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feat: expose env var in cust training class run func args #366

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merged 1 commit into from May 10, 2021
Merged

feat: expose env var in cust training class run func args #366

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120 changes: 120 additions & 0 deletions google/cloud/aiplatform/training_jobs.py
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Expand Up @@ -1805,6 +1805,7 @@ def run(
service_account: Optional[str] = None,
bigquery_destination: Optional[str] = None,
args: Optional[List[Union[str, float, int]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
replica_count: int = 0,
machine_type: str = "n1-standard-4",
accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
Expand Down Expand Up @@ -1880,6 +1881,13 @@ def run(
base_output_dir (str):
GCS output directory of job. If not provided a
timestamped directory in the staging directory will be used.

AI Platform sets the following environment variables when it runs your training code:

- AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/
- AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

service_account (str):
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
Expand All @@ -1900,6 +1908,16 @@ def run(
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"
args (List[Unions[str, int, float]]):
Command line arguments to be passed to the Python script.
environment_variables (Dict[str, str]):
Environment variables to be passed to the container.
Should be a dictionary where keys are environment variable names
and values are environment variable values for those names.
At most 10 environment variables can be specified.
The Name of the environment variable must be unique.

environment_variables = {
'MY_KEY': 'MY_VALUE'
}
replica_count (int):
The number of worker replicas. If replica count = 1 then one chief
replica will be provisioned. If replica_count > 1 the remainder will be
Expand Down Expand Up @@ -1960,6 +1978,7 @@ def run(
worker_pool_specs=worker_pool_specs,
managed_model=managed_model,
args=args,
environment_variables=environment_variables,
base_output_dir=base_output_dir,
service_account=service_account,
bigquery_destination=bigquery_destination,
Expand All @@ -1986,6 +2005,7 @@ def _run(
worker_pool_specs: _DistributedTrainingSpec,
managed_model: Optional[gca_model.Model] = None,
args: Optional[List[Union[str, float, int]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
base_output_dir: Optional[str] = None,
service_account: Optional[str] = None,
bigquery_destination: Optional[str] = None,
Expand Down Expand Up @@ -2018,9 +2038,26 @@ def _run(
Model proto if this script produces a Managed Model.
args (List[Unions[str, int, float]]):
Command line arguments to be passed to the Python script.
environment_variables (Dict[str, str]):
Environment variables to be passed to the container.
Should be a dictionary where keys are environment variable names
and values are environment variable values for those names.
At most 10 environment variables can be specified.
The Name of the environment variable must be unique.

environment_variables = {
'MY_KEY': 'MY_VALUE'
}
base_output_dir (str):
GCS output directory of job. If not provided a
timestamped directory in the staging directory will be used.

AI Platform sets the following environment variables when it runs your training code:

- AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/
- AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

service_account (str):
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
Expand Down Expand Up @@ -2083,6 +2120,9 @@ def _run(
if args:
spec["pythonPackageSpec"]["args"] = args

if environment_variables:
spec["pythonPackageSpec"]["env"] = environment_variables

(
training_task_inputs,
base_output_dir,
Expand Down Expand Up @@ -2334,6 +2374,7 @@ def run(
service_account: Optional[str] = None,
bigquery_destination: Optional[str] = None,
args: Optional[List[Union[str, float, int]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
replica_count: int = 0,
machine_type: str = "n1-standard-4",
accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
Expand Down Expand Up @@ -2402,6 +2443,13 @@ def run(
base_output_dir (str):
GCS output directory of job. If not provided a
timestamped directory in the staging directory will be used.

AI Platform sets the following environment variables when it runs your training code:

- AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/
- AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

service_account (str):
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
Expand All @@ -2422,6 +2470,16 @@ def run(
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"
args (List[Unions[str, int, float]]):
Command line arguments to be passed to the Python script.
environment_variables (Dict[str, str]):
Environment variables to be passed to the container.
Should be a dictionary where keys are environment variable names
and values are environment variable values for those names.
At most 10 environment variables can be specified.
The Name of the environment variable must be unique.

environment_variables = {
'MY_KEY': 'MY_VALUE'
}
replica_count (int):
The number of worker replicas. If replica count = 1 then one chief
replica will be provisioned. If replica_count > 1 the remainder will be
Expand Down Expand Up @@ -2481,6 +2539,7 @@ def run(
worker_pool_specs=worker_pool_specs,
managed_model=managed_model,
args=args,
environment_variables=environment_variables,
base_output_dir=base_output_dir,
service_account=service_account,
bigquery_destination=bigquery_destination,
Expand All @@ -2506,6 +2565,7 @@ def _run(
worker_pool_specs: _DistributedTrainingSpec,
managed_model: Optional[gca_model.Model] = None,
args: Optional[List[Union[str, float, int]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
base_output_dir: Optional[str] = None,
service_account: Optional[str] = None,
bigquery_destination: Optional[str] = None,
Expand Down Expand Up @@ -2535,9 +2595,26 @@ def _run(
Model proto if this script produces a Managed Model.
args (List[Unions[str, int, float]]):
Command line arguments to be passed to the Python script.
environment_variables (Dict[str, str]):
Environment variables to be passed to the container.
Should be a dictionary where keys are environment variable names
and values are environment variable values for those names.
At most 10 environment variables can be specified.
The Name of the environment variable must be unique.

environment_variables = {
'MY_KEY': 'MY_VALUE'
}
base_output_dir (str):
GCS output directory of job. If not provided a
timestamped directory in the staging directory will be used.

AI Platform sets the following environment variables when it runs your training code:

- AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/
- AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

service_account (str):
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
Expand Down Expand Up @@ -2593,6 +2670,9 @@ def _run(
if args:
spec["containerSpec"]["args"] = args

if environment_variables:
spec["containerSpec"]["env"] = environment_variables

(
training_task_inputs,
base_output_dir,
Expand Down Expand Up @@ -3625,6 +3705,7 @@ def run(
service_account: Optional[str] = None,
bigquery_destination: Optional[str] = None,
args: Optional[List[Union[str, float, int]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
replica_count: int = 0,
machine_type: str = "n1-standard-4",
accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
Expand Down Expand Up @@ -3693,6 +3774,13 @@ def run(
base_output_dir (str):
GCS output directory of job. If not provided a
timestamped directory in the staging directory will be used.

AI Platform sets the following environment variables when it runs your training code:

- AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/
- AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

service_account (str):
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
Expand All @@ -3713,6 +3801,16 @@ def run(
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"
args (List[Unions[str, int, float]]):
Command line arguments to be passed to the Python script.
environment_variables (Dict[str, str]):
Environment variables to be passed to the container.
Should be a dictionary where keys are environment variable names
and values are environment variable values for those names.
At most 10 environment variables can be specified.
The Name of the environment variable must be unique.

environment_variables = {
'MY_KEY': 'MY_VALUE'
}
replica_count (int):
The number of worker replicas. If replica count = 1 then one chief
replica will be provisioned. If replica_count > 1 the remainder will be
Expand Down Expand Up @@ -3767,6 +3865,7 @@ def run(
worker_pool_specs=worker_pool_specs,
managed_model=managed_model,
args=args,
environment_variables=environment_variables,
base_output_dir=base_output_dir,
service_account=service_account,
training_fraction_split=training_fraction_split,
Expand All @@ -3792,6 +3891,7 @@ def _run(
worker_pool_specs: _DistributedTrainingSpec,
managed_model: Optional[gca_model.Model] = None,
args: Optional[List[Union[str, float, int]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
base_output_dir: Optional[str] = None,
service_account: Optional[str] = None,
training_fraction_split: float = 0.8,
Expand Down Expand Up @@ -3822,9 +3922,26 @@ def _run(
Model proto if this script produces a Managed Model.
args (List[Unions[str, int, float]]):
Command line arguments to be passed to the Python script.
environment_variables (Dict[str, str]):
Environment variables to be passed to the container.
Should be a dictionary where keys are environment variable names
and values are environment variable values for those names.
At most 10 environment variables can be specified.
The Name of the environment variable must be unique.

environment_variables = {
'MY_KEY': 'MY_VALUE'
}
base_output_dir (str):
GCS output directory of job. If not provided a
timestamped directory in the staging directory will be used.

AI Platform sets the following environment variables when it runs your training code:

- AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/
- AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

service_account (str):
Specifies the service account for workload run-as account.
Users submitting jobs must have act-as permission on this run-as account.
Expand Down Expand Up @@ -3866,6 +3983,9 @@ def _run(
if args:
spec["pythonPackageSpec"]["args"] = args

if environment_variables:
spec["pythonPackageSpec"]["env"] = environment_variables

(
training_task_inputs,
base_output_dir,
Expand Down