Source code for dask_cloudprovider.gcp.instances

import asyncio
import os
import uuid
import json

import sqlite3

import dask
from dask.utils import tmpfile
from dask_cloudprovider.generic.vmcluster import (
from dask_cloudprovider.gcp.utils import build_request, is_inside_gce

from distributed.core import Status

    import googleapiclient.discovery
    from googleapiclient.errors import HttpError
except ImportError as e:
    msg = (
        "Dask Cloud Provider GCP requirements are not installed.\n\n"
        "Please either conda or pip install as follows:\n\n"
        "  conda install -c conda-forge dask-cloudprovider       # either conda install\n"
        '  pip install "dask-cloudprovider[gcp]" --upgrade       # or python -m pip install'
    raise ImportError(msg) from e

class GCPCredentialsError(Exception):
    """Raised when GCP credentials are missing"""

    def __init__(self, message=None):
        if message is None:
            message = (
                "GCP Credentials have not been provided. Either set the following environment variable: "
                "export GOOGLE_APPLICATION_CREDENTIALS=<Path-To-GCP-JSON-Credentials> "
                "or authenticate with "
                "gcloud auth login"

class GCPInstance(VMInterface):
    def __init__(

        self.cluster = cluster
        self.config = config
        self.projectid = projectid or self.config.get("projectid") = zone or self.config.get("zone")

        self.machine_type = machine_type or self.config.get("machine_type")

        self.source_image = self.expand_source_image(
            source_image or self.config.get("source_image")
        self.docker_image = docker_image or self.config.get("docker_image")
        self.env_vars = env_vars
        self.filesystem_size = filesystem_size or self.config.get("filesystem_size")
        self.ngpus = ngpus or self.config.get("ngpus") = network or self.config.get("network")
        self.gpu_type = gpu_type or self.config.get("gpu_type")
        self.gpu_instance = gpu_instance
        self.bootstrap = bootstrap
        self.auto_shutdown = auto_shutdown

        self.general_zone = "-".join("-")[:2])  # us-east1-c -> us-east1

    def create_gcp_config(self):
        config = {
            "machineType": f"zones/{}/machineTypes/{self.machine_type}",
            "displayDevice": {"enableDisplay": "false"},
            "tags": {"items": ["http-server", "https-server"]},
            # Specify the boot disk and the image to use as a source.
            "disks": [
                    "kind": "compute#attachedDisk",
                    "type": "PERSISTENT",
                    "boot": "true",
                    "mode": "READ_WRITE",
                    "autoDelete": "true",
                    "initializeParams": {
                        "sourceImage": self.source_image,
                        "diskType": f"projects/{self.projectid}/zones/{}/diskTypes/pd-standard",
                        "diskSizeGb": f"{self.filesystem_size}",  # nvidia-gpu-cloud cannot be smaller than 32 GB
                        "labels": {},
                        # "source": "projects/nv-ai-infra/zones/us-east1-c/disks/ngc-gpu-dask-rapids-docker-experiment",
                    "diskEncryptionKey": {},
            "canIpForward": "false",
            "networkInterfaces": [
                    "kind": "compute#networkInterface",
                    "subnetwork": f"projects/{self.projectid}/regions/{self.general_zone}/subnetworks/{}",
                    "aliasIpRanges": [],
            # Allow the instance to access cloud storage and logging.
            "serviceAccounts": [
                    "email": "default",
                    "scopes": [
            # Metadata is readable from the instance and allows you to
            # pass configuration from deployment scripts to instances.
            "metadata": {
                "items": [
                        # Startup script is automatically executed by the
                        # instance upon startup.
                        "key": "google-logging-enabled",
                        "value": "true",
                    {"key": "user-data", "value": self.cloud_init},
            "labels": {"container-vm": "dask-cloudprovider"},
            "scheduling": {
                "preemptible": "false",
                "onHostMaintenance": "TERMINATE",
                "automaticRestart": "true",
                "nodeAffinities": [],
            "shieldedInstanceConfig": {
                "enableSecureBoot": "false",
                "enableVtpm": "true",
                "enableIntegrityMonitoring": "true",
            "deletionProtection": "false",
            "reservationAffinity": {"consumeReservationType": "ANY_RESERVATION"},

        if self.config.get("public_ingress", True):
            config["networkInterfaces"][0]["accessConfigs"] = [
                    "kind": "compute#accessConfig",
                    "name": "External NAT",
                    "type": "ONE_TO_ONE_NAT",
                    "networkTier": "PREMIUM",

        if self.ngpus:
            config["guestAccelerators"] = [
                    "acceleratorCount": self.ngpus,
                    "acceleratorType": f"projects/{self.projectid}/zones/{}/acceleratorTypes/{self.gpu_type}",

        return config

    async def create_vm(self):

        self.cloud_init = self.cluster.render_process_cloud_init(self)

        self.gcp_config = self.create_gcp_config()

            inst = await self.cluster.call_async(
                .insert(project=self.projectid,, body=self.gcp_config)
            self.gcp_inst = inst
   = self.gcp_inst["id"]
        except HttpError as e:
            # something failed
            raise Exception(str(e))
        while await self.update_status() != "RUNNING":
            await asyncio.sleep(0.5)

        self.internal_ip = await self.get_internal_ip()
        if self.config.get("public_ingress", True):
            self.external_ip = await self.get_external_ip()
            self.external_ip = None
            f"{}\n\tInternal IP: {self.internal_ip}\n\tExternal IP: {self.external_ip}"
        return self.internal_ip, self.external_ip

    async def get_internal_ip(self):
        return (
            await self.cluster.call_async(
                    project=self.projectid,, filter=f"name={}"

    async def get_external_ip(self):
        return (
            await self.cluster.call_async(
                    project=self.projectid,, filter=f"name={}"

    async def update_status(self):
        d = await self.cluster.call_async(
            .list(project=self.projectid,, filter=f"name={}")
        self.gcp_inst = d

        if not d.get("items", None):
            self.cluster._log("Failed to find running VMI...")
            raise Exception(f"Missing Instance {}")

        return d["items"][0]["status"]

    def expand_source_image(self, source_image):
        if "/" not in source_image:
            return f"projects/{self.projectid}/global/images/{source_image}"
        if source_image.startswith(""):
            return source_image.replace("", "")
        return source_image

    async def close(self):
        self.cluster._log(f"Closing Instance: {}")
        await self.cluster.call_async(

class GCPScheduler(SchedulerMixin, GCPInstance):
    """Scheduler running in a GCP instance."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    async def start(self):
        await self.start_scheduler()
        self.status = Status.running

    async def start_scheduler(self):
            f"Launching cluster with the following configuration: "
            f"\n  Source Image: {self.source_image} "
            f"\n  Docker Image: {self.docker_image} "
            f"\n  Machine Type: {self.machine_type} "
            f"\n  Filesytsem Size: {self.filesystem_size} "
            f"\n  N-GPU Type: {self.ngpus} {self.gpu_type}"
            f"\n  Zone: {} "
        self.cluster._log("Creating scheduler instance")
        self.internal_ip, self.external_ip = await self.create_vm()

        if self.config.get("public_ingress", True) and not is_inside_gce():
            # scheduler must be publicly available, and firewall
            # needs to be in place to allow access to 8786 on
            # the external IP
            self.address = f"{self.cluster.protocol}://{self.external_ip}:8786"
            # if the client is running inside GCE environment
            # it's better to use internal IP, which doesn't
            # require firewall setup
            self.address = f"{self.cluster.protocol}://{self.internal_ip}:8786"
        await self.wait_for_scheduler()

        # need to reserve internal IP for workers
        # gcp docker containers can't see resolve ip address
        self.cluster.scheduler_internal_ip = self.internal_ip
        self.cluster.scheduler_external_ip = self.external_ip

class GCPWorker(GCPInstance):
    """Worker running in an GCP instance."""

    def __init__(
        scheduler: str,
        worker_class: str = "distributed.cli.dask_worker",
        worker_options: dict = {},
        self.scheduler = scheduler
        self.worker_class = worker_class = f"dask-{self.cluster.uuid}-worker-{str(uuid.uuid4())[:8]}"
        internal_scheduler = (
        self.command = " ".join(
                "''%s''"  # in yaml double single quotes escape the single quote
                % json.dumps(
                        "cls": self.worker_class,
                        "opts": {

    async def start(self):
        await super().start()
        await self.start_worker()

    async def start_worker(self):
        self.cluster._log("Creating worker instance")
        self.internal_ip, self.external_ip = await self.create_vm()
        if self.config.get("public_ingress", True):
            # scheduler is publicly available
            self.address = self.external_ip
            self.address = self.internal_ip

[docs]class GCPCluster(VMCluster): """Cluster running on GCP VM Instances. This cluster manager constructs a Dask cluster running on Google Cloud Platform 67VMs. When configuring your cluster you may find it useful to install the ``gcloud`` tool for querying the GCP API for available options. Parameters ---------- projectid: str Your GCP project ID. This must be set either here or in your Dask config. See the GCP docs page for more info. zone: str The GCP zone to launch you cluster in. A full list can be obtained with ``gcloud compute zones list``. network: str The GCP VPC network/subnetwork to use. The default is `default`. If using firewall rules, please ensure the follwing accesses are configured: - egress on all ports for downloading docker images and general data access - ingress on all ports for internal communication of workers - ingress on 8786-8787 for external accessibility of the dashboard/scheduler - (optional) ingress on 22 for ssh access machine_type: str The VM machine_type. You can get a full list with ``gcloud compute machine-types list``. The default is ``n1-standard-1`` which is 3.75GB RAM and 1 vCPU source_image: str The OS image to use for the VM. Dask Cloudprovider will boostrap Ubuntu based images automatically. Other images require Docker and for GPUs the NVIDIA Drivers and NVIDIA Docker. A list of available images can be found with ``gcloud compute images list`` Valid values are: - The short image name provided it is in ``projectid``. - The full image name ``projects/<projectid>/global/images/<source_image>``. - The full image URI such as those listed in ``gcloud compute images list --uri``. The default is ``projects/ubuntu-os-cloud/global/images/ubuntu-minimal-1804-bionic-v20201014``. docker_image: string (optional) The Docker image to run on all instances. This image must have a valid Python environment and have ``dask`` installed in order for the ``dask-scheduler`` and ``dask-worker`` commands to be available. It is recommended the Python environment matches your local environment where ``EC2Cluster`` is being created from. For GPU instance types the Docker image much have NVIDIA drivers and ``dask-cuda`` installed. By default the ``daskdev/dask:latest`` image will be used. docker_args: string (optional) Extra command line arguments to pass to Docker. ngpus: int (optional) The number of GPUs to atatch to the instance. Default is ``0``. gpu_type: str (optional) The name of the GPU to use. This must be set if ``ngpus>0``. You can see a list of GPUs available in each zone with ``gcloud compute accelerator-types list``. filesystem_size: int (optional) The VM filesystem size in GB. Defaults to ``50``. n_workers: int (optional) Number of workers to initialise the cluster with. Defaults to ``0``. bootstrap: bool (optional) Install Docker and NVIDIA drivers if ``ngpus>0``. Set to ``False`` if you are using a custom ``source_image`` which already has these requirements. Defaults to ``True``. worker_class: str The Python class to run for the worker. Defaults to ``dask.distributed.Nanny`` worker_options: dict (optional) Params to be passed to the worker class. See :class:`distributed.worker.Worker` for default worker class. If you set ``worker_class`` then refer to the docstring for the custom worker class. env_vars: dict (optional) Environment variables to be passed to the worker. scheduler_options: dict (optional) Params to be passed to the scheduler class. See :class:`distributed.scheduler.Scheduler`. silence_logs: bool (optional) Whether or not we should silence logging when setting up the cluster. asynchronous: bool (optional) If this is intended to be used directly within an event loop with async/await security : Security or bool (optional) Configures communication security in this cluster. Can be a security object, or True. If True, temporary self-signed credentials will be created automatically. Default is ``True``. Examples -------- Create the cluster. >>> from dask_cloudprovider.gcp import GCPCluster >>> cluster = GCPCluster(n_workers=1) Launching cluster with the following configuration: Source Image: projects/ubuntu-os-cloud/global/images/ubuntu-minimal-1804-bionic-v20201014 Docker Image: daskdev/dask:latest Machine Type: n1-standard-1 Filesytsem Size: 50 N-GPU Type: Zone: us-east1-c Creating scheduler instance dask-acc897b9-scheduler Internal IP: External IP: Waiting for scheduler to run Scheduler is running Creating worker instance dask-acc897b9-worker-bfbc94bc Internal IP: External IP: Connect a client. >>> from dask.distributed import Client >>> client = Client(cluster) Do some work. >>> import dask.array as da >>> arr = da.random.random((1000, 1000), chunks=(100, 100)) >>> arr.mean().compute() 0.5001550986751964 Close the cluster >>> cluster.close() Closing Instance: dask-acc897b9-worker-bfbc94bc Closing Instance: dask-acc897b9-scheduler You can also do this all in one go with context managers to ensure the cluster is created and cleaned up. >>> with GCPCluster(n_workers=1) as cluster: ... with Client(cluster) as client: ... print(da.random.random((1000, 1000), chunks=(100, 100)).mean().compute()) Launching cluster with the following configuration: Source Image: projects/ubuntu-os-cloud/global/images/ubuntu-minimal-1804-bionic-v20201014 Docker Image: daskdev/dask:latest Machine Type: n1-standard-1 Filesytsem Size: 50 N-GPU Type: Zone: us-east1-c Creating scheduler instance dask-19352f29-scheduler Internal IP: External IP: Waiting for scheduler to run Scheduler is running Creating worker instance dask-19352f29-worker-91a6bfe0 Internal IP: External IP: 0.5000812282861661 Closing Instance: dask-19352f29-worker-91a6bfe0 Closing Instance: dask-19352f29-scheduler """ def __init__( self, projectid=None, zone=None, network=None, machine_type=None, source_image=None, docker_image=None, ngpus=None, gpu_type=None, filesystem_size=None, auto_shutdown=None, bootstrap=True, **kwargs, ): self.compute = GCPCompute() self.config = dask.config.get("cloudprovider.gcp", {}) self.auto_shutdown = ( auto_shutdown if auto_shutdown is not None else self.config.get("auto_shutdown") ) self.scheduler_class = GCPScheduler self.worker_class = GCPWorker self.bootstrap = ( bootstrap if bootstrap is not None else self.config.get("bootstrap") ) self.machine_type = machine_type or self.config.get("machine_type") self.gpu_instance = "gpu" in self.machine_type or bool(ngpus) self.options = { "cluster": self, "config": self.config, "projectid": projectid or self.config.get("projectid"), "source_image": source_image or self.config.get("source_image"), "docker_image": docker_image or self.config.get("docker_image"), "filesystem_size": filesystem_size or self.config.get("filesystem_size"), "zone": zone or self.config.get("zone"), "machine_type": self.machine_type, "ngpus": ngpus or self.config.get("ngpus"), "network": network or self.config.get("network"), "gpu_type": gpu_type or self.config.get("gpu_type"), "gpu_instance": self.gpu_instance, "bootstrap": self.bootstrap, "auto_shutdown": self.auto_shutdown, } self.scheduler_options = {**self.options} self.worker_options = {**self.options} super().__init__(**kwargs)
class GCPCompute: """Wrapper for the ``googleapiclient`` compute object.""" def __init__(self): self._compute = self.refresh_client() def refresh_client(self): if os.environ.get("GOOGLE_APPLICATION_CREDENTIALS", False): import google.oauth2.service_account # google-auth creds = google.oauth2.service_account.Credentials.from_service_account_file( os.environ["GOOGLE_APPLICATION_CREDENTIALS"], scopes=[""], ) else: import google.auth.credentials # google-auth path = os.path.join( os.path.expanduser("~"), ".config/gcloud/credentials.db" ) if not os.path.exists(path): raise GCPCredentialsError() conn = sqlite3.connect(path) creds_rows = conn.execute("select * from credentials").fetchall() with tmpfile() as f: with open(f, "w") as f_: # take first row f_.write(creds_rows[0][1]) creds, _ = google.auth.load_credentials_from_file(filename=f) return "compute", "v1", credentials=creds, requestBuilder=build_request(creds) ) def instances(self): try: return self._compute.instances() except Exception: # noqa self._compute = self.refresh_client() return self._compute.instances() # Note: if you have trouble connecting make sure firewall rules in GCP are stetup for 8787,8786,22