def handle_pytorch_experiment(experiment, spawner, response):
# Get the number of jobs this experiment started
master = response[TaskType.MASTER]
job_uuid = master['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
create_job(job_uuid=job_uuid,
experiment=experiment,
definition=get_job_definition(master),
resources=spawner.spec.master_resources)
cluster, is_distributed, = spawner.spec.cluster_def
worker_resources = PytorchSpecification.get_worker_resources(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed
)
for i, worker in enumerate(response[TaskType.WORKER]):
job_uuid = worker['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
create_job(job_uuid=job_uuid,
experiment=experiment,
definition=get_job_definition(worker),
role=TaskType.WORKER,
resources=worker_resources.get(i))
def handle_base_experiment(experiment, spawner, response):
# Default case only master was created by the experiment spawner
master = response[TaskType.MASTER]
job_uuid = master['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
create_job(job_uuid=job_uuid,
experiment=experiment,
definition=get_job_definition(master),
resources=spawner.spec.master_resources)
def start_notebook(notebook):
# Update job status to show that its started
notebook.set_status(JobLifeCycle.SCHEDULED)
try:
image_name, image_tag = get_image_info(build_job=notebook.build_job)
except ValueError as e:
_logger.warning('Could not start the notebook, %s', e)
notebook.set_status(JobLifeCycle.FAILED, message='External git repo was note 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=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
in_cluster=True)
try:
results = spawner.start_notebook(image=job_docker_image,
resources=notebook.resources,
node_selectors=notebook.node_selectors)
except ApiException as e:
_logger.warning('Could not start notebook, please check your polyaxon spec %s', e)
notebook.set_status(
JobLifeCycle.FAILED,
message='Could not start notebook, encountered a Kubernetes ApiException.')
return
except Exception as e:
_logger.warning('Could not start notebook, please check your polyaxon spec %s', e)
notebook.set_status(
JobLifeCycle.FAILED,
message='Could not start notebook encountered an {} exception.'.format(
e.__class__.__name__
))
return
notebook.definition = get_job_definition(results)
notebook.save()
def start_dockerizer(build_job):
# Update job status to show that its started
build_job.set_status(JobLifeCycle.SCHEDULED)
spawner = DockerizerSpawner(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=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
in_cluster=True)
try:
results = spawner.start_dockerizer(
resources=build_job.resources,
node_selectors=build_job.node_selectors)
auditor.record(event_type=BUILD_JOB_STARTED,
instance=build_job,
target='project')
except ApiException as e:
_logger.warning(
'Could not start build job, please check your polyaxon spec %s', e)
build_job.set_status(
JobLifeCycle.FAILED,
message=
'Could not start build job, encountered a Kubernetes ApiException.'
)
return False
except Exception as e:
_logger.warning(
'Could not start build job, please check your polyaxon spec %s', e)
build_job.set_status(
JobLifeCycle.FAILED,
message='Could not start build job encountered an {} exception.'.
format(e.__class__.__name__))
return False
build_job.definition = get_job_definition(results)
build_job.save()
return True
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=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
in_cluster=True)
try:
results = spawner.start_tensorboard(
image=tensorboard.image,
resources=tensorboard.resources,
node_selectors=tensorboard.node_selectors)
except ApiException as e:
_logger.warning(
'Could not start tensorboard, please check your polyaxon spec %s',
e)
tensorboard.set_status(
JobLifeCycle.FAILED,
message=
'Could not start tensorboard, encountered a Kubernetes ApiException.'
)
return
except Exception as e:
_logger.warning(
'Could not start tensorboard, please check your polyaxon spec %s',
e)
tensorboard.set_status(
JobLifeCycle.FAILED,
message='Could not start tensorboard encountered an {} exception.'.
format(e.__class__.__name__))
return
tensorboard.definition = get_job_definition(results)
tensorboard.save()
def start_notebook(notebook):
# Update job status to show that its started
notebook.set_status(JobLifeCycle.SCHEDULED)
try:
image_name, image_tag = get_image_info(build_job=notebook.build_job)
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'),
job_docker_image=job_docker_image,
in_cluster=True)
error = {}
try:
mount_code_in_notebooks = conf.get('MOUNT_CODE_IN_NOTEBOOKS')
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,
secret_refs=notebook.secret_refs,
configmap_refs=notebook.configmap_refs,
node_selector=notebook.node_selector,
affinity=notebook.affinity,
tolerations=notebook.tolerations,
backend=notebook.backend,
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 VolumeNotFoundError 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_dockerizer(build_job):
# Update job status to show that its started
build_job.set_status(JobLifeCycle.SCHEDULED)
spawner = DockerizerSpawner(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'),
in_cluster=True)
error = {}
try:
results = spawner.start_dockerizer(
resources=build_job.resources,
node_selector=build_job.node_selector,
affinity=build_job.affinity,
tolerations=build_job.tolerations)
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 VolumeNotFoundError 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'))
def start_job(job):
# Update job status to show that its started
job.set_status(JobLifeCycle.SCHEDULED)
try:
image_name, image_tag = get_image_info(build_job=job.build_job)
except (ValueError, AttributeError):
_logger.error('Could not start the job.', exc_info=True)
job.set_status(JobLifeCycle.FAILED,
message='Image info was not found.')
return
job_docker_image = '{}:{}'.format(image_name, image_tag)
_logger.info('Start job with built image `%s`', job_docker_image)
spawner = JobSpawner(
project_name=job.project.unique_name,
project_uuid=job.project.uuid.hex,
job_name=job.unique_name,
job_uuid=job.uuid.hex,
spec=job.specification,
k8s_config=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
job_docker_image=job_docker_image,
in_cluster=True,
use_sidecar=True,
sidecar_config=config.get_requested_params(to_str=True))
error = {}
try:
results = spawner.start_job(
persistence_data=job.persistence_data,
persistence_outputs=job.persistence_outputs,
outputs_refs_jobs=job.outputs_refs_jobs,
outputs_refs_experiments=job.outputs_refs_experiments,
resources=job.resources,
node_selector=job.node_selector,
affinity=job.affinity,
tolerations=job.tolerations)
job.definition = get_job_definition(results)
job.save()
return
except ApiException:
_logger.error('Could not start job, 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 VolumeNotFoundError as e:
_logger.error(
'Could not start the job, 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 job, please check your polyaxon spec.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start job encountered an {} exception.'.format(
e.__class__.__name__)
}
finally:
if error.get('raised'):
job.set_status(JobLifeCycle.FAILED,
message=error.get('message'),
traceback=error.get('traceback'))
def start_job(job):
# Update job status to show that its started
job.set_status(JobLifeCycle.SCHEDULED)
try:
image_name, image_tag = get_image_info(build_job=job.build_job)
except ValueError as e:
_logger.warning('Could not start the job, %s', e)
job.set_status(JobLifeCycle.FAILED,
message='External git repo was note found.')
return
job_docker_image = '{}:{}'.format(image_name, image_tag)
_logger.info('Start job with built image `%s`', job_docker_image)
spawner = JobSpawner(
project_name=job.project.unique_name,
project_uuid=job.project.uuid.hex,
job_name=job.unique_name,
job_uuid=job.uuid.hex,
spec=job.specification,
k8s_config=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
job_docker_image=job_docker_image,
in_cluster=True,
use_sidecar=True,
sidecar_config=config.get_requested_params(to_str=True))
try:
results = spawner.start_job(
persistence_data=job.persistence_data,
persistence_outputs=job.persistence_outputs,
outputs_refs_jobs=job.outputs_refs_jobs,
outputs_refs_experiments=job.outputs_refs_experiments,
resources=job.resources,
node_selectors=job.node_selectors)
except ApiException as e:
_logger.warning(
'Could not start job, please check your polyaxon spec %s', e)
job.set_status(
JobLifeCycle.FAILED,
message=
'Could not start job, encountered a Kubernetes ApiException.')
return
except VolumeNotFoundError as e:
_logger.warning(
'Could not start the job, please check your volume definitions %s',
e)
job.set_status(JobLifeCycle.FAILED,
message='Could not start the job, '
'encountered a volume definition problem. %s' % e)
return False
except Exception as e:
_logger.warning(
'Could not start job, please check your polyaxon spec %s', e)
job.set_status(
JobLifeCycle.FAILED,
message='Could not start job encountered an {} exception.'.format(
e.__class__.__name__))
return
job.definition = get_job_definition(results)
job.save()
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=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
in_cluster=True)
error = {}
try:
results = spawner.start_tensorboard(
image=tensorboard.image,
outputs_path=tensorboard.outputs_path,
persistence_outputs=tensorboard.persistence_outputs,
outputs_refs_jobs=tensorboard.outputs_refs_jobs,
outputs_refs_experiments=tensorboard.outputs_refs_experiments,
resources=tensorboard.resources,
node_selector=tensorboard.node_selector,
affinity=tensorboard.affinity,
tolerations=tensorboard.tolerations)
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 VolumeNotFoundError 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 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_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),
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 start_notebook(notebook):
# Update job status to show that its started
notebook.set_status(JobLifeCycle.SCHEDULED)
try:
image_name, image_tag = get_image_info(build_job=notebook.build_job)
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=settings.K8S_CONFIG,
namespace=settings.K8S_NAMESPACE,
in_cluster=True)
try:
allow_commits = False
if settings.REPOS_CLAIM_NAME or notebook.node_selector:
allow_commits = True
results = spawner.start_notebook(
image=job_docker_image,
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,
node_selector=notebook.node_selector,
affinity=notebook.affinity,
tolerations=notebook.tolerations,
allow_commits=allow_commits)
except ApiException:
_logger.error(
'Could not start notebook, please check your polyaxon spec.',
exc_info=True)
notebook.set_status(
JobLifeCycle.FAILED,
message=
'Could not start notebook, encountered a Kubernetes ApiException.')
return
except VolumeNotFoundError as e:
_logger.error(
'Could not start the notebook, please check your volume definitions',
exc_info=True)
notebook.set_status(JobLifeCycle.FAILED,
message='Could not start the notebook, '
'encountered a volume definition problem. %s' % e)
return False
except Exception as e:
_logger.error(
'Could not start notebook, please check your polyaxon spec.',
exc_info=True)
notebook.set_status(
JobLifeCycle.FAILED,
message='Could not start notebook encountered an {} exception.'.
format(e.__class__.__name__))
return
notebook.definition = get_job_definition(results)
notebook.save()
def start_job(job):
# Update job status to show that its started
job.set_status(JobLifeCycle.SCHEDULED)
try:
registry_spec = get_registry_context(build_backend=None)
except ContainerRegistryError:
job.set_status(
JobLifeCycle.FAILED,
message=
'Could not start the job, please check your registry configuration.'
)
return
try:
image_name, image_tag = get_image_info(
build_job=job.build_job, registry_host=registry_spec.host)
except (ValueError, AttributeError):
_logger.error('Could not start the job.', exc_info=True)
job.set_status(JobLifeCycle.FAILED,
message='Image info was not found.')
return
job_docker_image = '{}:{}'.format(image_name, image_tag)
_logger.info('Start job with built image `%s`', job_docker_image)
spawner = JobSpawner(project_name=job.project.unique_name,
project_uuid=job.project.uuid.hex,
job_name=job.unique_name,
job_uuid=job.uuid.hex,
k8s_config=conf.get(K8S_CONFIG),
namespace=conf.get(K8S_NAMESPACE),
job_docker_image=job_docker_image,
in_cluster=True,
use_sidecar=True,
log_level=job.specification.log_level)
error = {}
try:
results = spawner.start_job(
container_cmd_callback=job.specification.run.get_container_cmd,
persistence_data=job.persistence_data,
persistence_outputs=job.persistence_outputs,
outputs_refs_jobs=job.outputs_refs_jobs,
outputs_refs_experiments=job.outputs_refs_experiments,
secret_refs=job.secret_refs,
config_map_refs=job.config_map_refs,
resources=job.resources,
labels=job.labels,
annotations=job.annotations,
node_selector=job.node_selector,
affinity=job.affinity,
tolerations=job.tolerations,
max_restarts=get_max_restart(job.max_restarts,
conf.get(MAX_RESTARTS_JOBS)),
reconcile_url=get_job_reconcile_url(job.unique_name))
job.definition = get_job_definition(results)
job.save(update_fields=['definition'])
return
except ApiException:
_logger.error('Could not start job, 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 job, 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 job, please check your polyaxon spec.',
exc_info=True)
error = {
'raised':
True,
'traceback':
traceback.format_exc(),
'message':
'Could not start job encountered an {} exception.'.format(
e.__class__.__name__)
}
finally:
if error.get('raised'):
job.set_status(JobLifeCycle.FAILED,
message=error.get('message'),
traceback=error.get('traceback'))
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)
local_build = build_job.backend in {BuildBackend.NATIVE, None}
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,
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, local=local_build),
build_steps=build_job.build_steps,
env_vars=build_job.build_env_vars,
nocache=build_job.build_nocache,
in_cluster_registry=conf.get('REGISTRY_IN_CLUSTER'),
spec=build_job.specification,
k8s_config=conf.get('K8S_CONFIG'),
namespace=conf.get('K8S_NAMESPACE'),
in_cluster=True,
use_sidecar=True)
error = {}
try:
results = spawner.start_dockerizer(
resources=build_job.resources,
node_selector=build_job.node_selector,
affinity=build_job.affinity,
tolerations=build_job.tolerations)
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 VolumeNotFoundError 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'))
def handle_tensorflow_experiment(experiment, spawner, response):
# Get the number of jobs this experiment started
master = response[TaskType.MASTER]
job_uuid = master['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
create_job(job_uuid=job_uuid,
experiment=experiment,
definition=get_job_definition(master),
resources=spawner.spec.master_resources,
node_selector=spawner.spec.master_node_selector,
affinity=spawner.spec.master_affinity,
tolerations=spawner.spec.master_tolerations)
cluster, is_distributed = spawner.spec.cluster_def
worker_resources = TensorflowSpecification.get_worker_resources(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
worker_node_selectors = TensorflowSpecification.get_worker_node_selectors(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
worker_affinities = TensorflowSpecification.get_worker_affinities(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
worker_tolerations = TensorflowSpecification.get_worker_tolerations(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
for i, worker in enumerate(response[TaskType.WORKER]):
job_uuid = worker['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
create_job(job_uuid=job_uuid,
experiment=experiment,
definition=get_job_definition(worker),
role=TaskType.WORKER,
sequence=i,
resources=worker_resources.get(i),
node_selector=worker_node_selectors.get(i),
affinity=worker_affinities.get(i),
tolerations=worker_tolerations.get(i))
ps_resources = TensorflowSpecification.get_ps_resources(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
ps_node_selectors = TensorflowSpecification.get_ps_node_selectors(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
ps_affinities = TensorflowSpecification.get_ps_affinities(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
ps_tolerations = TensorflowSpecification.get_ps_tolerations(
environment=spawner.spec.environment,
cluster=cluster,
is_distributed=is_distributed)
for i, ps in enumerate(response[TaskType.PS]):
job_uuid = ps['pod']['metadata']['labels']['job_uuid']
job_uuid = uuid.UUID(job_uuid)
create_job(job_uuid=job_uuid,
experiment=experiment,
definition=get_job_definition(ps),
role=TaskType.PS,
sequence=i,
resources=ps_resources.get(i),
node_selector=ps_node_selectors.get(i),
affinity=ps_affinities.get(i),
tolerations=ps_tolerations.get(i))
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,
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,
k8s_config=conf.get(K8S_CONFIG),
namespace=conf.get(K8S_NAMESPACE),
in_cluster=True,
use_sidecar=True,
log_level=build_job.specification.log_level)
error = {}
try:
results = spawner.start_dockerizer(
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'))