def gdmix_distributed_workflow(gdmix_config_file, namespace, secret_name, image, service_account):
""" Generate gdmix kubeflow pipeline using Kubeflow pipeline python DSL( kfp.dsl).
"""
gdmix_config_obj = yaml_config_file_to_obj(gdmix_config_file)
current_op = no_op("GDMix-training-start")
suffix = gen_random_string()
if not hasattr(gdmix_config_obj, FIXED_EFFECT_CONFIG):
raise ValueError(f"Need to define {FIXED_EFFECT_CONFIG}")
fe_tip_op = no_op("fixed-effect-training-start")
fe_tip_op.after(current_op)
fe_workflow = FixedEffectWorkflowGenerator(gdmix_config_obj,
namespace=namespace,
secret_name=secret_name,
image=image,
service_account=service_account,
job_suffix=suffix)
fe_start_op, current_op = fe_workflow.gen_workflow()
fe_start_op.after(fe_tip_op)
if hasattr(gdmix_config_obj, RANDOM_EFFECT_CONFIG):
re_tip_op = no_op("random-effect-training-start")
re_tip_op.after(current_op)
re_workflow = RandomEffectWorkflowGenerator(gdmix_config_obj, namespace=namespace, secret_name=secret_name, image=image,
service_account=service_account, job_suffix=suffix, prev_model_name=fe_workflow.fixed_effect_name)
re_start_op, _ = re_workflow.gen_workflow()
re_start_op.after(re_tip_op)
def no_op(msg):
""" Empty op as a placeholder on workflow to handle dependency.
"""
from kfp import dsl
return dsl.ContainerOp(name="{}-{}".format(msg, gen_random_string()),
image="alpine",
command=['sh', '-c'],
arguments=["echo {}".format(msg)])
def load_launcher_from_file(src_file, name_placeholder, name):
""" Load launcher's commponent yaml file and update launcher name """
dest_file = shutil.copyfile(src_file, gen_random_string(20) + ".tmp.yaml")
with fileinput.FileInput(dest_file, inplace=True) as f:
for line in f:
line = line.replace(name_placeholder, name)
print(line, end='')
from kfp import components
component = components.load_component_from_file(dest_file)
os.remove(dest_file)
return component