def test_simple_file_passes(self):
plxfile = PolyaxonFile(os.path.abspath('tests/fixtures/simple_file.yml'))
spec = plxfile.experiment_spec_at(0)
assert plxfile.version == 1
assert plxfile.project.name == 'project1'
assert plxfile.project_path == '/tmp/plx_logs/project1'
assert plxfile.matrix is None
assert plxfile.settings is None
assert plxfile.run_type == RunTypes.LOCAL
assert spec.environment is None
assert spec.experiment_path == '/tmp/plx_logs/project1/0'
assert spec.is_runnable
assert spec.cluster_def == ({TaskType.MASTER: 1}, False)
assert_equal_dict(spec.get_cluster().to_dict(), {TaskType.MASTER: ['127.0.0.1:10000'],
TaskType.PS: [],
TaskType.WORKER: []})
assert isinstance(spec.model, RegressorConfig)
assert isinstance(spec.model.loss, MeanSquaredErrorConfig)
assert isinstance(spec.model.optimizer, AdamConfig)
assert isinstance(spec.model.graph, GraphConfig)
assert len(spec.model.graph.layers) == 4
assert spec.model.graph.input_layers == [['images', 0, 0]]
last_layer = spec.model.graph.layers[-1].name
assert spec.model.graph.output_layers == [[last_layer, 0, 0]]
assert isinstance(spec.train.data_pipeline, TFRecordImagePipelineConfig)
assert spec.eval is None
def test_run_matrix_file_passes(self):
plxfile = PolyaxonFile(os.path.abspath('tests/fixtures/run_exec_matrix_file.yml'))
assert plxfile.version == 1
assert plxfile.project.name == 'video_prediction'
assert plxfile.project_path == get_vol_path(constants.LOGS_VOLUME,
RunTypes.MINIKUBE) + 'video_prediction'
assert isinstance(plxfile.matrix['model'], MatrixConfig)
assert plxfile.matrix['model'].to_dict() == {'values': ['CDNA', 'DNA', 'STP']}
assert plxfile.matrix_space == 3
declarations = []
for loss in plxfile.matrix['model'].to_numpy():
declarations.append({'model': loss})
assert sorted(
plxfile.matrix_declarations, key=lambda x: (x['model'])) == sorted(
declarations, key=lambda x: (x['model']))
assert isinstance(plxfile.settings, SettingsConfig)
assert plxfile.run_type == RunTypes.MINIKUBE
assert len(plxfile.experiment_specs) == plxfile.matrix_space
for xp in range(plxfile.matrix_space):
spec = plxfile.experiment_spec_at(xp)
assert spec.is_runnable
assert spec.environment is None
assert spec.cluster_def == ({TaskType.MASTER: 1}, False)
assert spec.model is None
run_exec = spec.run_exec
assert isinstance(run_exec, RunExecConfig)
declarations = plxfile.get_declarations_at(xp)
declarations['num_masks'] = 1 if declarations['model'] == 'DNA' else 10
assert run_exec.cmd == 'video_prediction_train --model="{model}" --num_masks={num_masks}'.format(
**declarations
)
def test_run_simple_file_passes(self):
plxfile = PolyaxonFile(os.path.abspath('tests/fixtures/run_exec_simple_file.yml'))
spec = plxfile.experiment_spec_at(0)
assert plxfile.version == 1
assert plxfile.project.name == 'video_prediction'
assert plxfile.settings is None
assert plxfile.run_type == RunTypes.LOCAL
assert plxfile.project_path == "/tmp/plx_logs/video_prediction"
assert spec.experiment_path == "/tmp/plx_logs/video_prediction/0"
assert spec.is_runnable
assert spec.environment is None
assert spec.cluster_def == ({TaskType.MASTER: 1}, False)
assert spec.model is None
run_exec = spec.run_exec
assert isinstance(run_exec, RunExecConfig)
assert run_exec.cmd == "video_prediction_train --model=DNA --num_masks=1"
def test_matrix_file_passes(self):
plxfile = PolyaxonFile(os.path.abspath('tests/fixtures/matrix_file.yml'))
assert plxfile.version == 1
assert plxfile.project.name == 'project1'
assert plxfile.project_path == '/tmp/plx_logs/project1'
assert isinstance(plxfile.matrix['lr'], MatrixConfig)
assert isinstance(plxfile.matrix['loss'], MatrixConfig)
assert plxfile.matrix['lr'].to_dict() == {
'logspace': {'start': 0.01, 'stop': 0.1, 'num': 5}}
assert plxfile.matrix['loss'].to_dict() == {'values': ['MeanSquaredError',
'AbsoluteDifference']}
assert plxfile.matrix_space == 10
declarations = []
for lr in plxfile.matrix['lr'].to_numpy():
for loss in plxfile.matrix['loss'].to_numpy():
declarations.append({'loss': loss, 'lr': lr})
assert sorted(
plxfile.matrix_declarations, key=lambda x: (x['lr'], x['loss'])) == sorted(
declarations, key=lambda x: (x['lr'], x['loss']))
assert isinstance(plxfile.settings, SettingsConfig)
assert plxfile.settings.concurrent_experiments == 2
assert plxfile.run_type == RunTypes.LOCAL
for xp in range(plxfile.matrix_space):
spec = plxfile.experiment_spec_at(xp)
assert spec.is_runnable
assert spec.environment is None
assert spec.cluster_def == ({TaskType.MASTER: 1}, False)
assert_equal_dict(spec.get_cluster().to_dict(),
{TaskType.MASTER: ['127.0.0.1:10000'],
TaskType.PS: [],
TaskType.WORKER: []})
model = spec.model
assert isinstance(model, RegressorConfig)
assert isinstance(model.loss, (MeanSquaredErrorConfig, AbsoluteDifferenceConfig))
assert isinstance(model.optimizer, AdamConfig)
assert isinstance(model.graph, GraphConfig)
assert len(model.graph.layers) == 4
assert model.graph.input_layers == [['images', 0, 0]]
last_layer = model.graph.layers[-1].name
assert model.graph.output_layers == [[last_layer, 0, 0]]
assert isinstance(spec.train.data_pipeline, TFRecordImagePipelineConfig)
def test_simple_generator_file_passes(self):
plxfile = PolyaxonFile(os.path.abspath('tests/fixtures/simple_generator_file.yml'))
spec = plxfile.experiment_spec_at(0)
assert plxfile.matrix is None
assert plxfile.version == 1
assert plxfile.project.name == 'project1'
assert plxfile.project_path == '/tmp/plx_logs/project1'
assert plxfile.settings is None
assert plxfile.run_type == RunTypes.LOCAL
assert spec.experiment_path == '/tmp/plx_logs/project1/0'
assert spec.environment is None
assert spec.is_runnable
assert spec.cluster_def == ({TaskType.MASTER: 1}, False)
assert_equal_dict(spec.get_cluster().to_dict(), {TaskType.MASTER: ['127.0.0.1:10000'],
TaskType.PS: [],
TaskType.WORKER: []})
assert isinstance(spec.model, GeneratorConfig)
assert isinstance(spec.model.loss, MeanSquaredErrorConfig)
assert isinstance(spec.model.optimizer, AdamConfig)
assert isinstance(spec.model.encoder, GraphConfig)
assert isinstance(spec.model.decoder, GraphConfig)
assert isinstance(spec.model.bridge, NoOpBridgeConfig)
assert isinstance(spec.train.data_pipeline, TFRecordImagePipelineConfig)
assert spec.eval is None
def test_advanced_file_passes(self):
plxfile = PolyaxonFile(os.path.abspath('tests/fixtures/advanced_file.yml'))
assert plxfile.version == 1
assert plxfile.project.name == 'project1'
assert plxfile.project_path == '/mypath/project1'
assert plxfile.matrix is None
assert plxfile.run_type == RunTypes.MINIKUBE
assert isinstance(plxfile.settings, SettingsConfig)
assert isinstance(plxfile.settings.logging, LoggingConfig)
spec = plxfile.experiment_spec_at(0)
assert spec.is_runnable
assert isinstance(spec.environment, EnvironmentConfig)
assert spec.environment.n_workers == 5
assert spec.environment.n_ps == 10
assert spec.environment.delay_workers_by_global_step is True
assert isinstance(spec.environment.run_config, RunConfig)
assert spec.environment.run_config.tf_random_seed == 100
assert spec.environment.run_config.save_summary_steps == 100
assert spec.environment.run_config.save_checkpoints_secs == 60
assert isinstance(spec.environment.run_config.session, SessionConfig)
assert spec.environment.run_config.session.allow_soft_placement is True
assert spec.environment.run_config.session.intra_op_parallelism_threads == 2
assert spec.environment.run_config.session.inter_op_parallelism_threads == 2
# check properties for returning worker configs and resources
assert spec.environment.worker_configs is None
assert spec.environment.ps_configs is None
assert spec.environment.resources is None
assert spec.environment.worker_resources is None
assert spec.environment.ps_resources is None
assert spec.worker_configs == {}
assert spec.ps_configs == {}
assert spec.worker_resources == {}
assert spec.ps_resources == {}
assert spec.cluster_def == ({TaskType.MASTER: 1,
TaskType.WORKER: 5,
TaskType.PS: 10}, True)
def task_name(task_type, task_idx):
return constants.TASK_NAME.format(project=plxfile.project.name,
experiment=0,
task_type=task_type,
task_idx=task_idx)
assert_equal_dict(spec.get_cluster().to_dict(),
{TaskType.MASTER: ['{}:2222'.format(task_name(TaskType.MASTER, 0))],
TaskType.WORKER: [
'{}:2222'.format(task_name(TaskType.WORKER, 0)),
'{}:2222'.format(task_name(TaskType.WORKER, 1)),
'{}:2222'.format(task_name(TaskType.WORKER, 2)),
'{}:2222'.format(task_name(TaskType.WORKER, 3)),
'{}:2222'.format(task_name(TaskType.WORKER, 4)),
],
TaskType.PS: [
'{}:2222'.format(task_name(TaskType.PS, 0)),
'{}:2222'.format(task_name(TaskType.PS, 1)),
'{}:2222'.format(task_name(TaskType.PS, 2)),
'{}:2222'.format(task_name(TaskType.PS, 3)),
'{}:2222'.format(task_name(TaskType.PS, 4)),
'{}:2222'.format(task_name(TaskType.PS, 5)),
'{}:2222'.format(task_name(TaskType.PS, 6)),
'{}:2222'.format(task_name(TaskType.PS, 7)),
'{}:2222'.format(task_name(TaskType.PS, 8)),
'{}:2222'.format(task_name(TaskType.PS, 9)),
]})
assert isinstance(spec.model, ClassifierConfig)
assert isinstance(spec.model.loss, MeanSquaredErrorConfig)
assert isinstance(spec.model.optimizer, AdamConfig)
assert spec.model.optimizer.learning_rate == 0.21
assert isinstance(spec.model.graph, GraphConfig)
assert len(spec.model.graph.layers) == 7
assert spec.model.graph.input_layers == [['images', 0, 0]]
assert len(spec.model.graph.output_layers) == 3
assert ['super_dense', 0, 0] in spec.model.graph.output_layers
assert isinstance(spec.train.data_pipeline, TFRecordImagePipelineConfig)
assert len(spec.train.data_pipeline.feature_processors.feature_processors) == 1
assert isinstance(spec.eval.data_pipeline, TFRecordImagePipelineConfig)
assert spec.eval.data_pipeline.feature_processors is None
