def get_init_container(self, persistence_outputs):
"""Pod init container for setting outputs path."""
if self.original_name is not None and self.cloning_strategy == CloningStrategy.RESUME:
return []
if self.original_name is not None and self.cloning_strategy == CloningStrategy.COPY:
command = InitCommands.COPY
original_outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.original_name)
else:
command = InitCommands.CREATE
original_outputs_path = None
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.experiment_name)
_, outputs_volume_mount = get_pod_outputs_volume(
persistence_outputs=persistence_outputs)
return [
client.V1Container(
name=self.init_container_name,
image=self.init_docker_image,
command=["/bin/sh", "-c"],
args=to_list(
get_output_args(
command=command,
outputs_path=outputs_path,
original_outputs_path=original_outputs_path)),
volume_mounts=outputs_volume_mount)
]
def get_init_path_args(self, persistence_outputs):
if self.original_name is not None and self.cloning_strategy == CloningStrategy.RESUME:
command = InitCommands.CREATE
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.original_name)
original_outputs_path = None
elif self.original_name is not None and self.cloning_strategy == CloningStrategy.COPY:
command = InitCommands.COPY
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.experiment_name)
original_outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.original_name)
else:
command = InitCommands.CREATE
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.experiment_name)
original_outputs_path = None
return get_output_args(command=command,
outputs_path=outputs_path,
original_outputs_path=original_outputs_path)
def get(self, request, *args, **kwargs):
filepath = request.query_params.get('path')
if not filepath:
raise ValidationError('Files view expect a path to the file.')
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name,
original_name=self.experiment.original_unique_name,
cloning_strategy=self.experiment.cloning_strategy)
download_filepath = archive_outputs_file(
persistence_outputs=self.experiment.persistence_outputs,
outputs_path=experiment_outputs_path,
namepath=self.experiment.unique_name,
filepath=filepath)
filename = os.path.basename(download_filepath)
chunk_size = 8192
try:
wrapped_file = FileWrapper(open(download_filepath, 'rb'), chunk_size)
response = StreamingHttpResponse(
wrapped_file, content_type=mimetypes.guess_type(download_filepath)[0])
response['Content-Length'] = os.path.getsize(download_filepath)
response['Content-Disposition'] = "attachment; filename={}".format(filename)
return response
except FileNotFoundError:
_logger.warning('Outputs file not found: log_path=%s', download_filepath)
return Response(status=status.HTTP_404_NOT_FOUND,
data='Outputs file not found: log_path={}'.format(download_filepath))
def test_copying_an_experiment(self):
with patch('scheduler.tasks.experiments.experiments_build.apply_async') as _: # noqa
experiment1 = ExperimentFactory()
# We create some outputs files for the experiment
path = stores.create_experiment_outputs_path(
persistence=experiment1.persistence_outputs,
experiment_name=experiment1.unique_name)
open(os.path.join(path, 'file'), 'w+')
# Create a new experiment that is a clone of the previous
with patch('scheduler.tasks.experiments.experiments_build.apply_async') as _: # noqa
experiment2 = ExperimentFactory(original_experiment=experiment1)
# Check that outputs path for experiment2 does not exist yet
experiment2_outputs_path = stores.get_experiment_outputs_path(
persistence=experiment2.persistence_outputs,
experiment_name=experiment2.unique_name)
assert os.path.exists(experiment2_outputs_path) is False
# Handle restart should create the outputs and copy the content of experiment 1
copy_experiment(experiment2)
assert os.path.exists(experiment2_outputs_path) is True
assert os.path.exists(os.path.join(experiment2_outputs_path, 'file')) is True
def get_experiments_outputs_spec(self):
import stores
if not self.experiments.count():
return None
annotation = {
'persistence_outputs': KeyTransform('outputs', 'persistence')
}
query = self.experiments.annotate(**annotation)
experiment_data = query.values_list('id', 'experiment_group__id',
'project__user__username',
'project__name',
'persistence_outputs')
outputs_spec_data = {}
for data in experiment_data:
project_name = PROJECT_UNIQUE_NAME_FORMAT.format(user=data[2],
project=data[3])
if data[1]:
group_name = GROUP_UNIQUE_NAME_FORMAT.format(
project_name=project_name, id=data[1])
experiment_name = EXPERIMENT_UNIQUE_NAME_FORMAT.format(
parent_name=group_name, id=data[0])
else:
experiment_name = EXPERIMENT_UNIQUE_NAME_FORMAT.format(
parent_name=project_name, id=data[0])
outputs_path = stores.get_experiment_outputs_path(
persistence=data[4], experiment_name=experiment_name)
outputs_spec_data[data[0]] = OutputsRefsSpec(path=outputs_path,
persistence=data[4])
return outputs_spec_data
def get_config_map(namespace, project_name, experiment_group_name,
experiment_name, project_uuid, experiment_group_uuid,
experiment_uuid, original_name, cloning_strategy,
cluster_def, persistence_outputs, persistence_data, params,
log_level):
name = constants.CONFIG_MAP_NAME.format(uuid=experiment_uuid)
labels = get_map_labels(project_name, experiment_group_name,
experiment_name, project_uuid,
experiment_group_uuid, experiment_uuid)
metadata = client.V1ObjectMeta(name=name,
labels=labels,
namespace=namespace)
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=experiment_name,
original_name=original_name,
cloning_strategy=cloning_strategy)
experiment_logs_path = stores.get_experiment_logs_path(
experiment_name=experiment_name, temp=False)
data = {
constants.CONFIG_MAP_CLUSTER_KEY_NAME: json.dumps(cluster_def),
constants.CONFIG_MAP_PARAMS_KEY_NAME: json.dumps(params) or '{}',
constants.CONFIG_MAP_EXPERIMENT_INFO_KEY_NAME: json.dumps(labels),
constants.CONFIG_MAP_LOG_LEVEL_KEY_NAME: log_level,
constants.CONFIG_MAP_RUN_OUTPUTS_PATH_KEY_NAME:
experiment_outputs_path,
constants.CONFIG_MAP_RUN_LOGS_PATH_KEY_NAME: experiment_logs_path,
constants.CONFIG_MAP_RUN_DATA_PATHS_KEY_NAME: persistence_data,
API_HTTP_URL: get_settings_http_api_url(),
API_WS_HOST: get_settings_ws_api_url(),
}
return client.V1ConfigMap(api_version=k8s_constants.K8S_API_VERSION_V1,
kind=k8s_constants.K8S_CONFIG_MAP_KIND,
metadata=metadata,
data=data)
def get(self, request, *args, **kwargs):
try:
store_manager = stores.get_outputs_store(
persistence_outputs=self.experiment.persistence_outputs)
except (PolyaxonStoresException, VolumeNotFoundError) as e:
raise ValidationError(e)
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name,
original_name=self.experiment.original_unique_name,
cloning_strategy=self.experiment.cloning_strategy)
if request.query_params.get('path'):
experiment_outputs_path = os.path.join(
experiment_outputs_path, request.query_params.get('path'))
try:
data = store_manager.ls(experiment_outputs_path)
except VolumeNotFoundError:
raise ValidationError(
'Store manager could not load the volume requested,'
' to get the outputs data.')
except Exception:
raise ValidationError(
'Experiment outputs path does not exists or bad configuration.'
)
return Response(data=data, status=200)
def get_init_container(self,
init_command,
init_args,
env_vars,
context_mounts,
persistence_outputs,
persistence_data):
"""Pod init container for setting outputs path."""
env_vars = to_list(env_vars, check_none=True)
if self.original_name is not None and self.cloning_strategy == CloningStrategy.RESUME:
return []
if self.original_name is not None and self.cloning_strategy == CloningStrategy.COPY:
command = InitCommands.COPY
original_outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.original_name)
else:
command = InitCommands.CREATE
original_outputs_path = None
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.experiment_name)
_, outputs_volume_mount = get_pod_outputs_volume(persistence_outputs=persistence_outputs)
volume_mounts = outputs_volume_mount + to_list(context_mounts, check_none=True)
init_command = init_command or ["/bin/sh", "-c"]
init_args = init_args or to_list(
get_output_args(command=command,
outputs_path=outputs_path,
original_outputs_path=original_outputs_path))
init_args += to_list(get_auth_context_args(entity='experiment',
entity_name=self.experiment_name))
return [
client.V1Container(
name=self.init_container_name,
image=self.init_docker_image,
image_pull_policy=self.init_docker_image_pull_policy,
command=init_command,
args=[''.join(init_args)],
env=env_vars,
resources=get_init_resources(),
volume_mounts=volume_mounts)
]
def get_env_vars(self, task_type, task_idx):
tf_config = {
'cluster': self.get_cluster(),
'task': {'type': task_type, 'index': task_idx},
'model_dir': stores.get_experiment_outputs_path(
persistence=self.persistence_config.outputs,
experiment_name=self.experiment_name,
cloning_strategy=self.cloning_strategy),
'environment': 'cloud'
}
return get_env_var(name='TF_CONFIG', value=tf_config)
def test_experiment_outputs_path_creation_deletion(self):
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name)
assert os.path.exists(experiment_outputs_path) is False
stores.create_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name)
assert os.path.exists(experiment_outputs_path) is True
stores_schedule_outputs_deletion(persistence=None, subpath=self.experiment.subpath)
assert os.path.exists(experiment_outputs_path) is False
def get_named_experiment_outputs_path(experiment):
persistence = experiment.persistence_outputs
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence,
experiment_name=experiment.unique_name,
original_name=experiment.original_unique_name,
cloning_strategy=experiment.cloning_strategy)
tensorboard_path = '{}:{}'.format(experiment.unique_name,
outputs_path)
return [
OutputsRefsSpec(path=outputs_path, persistence=persistence)
], tensorboard_path
def _get_experiment_outputs_path(self) -> Tuple[List, str]:
import stores
from stores.validators import validate_persistence_outputs
persistence = validate_persistence_outputs(
persistence_outputs=self.experiment.persistence_outputs)
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence,
experiment_name=self.experiment.unique_name,
original_name=self.experiment.original_unique_name,
cloning_strategy=self.experiment.cloning_strategy)
return [OutputsRefsSpec(path=outputs_path, persistence=persistence)], outputs_path
def get(self, request, *args, **kwargs):
auditor.record(event_type=EXPERIMENT_OUTPUTS_DOWNLOADED,
instance=self.experiment,
actor_id=self.request.user.id,
actor_name=self.request.user.username)
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name,
original_name=self.experiment.original_unique_name,
cloning_strategy=self.experiment.cloning_strategy)
archived_path, archive_name = archive_outputs(
outputs_path=experiment_outputs_path,
name=self.experiment.unique_name)
return self.redirect(path='{}/{}'.format(archived_path, archive_name))
def _get_named_experiment_outputs_path(experiment, persistence) -> Tuple[List, str]:
import stores
from stores.validators import validate_persistence_outputs
persistence = validate_persistence_outputs(persistence_outputs=persistence)
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence,
experiment_name=experiment.unique_name,
original_name=experiment.original_unique_name,
cloning_strategy=experiment.cloning_strategy)
tensorboard_path = '{}:{}'.format(
experiment.unique_name,
outputs_path)
return [OutputsRefsSpec(path=outputs_path, persistence=persistence)], tensorboard_path
def get_absolute_outputs_paths(self) -> str:
import stores
if self.experiment:
return stores.get_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name,
original_name=self.experiment.original_unique_name,
cloning_strategy=self.experiment.cloning_strategy)
if self.experiment_group:
return stores.get_experiment_group_outputs_path(
persistence=self.experiment_group.persistence_outputs,
experiment_group_name=self.experiment_group.unique_name)
return stores.get_project_outputs_path(
persistence_outputs=None, project_name=self.project.unique_name)
def test_experiment_group_outputs_path_creation_deletion(self):
experiment = ExperimentFactory(user=self.project.user,
project=self.project,
experiment_group=self.experiment_group)
stores.create_experiment_outputs_path(
persistence=experiment.persistence_outputs,
experiment_name=experiment.unique_name)
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=experiment.persistence_outputs,
experiment_name=experiment.unique_name)
experiment_group_outputs_path = stores.get_experiment_group_outputs_path(
persistence=self.experiment_group.persistence_outputs,
experiment_group_name=self.experiment_group.unique_name)
assert os.path.exists(experiment_outputs_path) is True
assert os.path.exists(experiment_group_outputs_path) is True
stores_schedule_outputs_deletion(persistence=None, subpath=self.experiment_group.subpath)
assert os.path.exists(experiment_outputs_path) is False
assert os.path.exists(experiment_group_outputs_path) is False
def test_project_outputs_path_creation_deletion(self):
with patch('scheduler.tasks.experiments.experiments_build.apply_async'
) as _: # noqa
experiment = ExperimentFactory(user=self.project.user,
project=self.project)
stores.create_experiment_outputs_path(
persistence=experiment.persistence_outputs,
experiment_name=experiment.unique_name)
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=experiment.persistence_outputs,
experiment_name=experiment.unique_name)
project_outputs_path = stores.get_project_outputs_path(
persistence=None, project_name=self.project.unique_name)
assert os.path.exists(experiment_outputs_path) is True
assert os.path.exists(project_outputs_path) is True
stores_schedule_outputs_deletion(persistence='outputs',
subpath=self.project.subpath)
assert os.path.exists(experiment_outputs_path) is False
assert os.path.exists(project_outputs_path) is False
def get(self, request, *args, **kwargs):
filepath = request.query_params.get('path')
if not filepath:
raise ValidationError('Files view expect a path to the file.')
experiment_outputs_path = stores.get_experiment_outputs_path(
persistence=self.experiment.persistence_outputs,
experiment_name=self.experiment.unique_name,
original_name=self.experiment.original_unique_name,
cloning_strategy=self.experiment.cloning_strategy)
download_filepath = archive_outputs_file(
persistence_outputs=self.experiment.persistence_outputs,
outputs_path=experiment_outputs_path,
namepath=self.experiment.unique_name,
filepath=filepath)
if not download_filepath:
return Response(status=status.HTTP_404_NOT_FOUND,
data='Outputs file not found: log_path={}'.format(download_filepath))
return stream_file(file_path=download_filepath, logger=_logger)
def _get_outputs_path(self, persistence_outputs):
return stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.experiment_name,
original_name=self.original_name,
cloning_strategy=self.cloning_strategy)
def get_pod_container(self,
volume_mounts,
env_vars=None,
command=None,
args=None,
persistence_outputs=None,
persistence_data=None,
outputs_refs_jobs=None,
outputs_refs_experiments=None,
secret_refs=None,
configmap_refs=None,
resources=None,
ephemeral_token=None):
"""Pod job container for task."""
assert self.cluster_def is not None
# Env vars preparations
env_vars = to_list(env_vars, check_none=True)
logs_path = stores.get_experiment_logs_path(
experiment_name=self.experiment_name, temp=False)
outputs_path = stores.get_experiment_outputs_path(
persistence=persistence_outputs,
experiment_name=self.experiment_name,
original_name=self.original_name,
cloning_strategy=self.cloning_strategy)
env_vars += get_job_env_vars(
persistence_outputs=persistence_outputs,
outputs_path=outputs_path,
persistence_data=persistence_data,
log_level=self.log_level,
logs_path=logs_path,
outputs_refs_jobs=outputs_refs_jobs,
outputs_refs_experiments=outputs_refs_experiments,
ephemeral_token=ephemeral_token,
)
env_vars += [
get_env_var(name=constants.CONFIG_MAP_CLUSTER_KEY_NAME,
value=json.dumps(self.cluster_def)),
get_env_var(name=constants.CONFIG_MAP_DECLARATIONS_KEY_NAME,
value=self.declarations),
get_env_var(name=constants.CONFIG_MAP_EXPERIMENT_INFO_KEY_NAME,
value=json.dumps(self.experiment_labels)),
]
env_vars += get_resources_env_vars(resources=resources)
# Env from configmap and secret refs
env_from = get_pod_env_from(secret_refs=secret_refs,
configmap_refs=configmap_refs)
ports = [
client.V1ContainerPort(container_port=port) for port in self.ports
]
return client.V1Container(name=self.job_container_name,
image=self.job_docker_image,
command=command,
args=args,
ports=ports,
env=env_vars,
env_from=env_from,
resources=get_resources(resources),
volume_mounts=volume_mounts)