows:

rm -rf env
rm -rf .tox
rm -rf .coverage
find . -name \*.pyc -type f -delete
find . -name \*.test.db -type f -delete
find . -depth -name __pycache__ -type d -exec rm -rf {} \;
rm -rf dist *.egg* build
rm -rf env
virtualenv -p python3.6 env
RuntimeError: failed to find interpreter for Builtin discoverer of python_spec='python3.6'
make: *** [Makefile:43: install] Error 1

So it's the command virtualenv -p python3.6 env which is failing, and this same failure happens when I try to run that command in shell.

As @ajslone suggested, changing the version to python3, i.e. using the command virtualenv -p python3 env by changing the variable PYTHON_BIN=python3 in the makefile does work.

`test_sql_roundtrip` fails sporadically

Situation: I was making changes to tests in test_measurementmanager.py, and while I was testing, the test_sql_roundtrip failed a couple times in succession, even though I hadn't touched that file.
A second test failed again, but a third passed. This seems stochastic.

Here is the failure:

========================================== FAILURES ==========================================                                                                                          
_____________________________________ test_sql_roundtrip _____________________________________
                                               
tmp_path = PosixPath('/tmp/pytest-of-ramasesh/pytest-18/test_sql_roundtrip0')
                                                                                              
    def test_sql_roundtrip(tmp_path):                                                         
      engine = u.sqlite_engine(str(tmp_path))                                                 
             
      # you can't make a reporter with an engine pointing to a nonexistent DB:                
      with pytest.raises(Exception):                                                          
        sr.SQLReporter(engine, sr.Experiment(id=10), 0)
                                               
      # Same goes for reader.                                                                 
      with pytest.raises(Exception):                                                          
        sr.SQLReader(engine, sr.Experiment(id=10), 0)                         
                                                                                              
      sr.create_tables(engine)                                                                
      experiment = sr.new_experiment(engine, {"learning_rate": 0.01})
                                               
      with closing(sr.SQLReporter(engine, experiment, 0)) as reporter:
        with closing(reporter.reader()) as reader:
          with closing(sr.SQLReader(engine, experiment, 0)) as reader2:
                                               
>           reporter.report_all(0, {"a": 1})

tests/uv/sql/test_sql.py:78: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
uv/sql/reporter.py:118: in report_all
    session.add_all([self._metric(step, k, v) for k, v in m.items()])
uv/sql/reporter.py:118: in <listcomp>
    session.add_all([self._metric(step, k, v) for k, v in m.items()])
uv/sql/reporter.py:110: in _metric
    return Metric(experiment_id=self._experiment.id,
env/lib/python3.8/site-packages/sqlalchemy/orm/attributes.py:287: in __get__
    return self.impl.get(instance_state(instance), dict_)
env/lib/python3.8/site-packages/sqlalchemy/orm/attributes.py:718: in get
    value = state._load_expired(state, passive) 
env/lib/python3.8/site-packages/sqlalchemy/orm/state.py:652: in _load_expired
    self.manager.deferred_scalar_loader(self, toload)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

mapper = <Mapper at 0x7fba3a2211f0; Experiment> 
state = <sqlalchemy.orm.state.InstanceState object at 0x7fba39f08e80>
attribute_names = {'id', 'params'}

    def load_scalar_attributes(mapper, state, attribute_names):
        """initiate a column-based attribute refresh operation."""
                                                                                                                                                                                             
        # assert mapper is _state_mapper(state) 
        session = state.session
        if not session:                        
>           raise orm_exc.DetachedInstanceError(
                "Instance %s is not bound to a Session; "
                "attribute refresh operation cannot proceed" % (state_str(state))
            )                                  
E           sqlalchemy.orm.exc.DetachedInstanceError: Instance <Experiment at 0x7fba39f08d00> is not bound to a Session; attribute refresh operation cannot proceed (Background on this error
 at: http://sqlalche.me/e/13/bhk3)             

env/lib/python3.8/site-packages/sqlalchemy/orm/loading.py:944: DetachedInstanceError

`make build` does not install sphinx in virtual environment

To start editing some docs, I ran make build in the root directory to get the env folder setup, and then ran sphinx-reload docs. I get the error

/bin/sh: ../env/bin/sphinx-build: No such file or directory

Looking in the env/bin folder, there is no sphinx-build. However, there is one in the Caliban env/bin folder, so somehow the setup is not happening in UV in the same way.

PyTest fails stochastically on the [tests/uv/sql/test_sql.py::test_of_sql_roundtrip]

An example of this can be seen here: https://github.com/google/uv-metrics/runs/1097920935

We get the following error message:

2020-09-10 17:52:54.571909: W tensorflow/stream_executor/cuda/cuda_driver.cc:312] failed call to cuInit: UNKNOWN ERROR (303)
2020-09-10 17:52:54.571931: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (fv-az12): /proc/driver/nvidia/version does not exist
2020-09-10 17:52:54.572223: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-09-10 17:52:54.578381: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2294685000 Hz
2020-09-10 17:52:54.578542: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x560b84667430 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-09-10 17:52:54.578560: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:From /home/runner/work/uv-metrics/uv-metrics/tests/uv/tensorflow/test_reporter.py:38: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and: 
`tf.data.TFRecordDataset(path)`

For some reason, this doesn't happen always, but seemingly randomly.

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