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prefect_gcp.aiplatform

Integrations with Google AI Platform.

Note this module is experimental. The intefaces within may change without notice.

Examples:

Run a job using Vertex AI Custom Training:
```python
from prefect_gcp.credentials import GcpCredentials
from prefect_gcp.aiplatform import VertexAICustomTrainingJob

gcp_credentials = GcpCredentials.load("BLOCK_NAME")
job = VertexAICustomTrainingJob(
    region="us-east1",
    image="us-docker.pkg.dev/cloudrun/container/job:latest",
    gcp_credentials=gcp_credentials,
)
job.run()
```

Run a job that runs the command `echo hello world` using Google Cloud Run Jobs:
```python
from prefect_gcp.credentials import GcpCredentials
from prefect_gcp.aiplatform import VertexAICustomTrainingJob

gcp_credentials = GcpCredentials.load("BLOCK_NAME")
job = VertexAICustomTrainingJob(
    command=["echo", "hello world"],
    region="us-east1",
    image="us-docker.pkg.dev/cloudrun/container/job:latest",
    gcp_credentials=gcp_credentials,
)
job.run()
```

Preview job specs:
```python
from prefect_gcp.credentials import GcpCredentials
from prefect_gcp.aiplatform import VertexAICustomTrainingJob

gcp_credentials = GcpCredentials.load("BLOCK_NAME")
job = VertexAICustomTrainingJob(
    command=["echo", "hello world"],
    region="us-east1",
    image="us-docker.pkg.dev/cloudrun/container/job:latest",
    gcp_credentials=gcp_credentials,
)
job.preview()
```

Classes

VertexAICustomTrainingJob

Bases: Infrastructure

Infrastructure block used to run Vertex AI custom training jobs.

Source code in prefect_gcp/aiplatform.py
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class VertexAICustomTrainingJob(Infrastructure):
    """
    Infrastructure block used to run Vertex AI custom training jobs.
    """

    _block_type_name = "Vertex AI Custom Training Job"
    _block_type_slug = "vertex-ai-custom-training-job"
    _logo_url = "https://cdn.sanity.io/images/3ugk85nk/production/10424e311932e31c477ac2b9ef3d53cefbaad708-250x250.png"  # noqa
    _documentation_url = "https://prefecthq.github.io/prefect-gcp/aiplatform/#prefect_gcp.aiplatform.VertexAICustomTrainingJob"  # noqa: E501

    type: Literal["vertex-ai-custom-training-job"] = Field(
        "vertex-ai-custom-training-job", description="The slug for this task type."
    )

    gcp_credentials: GcpCredentials = Field(
        default_factory=GcpCredentials,
        description=(
            "GCP credentials to use when running the configured Vertex AI custom "
            "training job. If not provided, credentials will be inferred from the "
            "environment. See `GcpCredentials` for details."
        ),
    )
    region: str = Field(
        default=...,
        description="The region where the Vertex AI custom training job resides.",
    )
    image: str = Field(
        default=...,
        title="Image Name",
        description=(
            "The image to use for a new Vertex AI custom training job. This value must "
            "refer to an image within either Google Container Registry "
            "or Google Artifact Registry, like `gcr.io/<project_name>/<repo>/`."
        ),
    )
    env: Dict[str, str] = Field(
        default_factory=dict,
        title="Environment Variables",
        description="Environment variables to be passed to your Cloud Run Job.",
    )
    machine_type: str = Field(
        default="n1-standard-4",
        description="The machine type to use for the run, which controls the available "
        "CPU and memory.",
    )
    accelerator_type: Optional[str] = Field(
        default=None, description="The type of accelerator to attach to the machine."
    )
    accelerator_count: Optional[int] = Field(
        default=None, description="The number of accelerators to attach to the machine."
    )
    boot_disk_type: str = Field(
        default="pd-ssd",
        title="Boot Disk Type",
        description="The type of boot disk to attach to the machine.",
    )
    boot_disk_size_gb: int = Field(
        default=100,
        title="Boot Disk Size",
        description="The size of the boot disk to attach to the machine, in gigabytes.",
    )
    maximum_run_time: datetime.timedelta = Field(
        default=datetime.timedelta(days=7), description="The maximum job running time."
    )
    network: Optional[str] = Field(
        default=None,
        description="The full name of the Compute Engine network"
        "to which the Job should be peered. Private services access must "
        "already be configured for the network. If left unspecified, the job "
        "is not peered with any network.",
    )
    reserved_ip_ranges: Optional[List[str]] = Field(
        default=None,
        description="A list of names for the reserved ip ranges under the VPC "
        "network that can be used for this job. If set, we will deploy the job "
        "within the provided ip ranges. Otherwise, the job will be deployed to "
        "any ip ranges under the provided VPC network.",
    )
    service_account: Optional[str] = Field(
        default=None,
        description=(
            "Specifies the service account to use "
            "as the run-as account in Vertex AI. The agent submitting jobs must have "
            "act-as permission on this run-as account. If unspecified, the AI "
            "Platform Custom Code Service Agent for the CustomJob's project is "
            "used. Takes precedence over the service account found in gcp_credentials, "
            "and required if a service account cannot be detected in gcp_credentials."
        ),
    )
    job_watch_poll_interval: float = Field(
        default=5.0,
        description=(
            "The amount of time to wait between GCP API calls while monitoring the "
            "state of a Vertex AI Job."
        ),
    )

    @property
    def job_name(self):
        """
        The name can be up to 128 characters long and can be consist of any UTF-8 characters. Reference:
        https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.CustomJob#google_cloud_aiplatform_CustomJob_display_name
        """  # noqa
        try:
            base_name = self.name or self.image.split("/")[2]
            return f"{base_name}-{uuid4().hex}"
        except IndexError:
            raise ValueError(
                "The provided image must be from either Google Container Registry "
                "or Google Artifact Registry"
            )

    def _get_compatible_labels(self) -> Dict[str, str]:
        """
        Ensures labels are compatible with GCP label requirements.
        https://cloud.google.com/resource-manager/docs/creating-managing-labels

        Ex: the Prefect provided key of prefect.io/flow-name -> prefect-io_flow-name
        """
        compatible_labels = {}
        for key, val in self.labels.items():
            new_key = slugify(
                key,
                lowercase=True,
                replacements=[("/", "_"), (".", "-")],
                max_length=63,
                regex_pattern=_DISALLOWED_GCP_LABEL_CHARACTERS,
            )
            compatible_labels[new_key] = slugify(
                val,
                lowercase=True,
                replacements=[("/", "_"), (".", "-")],
                max_length=63,
                regex_pattern=_DISALLOWED_GCP_LABEL_CHARACTERS,
            )
        return compatible_labels

    def preview(self) -> str:
        """Generate a preview of the job definition that will be sent to GCP."""
        job_spec = self._build_job_spec()
        custom_job = CustomJob(
            display_name=self.job_name,
            job_spec=job_spec,
            labels=self._get_compatible_labels(),
        )
        return str(custom_job)  # outputs a json string

    def get_corresponding_worker_type(self) -> str:
        """Return the corresponding worker type for this infrastructure block."""
        return "vertex-ai"

    async def generate_work_pool_base_job_template(self) -> dict:
        """
        Generate a base job template for a `Vertex AI` work pool with the same
        configuration as this block.
        Returns:
            - dict: a base job template for a `Vertex AI` work pool
        """
        base_job_template = await get_default_base_job_template_for_infrastructure_type(
            self.get_corresponding_worker_type(),
        )
        assert (
            base_job_template is not None
        ), "Failed to generate default base job template for Cloud Run worker."
        for key, value in self.dict(exclude_unset=True, exclude_defaults=True).items():
            if key == "command":
                base_job_template["variables"]["properties"]["command"][
                    "default"
                ] = shlex.join(value)
            elif key in [
                "type",
                "block_type_slug",
                "_block_document_id",
                "_block_document_name",
                "_is_anonymous",
            ]:
                continue
            elif key == "gcp_credentials":
                if not self.gcp_credentials._block_document_id:
                    raise BlockNotSavedError(
                        "It looks like you are trying to use a block that"
                        " has not been saved. Please call `.save` on your block"
                        " before publishing it as a work pool."
                    )
                base_job_template["variables"]["properties"]["credentials"][
                    "default"
                ] = {
                    "$ref": {
                        "block_document_id": str(
                            self.gcp_credentials._block_document_id
                        )
                    }
                }
            elif key == "maximum_run_time":
                base_job_template["variables"]["properties"]["maximum_run_time_hours"][
                    "default"
                ] = round(value.total_seconds() / 3600)
            elif key == "service_account":
                base_job_template["variables"]["properties"]["service_account_name"][
                    "default"
                ] = value
            elif key in base_job_template["variables"]["properties"]:
                base_job_template["variables"]["properties"][key]["default"] = value
            else:
                self.logger.warning(
                    f"Variable {key!r} is not supported by `Vertex AI` work pools."
                    " Skipping."
                )

        return base_job_template

    def _build_job_spec(self) -> "CustomJobSpec":
        """
        Builds a job spec by gathering details.
        """
        # gather worker pool spec
        env_list = [
            {"name": name, "value": value}
            for name, value in {
                **self._base_environment(),
                **self.env,
            }.items()
        ]
        container_spec = ContainerSpec(
            image_uri=self.image, command=self.command, args=[], env=env_list
        )
        machine_spec = MachineSpec(
            machine_type=self.machine_type,
            accelerator_type=self.accelerator_type,
            accelerator_count=self.accelerator_count,
        )
        worker_pool_spec = WorkerPoolSpec(
            container_spec=container_spec,
            machine_spec=machine_spec,
            replica_count=1,
            disk_spec=DiskSpec(
                boot_disk_type=self.boot_disk_type,
                boot_disk_size_gb=self.boot_disk_size_gb,
            ),
        )
        # look for service account
        service_account = (
            self.service_account or self.gcp_credentials._service_account_email
        )
        if service_account is None:
            raise ValueError(
                "A service account is required for the Vertex job. "
                "A service account could not be detected in the attached credentials; "
                "please set a service account explicitly, e.g. "
                '`VertexAICustomTrainingJob(service_acount="...")`'
            )

        # build custom job specs
        timeout = Duration().FromTimedelta(td=self.maximum_run_time)
        scheduling = Scheduling(timeout=timeout)
        job_spec = CustomJobSpec(
            worker_pool_specs=[worker_pool_spec],
            service_account=service_account,
            scheduling=scheduling,
            network=self.network,
            reserved_ip_ranges=self.reserved_ip_ranges,
        )
        return job_spec

    async def _create_and_begin_job(
        self, job_spec: "CustomJobSpec", job_service_client: "JobServiceClient"
    ) -> "CustomJob":
        """
        Builds a custom job and begins running it.
        """
        # create custom job
        custom_job = CustomJob(
            display_name=self.job_name,
            job_spec=job_spec,
            labels=self._get_compatible_labels(),
        )

        # run job
        self.logger.info(
            f"{self._log_prefix}: Job {self.job_name!r} starting to run "
            f"the command {' '.join(self.command)!r} in region "
            f"{self.region!r} using image {self.image!r}"
        )

        project = self.gcp_credentials.project
        resource_name = f"projects/{project}/locations/{self.region}"

        retry_policy = retry(
            stop=stop_after_attempt(3), wait=wait_fixed(1) + wait_random(0, 3)
        )

        custom_job_run = await run_sync_in_worker_thread(
            retry_policy(job_service_client.create_custom_job),
            parent=resource_name,
            custom_job=custom_job,
        )

        self.logger.info(
            f"{self._log_prefix}: Job {self.job_name!r} has successfully started; "
            f"the full job name is {custom_job_run.name!r}"
        )

        return custom_job_run

    async def _watch_job_run(
        self,
        full_job_name: str,  # different from self.job_name
        job_service_client: "JobServiceClient",
        current_state: "JobState",
        until_states: Tuple["JobState"],
        timeout: int = None,
    ) -> "CustomJob":
        """
        Polls job run to see if status changed.
        """
        state = JobState.JOB_STATE_UNSPECIFIED
        last_state = current_state
        t0 = time.time()

        while state not in until_states:
            job_run = await run_sync_in_worker_thread(
                job_service_client.get_custom_job,
                name=full_job_name,
            )
            state = job_run.state
            if state != last_state:
                state_label = (
                    state.name.replace("_", " ")
                    .lower()
                    .replace("state", "state is now:")
                )
                # results in "New job state is now: succeeded"
                self.logger.info(
                    f"{self._log_prefix}: {self.job_name} has new {state_label}"
                )
                last_state = state
            else:
                # Intermittently, the job will not be described. We want to respect the
                # watch timeout though.
                self.logger.debug(f"{self._log_prefix}: Job not found.")

            elapsed_time = time.time() - t0
            if timeout is not None and elapsed_time > timeout:
                raise RuntimeError(
                    f"Timed out after {elapsed_time}s while watching job for states "
                    "{until_states!r}"
                )
            time.sleep(self.job_watch_poll_interval)

        return job_run

    @sync_compatible
    async def run(
        self, task_status: Optional["TaskStatus"] = None
    ) -> VertexAICustomTrainingJobResult:
        """
        Run the configured task on VertexAI.

        Args:
            task_status: An optional `TaskStatus` to update when the container starts.

        Returns:
            The `VertexAICustomTrainingJobResult`.
        """
        client_options = ClientOptions(
            api_endpoint=f"{self.region}-aiplatform.googleapis.com"
        )

        job_spec = self._build_job_spec()
        with self.gcp_credentials.get_job_service_client(
            client_options=client_options
        ) as job_service_client:
            job_run = await self._create_and_begin_job(job_spec, job_service_client)

            if task_status:
                task_status.started(self.job_name)

            final_job_run = await self._watch_job_run(
                full_job_name=job_run.name,
                job_service_client=job_service_client,
                current_state=job_run.state,
                until_states=(
                    JobState.JOB_STATE_SUCCEEDED,
                    JobState.JOB_STATE_FAILED,
                    JobState.JOB_STATE_CANCELLED,
                    JobState.JOB_STATE_EXPIRED,
                ),
                timeout=self.maximum_run_time.total_seconds(),
            )

        error_msg = final_job_run.error.message
        if error_msg:
            raise RuntimeError(f"{self._log_prefix}: {error_msg}")

        status_code = 0 if final_job_run.state == JobState.JOB_STATE_SUCCEEDED else 1

        return VertexAICustomTrainingJobResult(
            identifier=final_job_run.display_name, status_code=status_code
        )

    @sync_compatible
    async def kill(self, identifier: str, grace_seconds: int = 30) -> None:
        """
        Kill a job running Cloud Run.

        Args:
            identifier: The Vertex AI full job name, formatted like
                "projects/{project}/locations/{location}/customJobs/{custom_job}".

        Returns:
            The `VertexAICustomTrainingJobResult`.
        """
        client_options = ClientOptions(
            api_endpoint=f"{self.region}-aiplatform.googleapis.com"
        )
        with self.gcp_credentials.get_job_service_client(
            client_options=client_options
        ) as job_service_client:
            await run_sync_in_worker_thread(
                self._kill_job,
                job_service_client=job_service_client,
                full_job_name=identifier,
            )
            self.logger.info(f"Requested to cancel {identifier}...")

    def _kill_job(
        self, job_service_client: "JobServiceClient", full_job_name: str
    ) -> None:
        """
        Thin wrapper around Job.delete, wrapping a try/except since
        Job is an independent class that doesn't have knowledge of
        CloudRunJob and its associated logic.
        """
        cancel_custom_job_request = CancelCustomJobRequest(name=full_job_name)
        try:
            job_service_client.cancel_custom_job(
                request=cancel_custom_job_request,
            )
        except Exception as exc:
            if "does not exist" in str(exc):
                raise InfrastructureNotFound(
                    f"Cannot stop Vertex AI job; the job name {full_job_name!r} "
                    "could not be found."
                ) from exc
            raise

    @property
    def _log_prefix(self) -> str:
        """
        Internal property for generating a prefix for logs where `name` may be null
        """
        if self.name is not None:
            return f"VertexAICustomTrainingJob {self.name!r}"
        else:
            return "VertexAICustomTrainingJob"

Attributes

job_name property

The name can be up to 128 characters long and can be consist of any UTF-8 characters. Reference: https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.CustomJob#google_cloud_aiplatform_CustomJob_display_name

Functions

generate_work_pool_base_job_template async

Generate a base job template for a Vertex AI work pool with the same configuration as this block. Returns: - dict: a base job template for a Vertex AI work pool

Source code in prefect_gcp/aiplatform.py
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async def generate_work_pool_base_job_template(self) -> dict:
    """
    Generate a base job template for a `Vertex AI` work pool with the same
    configuration as this block.
    Returns:
        - dict: a base job template for a `Vertex AI` work pool
    """
    base_job_template = await get_default_base_job_template_for_infrastructure_type(
        self.get_corresponding_worker_type(),
    )
    assert (
        base_job_template is not None
    ), "Failed to generate default base job template for Cloud Run worker."
    for key, value in self.dict(exclude_unset=True, exclude_defaults=True).items():
        if key == "command":
            base_job_template["variables"]["properties"]["command"][
                "default"
            ] = shlex.join(value)
        elif key in [
            "type",
            "block_type_slug",
            "_block_document_id",
            "_block_document_name",
            "_is_anonymous",
        ]:
            continue
        elif key == "gcp_credentials":
            if not self.gcp_credentials._block_document_id:
                raise BlockNotSavedError(
                    "It looks like you are trying to use a block that"
                    " has not been saved. Please call `.save` on your block"
                    " before publishing it as a work pool."
                )
            base_job_template["variables"]["properties"]["credentials"][
                "default"
            ] = {
                "$ref": {
                    "block_document_id": str(
                        self.gcp_credentials._block_document_id
                    )
                }
            }
        elif key == "maximum_run_time":
            base_job_template["variables"]["properties"]["maximum_run_time_hours"][
                "default"
            ] = round(value.total_seconds() / 3600)
        elif key == "service_account":
            base_job_template["variables"]["properties"]["service_account_name"][
                "default"
            ] = value
        elif key in base_job_template["variables"]["properties"]:
            base_job_template["variables"]["properties"][key]["default"] = value
        else:
            self.logger.warning(
                f"Variable {key!r} is not supported by `Vertex AI` work pools."
                " Skipping."
            )

    return base_job_template
get_corresponding_worker_type

Return the corresponding worker type for this infrastructure block.

Source code in prefect_gcp/aiplatform.py
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def get_corresponding_worker_type(self) -> str:
    """Return the corresponding worker type for this infrastructure block."""
    return "vertex-ai"
kill async

Kill a job running Cloud Run.

Parameters:

Name Type Description Default
identifier str

The Vertex AI full job name, formatted like "projects/{project}/locations/{location}/customJobs/{custom_job}".

required

Returns:

Type Description
None

The VertexAICustomTrainingJobResult.

Source code in prefect_gcp/aiplatform.py
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@sync_compatible
async def kill(self, identifier: str, grace_seconds: int = 30) -> None:
    """
    Kill a job running Cloud Run.

    Args:
        identifier: The Vertex AI full job name, formatted like
            "projects/{project}/locations/{location}/customJobs/{custom_job}".

    Returns:
        The `VertexAICustomTrainingJobResult`.
    """
    client_options = ClientOptions(
        api_endpoint=f"{self.region}-aiplatform.googleapis.com"
    )
    with self.gcp_credentials.get_job_service_client(
        client_options=client_options
    ) as job_service_client:
        await run_sync_in_worker_thread(
            self._kill_job,
            job_service_client=job_service_client,
            full_job_name=identifier,
        )
        self.logger.info(f"Requested to cancel {identifier}...")
preview

Generate a preview of the job definition that will be sent to GCP.

Source code in prefect_gcp/aiplatform.py
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def preview(self) -> str:
    """Generate a preview of the job definition that will be sent to GCP."""
    job_spec = self._build_job_spec()
    custom_job = CustomJob(
        display_name=self.job_name,
        job_spec=job_spec,
        labels=self._get_compatible_labels(),
    )
    return str(custom_job)  # outputs a json string
run async

Run the configured task on VertexAI.

Parameters:

Name Type Description Default
task_status Optional[TaskStatus]

An optional TaskStatus to update when the container starts.

None

Returns:

Type Description
VertexAICustomTrainingJobResult

The VertexAICustomTrainingJobResult.

Source code in prefect_gcp/aiplatform.py
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@sync_compatible
async def run(
    self, task_status: Optional["TaskStatus"] = None
) -> VertexAICustomTrainingJobResult:
    """
    Run the configured task on VertexAI.

    Args:
        task_status: An optional `TaskStatus` to update when the container starts.

    Returns:
        The `VertexAICustomTrainingJobResult`.
    """
    client_options = ClientOptions(
        api_endpoint=f"{self.region}-aiplatform.googleapis.com"
    )

    job_spec = self._build_job_spec()
    with self.gcp_credentials.get_job_service_client(
        client_options=client_options
    ) as job_service_client:
        job_run = await self._create_and_begin_job(job_spec, job_service_client)

        if task_status:
            task_status.started(self.job_name)

        final_job_run = await self._watch_job_run(
            full_job_name=job_run.name,
            job_service_client=job_service_client,
            current_state=job_run.state,
            until_states=(
                JobState.JOB_STATE_SUCCEEDED,
                JobState.JOB_STATE_FAILED,
                JobState.JOB_STATE_CANCELLED,
                JobState.JOB_STATE_EXPIRED,
            ),
            timeout=self.maximum_run_time.total_seconds(),
        )

    error_msg = final_job_run.error.message
    if error_msg:
        raise RuntimeError(f"{self._log_prefix}: {error_msg}")

    status_code = 0 if final_job_run.state == JobState.JOB_STATE_SUCCEEDED else 1

    return VertexAICustomTrainingJobResult(
        identifier=final_job_run.display_name, status_code=status_code
    )

VertexAICustomTrainingJobResult

Bases: InfrastructureResult

Result from a Vertex AI custom training job.

Source code in prefect_gcp/aiplatform.py
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class VertexAICustomTrainingJobResult(InfrastructureResult):
    """Result from a Vertex AI custom training job."""