def _run_instance_role_disabled(image_uri, ec2_client, ec2_instance,
ec2_connection):
expected_tag_key = "aws-dlc-autogenerated-tag-do-not-delete"
ec2_instance_id, _ = ec2_instance
account_id = test_utils.get_account_id_from_image_uri(image_uri)
image_region = test_utils.get_region_from_image_uri(image_uri)
repo_name, image_tag = test_utils.get_repository_and_tag_from_image_uri(
image_uri)
framework, _ = test_utils.get_framework_and_version_from_tag(image_uri)
job_type = test_utils.get_job_type_from_image(image_uri)
processor = test_utils.get_processor_from_image_uri(image_uri)
container_name = f"{repo_name}-telemetry_bad_instance_role-ec2"
docker_cmd = "nvidia-docker" if processor == "gpu" else "docker"
test_utils.login_to_ecr_registry(ec2_connection, account_id, image_region)
ec2_connection.run(f"{docker_cmd} pull -q {image_uri}")
preexisting_ec2_instance_tags = ec2_utils.get_ec2_instance_tags(
ec2_instance_id, ec2_client=ec2_client)
if expected_tag_key in preexisting_ec2_instance_tags:
ec2_client.remove_tags(Resources=[ec2_instance_id],
Tags=[{
"Key": expected_tag_key
}])
# Disable access to EC2 instance metadata
ec2_connection.run(f"sudo route add -host 169.254.169.254 reject")
if "tensorflow" in framework and job_type == "inference":
model_name = "saved_model_half_plus_two"
model_base_path = test_utils.get_tensorflow_model_base_path(image_uri)
env_vars_list = test_utils.get_tensorflow_inference_environment_variables(
model_name, model_base_path)
env_vars = " ".join([
f"-e {entry['name']}={entry['value']}" for entry in env_vars_list
])
inference_command = get_tensorflow_inference_command_tf27_above(
image_uri, model_name)
ec2_connection.run(
f"{docker_cmd} run {env_vars} --name {container_name} -id {image_uri} {inference_command}"
)
time.sleep(5)
else:
framework_to_import = framework.replace("huggingface_", "")
framework_to_import = "torch" if framework_to_import == "pytorch" else framework_to_import
ec2_connection.run(
f"{docker_cmd} run --name {container_name} -id {image_uri} bash")
output = ec2_connection.run(
f"{docker_cmd} exec -i {container_name} python -c 'import {framework_to_import}; import time; time.sleep(5)'",
warn=True)
assert output.ok, f"'import {framework_to_import}' fails when credentials not configured"
ec2_instance_tags = ec2_utils.get_ec2_instance_tags(ec2_instance_id,
ec2_client=ec2_client)
assert expected_tag_key not in ec2_instance_tags, (
f"{expected_tag_key} was applied as an instance tag."
"EC2 create_tags went through even though it should not have")
def _run_tag_success(image_uri, ec2_client, ec2_instance, ec2_connection):
expected_tag_key = "aws-dlc-autogenerated-tag-do-not-delete"
ec2_instance_id, _ = ec2_instance
account_id = test_utils.get_account_id_from_image_uri(image_uri)
image_region = test_utils.get_region_from_image_uri(image_uri)
repo_name, image_tag = test_utils.get_repository_and_tag_from_image_uri(
image_uri)
framework, _ = test_utils.get_framework_and_version_from_tag(image_uri)
job_type = test_utils.get_job_type_from_image(image_uri)
processor = test_utils.get_processor_from_image_uri(image_uri)
container_name = f"{repo_name}-telemetry_tag_instance_success-ec2"
docker_cmd = "nvidia-docker" if processor == "gpu" else "docker"
test_utils.login_to_ecr_registry(ec2_connection, account_id, image_region)
ec2_connection.run(f"{docker_cmd} pull -q {image_uri}")
preexisting_ec2_instance_tags = ec2_utils.get_ec2_instance_tags(
ec2_instance_id, ec2_client=ec2_client)
if expected_tag_key in preexisting_ec2_instance_tags:
ec2_client.remove_tags(Resources=[ec2_instance_id],
Tags=[{
"Key": expected_tag_key
}])
if framework == "tensorflow" and job_type == "inference":
env_vars_list = ecs_utils.get_ecs_tensorflow_environment_variables(
processor, "saved_model_half_plus_two")
env_vars = " ".join([
f"-e {entry['name']}={entry['value']}" for entry in env_vars_list
])
ec2_connection.run(
f"{docker_cmd} run {env_vars} --name {container_name} -id {image_uri}"
)
time.sleep(5)
else:
framework_to_import = framework.replace("huggingface_", "")
framework_to_import = "torch" if framework_to_import == "pytorch" else framework_to_import
ec2_connection.run(
f"{docker_cmd} run --name {container_name} -id {image_uri} bash")
output = ec2_connection.run(
f"{docker_cmd} exec -i {container_name} python -c 'import {framework_to_import}; import time; time.sleep(5)'",
warn=True)
ec2_instance_tags = ec2_utils.get_ec2_instance_tags(ec2_instance_id,
ec2_client=ec2_client)
assert expected_tag_key in ec2_instance_tags, f"{expected_tag_key} was not applied as an instance tag"