def handle_pytorch_experiment(response):
master = response[TaskType.MASTER]
job_uuid = master['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
set_job_definition(job_uuid=job_uuid, definition=get_job_definition(master))
for worker in response[TaskType.WORKER]:
job_uuid = worker['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
set_job_definition(job_uuid=job_uuid, definition=get_job_definition(worker))
def start_notebook(notebook):
# Update job status to show that its started
notebook.set_status(JobLifeCycle.SCHEDULED)
try:
registry_spec = get_registry_context(build_backend=None)
except ContainerRegistryError:
notebook.set_status(
JobLifeCycle.FAILED,
message='Could not start the notebook, please check your registry configuration.')
return
try:
image_name, image_tag = get_image_info(build_job=notebook.build_job,
registry_host=registry_spec.host)
except (ValueError, AttributeError):
_logger.error('Could not start the notebook.', exc_info=True)
notebook.set_status(JobLifeCycle.FAILED,
message='Image info was not found.')
return
job_docker_image = '{}:{}'.format(image_name, image_tag)
_logger.info('Start notebook with built image `%s`', job_docker_image)
spawner = NotebookSpawner(
project_name=notebook.project.unique_name,
project_uuid=notebook.project.uuid.hex,
job_name=notebook.unique_name,
job_uuid=notebook.uuid.hex,
k8s_config=conf.get(K8S_CONFIG),
namespace=conf.get(K8S_NAMESPACE),
version=conf.get(CHART_VERSION),
job_docker_image=job_docker_image,
in_cluster=True)
error = {}
try:
mount_code_in_notebooks = conf.get(NOTEBOOKS_MOUNT_CODE)
results = spawner.start_notebook(
persistence_outputs=notebook.persistence_outputs,
persistence_data=notebook.persistence_data,
outputs_refs_jobs=notebook.outputs_refs_jobs,
outputs_refs_experiments=notebook.outputs_refs_experiments,
resources=notebook.resources,
labels=notebook.labels,
annotations=notebook.annotations,
secret_refs=notebook.secret_refs,
config_map_refs=notebook.config_map_refs,
node_selector=notebook.node_selector,
affinity=notebook.affinity,
tolerations=notebook.tolerations,
backend=notebook.backend,
max_restarts=get_max_restart(notebook.max_restarts, conf.get(MAX_RESTARTS_NOTEBOOKS)),
reconcile_url=get_notebook_reconcile_url(notebook.unique_name),
mount_code_in_notebooks=mount_code_in_notebooks)
notebook.definition = get_job_definition(results)
notebook.save(update_fields=['definition'])
return
except ApiException:
_logger.error('Could not start notebook, please check your polyaxon spec.',
exc_info=True)
error = {
'raised': True,
'traceback': traceback.format_exc(),
'message': 'Could not start the job, encountered a Kubernetes ApiException.',
}
except StoreNotFoundError as e:
_logger.error('Could not start the notebook, please check your volume definitions',
exc_info=True)
error = {
'raised': True,
'traceback': traceback.format_exc(),
'message': 'Could not start the job, encountered a volume definition problem. %s' % e,
}
except Exception as e:
_logger.error('Could not start notebook, please check your polyaxon spec.',
exc_info=True)
error = {
'raised': True,
'traceback': traceback.format_exc(),
'message': 'Could not start notebook encountered an {} exception.'.format(
e.__class__.__name__)
}
finally:
if error.get('raised'):
notebook.set_status(
JobLifeCycle.FAILED,
message=error.get('message'),
traceback=error.get('traceback'))
def start_tensorboard(tensorboard):
# Update job status to show that its started
tensorboard.set_status(JobLifeCycle.SCHEDULED)
spawner = TensorboardSpawner(project_name=tensorboard.project.unique_name,
project_uuid=tensorboard.project.uuid.hex,
job_name=tensorboard.unique_name,
job_uuid=tensorboard.uuid.hex,
k8s_config=conf.get(K8S_CONFIG),
namespace=conf.get(K8S_NAMESPACE),
version=conf.get(CHART_VERSION),
job_docker_image=tensorboard.build_image,
in_cluster=True)
error = {}
outputs_specs, tensorboard_paths = tensorboard.outputs_path
try:
results = spawner.start_tensorboard(
outputs_path=tensorboard_paths,
persistence_outputs=tensorboard.persistence_outputs,
outputs_specs=outputs_specs,
outputs_refs_jobs=tensorboard.outputs_refs_jobs,
outputs_refs_experiments=tensorboard.outputs_refs_experiments,
resources=tensorboard.resources,
labels=tensorboard.labels,
annotations=tensorboard.annotations,
node_selector=tensorboard.node_selector,
affinity=tensorboard.affinity,
tolerations=tensorboard.tolerations,
max_restarts=get_max_restart(tensorboard.max_restarts,
conf.get(MAX_RESTARTS_TENSORBOARDS)),
reconcile_url=get_tensorboard_reconcile_url(
tensorboard.unique_name))
tensorboard.definition = get_job_definition(results)
tensorboard.save(update_fields=['definition'])
return
except ApiException:
_logger.error(
'Could not start tensorboard, please check your polyaxon spec.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start the job, encountered a Kubernetes ApiException.',
}
except StoreNotFoundError as e:
_logger.error(
'Could not start the tensorboard, please check your volume definitions.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start the job, encountered a volume definition problem. %s'
% e,
}
except TensorboardValidation as e:
_logger.error(
'Could not start the tensorboard, '
'some experiments require authenticating to stores with different access.',
exc_info=True)
error = {
'raised':
True,
'traceback':
None,
'message':
'Could not start the tensorboard, '
'some experiments require authenticating '
'to stores with different access. %s' % e,
}
except Exception as e:
_logger.error(
'Could not start tensorboard, please check your polyaxon spec.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start tensorboard encountered an {} exception.'.format(
e.__class__.__name__)
}
finally:
if error.get('raised'):
tensorboard.set_status(JobLifeCycle.FAILED,
message=error.get('message'),
traceback=error.get('traceback'))
def handle_base_experiment(response):
master = response[TaskType.MASTER]
job_uuid = master['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
set_job_definition(job_uuid=job_uuid, definition=get_job_definition(master))
def start_dockerizer(build_job):
# Update job status to show that its started
build_job.set_status(JobLifeCycle.SCHEDULED)
spawner_class = get_spawner_class(build_job.backend)
try:
registry_spec = get_registry_context(build_backend=build_job.backend)
except ContainerRegistryError:
build_job.set_status(
JobLifeCycle.FAILED,
message=
'Could not start the dockerizer job, please check your registry configuration.'
)
return
spawner = spawner_class(project_name=build_job.project.unique_name,
project_uuid=build_job.project.uuid.hex,
job_name=build_job.unique_name,
job_uuid=build_job.uuid.hex,
k8s_config=conf.get(K8S_CONFIG),
namespace=conf.get(K8S_NAMESPACE),
version=conf.get(CHART_VERSION),
in_cluster=True,
use_sidecar=True,
log_level=build_job.specification.log_level)
error = {}
try:
results = spawner.start_dockerizer(
commit=build_job.commit,
from_image=build_job.build_image,
dockerfile_path=build_job.build_dockerfile,
context_path=build_job.build_context,
image_tag=build_job.uuid.hex,
image_name=get_image_name(build_job=build_job,
registry_host=registry_spec.host),
build_steps=build_job.build_steps,
env_vars=build_job.build_env_vars,
lang_env=build_job.build_lang_env,
nocache=build_job.build_nocache,
insecure=registry_spec.insecure,
creds_secret_ref=registry_spec.secret,
creds_secret_items=registry_spec.secret_items,
secret_refs=build_job.secret_refs,
config_map_refs=build_job.config_map_refs,
resources=build_job.resources,
labels=build_job.labels,
annotations=build_job.annotations,
node_selector=build_job.node_selector,
affinity=build_job.affinity,
tolerations=build_job.tolerations,
max_restarts=get_max_restart(build_job.max_restarts,
conf.get(MAX_RESTARTS_BUILD_JOBS)),
reconcile_url=get_build_reconcile_url(build_job.unique_name))
auditor.record(event_type=BUILD_JOB_STARTED, instance=build_job)
build_job.definition = get_job_definition(results)
build_job.save(update_fields=['definition'])
return True
except ApiException:
_logger.error(
'Could not start build job, please check your polyaxon spec',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start build job, encountered a Kubernetes ApiException.'
}
except StoreNotFoundError as e:
_logger.error(
'Could not start build job, please check your volume definitions.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start build job, encountered a volume definition problem. %s'
% e
}
except Exception as e:
_logger.error(
'Could not start build job, please check your polyaxon spec.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start build job encountered an {} exception.'.format(
e.__class__.__name__)
}
finally:
if error.get('raised'):
build_job.set_status(JobLifeCycle.FAILED,
message=error.get('message'),
traceback=error.get('traceback'))