feat: expose boot disk type and size for CustomTrainingJob, CustomPythonPackageTrainingJob, and CustomContainerTrainingJob

google/cloud/aiplatform/jobs.py

@@ -1070,13 +1070,15 @@ def from_local_script(
         display_name: str,
         script_path: str,
         container_uri: str,
-        args: Optional[List[Union[str, float, int]]] = None,
+        args: Optional[Sequence[str]] = None,
         requirements: Optional[Sequence[str]] = None,
         environment_variables: Optional[Dict[str, str]] = None,
         replica_count: int = 1,
         machine_type: str = "n1-standard-4",
         accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
         accelerator_count: int = 0,
+        boot_disk_type: str = "pd-ssd",
+        boot_disk_size_gb: int = 100,
         base_output_dir: Optional[str] = None,
         project: Optional[str] = None,
         location: Optional[str] = None,
@@ -1110,7 +1112,7 @@ def from_local_script(
                 Required. Local path to training script.
             container_uri (str):
                 Required: Uri of the training container image to use for custom job.
-            args (Optional[List[Union[str, float, int]]]):
+            args (Optional[Sequence[str]]):
                 Optional. Command line arguments to be passed to the Python task.
             requirements (Sequence[str]):
                 Optional. List of python packages dependencies of script.
@@ -1136,6 +1138,13 @@ def from_local_script(
                 NVIDIA_TESLA_T4
             accelerator_count (int):
                 Optional. The number of accelerators to attach to a worker replica.
+            boot_disk_type (str):
+                Optional. Type of the boot disk, default is `pd-ssd`.
+                Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
+                `pd-standard` (Persistent Disk Hard Disk Drive).
+            boot_disk_size_gb (int):
+                Optional. Size in GB of the boot disk, default is 100GB.
+                boot disk size must be within the range of [100, 64000].
             base_output_dir (str):
                 Optional. GCS output directory of job. If not provided a
                 timestamped directory in the staging directory will be used.
@@ -1188,6 +1197,8 @@ def from_local_script(
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         ).pool_specs
 
         python_packager = source_utils._TrainingScriptPythonPackager(

google/cloud/aiplatform/training_jobs.py

@@ -1139,6 +1139,8 @@ def _prepare_and_validate_run(
         machine_type: str = "n1-standard-4",
         accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
         accelerator_count: int = 0,
+        boot_disk_type: str = "pd-ssd",
+        boot_disk_size_gb: int = 100,
     ) -> Tuple[worker_spec_utils._DistributedTrainingSpec, Optional[gca_model.Model]]:
         """Create worker pool specs and managed model as well validating the
         run.
@@ -1172,6 +1174,13 @@ def _prepare_and_validate_run(
                 NVIDIA_TESLA_T4
             accelerator_count (int):
                 The number of accelerators to attach to a worker replica.
+            boot_disk_type (str):
+                Type of the boot disk, default is `pd-ssd`.
+                Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
+                `pd-standard` (Persistent Disk Hard Disk Drive).
+            boot_disk_size_gb (int):
+                Size in GB of the boot disk, default is 100GB.
+                boot disk size must be within the range of [100, 64000].
         Returns:
             Worker pools specs and managed model for run.
 
@@ -1204,6 +1213,8 @@ def _prepare_and_validate_run(
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         ).pool_specs
 
         managed_model = self._managed_model
@@ -1588,6 +1599,8 @@ def run(
         machine_type: str = "n1-standard-4",
         accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
         accelerator_count: int = 0,
+        boot_disk_type: str = "pd-ssd",
+        boot_disk_size_gb: int = 100,
         training_fraction_split: float = 0.8,
         validation_fraction_split: float = 0.1,
         test_fraction_split: float = 0.1,
@@ -1724,6 +1737,13 @@ def run(
                 NVIDIA_TESLA_T4
             accelerator_count (int):
                 The number of accelerators to attach to a worker replica.
+            boot_disk_type (str):
+                Type of the boot disk, default is `pd-ssd`.
+                Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
+                `pd-standard` (Persistent Disk Hard Disk Drive).
+            boot_disk_size_gb (int):
+                Size in GB of the boot disk, default is 100GB.
+                boot disk size must be within the range of [100, 64000].
             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.
@@ -1774,6 +1794,8 @@ def run(
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         )
 
         # make and copy package
@@ -2241,6 +2263,8 @@ def run(
         machine_type: str = "n1-standard-4",
         accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
         accelerator_count: int = 0,
+        boot_disk_type: str = "pd-ssd",
+        boot_disk_size_gb: int = 100,
         training_fraction_split: float = 0.8,
         validation_fraction_split: float = 0.1,
         test_fraction_split: float = 0.1,
@@ -2370,6 +2394,13 @@ def run(
                 NVIDIA_TESLA_T4
             accelerator_count (int):
                 The number of accelerators to attach to a worker replica.
+            boot_disk_type (str):
+                Type of the boot disk, default is `pd-ssd`.
+                Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
+                `pd-standard` (Persistent Disk Hard Disk Drive).
+            boot_disk_size_gb (int):
+                Size in GB of the boot disk, default is 100GB.
+                boot disk size must be within the range of [100, 64000].
             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.
@@ -2425,6 +2456,8 @@ def run(
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         )
 
         return self._run(
@@ -4402,6 +4435,8 @@ def run(
         machine_type: str = "n1-standard-4",
         accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
         accelerator_count: int = 0,
+        boot_disk_type: str = "pd-ssd",
+        boot_disk_size_gb: int = 100,
         training_fraction_split: float = 0.8,
         validation_fraction_split: float = 0.1,
         test_fraction_split: float = 0.1,
@@ -4531,6 +4566,13 @@ def run(
                 NVIDIA_TESLA_T4
             accelerator_count (int):
                 The number of accelerators to attach to a worker replica.
+            boot_disk_type (str):
+                Type of the boot disk, default is `pd-ssd`.
+                Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
+                `pd-standard` (Persistent Disk Hard Disk Drive).
+            boot_disk_size_gb (int):
+                Size in GB of the boot disk, default is 100GB.
+                boot disk size must be within the range of [100, 64000].
             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.
@@ -4581,6 +4623,8 @@ def run(
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         )
 
         return self._run(

google/cloud/aiplatform/utils/worker_spec_utils.py

@@ -22,16 +22,19 @@
 )
 
 
-class _MachineSpec(NamedTuple):
-    """Specification container for Machine specs used for distributed training.
+class _WorkerPoolSpec(NamedTuple):
+    """Specification container for Worker Pool specs used for distributed training.
 
     Usage:
 
-    spec = _MachineSpec(
+    spec = _WorkerPoolSpec(
                 replica_count=10,
                 machine_type='n1-standard-4',
                 accelerator_count=2,
-                accelerator_type='NVIDIA_TESLA_K80')
+                accelerator_type='NVIDIA_TESLA_K80',
+                boot_disk_type='pd-ssd',
+                boot_disk_size_gb=100,
+            )
 
     Note that container and python package specs are not stored with this spec.
     """
@@ -40,6 +43,8 @@ class _MachineSpec(NamedTuple):
     machine_type: str = "n1-standard-4"
     accelerator_count: int = 0
     accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED"
+    boot_disk_type: str = "pd-ssd"
+    boot_disk_size_gb: int = 100
 
     def _get_accelerator_type(self) -> Optional[str]:
         """Validates accelerator_type and returns the name of the accelerator.
@@ -70,7 +75,12 @@ def spec_dict(self) -> Dict[str, Union[int, str, Dict[str, Union[int, str]]]]:
         spec = {
             "machine_spec": {"machine_type": self.machine_type},
             "replica_count": self.replica_count,
+            "disk_spec": {
+                "boot_disk_type": self.boot_disk_type,
+                "boot_disk_size_gb": self.boot_disk_size_gb,
+            },
         }
+
         accelerator_type = self._get_accelerator_type()
         if accelerator_type and self.accelerator_count:
             spec["machine_spec"]["accelerator_type"] = accelerator_type
@@ -98,25 +108,29 @@ class _DistributedTrainingSpec(NamedTuple):
     Usage:
 
     dist_training_spec = _DistributedTrainingSpec(
-        chief_spec = _MachineSpec(
+        chief_spec = _WorkerPoolSpec(
                 replica_count=1,
                 machine_type='n1-standard-4',
                 accelerator_count=2,
-                accelerator_type='NVIDIA_TESLA_K80'
-                ),
-        worker_spec = _MachineSpec(
+                accelerator_type='NVIDIA_TESLA_K80',
+                boot_disk_type='pd-ssd',
+                boot_disk_size_gb=100,
+            ),
+        worker_spec = _WorkerPoolSpec(
                 replica_count=10,
                 machine_type='n1-standard-4',
                 accelerator_count=2,
-                accelerator_type='NVIDIA_TESLA_K80'
-                )
+                accelerator_type='NVIDIA_TESLA_K80',
+                boot_disk_type='pd-ssd',
+                boot_disk_size_gb=100,
+            ),
     )
     """
 
-    chief_spec: _MachineSpec = _MachineSpec()
-    worker_spec: _MachineSpec = _MachineSpec()
-    parameter_server_spec: _MachineSpec = _MachineSpec()
-    evaluator_spec: _MachineSpec = _MachineSpec()
+    chief_spec: _WorkerPoolSpec = _WorkerPoolSpec()
+    worker_spec: _WorkerPoolSpec = _WorkerPoolSpec()
+    parameter_server_spec: _WorkerPoolSpec = _WorkerPoolSpec()
+    evaluator_spec: _WorkerPoolSpec = _WorkerPoolSpec()
 
     @property
     def pool_specs(
@@ -156,6 +170,8 @@ def chief_worker_pool(
         machine_type: str = "n1-standard-4",
         accelerator_count: int = 0,
         accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
+        boot_disk_type: str = "pd-ssd",
+        boot_disk_size_gb: int = 100,
     ) -> "_DistributedTrainingSpec":
         """Parameterizes Config to support only chief with worker replicas.
 
@@ -174,6 +190,13 @@ def chief_worker_pool(
                 NVIDIA_TESLA_T4
             accelerator_count (int):
                 The number of accelerators to attach to a worker replica.
+            boot_disk_type (str):
+                Type of the boot disk (default is `pd-ssd`).
+                Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
+                `pd-standard` (Persistent Disk Hard Disk Drive).
+            boot_disk_size_gb (int):
+                Size in GB of the boot disk (default is 100GB).
+                boot disk size must be within the range of [100, 64000].
 
         Returns:
             _DistributedTrainingSpec representing one chief and n workers all of same
@@ -182,18 +205,22 @@ def chief_worker_pool(
         if replica_count <= 0:
             return cls()
 
-        chief_spec = _MachineSpec(
+        chief_spec = _WorkerPoolSpec(
             replica_count=1,
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         )
 
-        worker_spec = _MachineSpec(
+        worker_spec = _WorkerPoolSpec(
             replica_count=replica_count - 1,
             machine_type=machine_type,
             accelerator_count=accelerator_count,
             accelerator_type=accelerator_type,
+            boot_disk_type=boot_disk_type,
+            boot_disk_size_gb=boot_disk_size_gb,
         )
 
         return cls(chief_spec=chief_spec, worker_spec=worker_spec)

tests/unit/aiplatform/test_custom_job.py

@@ -54,6 +54,8 @@
 
 _TEST_TRAINING_CONTAINER_IMAGE = "gcr.io/test-training/container:image"
 
+_TEST_RUN_ARGS = ["-v", "0.1", "--test=arg"]
+
 _TEST_WORKER_POOL_SPEC = [
     {
         "machine_spec": {
@@ -62,10 +64,11 @@
             "accelerator_count": 1,
         },
         "replica_count": 1,
+        "disk_spec": {"boot_disk_type": "pd-ssd", "boot_disk_size_gb": 100},
         "container_spec": {
             "image_uri": _TEST_TRAINING_CONTAINER_IMAGE,
             "command": [],
-            "args": [],
+            "args": _TEST_RUN_ARGS,
         },
     }
 ]
@@ -490,3 +493,41 @@ def test_create_custom_job_without_base_output_dir(self,):
         assert job.job_spec.base_output_directory.output_uri_prefix.startswith(
             f"{_TEST_STAGING_BUCKET}/aiplatform-custom-job"
         )
+
+    @pytest.mark.usefixtures("mock_python_package_to_gcs")
+    @pytest.mark.parametrize("sync", [True, False])
+    def test_create_from_local_script_with_all_args(
+        self, get_custom_job_mock, create_custom_job_mock, sync
+    ):
+        aiplatform.init(
+            project=_TEST_PROJECT,
+            location=_TEST_LOCATION,
+            staging_bucket=_TEST_STAGING_BUCKET,
+            encryption_spec_key_name=_TEST_DEFAULT_ENCRYPTION_KEY_NAME,
+        )
+
+        # configuration on this is tested in test_training_jobs.py
+        job = aiplatform.CustomJob.from_local_script(
+            display_name=_TEST_DISPLAY_NAME,
+            script_path=test_training_jobs._TEST_LOCAL_SCRIPT_FILE_NAME,
+            container_uri=_TEST_TRAINING_CONTAINER_IMAGE,
+            args=_TEST_RUN_ARGS,
+            requirements=test_training_jobs._TEST_REQUIREMENTS,
+            environment_variables=test_training_jobs._TEST_ENVIRONMENT_VARIABLES,
+            replica_count=test_training_jobs._TEST_REPLICA_COUNT,
+            machine_type=test_training_jobs._TEST_MACHINE_TYPE,
+            accelerator_type=test_training_jobs._TEST_ACCELERATOR_TYPE,
+            accelerator_count=test_training_jobs._TEST_ACCELERATOR_COUNT,
+            boot_disk_type=test_training_jobs._TEST_BOOT_DISK_TYPE,
+            boot_disk_size_gb=test_training_jobs._TEST_BOOT_DISK_SIZE_GB,
+            base_output_dir=_TEST_BASE_OUTPUT_DIR,
+            labels=_TEST_LABELS,
+        )
+
+        job.run(sync=sync)
+
+        job.wait()
+
+        assert (
+            job._gca_resource.state == gca_job_state_compat.JobState.JOB_STATE_SUCCEEDED
+        )

tests/unit/aiplatform/test_end_to_end.py

@@ -211,6 +211,10 @@ def test_dataset_create_to_model_predict(
                 "accelerator_type": test_training_jobs._TEST_ACCELERATOR_TYPE,
                 "accelerator_count": test_training_jobs._TEST_ACCELERATOR_COUNT,
             },
+            "disk_spec": {
+                "boot_disk_type": test_training_jobs._TEST_BOOT_DISK_TYPE_DEFAULT,
+                "boot_disk_size_gb": test_training_jobs._TEST_BOOT_DISK_SIZE_GB_DEFAULT,
+            },
             "python_package_spec": {
                 "executor_image_uri": test_training_jobs._TEST_TRAINING_CONTAINER_IMAGE,
                 "python_module": source_utils._TrainingScriptPythonPackager.module_name,
@@ -394,6 +398,10 @@ def test_dataset_create_to_model_predict_with_pipeline_fail(
                 "accelerator_type": test_training_jobs._TEST_ACCELERATOR_TYPE,
                 "accelerator_count": test_training_jobs._TEST_ACCELERATOR_COUNT,
             },
+            "disk_spec": {
+                "boot_disk_type": test_training_jobs._TEST_BOOT_DISK_TYPE_DEFAULT,
+                "boot_disk_size_gb": test_training_jobs._TEST_BOOT_DISK_SIZE_GB_DEFAULT,
+            },
             "python_package_spec": {
                 "executor_image_uri": test_training_jobs._TEST_TRAINING_CONTAINER_IMAGE,
                 "python_module": source_utils._TrainingScriptPythonPackager.module_name,