def submit_serving_with_pvc(capsys,
namespace='default',
pvc_name=None,
pvc_mount_path=None): # pylint:disable=unused-argument
fairing.config.set_builder(
'docker',
registry=DOCKER_REGISTRY,
base_image="seldonio/seldon-core-s2i-python3:0.4")
if pvc_mount_path:
pod_spec_mutators = [
mounting_pvc(pvc_name=pvc_name, pvc_mount_path=pvc_mount_path)
]
else:
pod_spec_mutators = [mounting_pvc(pvc_name=pvc_name)]
expected_result = str(uuid.uuid4())
fairing.config.set_deployer('serving',
serving_class="TestServe",
labels={'pytest-id': expected_result},
service_type='ClusterIP',
pod_spec_mutators=pod_spec_mutators)
fairing.config.run()
created_deployment = get_deployment_with_labels(
namespace, 'pytest-id=' + expected_result)
assert pvc_name == created_deployment.items[0].spec.template.spec.volumes[0]\
.persistent_volume_claim.claim_name
if pvc_mount_path:
assert pvc_mount_path == created_deployment.items[
0].spec.template.spec.containers[0].volume_mounts[0].mount_path
else:
assert constants.PVC_DEFAULT_MOUNT_PATH == created_deployment.items[
0].spec.template.spec.containers[0].volume_mounts[0].mount_path
def submit_jobs_with_pvc(
capsys,
cleanup=False,
namespace="default", # pylint:disable=unused-argument
pvc_name=None,
pvc_mount_path=None):
py_version = ".".join([str(x) for x in sys.version_info[0:3]])
base_image = 'registry.hub.docker.com/library/python:{}'.format(py_version)
fairing.config.set_builder('append',
base_image=base_image,
registry=DOCKER_REGISTRY)
if pvc_mount_path:
pod_spec_mutators = [
mounting_pvc(pvc_name=pvc_name, pvc_mount_path=pvc_mount_path)
]
else:
pod_spec_mutators = [mounting_pvc(pvc_name=pvc_name)]
expected_result = str(uuid.uuid4())
fairing.config.set_deployer('job',
namespace=namespace,
cleanup=cleanup,
labels={'pytest-id': expected_result},
stream_log=False,
pod_spec_mutators=pod_spec_mutators)
remote_train = fairing.config.fn(lambda: train_fn(expected_result))
remote_train()
created_job = get_job_with_labels(namespace,
'pytest-id=' + expected_result)
assert pvc_name == created_job.items[0].spec.template.spec.volumes[0]\
.persistent_volume_claim.claim_name
if pvc_mount_path:
assert pvc_mount_path == created_job.items[0].spec.template.spec.containers[0]\
.volume_mounts[0].mount_path
else:
assert constants.PVC_DEFAULT_MOUNT_PATH == created_job.items[
0].spec.template.spec.containers[0].volume_mounts[0].mount_path
if __name__ == '__main__':
if os.getenv('FAIRING_RUNTIME', None) is None:
from kubeflow import fairing
from kubeflow.fairing.kubernetes import utils as k8s_utils
DOCKER_REGISTRY = 'kubeflow-registry.default.svc.cluster.local:30000'
fairing.config.set_builder(
'append',
image_name='katib-job',
base_image='brightfly/kubeflow-jupyter-lab:tf2.0-gpu',
registry=DOCKER_REGISTRY,
push=True)
# cpu 2, memory 5GiB
fairing.config.set_deployer(
'job',
namespace='dudaji',
pod_spec_mutators=[
k8s_utils.mounting_pvc(pvc_name="fashion-mnist",
pvc_mount_path="/result"),
k8s_utils.get_resource_mutator(cpu=2, memory=5)
])
fairing.config.run()
else:
remote_train = MyFashionMnist()
remote_train.train()
# In[ ]:
# In[ ]:
"python", "tensorflow_mnist.py", "--model_path",
"/mnt/pv/models/tensorflow/mnist"
]
output_map = {
"Dockerfile": "Dockerfile",
"tensorflow_mnist.py": "tensorflow_mnist.py"
}
fairing.config.set_preprocessor('python',
command=command,
path_prefix="/app",
output_map=output_map)
fairing.config.set_builder('docker',
registry=CONTAINER_REGISTRY,
image_name="tensorflow-mnist",
dockerfile_path="Dockerfile")
fairing.config.set_deployer('job',
namespace=namespace,
job_name=job_name,
pod_spec_mutators=[
k8s_utils.mounting_pvc(
pvc_name='kfserving-models-pvc',
pvc_mount_path='/mnt/pv')
],
cleanup=True,
stream_log=True)
fairing.config.run()
if __name__ == '__main__':
if os.getenv('FAIRING_RUNTIME', None) is None:
from kubeflow import fairing
from kubeflow.fairing.kubernetes import utils as k8s_utils
DOCKER_REGISTRY = 'kubeflow-registry.default.svc.cluster.local:30000'
fairing.config.set_builder('append',
image_name='caltech-katib-job',
base_image='brightfly/tf-fairing:2.0-gpu',
registry=DOCKER_REGISTRY,
push=True)
# cpu 1, memory 1GiB
fairing.config.set_deployer('job',
namespace='dudaji',
pod_spec_mutators=[
k8s_utils.mounting_pvc(
pvc_name="caltech101",
pvc_mount_path="/result")
])
# python3
import IPython
ipy = IPython.get_ipython()
if ipy is None:
fairing.config.set_preprocessor('python', input_files=[__file__])
fairing.config.run()
else:
train = Caltech101()
train.run()
# In[4]:
if __name__ == '__main__':
if os.getenv('FAIRING_RUNTIME', None) is None:
from kubeflow import fairing
from kubeflow.fairing.kubernetes import utils as k8s_utils
DOCKER_REGISTRY = 'jaewoo201'
fairing.config.set_builder(
'append',
base_image='jaewoo201/kubeflow-jupyter-lab:tf2.0-cpu',
registry=DOCKER_REGISTRY,
push=True)
# cpu 2, memory 5GiB
fairing.config.set_deployer('job',
namespace='kf-namespace',
pod_spec_mutators=[
k8s_utils.mounting_pvc(pvc_name="azurefile",
pvc_mount_path="/result"),
k8s_utils.get_resource_mutator(cpu=1,
memory=3)]
)
fairing.config.run()
else:
remote_train = MyFashionMnist()
remote_train.train()
# In[ ]: