def make_flow(obj, token, name, tags, namespace, max_workers, workflow_timeout,
is_project):
if obj.flow_datastore.TYPE != "s3":
raise MetaflowException("AWS Step Functions requires --datastore=s3.")
# Attach AWS Batch decorator to the flow
decorators._attach_decorators(obj.flow, [BatchDecorator.name])
decorators._init_step_decorators(obj.flow, obj.graph, obj.environment,
obj.flow_datastore, obj.logger)
obj.package = MetaflowPackage(obj.flow, obj.environment, obj.echo,
obj.package_suffixes)
package_url, package_sha = obj.flow_datastore.save_data([obj.package.blob],
len_hint=1)[0]
return StepFunctions(
name,
obj.graph,
obj.flow,
package_sha,
package_url,
token,
obj.metadata,
obj.flow_datastore,
obj.environment,
obj.event_logger,
obj.monitor,
tags=tags,
namespace=namespace,
max_workers=max_workers,
username=get_username(),
workflow_timeout=workflow_timeout,
is_project=is_project,
)
def create(obj,
image,
image_pull_secrets,
env,
env_from,
labels,
annotations,
k8s_namespace,
embedded,
max_workers,
volumes,
workflow_timeout=None,
only_json=False):
obj.echo("Deploying *%s* to Argo Workflow Templates..." %
obj.workflow_template_name,
bold=True)
if obj.flow_datastore.TYPE != 's3':
raise MetaflowException("Argo Workflows require --datastore=s3.")
# When using conda attach AWS Batch decorator to the flow.
# This results in 'linux-64' libraries to be packaged.
decorators._attach_decorators(obj.flow, [BatchDecorator.name])
decorators._init_step_decorators(obj.flow, obj.graph, obj.environment,
obj.flow_datastore, obj.logger)
obj.package = MetaflowPackage(obj.flow, obj.environment, obj.echo,
obj.package_suffixes)
package_url, package_sha = obj.flow_datastore.save_data([obj.package.blob],
len_hint=1)[0]
warn_use_argo_image(obj)
workflow = ArgoWorkflow(obj.workflow_template_name, obj.flow, obj.graph,
obj.package, package_url if not embedded else None,
obj.metadata, obj.flow_datastore, obj.environment,
obj.event_logger, obj.monitor, image,
image_pull_secrets, env, env_from, labels,
annotations, max_workers, volumes,
workflow_timeout)
if only_json:
obj.echo_always(workflow.to_json(), err=False, no_bold=True, nl=False)
else:
workflow.deploy(k8s_namespace)
obj.echo(
"WorkflowTemplate *{name}* is pushed to Argo Workflows successfully.\n"
.format(name=obj.workflow_template_name),
bold=True)
workflow.schedule(k8s_namespace)
obj.echo("What will trigger execution of the workflow:", bold=True)
obj.echo(workflow.trigger_explanation(), indent=True)
def make_flow(obj,
token,
name,
tags,
namespace,
max_workers,
workflow_timeout,
is_project):
datastore = obj.datastore(obj.flow.name,
mode='w',
metadata=obj.metadata,
event_logger=obj.event_logger,
monitor=obj.monitor)
if datastore.TYPE != 's3':
raise MetaflowException("AWS Step Functions requires --datastore=s3.")
# Attach AWS Batch decorator to the flow
decorators._attach_decorators(obj.flow, [BatchDecorator.name])
decorators._init_step_decorators(
obj.flow, obj.graph, obj.environment, obj.datastore, obj.logger)
obj.package = MetaflowPackage(
obj.flow, obj.environment, obj.echo, obj.package_suffixes)
package_url = datastore.save_data(
obj.package.sha, TransformableObject(obj.package.blob))
return StepFunctions(name,
obj.graph,
obj.flow,
obj.package,
package_url,
token,
obj.metadata,
obj.datastore,
obj.environment,
obj.event_logger,
obj.monitor,
tags=tags,
namespace=namespace,
max_workers=max_workers,
username=get_username(),
workflow_timeout=workflow_timeout,
is_project=is_project)
def make_flow(obj, token, name, tags, namespace, max_workers, workflow_timeout,
workflow_priority):
# TODO: Make this check less specific to Amazon S3 as we introduce
# support for more cloud object stores.
if obj.flow_datastore.TYPE != "s3":
raise MetaflowException("Argo Workflows requires --datastore=s3.")
# Attach @kubernetes and @environment decorator to the flow to
# ensure that the related decorator hooks are invoked.
decorators._attach_decorators(
obj.flow, [KubernetesDecorator.name, EnvironmentDecorator.name])
decorators._init_step_decorators(obj.flow, obj.graph, obj.environment,
obj.flow_datastore, obj.logger)
# Save the code package in the flow datastore so that both user code and
# metaflow package can be retrieved during workflow execution.
obj.package = MetaflowPackage(obj.flow, obj.environment, obj.echo,
obj.package_suffixes)
package_url, package_sha = obj.flow_datastore.save_data([obj.package.blob],
len_hint=1)[0]
return ArgoWorkflows(
name,
obj.graph,
obj.flow,
package_sha,
package_url,
token,
obj.metadata,
obj.flow_datastore,
obj.environment,
obj.event_logger,
obj.monitor,
tags=tags,
namespace=namespace,
max_workers=max_workers,
username=get_username(),
workflow_timeout=workflow_timeout,
workflow_priority=workflow_priority,
)
