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How to use _get_session method in Molotov

Best Python code snippet using molotov_python

cli.py

Source:cli.py

...125        'profile': _train_keys,126        'test': _test_keys,127        'validate': _validate_keys,128    }129    def _get_session(self, action=None):130        if not action:131            if self.session:132                return self.session133            keys = self._train_keys134            if self._validate_config(keys, 'train', test=True):135                self.session = self._get_session('train')136            else:137                self.session = self._get_session('validate')138            return self.session139        keys = self._model_keys + self._dataset_keys140        try:141            cls = self._session_map[action]142            keys += self._keys_map[action]143        except KeyError:144            raise TypeError('Action {!r} not recognized.'.format(action))145        self._validate_config(keys, action)146        if not isinstance(self.session, cls):147            log.info('Starting a {} session...'.format(action))148            self.session = cls(self.config)149        return self.session150    def cli_profile_timeline(self):151        """Performs training profiling to produce timeline.json.  """152        # TODO integrate this into Profile.153        from tensorflow.python.client import timeline154        options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)155        run_metadata = tf.RunMetadata()156        session = self._get_session('train')157        # run 100 iterations to warm up158        max_iterations = 100159        for i in range(max_iterations):160            log.info(161                'Running {}/{} iterations to warm up...'162                .format(i, max_iterations), update=True)163            session.run(session._train_op)164        log.info('Running the final iteration to generate timeline...')165        session.run(166            session._train_op, options=options, run_metadata=run_metadata)167        fetched_timeline = timeline.Timeline(run_metadata.step_stats)168        chrome_trace = fetched_timeline.generate_chrome_trace_format()169        with open('timeline.json', 'w') as f:170            f.write(chrome_trace)171    def cli_plot(self):172        """Plots activation maps as images and parameters as histograms."""173        return self._get_session('validate').plot()174    def cli_train(self):175        """Performs training.  """176        return self._get_session('train').train()177    def cli_search(self):178        """Performs automated hyperparameter search.  """179        return self._get_session('search').search()180    def cli_profile(self):181        """Performs profiling.  """182        return self._get_session('profile').profile()183    def cli_eval(self):184        """Evaluates the accuracy of a saved model.  """185        return self._get_session('validate').eval()186    def cli_eval_all(self):187        """Evaluates all checkpoints for accuracy.  """188        result = self._get_session('validate').eval_all()189        file_name = 'eval_all.csv'190        with open(file_name, 'w') as f:191            f.write(result.csv())192        log.info(193            'Evaluation results saved in {!r}.'.format(file_name))194    def cli_test(self):195        """Perform inference for custom test data.  """196        return self._get_session('test').test()197    def cli_overriders_update(self):198        """Updates variable overriders in the training session.  """199        self._get_session('train').overriders_update()200    def cli_overriders_assign(self):201        """Assign overridden values to original parameters.  """202        self._get_session('train').overriders_assign()203        self._get_session('train').save_checkpoint('assigned')204    def cli_overriders_reset(self):205        """Reset the internal state of overriders.  """206        self._get_session('train').overriders_reset()207    def cli_overriders_dump(self):208        """Export the internal parameters of overriders.  """209        self._get_session().overriders_dump()210    def cli_reset_num_epochs(self):211        """Resets the number of training epochs.  """212        self._get_session('train').reset_num_epochs()213    def cli_export(self):214        """Exports the current config.  """215        name = 'export.yaml'216        with open(name, 'w') as f:217            f.write(self.config.to_yaml())218        log.info('Config successfully exported to {!r}.'.format(name))219    def cli_info(self):220        """Prints parameter and layer info of the model.  """221        plumbing = self.config.system.info.get('plumbing')222        info = self._get_session().info(plumbing)223        if plumbing:224            with open('info.yaml', 'w') as f:225                yaml.dump(info, f)226        else:227            for key in ('trainables', 'nontrainables', 'layers'):228                print(info[key].format())229            for table in info.get('overriders', {}).values():230                print(table.format())231    def cli_interact(self):232        """Interacts with the train/eval session using iPython.  """233        self._get_session().interact()234    def cli_save(self):235        """Saves the latest checkpoint.  """236        self.session.checkpoint.save('latest')237    def _purge_session(self):238        if not self.session:239            return240        log.info('Purging current session because config is updated...')241        del self.session242        self.session = None243    def main(self, args=None):244        if args is None:245            args = docopt(self.usage(), version=meta()['__version__'])246        anything = args['<anything>']247        commands = self.commands()...

test_pyxis.py

Source:test_pyxis.py

...9    monkeypatch.setenv("PYXIS_KEY_PATH", "/path/to/key.key")10    assert pyxis.is_internal()11def test_get_session_api_key(monkeypatch: Any) -> None:12    monkeypatch.setenv("PYXIS_API_KEY", "123")13    session = pyxis._get_session()14    assert session.headers["X-API-KEY"] == "123"15def test_get_session_cert(monkeypatch: Any) -> None:16    monkeypatch.setenv("PYXIS_CERT_PATH", "/path/to/cert.pem")17    monkeypatch.setenv("PYXIS_KEY_PATH", "/path/to/key.key")18    session = pyxis._get_session()19    assert session.cert == ("/path/to/cert.pem", "/path/to/key.key")20def test_get_session_no_auth(monkeypatch: Any) -> None:21    with pytest.raises(Exception):22        pyxis._get_session()23@patch("operatorcert.pyxis._get_session")24def test_post(mock_session: MagicMock) -> None:25    mock_session.return_value.post.return_value.json.return_value = {"key": "val"}26    resp = pyxis.post("https://foo.com/v1/bar", {})27    assert resp == {"key": "val"}28@patch("operatorcert.pyxis._get_session")29def test_patch(mock_session: MagicMock) -> None:30    mock_session.return_value.patch.return_value.json.return_value = {"key": "val"}31    resp = pyxis.patch("https://foo.com/v1/bar", {})32    assert resp == {"key": "val"}33@patch("operatorcert.pyxis._get_session")34def test_patch_error(mock_session: MagicMock) -> None:35    response = Response()36    response.status_code = 400...

m.py

Source:m.py

...55    def set_lr(self, lr):56        '''set_lr to update learning rate. call this at least once.'''57        self.lr = lr58        print("learning rate to: ", lr)59    def _get_session(self):60        if self.session is None:61            global_step = tf.contrib.framework.get_or_create_global_step()62            self.lrt = tf.placeholder(tf.float32, shape=[])63            self.train_op = self.optimizer_cls(learning_rate=self.lrt).minimize(self.loss,64                                                                                global_step=global_step)65            self.session = tf.Session()66            self.session.run(tf.global_variables_initializer())67        return self.session68    def fit(self, x, y, epochs=1, verbose=True, log_every=1):69        '''fit to fix predictions to labels.'''70        losses = []71        with self.graph.as_default():72            sess = self._get_session()73            for _ in range(epochs):74                ops = [self.loss, self.train_op]75                if self.tensorboard_dir:76                    ops.append(self.merged)77                r = sess.run(ops, feed_dict={78                    self.input: x, self.labels: y, self.lrt: self.lr})79                self.fit_iterations += 180                if verbose:81                    if self.fit_iterations % log_every == 0:82                        print("Loss: ", r[0])83                losses.append(r[0])84                if self.tensorboard_dir:85                    self.tb_writer.add_summary(r[2], self.fit_iterations)86        return losses87    def predict(self, x):88        '''predict to make prediction from observation.'''89        with self.graph.as_default():90            sess = self._get_session()91            res = sess.run(self.prediction, feed_dict={self.input: x})92            return res93    def evaluate(self, x, y):94        with self.graph.as_default():95            sess = self._get_session()96            loss = sess.run(self.loss, feed_dict={97                self.input: x, self.labels: y, self.lrt: self.lr})98        return loss99    def add(self, l):100        '''101        Add this later to the model. We will take care of the unique naming102        '''103        if len(self._layers) == 0:104            self.input = l105        self.output = l106        self._layers.append(l)107    def __repr__(self):108        return '\n\n'.join([str(x) + ': ' + str(l) for x, l in enumerate(self._layers)])109    def save(self, filename):110        '''save graph'''111        with self.graph.as_default():112            saver = tf.train.Saver()113            saver.save(self._get_session(), filename)114    def load(self, filename):115        '''load graph'''116        with self.graph.as_default():117            saver = tf.train.Saver()118            saver.restore(self._get_session(), filename)119    def close(self):120        if self.session:121            self.session.close()...

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