/
create_hyperparameter_tuning_job_python_package_sample.py
101 lines (92 loc) · 3.62 KB
/
create_hyperparameter_tuning_job_python_package_sample.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# [START aiplatform_create_hyperparameter_tuning_job_python_package_sample]
from google.cloud import aiplatform
def create_hyperparameter_tuning_job_python_package_sample(
project: str,
display_name: str,
executor_image_uri: str,
package_uri: str,
python_module: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.JobServiceClient(client_options=client_options)
# study_spec
metric = {
"metric_id": "val_rmse",
"goal": aiplatform.gapic.StudySpec.MetricSpec.GoalType.MINIMIZE,
}
conditional_parameter_decay = {
"parameter_spec": {
"parameter_id": "decay",
"double_value_spec": {"min_value": 1e-07, "max_value": 1},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [32, 64]},
}
conditional_parameter_learning_rate = {
"parameter_spec": {
"parameter_id": "learning_rate",
"double_value_spec": {"min_value": 1e-07, "max_value": 1},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [4, 8, 16]},
}
parameter = {
"parameter_id": "batch_size",
"discrete_value_spec": {"values": [4, 8, 16, 32, 64, 128]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
"conditional_parameter_specs": [
conditional_parameter_decay,
conditional_parameter_learning_rate,
],
}
# trial_job_spec
machine_spec = {
"machine_type": "n1-standard-4",
"accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
"accelerator_count": 1,
}
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": [],
},
}
# hyperparameter_tuning_job
hyperparameter_tuning_job = {
"display_name": display_name,
"max_trial_count": 4,
"parallel_trial_count": 2,
"study_spec": {
"metrics": [metric],
"parameters": [parameter],
"algorithm": aiplatform.gapic.StudySpec.Algorithm.RANDOM_SEARCH,
},
"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("response:", response)
# [END aiplatform_create_hyperparameter_tuning_job_python_package_sample]