Google Vertex AI hyperparameter tuning: 500 Internal Error encountered - google-cloud-ml

Question

I was trying to run a hyperparameter tuning job on Vertex AI using the Python SDK described here. Around 2 hours ago it was successful sending the job to run. I noticed there are some errors in my code so the run failed, and I went back and fix it, and I re-run the code and what I got was as below.
Traceback (most recent call last):
File "/workspace/.pip-modules/lib/python3.8/site-packages/google/api_core/grpc_helpers.py", line 67, in error_remapped_callable
return callable_(*args, **kwargs)
File "/workspace/.pip-modules/lib/python3.8/site-packages/grpc/_channel.py", line 946, in __call__
return _end_unary_response_blocking(state, call, False, None)
File "/workspace/.pip-modules/lib/python3.8/site-packages/grpc/_channel.py", line 849, in _end_unary_response_blocking
raise _InactiveRpcError(state)
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
status = StatusCode.INTERNAL
details = "Internal error encountered."
debug_error_string = "{"created":"#1623393121.374988331","description":"Error received from peer ipv4:142.251.33.74:443","file":"src/core/lib/surface/call.cc","file_line":1066,"grpc_message":"Internal error encountered.","grpc_status":13}"
>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/workspace/ariel_ml_2021/hparam_tuning.py", line 140, in <module>
create_hyperparameter_tuning_job_python_package()
File "/workspace/ariel_ml_2021/hparam_tuning.py", line 133, in create_hyperparameter_tuning_job_python_package
response = client.create_hyperparameter_tuning_job(
File "/workspace/.pip-modules/lib/python3.8/site-packages/google/cloud/aiplatform_v1/services/job_service/client.py", line 1363, in create_hyperparameter_tuning_job
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
File "/workspace/.pip-modules/lib/python3.8/site-packages/google/api_core/gapic_v1/method.py", line 145, in __call__
return wrapped_func(*args, **kwargs)
File "/workspace/.pip-modules/lib/python3.8/site-packages/google/api_core/grpc_helpers.py", line 69, in error_remapped_callable
six.raise_from(exceptions.from_grpc_error(exc), exc)
File "<string>", line 3, in raise_from
google.api_core.exceptions.InternalServerError: 500 Internal error encountered.
I thought this might be due to my changes in the python code that causes the error, so I try with the original copy (without making any changes), and the error persists. The code for hyperparameter tuning is as below if required.
from google.cloud import aiplatform
def create_hyperparameter_tuning_job_python_package(
project: str = "<my_project_id>",
display_name: str = "<some_description>",
executor_image_uri: str = "us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-4:latest",
package_uri: str = "gs://<bucket_name>/",
python_module: str = "train_second", # located at gs://<bucket_name>/train_second.py
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
client_options = {"api_endpoint": api_endpoint}
client = aiplatform.gapic.JobServiceClient(client_options=client_options)
metric = {
"metric_id": "ariel_score",
"goal": aiplatform.gapic.StudySpec.MetricSpec.GoalType.MAXIMIZE,
}
conditional_param_H1 = {
"parameter_spec": {
"parameter_id": "H1",
"discrete_value_spec": {"values": [4, 8, 16, 32, 64, 128, 256, 512, 1024]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
conditional_param_H2 = {
"parameter_spec": {
"parameter_id": "H2",
"discrete_value_spec": {"values": [64, 128, 256, 512, 1024]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
conditional_param_H3 = {
"parameter_spec": {
"parameter_id": "H3",
"discrete_value_spec": {"values": [4, 8, 16, 32, 64, 128, 256, 512, 1024]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
conditional_param_D1 = {
"parameter_spec": {
"parameter_id": "D1",
"double_value_spec": {"min_value": 0.01, "max_value": 0.5},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
conditional_param_mean = {
"parameter_spec": {
"parameter_id": "mean",
"discrete_value_spec": {"values": [0., 1.]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
conditional_param_std = {
"parameter_spec": {
"parameter_id": "std",
"double_value_spec": {"min_value": 0.005, "max_value": 0.5},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
conditional_param_lr = {
"parameter_spec": {
"parameter_id": "lr",
"discrete_value_spec": {"values": [0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [10, 25, 50, 100]}
}
parameter = {
"parameter_id": "batch_size",
"discrete_value_spec": {"values": [10, 25, 50, 100]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
"conditional_parameter_specs": [
conditional_param_H1,
conditional_param_H2,
conditional_param_H3,
conditional_param_D1,
conditional_param_mean,
conditional_param_std,
conditional_param_lr,
],
}
# Trial job spec
machine_spec = {
"machine_type": "e2-standard-4",
}
worker_pool_spec = {
"machine_spec": machine_spec,
"replica_count": 1,
"python_package_spec": {
"executor_image_uri": executor_image_uri,
"package_uris": [package_uri],
"python_module": python_module,
"args": [],
}
}
# hparam tuning job
hyperparameter_tuning_job = {
"display_name": display_name,
"max_trial_count": 2,
"parallel_trial_count": 2,
"study_spec": {
"metrics": [metric],
"parameters": [parameter],
},
"trial_job_spec": {"worker_pool_specs": [worker_pool_spec]},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_hyperparameter_tuning_job(
parent=parent, hyperparameter_tuning_job=hyperparameter_tuning_job
)
print(f"response:", response)
if __name__ == "__main__":
create_hyperparameter_tuning_job_python_package()
Thanks in advance.

Answer 1

It seems that the endpoint at us-central1 is experiencing the problem. The workaround is to use another endpoint such as with us-east1 and the problem is solved.

Answer 2

Your package_uri is incorrect. It should point to a file containing the Python package (i.e. a tar.bz file with all the code), not a directory or a bucket.