def setup_eks_cluster(framework_name, is_neuron):
frameworks = {
"tensorflow": "tf",
"mxnet": "mx",
"pytorch": "pt",
}
long_name = framework_name
short_name = frameworks[long_name]
codebuild_version = os.getenv("CODEBUILD_RESOLVED_SOURCE_VERSION")[0:7]
num_nodes = 1 if is_pr_context() else 3 if long_name != "pytorch" else 4
cluster_name = f"dlc-{short_name}-cluster-{codebuild_version}-{random.randint(1, 10000)}"
# default volume size
volume_size = 80
try:
eks_utils.eks_setup()
if is_neuron:
#TODO the eks AMI used for neuron has a snapshot size of 500GB, if we pass the default 80GB the cluster
#creation will fail. Once official EKS AMI for neuron 1.1 is released, revert this change.
volume_size = 500
eks_utils.create_eks_cluster(cluster_name, "neuron", num_nodes, volume_size, "inf1.xlarge", "pytest.pem")
else:
eks_utils.create_eks_cluster(cluster_name, "gpu", num_nodes, volume_size, "p3.16xlarge", "pytest.pem")
except Exception:
eks_utils.delete_eks_cluster(cluster_name)
raise
return cluster_name
def setup_eks_cluster(framework_name, is_neuron):
frameworks = {
"tensorflow": "tf",
"mxnet": "mx",
"pytorch": "pt",
}
long_name = framework_name
short_name = frameworks[long_name]
codebuild_version = os.getenv("CODEBUILD_RESOLVED_SOURCE_VERSION")[0:7]
num_nodes = 1 if is_pr_context() else 3 if long_name != "pytorch" else 4
cluster_name = f"dlc-{short_name}-cluster-{codebuild_version}-{random.randint(1, 10000)}"
# default volume size
volume_size = 80
try:
eks_utils.eks_setup()
if is_neuron:
eks_utils.create_eks_cluster(cluster_name, num_nodes, volume_size,
"inf1.xlarge", "pytest.pem")
else:
eks_utils.create_eks_cluster(cluster_name, num_nodes, volume_size,
"p3.16xlarge", "pytest.pem")
except Exception:
eks_utils.delete_eks_cluster(cluster_name)
raise
return cluster_name
def setup_eks_clusters(dlc_images):
terminable_clusters = []
frameworks = {"tensorflow": "tf", "pytorch": "pt", "mxnet": "mx"}
for long_name, short_name in frameworks.items():
if long_name in dlc_images:
cluster_name = None
if not is_pr_context():
num_nodes = 3 if long_name != "pytorch" else 4
cluster_name = f"dlc-{short_name}-cluster-" \
f"{os.getenv('CODEBUILD_RESOLVED_SOURCE_VERSION')}-{random.randint(1, 10000)}"
eks_utils.create_eks_cluster(cluster_name, "gpu", num_nodes,
"p3.16xlarge", "pytest.pem")
terminable_clusters.append(cluster_name)
eks_utils.eks_setup(long_name, cluster_name)
return terminable_clusters
def setup_eks_cluster(framework_name):
frameworks = {"tensorflow": "tf", "pytorch": "pt", "mxnet": "mx"}
long_name = framework_name
short_name = frameworks[long_name]
codebuild_version = os.getenv('CODEBUILD_RESOLVED_SOURCE_VERSION')[0:7]
num_nodes = 1 if is_pr_context() else 3 if long_name != "pytorch" else 4
cluster_name = f"dlc-{short_name}-cluster-" \
f"{codebuild_version}-{random.randint(1, 10000)}"
try:
eks_utils.eks_setup()
eks_utils.create_eks_cluster(cluster_name, "gpu", num_nodes, "p3.16xlarge", "pytest.pem")
except Exception:
eks_utils.delete_eks_cluster(cluster_name)
raise
return cluster_name
def setup_eks_clusters(dlc_images):
frameworks = {"tensorflow": "tf", "pytorch": "pt", "mxnet": "mx"}
frameworks_in_images = [
framework for framework in frameworks.keys() if framework in dlc_images
]
if len(frameworks_in_images) != 1:
raise ValueError(
f"All images in dlc_images must be of a single framework for EKS tests.\n"
f"Instead seeing {frameworks_in_images} frameworks.")
long_name = frameworks_in_images[0]
short_name = frameworks[long_name]
num_nodes = 2 if is_pr_context() else 3 if long_name != "pytorch" else 4
cluster_name = f"dlc-{short_name}-cluster-" \
f"{os.getenv('CODEBUILD_RESOLVED_SOURCE_VERSION')}-{random.randint(1, 10000)}"
eks_utils.create_eks_cluster(cluster_name, "gpu", num_nodes, "p3.16xlarge",
"pytest.pem")
eks_utils.eks_setup(long_name, cluster_name)
return cluster_name
def generate_sagemaker_pytest_cmd(image, sagemaker_test_type):
"""
Parses the image ECR url and returns appropriate pytest command
:param image: ECR url of image
:param sagemaker_test_type: local or remote test type
:return: <tuple> pytest command to be run, path where it should be executed, image tag
"""
region = os.getenv("AWS_REGION", DEFAULT_REGION)
account_id = os.getenv("ACCOUNT_ID", image.split(".")[0])
print("image name {}".format(image))
sm_remote_docker_base_name, tag = image.split("/")[1].split(":")
sm_local_docker_repo_uri = image.split(":")[0]
# Assign instance type
instance_type = assign_sagemaker_remote_job_instance_type(image)
# Get path to test directory
find_path = sm_remote_docker_base_name.split("-")
# NOTE: We are relying on the fact that repos are defined as <context>-<framework>-<job_type> in our infrastructure
framework, framework_version = get_framework_and_version_from_tag(image)
framework_major_version = framework_version.split(".")[0]
job_type = get_job_type_from_image(image)
path = os.path.join("test", "sagemaker_tests", framework, job_type)
aws_id_arg = "--aws-id"
docker_base_arg = "--docker-base-name"
instance_type_arg = "--instance-type"
accelerator_type_arg = "--accelerator-type"
framework_version_arg = "--framework-version"
eia_arg = "ml.eia1.large"
processor = ("neuron" if "neuron" in image else "gpu"
if "gpu" in image else "eia" if "eia" in image else "cpu")
py_version = re.search(r"py\d+", tag).group()
sm_local_py_version = "37" if py_version == "py37" else "38" if py_version == "py38" else "2" if py_version == "py27" else "3"
if framework == "tensorflow" and job_type == "inference":
# Tf Inference tests have an additional sub directory with test
integration_path = os.path.join("test", "integration",
sagemaker_test_type)
else:
integration_path = os.path.join("integration", sagemaker_test_type)
# Conditions for modifying tensorflow SageMaker pytest commands
if framework == "tensorflow" and sagemaker_test_type == SAGEMAKER_REMOTE_TEST_TYPE:
if job_type == "inference":
aws_id_arg = "--registry"
docker_base_arg = "--repo"
instance_type_arg = "--instance-types"
framework_version_arg = "--versions"
integration_path = os.path.join(
integration_path,
"test_tfs.py") if processor != "eia" else os.path.join(
integration_path, "test_ei.py")
if framework == "tensorflow" and job_type == "training":
aws_id_arg = "--account-id"
test_report = os.path.join(os.getcwd(), "test", f"{job_type}_{tag}.xml")
local_test_report = os.path.join(UBUNTU_HOME_DIR, "test",
f"{job_type}_{tag}_sm_local.xml")
# Explanation of why we need the if-condition below:
# We have separate Pipeline Actions that run EFA tests, which have the env variable "EFA_DEDICATED=True" configured
# so that those Actions only run the EFA tests.
# However, there is no such dedicated CB job dedicated to EFA tests in the PR context. This means that when in the
# PR context, setting "DISABLE_EFA_TESTS" to True should skip EFA tests, but setting it to False should enable
# not just the EFA tests, but also all other tests as well.
if is_pr_context():
efa_tests_disabled = os.getenv("DISABLE_EFA_TESTS",
"False").lower() == "true"
efa_flag = "-m \"not efa\"" if efa_tests_disabled else ""
else:
efa_dedicated = os.getenv("EFA_DEDICATED", "False").lower() == "true"
efa_flag = '--efa' if efa_dedicated else '-m \"not efa\"'
region_list = ",".join(SAGEMAKER_EXECUTION_REGIONS)
sagemaker_regions_list = f"--sagemaker-regions {region_list}"
remote_pytest_cmd = (
f"pytest -rA {integration_path} --region {region} --processor {processor} {docker_base_arg} "
f"{sm_remote_docker_base_name} --tag {tag} {framework_version_arg} {framework_version} "
f"{aws_id_arg} {account_id} {instance_type_arg} {instance_type} {efa_flag} {sagemaker_regions_list} --junitxml {test_report}"
)
if processor == "eia":
remote_pytest_cmd += f"{accelerator_type_arg} {eia_arg}"
local_pytest_cmd = (
f"pytest -s -v {integration_path} {docker_base_arg} "
f"{sm_local_docker_repo_uri} --tag {tag} --framework-version {framework_version} "
f"--processor {processor} {aws_id_arg} {account_id} --junitxml {local_test_report}"
)
if framework == "tensorflow" and job_type != "inference":
local_pytest_cmd = f"{local_pytest_cmd} --py-version {sm_local_py_version} --region {region}"
if framework == "tensorflow" and job_type == "training":
path = os.path.join(os.path.dirname(path),
f"{framework}{framework_major_version}_training")
if "huggingface" in framework and job_type == "inference":
path = os.path.join("test", "sagemaker_tests", "huggingface",
"inference")
return (
remote_pytest_cmd if sagemaker_test_type == SAGEMAKER_REMOTE_TEST_TYPE
else local_pytest_cmd,
path,
tag,
job_type,
)
def main():
# Define constants
start_time = datetime.now()
test_type = os.getenv("TEST_TYPE")
efa_dedicated = os.getenv("EFA_DEDICATED", "False").lower() == "true"
executor_mode = os.getenv("EXECUTOR_MODE", "False").lower() == "true"
dlc_images = os.getenv("DLC_IMAGE") if executor_mode else get_dlc_images()
LOGGER.info(f"Images tested: {dlc_images}")
all_image_list = dlc_images.split(" ")
standard_images_list = [image_uri for image_uri in all_image_list if "example" not in image_uri]
# Do not create EKS cluster for when EIA Only Images are present
is_all_images_list_eia = all("eia" in image_uri for image_uri in all_image_list)
eks_cluster_name = None
benchmark_mode = "benchmark" in test_type or is_benchmark_dev_context()
specific_test_type = re.sub("benchmark-", "", test_type) if "benchmark" in test_type else test_type
test_path = os.path.join("benchmark", specific_test_type) if benchmark_mode else specific_test_type
# Skipping non HuggingFace specific tests to execute only sagemaker tests
if any("huggingface" in image_uri for image_uri in all_image_list) and \
specific_test_type in ("ecs", "ec2", "eks", "bai"):
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "huggingface")
return
if specific_test_type in ("sanity", "ecs", "ec2", "eks", "canary", "bai"):
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
# The following two report files will only be used by EKS tests, as eks_train.xml and eks_infer.xml.
# This is to sequence the tests and prevent one set of tests from waiting too long to be scheduled.
report_train = os.path.join(os.getcwd(), "test", f"{test_type}_train.xml")
report_infer = os.path.join(os.getcwd(), "test", f"{test_type}_infer.xml")
report_multinode_train = os.path.join(os.getcwd(), "test", f"eks_multinode_train.xml")
# PyTest must be run in this directory to avoid conflicting w/ sagemaker_tests conftests
os.chdir(os.path.join("test", "dlc_tests"))
# Pull images for necessary tests
if specific_test_type == "sanity":
pull_dlc_images(all_image_list)
if specific_test_type == "bai":
build_bai_docker_container()
if specific_test_type == "eks" and not is_all_images_list_eia:
frameworks_in_images = [
framework for framework in ("mxnet", "pytorch", "tensorflow") if framework in dlc_images
]
if len(frameworks_in_images) != 1:
raise ValueError(
f"All images in dlc_images must be of a single framework for EKS tests.\n"
f"Instead seeing {frameworks_in_images} frameworks."
)
framework = frameworks_in_images[0]
is_neuron = "neuron" in dlc_images
eks_cluster_name = setup_eks_cluster(framework, is_neuron)
setup_ssm_eks_cluster(eks_cluster_name)
if not is_neuron:
# setup kubeflow
eks_utils.setup_kubeflow(eks_cluster_name)
# Change 1: Split training and inference, and run one after the other, to prevent scheduling issues
# Set -n=4, instead of -n=auto, because initiating too many pods simultaneously has been resulting in
# pods timing-out while they were in the Pending state. Scheduling 4 tests (and hence, 4 pods) at once
# seems to be an optimal configuration.
# Change 2: Separate multi-node EKS tests from single-node tests in execution to prevent resource contention
if not is_neuron:
pytest_cmds = [
[
"-s",
"-rA",
os.path.join(test_path, framework, "training"),
f"--junitxml={report_train}",
"-n=4",
"-m",
"not multinode",
],
[
"-s",
"-rA",
os.path.join(test_path, framework, "inference"),
f"--junitxml={report_infer}",
"-n=4",
"-m",
"not multinode",
],
["-s", "-rA", test_path, f"--junitxml={report_multinode_train}", "--multinode"],
]
else:
pytest_cmds = [
[
"-s",
"-rA",
os.path.join(test_path, framework, "inference"),
f"--junitxml={report_infer}",
"-n=4",
"-m",
"not multinode",
],
["-s", "-rA", test_path, f"--junitxml={report_multinode_train}", "--multinode"],
]
if is_pr_context():
for cmd in pytest_cmds:
cmd.append("--timeout=2340")
else:
# Execute dlc_tests pytest command
pytest_cmd = ["-s", "-rA", test_path, f"--junitxml={report}", "-n=auto"]
if specific_test_type == "ec2":
pytest_cmd += ["--reruns=1", "--reruns-delay=10"]
if is_pr_context():
pytest_cmd.append("--timeout=4860")
pytest_cmds = [pytest_cmd]
# Execute separate cmd for canaries
if specific_test_type == "canary":
pytest_cmds = [["-s", "-rA", f"--junitxml={report}", "-n=auto", "--canary", "--ignore=container_tests/"]]
try:
# Note:- Running multiple pytest_cmds in a sequence will result in the execution log having two
# separate pytest reports, both of which must be examined in case of a manual review of results.
cmd_exit_statuses = [pytest.main(pytest_cmd) for pytest_cmd in pytest_cmds]
if all([status == 0 for status in cmd_exit_statuses]):
sys.exit(0)
else:
raise RuntimeError(pytest_cmds)
finally:
if specific_test_type == "eks" and eks_cluster_name:
delete_eks_cluster(eks_cluster_name)
# Delete dangling EC2 KeyPairs
if os.path.exists(KEYS_TO_DESTROY_FILE):
delete_key_pairs(KEYS_TO_DESTROY_FILE)
elif specific_test_type == "sagemaker":
if "neuron" in dlc_images:
LOGGER.info(f"Skipping sagemaker tests because Neuron is not yet supported on SM. Images: {dlc_images}")
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "neuron")
return
if benchmark_mode:
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
os.chdir(os.path.join("test", "dlc_tests"))
setup_sm_benchmark_env(dlc_images, test_path)
pytest_cmd = ["-s", "-rA", test_path, f"--junitxml={report}", "-n=auto", "-o", "norecursedirs=resources"]
if not is_pr_context():
pytest_cmd += ["--efa"] if efa_dedicated else ["-m", "not efa"]
sys.exit(pytest.main(pytest_cmd))
else:
run_sagemaker_remote_tests(
[image for image in standard_images_list if not ("tensorflow-inference" in image and "py2" in image)]
)
metrics_utils.send_test_duration_metrics(start_time)
elif specific_test_type == "sagemaker-local":
if "neuron" in dlc_images:
LOGGER.info(f"Skipping sagemaker tests because Neuron is not yet supported on SM. Images: {dlc_images}")
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "neuron")
return
testing_image_list = [
image
for image in standard_images_list
if not (("tensorflow-inference" in image and "py2" in image) or ("eia" in image))
]
run_sagemaker_local_tests(testing_image_list)
# for EIA Images
if len(testing_image_list) == 0:
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "eia")
else:
raise NotImplementedError(
f"{test_type} test is not supported. Only support ec2, ecs, eks, sagemaker and sanity currently"
)
def main():
# Define constants
start_time = datetime.now()
test_type = os.getenv("TEST_TYPE")
efa_dedicated = os.getenv("EFA_DEDICATED", "False").lower() == "true"
executor_mode = os.getenv("EXECUTOR_MODE", "False").lower() == "true"
dlc_images = os.getenv("DLC_IMAGE") if executor_mode else get_dlc_images()
# Executing locally ona can provide commit_id or may ommit it. Assigning default value for local executions:
commit_id = os.getenv("CODEBUILD_RESOLVED_SOURCE_VERSION",
default="unrecognised_commit_id")
LOGGER.info(f"Images tested: {dlc_images}")
all_image_list = dlc_images.split(" ")
standard_images_list = [
image_uri for image_uri in all_image_list if "example" not in image_uri
]
# Do not create EKS cluster for when EIA Only Images are present
is_all_images_list_eia = all("eia" in image_uri
for image_uri in all_image_list)
eks_cluster_name = None
benchmark_mode = "benchmark" in test_type or is_benchmark_dev_context()
specific_test_type = re.sub(
"benchmark-", "", test_type) if "benchmark" in test_type else test_type
build_context = get_build_context()
# quick_checks tests don't have images in it. Using a placeholder here for jobs like that
try:
framework, version = get_framework_and_version_from_tag(
all_image_list[0])
except:
framework, version = "general_test", "none"
pytest_cache_params = {
"commit_id": commit_id,
"framework": framework,
"version": version,
"build_context": build_context,
"test_type": test_type,
}
# In PR context, allow us to switch sagemaker tests to RC tests.
# Do not allow them to be both enabled due to capacity issues.
if specific_test_type == "sagemaker" and is_rc_test_context(
) and is_pr_context():
specific_test_type = "release_candidate_integration"
test_path = os.path.join(
"benchmark",
specific_test_type) if benchmark_mode else specific_test_type
# Skipping non HuggingFace/AG specific tests to execute only sagemaker tests
is_hf_image_present = any("huggingface" in image_uri
for image_uri in all_image_list)
is_ag_image_present = any("autogluon" in image_uri
for image_uri in all_image_list)
if (is_hf_image_present
or is_ag_image_present) and specific_test_type in ("ecs", "ec2",
"eks", "bai"):
# Creating an empty file for because codebuild job fails without it
LOGGER.info(
f"NOTE: {specific_test_type} tests not supported on HF or AG. Skipping..."
)
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "huggingface")
return
if specific_test_type in (
"sanity",
"ecs",
"ec2",
"eks",
"canary",
"bai",
"quick_checks",
"release_candidate_integration",
):
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
# The following two report files will only be used by EKS tests, as eks_train.xml and eks_infer.xml.
# This is to sequence the tests and prevent one set of tests from waiting too long to be scheduled.
report_train = os.path.join(os.getcwd(), "test",
f"{test_type}_train.xml")
report_infer = os.path.join(os.getcwd(), "test",
f"{test_type}_infer.xml")
report_multinode_train = os.path.join(os.getcwd(), "test",
f"eks_multinode_train.xml")
# PyTest must be run in this directory to avoid conflicting w/ sagemaker_tests conftests
os.chdir(os.path.join("test", "dlc_tests"))
# Pull images for necessary tests
if specific_test_type == "sanity":
pull_dlc_images(all_image_list)
if specific_test_type == "bai":
build_bai_docker_container()
if specific_test_type == "eks" and not is_all_images_list_eia:
frameworks_in_images = [
framework for framework in ("mxnet", "pytorch", "tensorflow")
if framework in dlc_images
]
if len(frameworks_in_images) != 1:
raise ValueError(
f"All images in dlc_images must be of a single framework for EKS tests.\n"
f"Instead seeing {frameworks_in_images} frameworks.")
framework = frameworks_in_images[0]
eks_cluster_name = f"{framework}-{build_context}"
eks_utils.eks_setup()
if eks_utils.is_eks_cluster_active(eks_cluster_name):
eks_utils.eks_write_kubeconfig(eks_cluster_name)
else:
raise Exception(
f"EKS cluster {eks_cluster_name} is not in active state")
# Execute dlc_tests pytest command
pytest_cmd = [
"-s", "-rA", test_path, f"--junitxml={report}", "-n=auto"
]
is_habana_image = any("habana" in image_uri
for image_uri in all_image_list)
if specific_test_type == "ec2":
if is_habana_image:
context = Context()
context.run(
"git clone https://github.com/HabanaAI/gaudi-test-suite.git"
)
context.run(
"tar -c -f gaudi-test-suite.tar.gz gaudi-test-suite")
else:
pytest_cmd += ["--reruns=1", "--reruns-delay=10"]
if is_pr_context():
if specific_test_type == "eks":
pytest_cmd.append("--timeout=2340")
else:
if is_habana_image:
pytest_cmd.append("--timeout=18000")
else:
pytest_cmd.append("--timeout=4860")
pytest_cmds = [pytest_cmd]
# Execute separate cmd for canaries
if specific_test_type in ("canary", "quick_checks"):
pytest_cmds = [[
"-s", "-rA", f"--junitxml={report}", "-n=auto",
f"--{specific_test_type}", "--ignore=container_tests/"
]]
pytest_cmds = [
pytest_cmd + ["--last-failed", "--last-failed-no-failures", "all"]
for pytest_cmd in pytest_cmds
]
pytest_cache_util.download_pytest_cache_from_s3_to_local(
os.getcwd(), **pytest_cache_params)
try:
# Note:- Running multiple pytest_cmds in a sequence will result in the execution log having two
# separate pytest reports, both of which must be examined in case of a manual review of results.
cmd_exit_statuses = [
pytest.main(pytest_cmd) for pytest_cmd in pytest_cmds
]
if all([status == 0 for status in cmd_exit_statuses]):
sys.exit(0)
else:
raise RuntimeError(pytest_cmds)
finally:
pytest_cache_util.upload_pytest_cache_from_local_to_s3(
os.getcwd(), **pytest_cache_params)
# Delete dangling EC2 KeyPairs
if os.path.exists(KEYS_TO_DESTROY_FILE):
delete_key_pairs(KEYS_TO_DESTROY_FILE)
elif specific_test_type == "sagemaker":
if "habana" in dlc_images:
LOGGER.info(f"Skipping SM tests for Habana. Images: {dlc_images}")
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "habana")
return
if benchmark_mode:
if "neuron" in dlc_images:
LOGGER.info(
f"Skipping benchmark sm tests for Neuron. Images: {dlc_images}"
)
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "neuron")
return
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
os.chdir(os.path.join("test", "dlc_tests"))
setup_sm_benchmark_env(dlc_images, test_path)
pytest_cmd = [
"-s", "-rA", test_path, f"--junitxml={report}", "-n=auto",
"-o", "norecursedirs=resources"
]
if not is_pr_context():
pytest_cmd += ["--efa"] if efa_dedicated else ["-m", "not efa"]
sys.exit(pytest.main(pytest_cmd))
else:
sm_remote_images = [
image for image in standard_images_list
if not (("tensorflow-inference" in image and "py2" in image)
or is_e3_image(image))
]
run_sagemaker_remote_tests(sm_remote_images, pytest_cache_params)
if standard_images_list and not sm_remote_images:
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type,
"sm_remote_unsupported")
metrics_utils.send_test_duration_metrics(start_time)
elif specific_test_type == "sagemaker-local":
if "neuron" in dlc_images:
LOGGER.info(
f"Skipping sagemaker tests because Neuron is not yet supported on SM. Images: {dlc_images}"
)
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "neuron")
return
if "habana" in dlc_images:
LOGGER.info(
f"Skipping sagemaker tests because Habana is not yet supported on SM. Images: {dlc_images}"
)
# Creating an empty file for because codebuild job fails without it
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "habana")
return
testing_image_list = [
image for image in standard_images_list
if not (("tensorflow-inference" in image and "py2" in image) or
("eia" in image) or (is_e3_image(image)))
]
run_sagemaker_local_tests(testing_image_list, pytest_cache_params)
# for EIA Images
if len(testing_image_list) == 0:
report = os.path.join(os.getcwd(), "test", f"{test_type}.xml")
sm_utils.generate_empty_report(report, test_type, "eia")
else:
raise NotImplementedError(
f"{test_type} test is not supported. Only support ec2, ecs, eks, sagemaker and sanity currently"
)
