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 setup_ecs_inference_service(
docker_image_uri,
framework,
cluster_arn,
model_name,
worker_instance_id,
ei_accelerator_type=None,
num_gpus=None,
num_neurons=None,
region=DEFAULT_REGION,
):
"""
Function to setup Inference service on ECS
:param docker_image_uri:
:param framework:
:param cluster_arn:
:param model_name:
:param worker_instance_id:
:param num_gpus:
:param num_neurons:
:param region:
:return: <tuple> service_name, task_family, revision if all steps passed else Exception
Cleans up the resources if any step fails
"""
datetime_suffix = datetime.datetime.now().strftime("%Y%m%d-%H-%M-%S")
processor = "gpu" if "gpu" in docker_image_uri else "eia" if "eia" in docker_image_uri else "neuron" if "neuron" in docker_image_uri else "cpu"
port_mappings = get_ecs_port_mappings(framework)
log_group_name = f"/ecs/{framework}-inference-{processor}"
num_cpus = ec2_utils.get_instance_num_cpus(worker_instance_id,
region=region)
# We assume that about 80% of RAM is free on the instance, since we are not directly querying it to find out
# what the memory utilization is.
memory = int(
ec2_utils.get_instance_memory(worker_instance_id, region=region) * 0.8)
cluster_name = get_ecs_cluster_name(cluster_arn, region=region)
# Below values here are just for sanity
arguments_dict = {
"family_name": cluster_name,
"image": docker_image_uri,
"log_group_name": log_group_name,
"log_stream_prefix": datetime_suffix,
"port_mappings": port_mappings,
"num_cpu": num_cpus,
"memory": memory,
"region": region,
}
if processor == "gpu" and num_gpus:
arguments_dict["num_gpu"] = num_gpus
if framework == "tensorflow":
model_name = get_tensorflow_model_name(processor, model_name)
model_base_path = get_tensorflow_model_base_path(docker_image_uri)
_, image_framework_version = get_framework_and_version_from_tag(
docker_image_uri)
if Version(image_framework_version) in SpecifierSet(">=2.7"):
arguments_dict["container_command"] = [
build_tensorflow_inference_command_tf27_and_above(model_name)
]
arguments_dict["entrypoint"] = ["sh", "-c"]
arguments_dict[
"environment"] = get_tensorflow_inference_environment_variables(
model_name, model_base_path)
print(f"Added environment variables: {arguments_dict['environment']}")
elif framework in ["mxnet", "pytorch"]:
arguments_dict["container_command"] = [
get_inference_run_command(docker_image_uri, model_name, processor)
]
if processor == "eia":
arguments_dict["health_check"] = {
"retries":
2,
"command": [
"CMD-SHELL",
"LD_LIBRARY_PATH=/opt/ei_health_check/lib /opt/ei_health_check/bin/health_check"
],
"timeout":
5,
"interval":
30,
"startPeriod":
60,
}
arguments_dict["inference_accelerators"] = {
"deviceName": "device_1",
"deviceType": ei_accelerator_type
}
if processor == "neuron" and num_neurons:
arguments_dict["num_neurons"] = num_neurons
try:
task_family, revision = register_ecs_task_definition(**arguments_dict)
print(f"Created Task definition - {task_family}:{revision}")
service_name = create_ecs_service(cluster_name,
f"service-{cluster_name}",
f"{task_family}:{revision}",
region=region)
print(
f"Created ECS service - {service_name} with cloudwatch log group - {log_group_name} "
f"log stream prefix - {datetime_suffix}/{cluster_name}")
if check_running_task_for_ecs_service(cluster_name,
service_name,
region=region):
print("Service status verified as running. Running inference ...")
else:
raise Exception(f"No task running in the service: {service_name}")
return service_name, task_family, revision
except Exception as e:
raise ECSServiceCreationException(
f"Setup Inference Service Exception - {e}")
