def create_pr_symlink(args):
"""Create a 'symlink' in GCS pointing at the results for a PR.
This is a null op if PROW environment variables indicate this is not a PR
job.
"""
gcs_client = storage.Client()
# GCS layout is defined here:
# https://github.com/kubernetes/test-infra/tree/master/gubernator#job-artifact-gcs-layout
pull_number = os.getenv("PULL_NUMBER")
if not pull_number:
# Symlinks are only created for pull requests.
return
path = "pr-logs/directory/{job}/{build}.txt".format(
job=os.getenv("JOB_NAME"), build=os.getenv("BUILD_NUMBER"))
pull_number = os.getenv("PULL_NUMBER")
source = util.to_gcs_uri(args.bucket, path)
target = get_gcs_dir(args.bucket)
logging.info("Creating symlink %s pointing to %s", source, target)
bucket = gcs_client.get_bucket(args.bucket)
blob = bucket.blob(path)
blob.upload_from_string(target)
def check_no_errors(gcs_client, artifacts_dir):
"""Check that all the XML files exist and there were no errors.
Args:
gcs_client: The GCS client.
artifacts_dir: The directory where artifacts should be stored.
Returns:
True if there were no errors and false otherwise.
"""
bucket_name, prefix = util.split_gcs_uri(artifacts_dir)
bucket = gcs_client.get_bucket(bucket_name)
no_errors = True
for b in bucket.list_blobs(prefix=os.path.join(prefix, "junit")):
full_path = util.to_gcs_uri(b.bucket, b.path)
if not os.path.splitext(b.path)[-1] == ".xml":
logging.info("Skipping %s; not an xml file", full_path)
continue
logging.info("Checking %s", full_path)
xml_contents = b.download_as_string()
if test_util.get_num_failures(xml_contents) > 0:
logging.info("Test failures in %s", full_path)
no_errors = False
return no_errors
def run_papermill_job(
notebook_path,
name,
namespace, # pylint: disable=too-many-branches,too-many-statements
repos,
image):
"""Generate a K8s job to run a notebook using papermill
Args:
notebook_path: Path to the notebook. This should be in the form
"{REPO_OWNER}/{REPO}/path/to/notebook.ipynb"
name: Name for the K8s job
namespace: The namespace where the job should run.
repos: Which repos to checkout; if None or empty tries
to infer based on PROW environment variables
image: The docker image to run the notebook in.
"""
util.maybe_activate_service_account()
with open("job.yaml") as hf:
job = yaml.load(hf)
if notebook_path.startswith("/"):
raise ValueError(
"notebook_path={0} should not start with /".format(notebook_path))
# We need to checkout the correct version of the code
# in presubmits and postsubmits. We should check the environment variables
# for the prow environment variables to get the appropriate values.
# We should probably also only do that if the
# See
# https://github.com/kubernetes/test-infra/blob/45246b09ed105698aa8fb928b7736d14480def29/prow/jobs.md#job-environment-variables
if not repos:
repos = argo_build_util.get_repo_from_prow_env()
if not repos:
raise ValueError("Could not get repos from prow environment variable "
"and --repos isn't explicitly set")
repos += ",kubeflow/testing@HEAD"
logging.info("Repos set to %s", repos)
job["spec"]["template"]["spec"]["initContainers"][0]["command"] = [
"/usr/local/bin/checkout_repos.sh",
"--repos=" + repos,
"--src_dir=/src",
"--depth=all",
]
job["spec"]["template"]["spec"]["containers"][0]["image"] = image
full_notebook_path = os.path.join("/src", notebook_path)
job["spec"]["template"]["spec"]["containers"][0]["command"] = [
"python3", "-m", "kubeflow.examples.notebook_tests.execute_notebook",
"--notebook_path", full_notebook_path
]
job["spec"]["template"]["spec"]["containers"][0][
"workingDir"] = os.path.dirname(full_notebook_path)
# The prow bucket to use for results/artifacts
prow_bucket = prow_artifacts.PROW_RESULTS_BUCKET
if os.getenv("REPO_OWNER") and os.getenv("REPO_NAME"):
# Running under prow
prow_dir = prow_artifacts.get_gcs_dir(prow_bucket)
logging.info("Prow artifacts dir: %s", prow_dir)
prow_dir = os.path.join(prow_dir, "artifacts")
if os.getenv("TEST_TARGET_NAME"):
prow_dir = os.path.join(prow_dir,
os.getenv("TEST_TARGET_NAME").lstrip("/"))
prow_bucket, prow_path = util.split_gcs_uri(prow_dir)
else:
prow_path = "notebook-test" + datetime.datetime.now().strftime(
"%H%M%S")
prow_path = prow_path + "-" + uuid.uuid4().hex[0:3]
prow_dir = util.to_gcs_uri(prow_bucket, prow_path)
prow_path = os.path.join(prow_path, name + ".html")
output_gcs = util.to_gcs_uri(NB_BUCKET, prow_path)
job["spec"]["template"]["spec"]["containers"][0]["env"] = [
{
"name": "OUTPUT_GCS",
"value": output_gcs
},
{
"name": "PYTHONPATH",
"value": "/src/kubeflow/testing/py:/src/kubeflow/examples/py"
},
]
logging.info("Notebook will be written to %s", output_gcs)
util.load_kube_config(persist_config=False)
if name:
job["metadata"]["name"] = name
else:
job["metadata"]["name"] = ("notebook-test-" +
datetime.datetime.now().strftime("%H%M%S") +
"-" + uuid.uuid4().hex[0:3])
name = job["metadata"]["name"]
job["metadata"]["namespace"] = namespace
# Create an API client object to talk to the K8s master.
api_client = k8s_client.ApiClient()
batch_api = k8s_client.BatchV1Api(api_client)
logging.info("Creating job:\n%s", yaml.dump(job))
actual_job = batch_api.create_namespaced_job(job["metadata"]["namespace"],
job)
logging.info("Created job %s.%s:\n%s", namespace, name,
yaml.safe_dump(actual_job.to_dict()))
final_job = util.wait_for_job(api_client,
namespace,
name,
timeout=datetime.timedelta(minutes=30))
logging.info("Final job:\n%s", yaml.safe_dump(final_job.to_dict()))
# Download notebook html to artifacts
logging.info("Copying %s to bucket %s", output_gcs, prow_bucket)
storage_client = storage.Client()
bucket = storage_client.get_bucket(NB_BUCKET)
blob = bucket.get_blob(prow_path)
destination_bucket = storage_client.get_bucket(prow_bucket)
bucket.copy_blob(blob, destination_bucket)
if not final_job.status.conditions:
raise RuntimeError("Job {0}.{1}; did not complete".format(
namespace, name))
last_condition = final_job.status.conditions[-1]
if last_condition.type not in ["Complete"]:
logging.error("Job didn't complete successfully")
raise RuntimeError("Job {0}.{1} failed".format(namespace, name))
def test_xgboost_synthetic(
record_xml_attribute,
name,
namespace, # pylint: disable=too-many-branches,too-many-statements
repos,
image,
notebook_artifacts_dir):
'''Generate Job and summit.'''
util.set_pytest_junit(record_xml_attribute, "test_xgboost_synthetic")
util.maybe_activate_service_account()
with open("job.yaml") as hf:
job = yaml.load(hf)
# We need to checkout the correct version of the code
# in presubmits and postsubmits. We should check the environment variables
# for the prow environment variables to get the appropriate values.
# We should probably also only do that if the
# See
# https://github.com/kubernetes/test-infra/blob/45246b09ed105698aa8fb928b7736d14480def29/prow/jobs.md#job-environment-variables
if not repos:
repos = argo_build_util.get_repo_from_prow_env()
repos += ",kubeflow/testing@HEAD"
logging.info("Repos set to %s", repos)
job["spec"]["template"]["spec"]["initContainers"][0]["command"] = [
"/usr/local/bin/checkout_repos.sh",
"--repos=" + repos,
"--src_dir=/src",
"--depth=all",
]
nb_bucket = "kubeflow-ci-deployment"
nb_path = os.path.join("xgboost_synthetic_testing", os.getenv("JOB_TYPE"),
os.getenv("HOSTNAME"), "notebook.html")
output_gcs = util.to_gcs_uri(nb_bucket, nb_path)
logging.info("Tested notebook will be outputed to: %s", output_gcs)
job["spec"]["template"]["spec"]["containers"][0]["env"] = [
{
"name": "PYTHONPATH",
"value": "/src/kubeflow/testing/py"
},
{
"name": "OUTPUT_GCS",
"value": output_gcs
},
]
job["spec"]["template"]["spec"]["containers"][0]["image"] = image
util.load_kube_config(persist_config=False)
if name:
job["metadata"]["name"] = name
else:
job["metadata"]["name"] = ("xgboost-test-" +
datetime.datetime.now().strftime("%H%M%S") +
"-" + uuid.uuid4().hex[0:3])
name = job["metadata"]["name"]
job["metadata"]["namespace"] = namespace
# Create an API client object to talk to the K8s master.
api_client = k8s_client.ApiClient()
batch_api = k8s_client.BatchV1Api(api_client)
logging.info("Creating job:\n%s", yaml.dump(job))
actual_job = batch_api.create_namespaced_job(job["metadata"]["namespace"],
job)
logging.info("Created job %s.%s:\n%s", namespace, name,
yaml.safe_dump(actual_job.to_dict()))
final_job = util.wait_for_job(api_client,
namespace,
name,
timeout=datetime.timedelta(minutes=30))
logging.info("Final job:\n%s", yaml.safe_dump(final_job.to_dict()))
if not final_job.status.conditions:
raise RuntimeError("Job {0}.{1}; did not complete".format(
namespace, name))
last_condition = final_job.status.conditions[-1]
# Download notebook html to artifacts
notebook_artifacts_path = os.path.join(notebook_artifacts_dir,
"notebook.html")
logging.info("Writing notebook artifact to: %s", notebook_artifacts_path)
os.makedirs(notebook_artifacts_dir, exist_ok=True)
storage_client = storage.Client()
bucket = storage_client.get_bucket(nb_bucket)
blob = bucket.get_blob(nb_path)
blob.download_to_filename(notebook_artifacts_path)
if last_condition.type not in ["Complete"]:
logging.error("Job didn't complete successfully")
raise RuntimeError("Job {0}.{1} failed".format(namespace, name))
