def test_run_std_server(self, mock_server):
# Arrange.
tf_config = {
'cluster': self._cluster_spec(),
'task': {
'type': run_config_lib.TaskType.PS,
'index': 1
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = RunConfig(
master='host2:2222',
num_cores=15,
gpu_memory_fraction=0.314, )
for est in self._estimators_for_tests(config):
ex = Experiment(
est, train_input_fn='train_input', eval_input_fn='eval_input')
# Act.
ex.run_std_server()
# Assert.
mock_server.assert_has_calls(
[test.mock.call().start(), test.mock.call().join()])
def test_run_std_server(self, mock_server):
# Arrange.
tf_config = {
'cluster': self._cluster_spec(),
'task': {
'type': run_config_lib.TaskType.PS,
'index': 1
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = RunConfig(
master='host2:2222',
num_cores=15,
gpu_memory_fraction=0.314,
)
for est in self._estimators_for_tests(config):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
# Act.
ex.run_std_server()
# Assert.
mock_server.assert_has_calls(
[test.mock.call().start(),
test.mock.call().join()])
def test_continuous_eval_evaluates_checkpoint_once(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
est.fake_checkpoint()
result = {
'called': 0,
'called_with_eval_result': 0,
}
# pylint: disable=cell-var-from-loop
def _predicate_fn(eval_result):
result['called'] += 1
if eval_result:
# If eval_result is not empty nor None, the checkpoint has been
# evaluated.
result['called_with_eval_result'] += 1
# With 300 times of evaluation, this should prove something.
return result['called'] < 300
# pylint: enable=cell-var-from-loop
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_delay_secs=0,
continuous_eval_throttle_secs=0)
ex.continuous_eval(evaluate_checkpoint_only_once=True,
continuous_eval_predicate_fn=_predicate_fn)
self.assertEqual(0, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual(300, result['called'])
self.assertEqual(1, result['called_with_eval_result'])
def test_min_eval_frequency_defaults(self):
def dummy_model_fn(features, labels): # pylint: disable=unused-argument
pass
# The default value when model_dir is on GCS is 1000
estimator = Estimator(dummy_model_fn, 'gs://dummy_bucket')
ex = Experiment(estimator, train_input_fn=None, eval_input_fn=None)
self.assertEquals(ex._eval_every_n_steps, 1)
# The default value when model_dir is not on GCS is 1
estimator = Estimator(dummy_model_fn, '/tmp/dummy')
ex = Experiment(estimator, train_input_fn=None, eval_input_fn=None)
self.assertEquals(ex._eval_every_n_steps, 1)
# Make sure default not used when explicitly set
estimator = Estimator(dummy_model_fn, 'gs://dummy_bucket')
ex = Experiment(estimator,
eval_every_n_steps=123,
train_input_fn=None,
eval_input_fn=None)
self.assertEquals(ex._eval_every_n_steps, 123)
# Make sure default not used when explicitly set as 0
estimator = Estimator(dummy_model_fn, 'gs://dummy_bucket')
ex = Experiment(estimator,
eval_every_n_steps=0,
train_input_fn=None,
eval_input_fn=None)
self.assertEquals(ex._eval_every_n_steps, 0)
def test_continuous_eval_evaluates_checkpoint_once(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
est.fake_checkpoint()
result = {
'called': 0,
'called_with_eval_result': 0,
}
# pylint: disable=cell-var-from-loop
def _predicate_fn(eval_result):
result['called'] += 1
if eval_result:
# If eval_result is not empty nor None, the checkpoint has been
# evaluated.
result['called_with_eval_result'] += 1
# With 300 times of evaluation, this should prove something.
return result['called'] < 300
# pylint: enable=cell-var-from-loop
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_delay_secs=0,
continuous_eval_throttle_secs=0)
ex.continuous_eval(evaluate_checkpoint_only_once=True,
continuous_eval_predicate_fn=_predicate_fn)
self.assertEqual(0, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual(300, result['called'])
self.assertEqual(1, result['called_with_eval_result'])
def test_train(self):
for est in self._estimators_for_tests():
ex = Experiment(est, train_input_fn='train_input', train_steps='train_steps',
eval_input_fn='eval_input')
fit_args = ex.train(delay_secs=0)
self.assertEqual(1, est.fit_count)
self.assertIn(('max_steps', 'train_steps'), fit_args)
self.assertEqual(0, est.eval_count)
def test_run_std_server_raises_without_cluster_spec(self, mock_server):
config = run_config_lib.RunConfig(master='host4:2222')
for est in self._estimators_for_tests(config):
with self.assertRaises(ValueError):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.run_std_server()
def test_continuous_train_and_eval_with_invalid_predicate_fn(self):
for est in self._estimators_for_tests():
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input')
with self.assertRaisesRegexp(ValueError,
'`continuous_eval_predicate_fn` must be a callable'):
ex.continuous_train_and_evaluate(continuous_eval_predicate_fn='fn')
def test_run_std_server_raises_without_cluster_spec(self, mock_server):
config = run_config_lib.RunConfig(master='host4:2222')
for est in self._estimators_for_tests(config):
with self.assertRaises(ValueError):
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.run_std_server()
def test_test(self):
for est in self._estimators_for_tests():
export_strategy = make_export_strategy(est, None, exports_to_keep=None)
ex = Experiment(est, train_input_fn='train_input', eval_input_fn='eval_input',
export_strategies=export_strategy)
ex.test()
self.assertEqual(1, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual(1, est.export_count)
def test_train_delay(self):
for est in self._estimators_for_tests():
ex = Experiment(est, train_input_fn='train_input', eval_input_fn='eval_input')
for delay in [0, 1, 3]:
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.train(delay_secs=delay)
self.assertAlmostEqual(delay, sheep.time(), delta=1e-4)
def test_train(self):
for est in self._estimators_for_tests():
ex = Experiment(est,
train_input_fn='train_input',
train_steps='train_steps',
eval_input_fn='eval_input')
fit_args = ex.train(delay_secs=0)
self.assertEqual(1, est.fit_count)
self.assertIn(('max_steps', 'train_steps'), fit_args)
self.assertEqual(0, est.eval_count)
def test_continuous_train_and_eval_with_invalid_predicate_fn(self):
for est in self._estimators_for_tests():
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
with self.assertRaisesRegexp(
ValueError,
'`continuous_eval_predicate_fn` must be a callable'):
ex.continuous_train_and_evaluate(
continuous_eval_predicate_fn='fn')
def test_export_strategies_reset(self):
for est in self._estimators_for_tests():
export_strategy_1 = make_export_strategy(est, None, exports_to_keep=None)
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps=100,
eval_steps=100,
export_strategies=(export_strategy_1,))
ex.train_and_evaluate()
self.assertEqual(1, est.export_count)
# After reset with empty list (None), the count does not change and the
# user provided export strategy list should remain intact.
old_es = ex.reset_export_strategies()
ex.train_and_evaluate()
self.assertAllEqual([export_strategy_1], old_es)
self.assertEqual(1, est.export_count)
# After reset with list, the count should increase with the number of
# items.
export_strategy_2 = make_export_strategy(est, None, exports_to_keep=None)
export_strategy_3 = make_export_strategy(est, None, exports_to_keep=None)
old_es = ex.reset_export_strategies([export_strategy_2, export_strategy_3])
ex.train_and_evaluate()
self.assertAllEqual([], old_es)
self.assertEqual(3, est.export_count)
def test_train_server_does_not_start_without_cluster_spec(self, mock_server):
config = run_config_lib.RunConfig(master='host4:2222')
for est in self._estimators_for_tests(config):
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.train()
# The server should not have started because there was no ClusterSpec.
self.assertFalse(mock_server.called)
def test_train_delay(self):
for est in self._estimators_for_tests():
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
for delay in [0, 1, 3]:
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.train(delay_secs=delay)
self.assertAlmostEqual(delay, sheep.time(), delta=1e-4)
def test_train_server_does_not_start_without_cluster_spec(
self, mock_server):
config = run_config_lib.RunConfig(master='host4:2222')
for est in self._estimators_for_tests(config):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.train()
# The server should not have started because there was no ClusterSpec.
self.assertFalse(mock_server.called)
def test_train_server_does_not_start_with_empty_master(self, mock_server):
tf_config = {'cluster': self._cluster_spec()}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config_lib.RunConfig(master='')
for est in self._estimators_for_tests(config):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.train()
# The server should not have started because master was the empty string.
self.assertFalse(mock_server.called)
def test_train_and_evaluate_with_no_eval_during_training(self):
for est in self._estimators_for_tests():
noop_hook = _NoopHook()
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
train_steps=100,
eval_steps=100)
ex.train_and_evaluate()
self.assertEqual(1, est.fit_count)
self.assertEqual(1, est.eval_count)
def test_continuous_train_and_eval(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
noop_hook = _NoopHook()
export_strategy = make_export_strategy(est, None, exports_to_keep=None)
ex = Experiment(est, train_input_fn='train_input', eval_input_fn='eval_input',
eval_hooks=[noop_hook], train_steps=100, eval_steps=100,
export_strategies=export_strategy)
ex.continuous_train_and_evaluate()
self.assertEqual(1, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual(1, est.export_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_train_server_does_not_start_with_empty_master(self, mock_server):
tf_config = {'cluster': self._cluster_spec()}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config_lib.RunConfig(master='')
for est in self._estimators_for_tests(config):
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.train()
# The server should not have started because master was the empty string.
self.assertFalse(mock_server.called)
def test_train_and_evaluate_with_no_eval_during_training(self):
for est in self._estimators_for_tests():
noop_hook = _NoopHook()
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
train_steps=100,
eval_steps=100)
ex.train_and_evaluate()
self.assertEqual(1, est.fit_count)
self.assertEqual(1, est.eval_count)
def test_test(self):
for est in self._estimators_for_tests():
export_strategy = make_export_strategy(est,
None,
exports_to_keep=None)
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
export_strategies=export_strategy)
ex.test()
self.assertEqual(1, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual(1, est.export_count)
def test_evaluate(self):
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
eval_steps='steps',
eval_delay_secs=0)
ex.evaluate()
self.assertEqual(0, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_evaluate_delay(self):
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(
est, train_input_fn='train_input', eval_input_fn='eval_input',
eval_hooks=[noop_hook])
for delay in [0, 1, 3]:
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.evaluate(delay_secs=delay)
self.assertAlmostEqual(delay, sheep.time(), delta=1e-4)
self.assertEqual([noop_hook], est.eval_hooks)
def test_continuous_eval_ends_after_train_step(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
eval_delay_secs=0,
continuous_eval_throttle_secs=0,
train_steps=100)
ex.continuous_eval()
self.assertEqual(0, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_train_default_delay(self):
for task_id in [0, 1, 3]:
tf_config = {'task': {'index': task_id}}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = RunConfig()
for est in self._estimators_for_tests(config):
ex = Experiment(
est, train_input_fn='train_input', eval_input_fn='eval_input')
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.train()
self.assertAlmostEqual(task_id * 5, sheep.time(), delta=1e-4)
def test_evaluate(self):
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
eval_steps='steps',
eval_delay_secs=0)
ex.evaluate()
self.assertEqual(0, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_invalid_export_strategies(self):
for est in self._estimators_for_tests():
with self.assertRaisesRegexp(ValueError, 'ExportStrategy'):
Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps=100,
eval_steps=100,
export_strategies='not_an_export_strategy')
with self.assertRaisesRegexp(ValueError, 'ExportStrategy'):
Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps=100,
eval_steps=100,
export_strategies=['not_an_export_srategy'])
def test_continuous_eval_predicate_fn(self):
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
def _predicate_fn(unused_eval_result):
return est.eval_count < 3 # pylint: disable=cell-var-from-loop
ex = Experiment(est, train_input_fn='train_input', eval_input_fn='eval_input',
eval_hooks=[noop_hook], eval_delay_secs=0,
continuous_eval_throttle_secs=0)
ex.continuous_eval(evaluate_checkpoint_only_once=False,
continuous_eval_predicate_fn=_predicate_fn)
self.assertEqual(0, est.fit_count)
self.assertEqual(3, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_evaluate_delay(self):
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook])
for delay in [0, 1, 3]:
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.evaluate(delay_secs=delay)
self.assertAlmostEqual(delay, sheep.time(), delta=1e-4)
self.assertEqual([noop_hook], est.eval_hooks)
def test_continuous_eval_ends_after_train_step(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
eval_delay_secs=0,
continuous_eval_throttle_secs=0,
train_steps=100)
ex.continuous_eval()
self.assertEqual(0, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_train_hooks_extend_does_not_mutate_input_hooks(self):
for est in self._estimators_for_tests():
noop_hook = _NoopHook()
input_hooks = [noop_hook]
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_hooks=input_hooks)
self.assertAllEqual([noop_hook], ex._train_hooks)
another_noop_hook = _NoopHook()
# Assert that the extend API mutates the hooks, but not the input hooks
ex.extend_train_hooks([another_noop_hook])
self.assertAllEqual([noop_hook, another_noop_hook], ex._train_hooks)
self.assertAllEqual([noop_hook], input_hooks)
def test_train_hooks_extend_does_not_mutate_input_hooks(self):
for est in self._estimators_for_tests():
noop_hook = _NoopHook()
input_hooks = [noop_hook]
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_hooks=input_hooks)
self.assertAllEqual([noop_hook], ex._train_hooks)
another_noop_hook = _NoopHook()
# Assert that the extend API mutates the hooks, but not the input hooks
ex.extend_train_hooks([another_noop_hook])
self.assertAllEqual([noop_hook, another_noop_hook],
ex._train_hooks)
self.assertAllEqual([noop_hook], input_hooks)
def test_continuous_train_and_eval_with_adapted_steps_per_iteration(self):
mock_estimator = test.mock.Mock(Estimator)
type(mock_estimator).model_dir = test.mock.PropertyMock(return_value='test_dir')
total_steps = 100000000000000
ex = Experiment(mock_estimator, train_input_fn='train_input', eval_input_fn='eval_input',
train_steps=total_steps, train_steps_per_iteration=None)
def predicate_fn(eval_result):
# Allows the first invoke only.
return eval_result is None
ex.continuous_train_and_evaluate(continuous_eval_predicate_fn=predicate_fn)
mock_estimator.train.assert_called_once_with(
input_fn='train_input',
steps=int(total_steps / 10),
max_steps=None,
hooks=[])
def test_train_default_delay(self):
for task_id in [0, 1, 3]:
tf_config = {'task': {'index': task_id}}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = RunConfig()
for est in self._estimators_for_tests(config):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.train()
self.assertAlmostEqual(task_id * 5,
sheep.time(),
delta=1e-4)
def test_continuous_train_and_eval(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
noop_hook = _NoopHook()
export_strategy = make_export_strategy(est,
None,
exports_to_keep=None)
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
train_steps=100,
eval_steps=100,
export_strategies=export_strategy)
ex.continuous_train_and_evaluate()
self.assertEqual(1, est.fit_count)
self.assertEqual(1, est.eval_count)
self.assertEqual(1, est.export_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_continuous_eval_predicate_fn(self):
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
def _predicate_fn(unused_eval_result):
return est.eval_count < 3 # pylint: disable=cell-var-from-loop
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
eval_delay_secs=0,
continuous_eval_throttle_secs=0)
ex.continuous_eval(evaluate_checkpoint_only_once=False,
continuous_eval_predicate_fn=_predicate_fn)
self.assertEqual(0, est.fit_count)
self.assertEqual(3, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def test_continuous_train_and_eval_with_predicate_fn(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
export_strategy = make_export_strategy(est, None, exports_to_keep=None)
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps=100000000000, # a value will make `ex` never stops.
eval_steps=100,
export_strategies=export_strategy)
def predicate_fn(eval_result):
del eval_result # unused. for fn signature.
return False
ex.continuous_train_and_evaluate(continuous_eval_predicate_fn=predicate_fn)
self.assertEqual(0, est.fit_count)
self.assertEqual(0, est.eval_count)
self.assertEqual(1, est.export_count)
def test_train_starts_server(self, mock_server):
# Arrange.
tf_config = {
'cluster': self._cluster_spec(),
'environment': run_config_lib.Environment.CLOUD,
'task': {
'type': run_config_lib.TaskType.WORKER,
'index': 1
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)}):
config = run_config_lib.RunConfig(master='host4:2222',
num_cores=15,
gpu_memory_fraction=0.314)
for est in self._estimators_for_tests(config):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
# Act.
# We want to make sure we discount the time it takes to start the server
# in our accounting of the delay, so we set a small delay here.
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.train(delay_secs=1)
# Ensure that the delay takes into account the time to start server.
self.assertAlmostEqual(1, sheep.time(), delta=1e-4)
# Assert.
expected_config_proto = config_pb2.ConfigProto()
expected_config_proto.inter_op_parallelism_threads = 15
expected_config_proto.intra_op_parallelism_threads = 15
expected_config_proto.gpu_options.per_process_gpu_memory_fraction = 0.314
mock_server.assert_called_with(
config.cluster_spec,
job_name=run_config_lib.TaskType.WORKER,
task_index=1,
config=expected_config_proto,
start=False)
mock_server.assert_has_calls([test.mock.call().start()])
def test_continuous_train_and_eval_with_invalid_train_steps_iterations(
self):
for est in self._estimators_for_tests():
with self.assertRaisesRegexp(
ValueError,
'`train_steps_per_iteration` must be an integer.'):
Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps_per_iteration='123')
def test_train_raises_if_job_name_is_missing(self):
tf_config = {
'cluster': self._cluster_spec(),
'environment': run_config_lib.Environment.CLOUD,
'task': {
'index': 1
}
}
with test.mock.patch.dict('os.environ',
{'TF_CONFIG': json.dumps(tf_config)
}), self.assertRaises(ValueError):
config = run_config_lib.RunConfig(
master='host3:2222' # Normally selected by task type.
)
for est in self._estimators_for_tests(config):
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.train()
def test_train_raises_if_job_name_is_missing(self):
tf_config = {
'cluster': self._cluster_spec(),
'environment': run_config_lib.Environment.CLOUD,
'task': {
'index': 1
}
}
with test.mock.patch.dict(
'os.environ',
{'TF_CONFIG': json.dumps(tf_config)}), self.assertRaises(ValueError):
config = run_config_lib.RunConfig(
master='host3:2222' # Normally selected by task type.
)
for est in self._estimators_for_tests(config):
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input')
ex.train()
def test_continuous_train_and_eval_with_default_steps_per_iteration(self):
mock_estimator = test.mock.Mock(Estimator)
type(mock_estimator).model_dir = test.mock.PropertyMock(
return_value='test_dir')
ex = Experiment(mock_estimator,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps_per_iteration=None,
train_steps=None)
def predicate_fn(eval_result):
# Allows the first invoke only.
return eval_result is None
ex.continuous_train_and_evaluate(
continuous_eval_predicate_fn=predicate_fn)
mock_estimator.train.assert_called_once_with(input_fn='train_input',
steps=1000,
max_steps=test.mock.ANY,
hooks=test.mock.ANY)
def test_train_starts_server(self, mock_server):
# Arrange.
tf_config = {
'cluster': self._cluster_spec(),
'environment': run_config_lib.Environment.CLOUD,
'task': {
'type': run_config_lib.TaskType.WORKER,
'index': 1
}
}
with test.mock.patch.dict('os.environ', {'TF_CONFIG': json.dumps(tf_config)}):
config = run_config_lib.RunConfig(
master='host4:2222', num_cores=15, gpu_memory_fraction=0.314)
for est in self._estimators_for_tests(config):
ex = Experiment(est, train_input_fn='train_input', eval_input_fn='eval_input')
# Act.
# We want to make sure we discount the time it takes to start the server
# in our accounting of the delay, so we set a small delay here.
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
ex.train(delay_secs=1)
# Ensure that the delay takes into account the time to start server.
self.assertAlmostEqual(1, sheep.time(), delta=1e-4)
# Assert.
expected_config_proto = config_pb2.ConfigProto()
expected_config_proto.inter_op_parallelism_threads = 15
expected_config_proto.intra_op_parallelism_threads = 15
expected_config_proto.gpu_options.per_process_gpu_memory_fraction = 0.314
mock_server.assert_called_with(
config.cluster_spec,
job_name=run_config_lib.TaskType.WORKER,
task_index=1,
config=expected_config_proto,
start=False)
mock_server.assert_has_calls([test.mock.call().start()])
def test_continuous_train_and_eval_with_predicate_fn(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
export_strategy = make_export_strategy(est,
None,
exports_to_keep=None)
ex = Experiment(
est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps=100000000000, # a value will make `ex` never stops.
eval_steps=100,
export_strategies=export_strategy)
def predicate_fn(eval_result):
del eval_result # unused. for fn signature.
return False
ex.continuous_train_and_evaluate(
continuous_eval_predicate_fn=predicate_fn)
self.assertEqual(0, est.fit_count)
self.assertEqual(0, est.eval_count)
self.assertEqual(1, est.export_count)
def test_export_strategies_reset(self):
for est in self._estimators_for_tests():
export_strategy_1 = make_export_strategy(est,
None,
exports_to_keep=None)
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
train_steps=100,
eval_steps=100,
export_strategies=(export_strategy_1, ))
ex.train_and_evaluate()
self.assertEqual(1, est.export_count)
# After reset with empty list (None), the count does not change and the
# user provided export strategy list should remain intact.
old_es = ex.reset_export_strategies()
ex.train_and_evaluate()
self.assertAllEqual([export_strategy_1], old_es)
self.assertEqual(1, est.export_count)
# After reset with list, the count should increase with the number of
# items.
export_strategy_2 = make_export_strategy(est,
None,
exports_to_keep=None)
export_strategy_3 = make_export_strategy(est,
None,
exports_to_keep=None)
old_es = ex.reset_export_strategies(
[export_strategy_2, export_strategy_3])
ex.train_and_evaluate()
self.assertAllEqual([], old_es)
self.assertEqual(3, est.export_count)
def test_continuous_eval(self):
for est in self._estimators_for_tests(eval_dict={'global_step': 100}):
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
eval_delay_secs=0,
continuous_eval_throttle_secs=0)
self.assertRaises(StopIteration,
ex.continuous_eval,
evaluate_checkpoint_only_once=False)
self.assertEqual(0, est.fit_count)
self.assertEqual(6, est.eval_count)
self.assertEqual([noop_hook], est.eval_hooks)
def prepare_experiment_run(polyaxonfile,
experiment_id,
task_type=TaskType.MASTER,
task_id=0):
plx_file = PolyaxonFile.read(polyaxonfile)
cluster, _ = plx_file.get_cluster_def_at(experiment_id)
if (task_type not in cluster or not isinstance(cluster[task_type], int)
or task_id >= cluster[task_type]):
raise ValueError('task_type, task_id `{}, {}` is not supported by '
'the specification file passed.'.format(
task_type, task_id))
env = plx_file.get_environment_at(experiment_id)
if not env:
tf.logging.set_verbosity(tf.logging.INFO)
configs = {TaskType.MASTER: [RunConfig()]}
delay_workers_by_global_step = False
else:
tf.logging.set_verbosity(
LOGGING_LEVEL[plx_file.settings.logging.level])
configs, _ = _get_run_configs(plx_file, experiment_id)
delay_workers_by_global_step = env.delay_workers_by_global_step
train_input_fn, train_steps, train_hooks = _get_train(
plx_file.get_train_at(experiment_id))
(eval_input_fn, eval_steps, eval_hooks, eval_delay_secs,
continuous_eval_throttle_secs) = _get_eval(
plx_file.get_eval_at(experiment_id))
estimator = getters.get_estimator(
plx_file.get_model_at(experiment_id),
configs[task_type][task_id],
output_dir=plx_file.get_project_path_at(experiment_id))
return Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=train_steps,
eval_steps=eval_steps,
train_hooks=train_hooks,
eval_hooks=eval_hooks,
eval_delay_secs=eval_delay_secs,
continuous_eval_throttle_secs=continuous_eval_throttle_secs,
delay_workers_by_global_step=delay_workers_by_global_step,
export_strategies=plx_file.settings.export_strategies)
def get_experiment(config):
estimator = getters.get_estimator(plx_file.model,
config,
output_dir=plx_file.project_path)
return Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=train_steps,
eval_steps=eval_steps,
train_hooks=train_hooks,
eval_hooks=eval_hooks,
eval_delay_secs=eval_delay_secs,
continuous_eval_throttle_secs=continuous_eval_throttle_secs,
delay_workers_by_global_step=delay_workers_by_global_step,
export_strategies=plx_file.settings.export_strategies)
def create_experiment(experiment_config):
"""Creates a new `Experiment` instance.
Args:
experiment_config: the config to use for creating the experiment.
"""
# Creates training input function
train_input_data_config = experiment_config.train_input_data_config
train_input_fn = create_input_data_fn(
pipeline_config=train_input_data_config.pipeline_config,
mode=Modes.TRAIN, scope='train_input_fn',
input_type=train_input_data_config.input_type,
x=train_input_data_config.x, y=train_input_data_config.y)
# Creates eval_input_fn input function
eval_input_data_config = experiment_config.eval_input_data_config
eval_input_fn = create_input_data_fn(
pipeline_config=eval_input_data_config.pipeline_config,
mode=Modes.EVAL, scope='eval_input_fn',
input_type=eval_input_data_config.input_type,
x=eval_input_data_config.x, y=eval_input_data_config.y)
estimator = getters.get_estimator(experiment_config.estimator_config,
experiment_config.model_config,
experiment_config.run_config)
train_hooks = getters.get_hooks(experiment_config.train_hooks_config)
eval_hooks = getters.get_hooks(experiment_config.eval_hooks_config)
experiment = Experiment(
estimator=estimator,
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=experiment_config.train_steps,
eval_steps=experiment_config.eval_steps,
train_hooks=train_hooks,
eval_hooks=eval_hooks,
eval_delay_secs=experiment_config.eval_delay_secs,
continuous_eval_throttle_secs=experiment_config.continuous_eval_throttle_secs,
eval_every_n_steps=experiment_config.eval_every_n_steps,
delay_workers_by_global_step=experiment_config.delay_workers_by_global_step,
export_strategies=experiment_config.export_strategies,
train_steps_per_iteration=experiment_config.train_steps_per_iteration)
return experiment
def test_continuous_eval_throttle_delay(self):
for delay in [0, 1, 2]:
for est in self._estimators_for_tests():
est.fake_checkpoint()
noop_hook = _NoopHook()
ex = Experiment(est,
train_input_fn='train_input',
eval_input_fn='eval_input',
eval_hooks=[noop_hook],
continuous_eval_throttle_secs=delay,
eval_delay_secs=0)
sheep = SheepCounter()
with test.mock.patch.object(time, 'time', sheep.time):
with test.mock.patch.object(time, 'sleep', sheep.sleep):
self.assertRaises(StopIteration,
ex.continuous_eval,
evaluate_checkpoint_only_once=False)
self.assertAlmostEqual(5 * delay,
sheep.time(),
delta=1e-4)
