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CustomTrainingJob(
display_name: str,
script_path: str,
container_uri: str,
requirements: Optional[Sequence[str]] = None,
model_serving_container_image_uri: Optional[str] = None,
model_serving_container_predict_route: Optional[str] = None,
model_serving_container_health_route: Optional[str] = None,
model_serving_container_command: Optional[Sequence[str]] = None,
model_serving_container_args: Optional[Sequence[str]] = None,
model_serving_container_environment_variables: Optional[Dict[str, str]] = None,
model_serving_container_ports: Optional[Sequence[int]] = None,
model_description: Optional[str] = None,
model_instance_schema_uri: Optional[str] = None,
model_parameters_schema_uri: Optional[str] = None,
model_prediction_schema_uri: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
staging_bucket: Optional[str] = None,
)
Class to launch a Custom Training Job in Vertex AI using a script.
Takes a training implementation as a python script and executes that script in Cloud Vertex AI Training.
Inheritance
builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.training_jobs._TrainingJob > google.cloud.aiplatform.training_jobs._CustomTrainingJob > CustomTrainingJobMethods
CustomTrainingJob
CustomTrainingJob(
display_name: str,
script_path: str,
container_uri: str,
requirements: Optional[Sequence[str]] = None,
model_serving_container_image_uri: Optional[str] = None,
model_serving_container_predict_route: Optional[str] = None,
model_serving_container_health_route: Optional[str] = None,
model_serving_container_command: Optional[Sequence[str]] = None,
model_serving_container_args: Optional[Sequence[str]] = None,
model_serving_container_environment_variables: Optional[Dict[str, str]] = None,
model_serving_container_ports: Optional[Sequence[int]] = None,
model_description: Optional[str] = None,
model_instance_schema_uri: Optional[str] = None,
model_parameters_schema_uri: Optional[str] = None,
model_prediction_schema_uri: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
staging_bucket: Optional[str] = None,
)
Constructs a Custom Training Job from a Python script.
job = aiplatform.CustomTrainingJob( display_name='test-train', script_path='test_script.py', requirements=['pandas', 'numpy'], container_uri='gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest', model_serving_container_image_uri='gcr.io/my-trainer/serving:1', model_serving_container_predict_route='predict', model_serving_container_health_route='metadata)
Usage with Dataset:
ds = aiplatform.TabularDataset( 'projects/my-project/locations/us-central1/datasets/12345')
job.run(ds, replica_count=1, model_display_name='my-trained-model')
Usage without Dataset:
job.run(replica_count=1, model_display_name='my-trained-model)
TODO(b/169782082) add documentation about traning utilities To ensure your model gets saved in Vertex AI, write your saved model to os.environ["AIP_MODEL_DIR"] in your provided training script.
Name | Description |
display_name |
str
Required. The user-defined name of this TrainingPipeline. |
script_path |
str
Required. Local path to training script. |
container_uri |
str
Required: Uri of the training container image in the GCR. |
requirements |
Sequence[str]
List of python packages dependencies of script. |
model_serving_container_image_uri |
str
If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script. |
model_serving_container_predict_route |
str
If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI. |
model_serving_container_health_route |
str
If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform. |
model_serving_container_command |
Sequence[str]
The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. |
model_serving_container_args |
Sequence[str]
The arguments to the command. The Docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. |
model_serving_container_environment_variables |
Dict[str, str]
The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. |
model_serving_container_ports |
Sequence[int]
Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network. |
model_description |
str
The description of the Model. |
model_instance_schema_uri |
str
Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in |
model_parameters_schema_uri |
str
Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via |
model_prediction_schema_uri |
str
Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via |
project |
str
Project to run training in. Overrides project set in aiplatform.init. |
location |
str
Location to run training in. Overrides location set in aiplatform.init. |
credentials |
auth_credentials.Credentials
Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init. |
training_encryption_spec_key_name |
Optional[str]
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: |
model_encryption_spec_key_name |
Optional[str]
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: |
staging_bucket |
str
Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init. |
run
run(
dataset: Optional[
Union[
google.cloud.aiplatform.datasets.image_dataset.ImageDataset,
google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset,
google.cloud.aiplatform.datasets.text_dataset.TextDataset,
google.cloud.aiplatform.datasets.video_dataset.VideoDataset,
]
] = None,
annotation_schema_uri: Optional[str] = None,
model_display_name: Optional[str] = None,
base_output_dir: Optional[str] = None,
service_account: Optional[str] = None,
network: Optional[str] = None,
bigquery_destination: Optional[str] = None,
args: Optional[List[Union[float, int, str]]] = None,
environment_variables: Optional[Dict[str, str]] = None,
replica_count: int = 0,
machine_type: str = "n1-standard-4",
accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
accelerator_count: int = 0,
training_fraction_split: float = 0.8,
validation_fraction_split: float = 0.1,
test_fraction_split: float = 0.1,
predefined_split_column_name: Optional[str] = None,
tensorboard: Optional[str] = None,
sync=True,
)
Runs the custom training job.
Distributed Training Support: If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool. ie: replica_count = 10 will result in 1 chief and 9 workers All replicas have same machine_type, accelerator_type, and accelerator_count
Data fraction splits:
Any of training_fraction_split
, validation_fraction_split
and
test_fraction_split
may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI.If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.
Name | Description |
annotation_schema_uri |
str
Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with |
model_display_name |
str
If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used. |
base_output_dir |
str
GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. Vertex AI 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. |
network |
str
The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. |
bigquery_destination |
str
Provide this field if |
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 provisioned as a worker replica pool. |
machine_type |
str
The type of machine to use for training. |
accelerator_type |
str
Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 |
accelerator_count |
int
The number of accelerators to attach to a worker replica. |
training_fraction_split |
float
The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided. |
validation_fraction_split |
float
The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided. |
test_fraction_split |
float
The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided. |
predefined_split_column_name |
str
Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of { |
tensorboard |
str
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: |
sync |
bool
Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. |
Type | Description |
model | The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model. |