Google Cloud Platform¶

GCPCluster([projectid, zone, network, …]) Cluster running on GCP VM Instances.

Overview¶

Authentication¶

In order to create clusters on GCP you need to set your authentication credentials. You can do this via the gcloud command line tool.

$ gcloud auth login

Alternatively you can use a service account which provides credentials in a JSON file. You must set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path to the JSON file.

$ export GOOGLE_APPLICATION_CREDENTIALS=/path/to/credentials.json

Project ID¶

To use Dask Cloudprovider with GCP you must also configure your Project ID. Generally when creating a GCP account you will create a default project. This can be found at the top of the GCP dashboard.

Your Project ID must be added to your Dask config file.

# ~/.config/dask/cloudprovider.yaml
cloudprovider:
  gcp:
    projectid: "YOUR PROJECT ID"

Or via an environment variable.

$ export DASK_CLOUDPROVIDER__GCP__PROJECTID="YOUR PROJECT ID"

Google Cloud VMs¶

class dask_cloudprovider.gcp.GCPCluster(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)[source]¶

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.

https://cloud.google.com/sdk/gcloud

Parameters:

projectid: str

Your GCP project ID. This must be set either here or in your Dask config.

https://cloud.google.com/resource-manager/docs/creating-managing-projects

See the GCP docs page for more info.

https://cloudprovider.dask.org/en/latest/gcp.html#project-id

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 0.0.0.0/0 on all ports for downloading docker images and general data access

ingress 10.0.0.0/8 on all ports for internal communication of workers

ingress 0.0.0.0/0 on 8786-8787 for external accessibility of the dashboard/scheduler

(optional) ingress 0.0.0.0./0 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 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 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: 10.142.0.37
        External IP: 34.75.60.62
Waiting for scheduler to run
Scheduler is running
Creating worker instance
dask-acc897b9-worker-bfbc94bc
        Internal IP: 10.142.0.39
        External IP: 34.73.245.220

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: 10.142.0.41
        External IP: 34.73.217.251
Waiting for scheduler to run
Scheduler is running
Creating worker instance
dask-19352f29-worker-91a6bfe0
        Internal IP: 10.142.0.48
        External IP: 34.73.245.220
0.5000812282861661
Closing Instance: dask-19352f29-worker-91a6bfe0
Closing Instance: dask-19352f29-scheduler

Attributes:	asynchronous auto_shutdown bootstrap command dashboard_link docker_image gpu_instance name observed plan requested scheduler_address scheduler_class worker_class

Methods

`adapt`(*args[, minimum, maximum])	Turn on adaptivity
`call_async`(f, args, *kwargs)	Run a blocking function in a thread as a coroutine.
`from_name`(name)	Create an instance of this class to represent an existing cluster by name.
`get_logs`([cluster, scheduler, workers])	Return logs for the cluster, scheduler and workers
`get_tags`()	Generate tags to be applied to all resources.
`new_worker_spec`()	Return name and spec for the next worker
`scale`([n, memory, cores])	Scale cluster to n workers
`scale_up`([n, memory, cores])	Scale cluster to n workers

close
get_cloud_init
logs
render_cloud_init
render_process_cloud_init
scale_down
sync