API#

Python API Reference#

`coiled.Cluster`([name, software, container, ...])	Create a Dask cluster with Coiled
`coiled.cluster_logs`(cluster_id[, account, ...])	Returns cluster logs as a dictionary, with a key for the scheduler and each worker.
`coiled.create_software_environment`([name, ...])	Create a software environment
`coiled.delete_cluster`(name[, account])	Delete a cluster
`coiled.delete_software_environment`(name[, ...])	Delete a software environment
`coiled.diagnostics`([account])	Run a diagnose check aimed to help support with any issues.
`coiled.get_billing_activity`([account, ...])	Retrieve Billing information.
`coiled.get_software_info`(name[, account])	Retrieve solved spec for a Coiled software environment
`coiled.list_clusters`([account, max_pages])	List clusters
`coiled.list_core_usage`([account])	Get a list of used cores.
`coiled.list_gpu_types`()	List allowed GPU Types.
`coiled.list_instance_types`([backend, ...])	List allowed instance types for the cloud provider configured on your account.
`coiled.BackendOptions`	A dictionary with the following key/value pairs
`coiled.AWSOptions`	A dictionary with the following key/value pairs plus any pairs in `BackendOptions`
`coiled.GCPOptions`	A dictionary with GCP specific key/value pairs plus any pairs in `BackendOptions`
`coiled.list_local_versions`()	Get information about local versions.
`coiled.list_performance_reports`([account])	List performance reports stored on Coiled Cloud
`coiled.list_software_environments`([account])	List software environments
`coiled.list_user_information`()	List information about your user.
`coiled.performance_report`([filename, ...])	Generates a static performance report and saves it to Coiled Cloud
`coiled.set_backend_options`([account, ...])	Configure account level settings for cloud provider and container registry.
`coiled.function`(*[, software, container, ...])	Decorate a function to run on cloud infrastructure

Software Environments#

coiled.create_software_environment(name=None, *, account=None, conda=None, pip=None, container=None, log_output=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, force_rebuild=False, use_entrypoint=True, gpu_enabled=False, architecture=ArchitectureTypesEnum.X86_64, region_name=None)[source]#

Create a software environment

Clusters#

class coiled.Cluster(name=None, *, software=None, container=None, n_workers=None, worker_class=None, worker_options=None, worker_vm_types=None, worker_cpu=None, worker_memory=None, worker_disk_size=None, worker_disk_throughput=None, worker_gpu=None, worker_gpu_type=None, scheduler_options=None, scheduler_vm_types=None, scheduler_cpu=None, scheduler_memory=None, scheduler_disk_size=None, scheduler_gpu=None, asynchronous=False, cloud=None, account=None, shutdown_on_close=None, idle_timeout=None, use_scheduler_public_ip=None, use_dashboard_https=None, credentials='local', credentials_duration_seconds=None, timeout=None, environ=None, tags=None, send_dask_config=True, backend_options=None, show_widget=True, custom_widget=None, configure_logging=None, wait_for_workers=None, package_sync=False, package_sync_strict=False, package_sync_ignore=None, package_sync_fail_on='critical-only', private_to_creator=None, use_best_zone=True, compute_purchase_option=None, spot_policy=None, extra_worker_on_scheduler=None, _n_worker_specs_per_host=None, scheduler_port=None, allow_ingress_from=None, allow_ssh=None, allow_spark=None, jupyter=None, region=None, arm=None)#

Create a Dask cluster with Coiled

Parameters

n_workers (Optional[int]) – Number of workers in this cluster. Defaults to 4. If argument this is not specified, adaptive scaling is enabled.
name (Optional[str]) – Name to use for identifying this cluster. Defaults to None.
software (Optional[str]) – Name of the software environment to use; this allows you to use and re-use existing Coiled software environments, and should not be used with package sync or when specifying a container to use for this specific cluster.
container (Optional[str]) – Name or URI of container image to use; when using a pre-made container image with Coiled, this allows you to skip the step of explicitly creating a Coiled software environment from that image. Note that this should not be used with package sync or when specifying an existing Coiled software environment.
worker_class (Optional[str]) – Worker class to use. Defaults to distributed.nanny.Nanny.
worker_options (Optional[dict]) – Mapping with keyword arguments to pass to worker_class. Defaults to {}.
worker_vm_types (Optional[list]) – List of instance types that you would like workers to use, default instance type selected contains 4 cores. You can use the command coiled.list_instance_types() to see a list of allowed types.
worker_cpu (Union[int, List[int], None]) – Number, or range, of CPUs requested for each worker. Specify a range by using a list of two elements, for example: worker_cpu=[2, 8].
worker_memory (Union[str, List[str], None]) – Amount of memory to request for each worker, Coiled will use a +/- 10% buffer from the memory that you specify. You may specify a range of memory by using a list of two elements, for example: worker_memory=["2GiB", "4GiB"].
worker_disk_size (Union[int, str, None]) – Non-default size of persistent disk attached to each worker instance, specified as string with units or integer for GiB.
worker_disk_throughput (Optional[int]) – EXPERIMENTAL. For AWS, non-default throughput (in MB/s) for EBS gp3 volumes attached to workers.
worker_gpu (Union[int, bool, None]) – Number of GPUs to attach to each worker. Default is 0, True is interpreted as 1. Note that this is ignored if you’re explicitly specifying an instance type which includes a fixed number of GPUs.
worker_gpu_type (Optional[str]) – For GCP, this lets you specify type of guest GPU for instances. Should match the way the cloud provider specifies the GPU, for example: worker_gpu_type="nvidia-tesla-t4". By default, Coiled will request NVIDIA T4 if GPU type isn’t specified. For AWS, if you want GPU other than T4, you’ll need to explicitly specify the VM instance type (e.g., p3.2xlarge for instance with one NVIDIA Tesla V100).
scheduler_options (Optional[dict]) – Mapping with keyword arguments to pass to the Scheduler __init__. Defaults to {}.
scheduler_vm_types (Optional[list]) – List of instance types that you would like the scheduler to use, default instances type selected contains 4 cores. You can use the command coiled.list_instance_types() to se a list of allowed types.
scheduler_cpu (Union[int, List[int], None]) – Number, or range, of CPUs requested for the scheduler. Specify a range by using a list of two elements, for example: scheduler_cpu=[2, 8].
scheduler_memory (Union[str, List[str], None]) – Amount of memory to request for the scheduler, Coiled will use a +/-10% buffer from the memory what you specify. You may specify a range of memory by using a list of two elements, for example: scheduler_memory=["2GiB", "4GiB"].
scheduler_gpu (Optional[bool]) – Whether to attach GPU to scheduler; this would be a single NVIDIA T4. The best practice for Dask is to have a GPU on the scheduler if you are using GPUs on your workers, so if you don’t explicitly specify, Coiled will follow this best practice and give you a scheduler GPU just in case you have worker_gpu set.
asynchronous (bool) – Set to True if using this Cloud within async/await functions or within Tornado gen.coroutines. Otherwise this should remain False for normal use. Default is False.
cloud (Optional[CloudV2]) – Cloud object to use for interacting with Coiled. This object contains user/authentication/account information. If this is None (default), we look for a recently-cached Cloud object, and if none exists create one.
account (Optional[str]) – Name of Coiled account to use. If not provided, will default to the user account for the cloud object being used.
shutdown_on_close – Whether or not to shut down the cluster when it finishes. Defaults to True, unless name points to an existing cluster.
idle_timeout (Optional[str]) – Shut down the cluster after this duration if no activity has occurred. E.g. “30 minutes” Default: “20 minutes”
use_scheduler_public_ip (Optional[bool]) – Boolean value that determines if the Python client connects to the Dask scheduler using the scheduler machine’s public IP address. The default behaviour when set to True is to connect to the scheduler using its public IP address, which means traffic will be routed over the public internet. When set to False, traffic will be routed over the local network the scheduler lives in, so make sure the scheduler private IP address is routable from where this function call is made when setting this to False.
use_dashboard_https (Optional[bool]) – When public IP address is used for dashboard, we’ll enable HTTPS + auth by default. You may want to disable this if using something that needs to connect directly to the scheduler dashboard without authentication, such as jupyter dask-labextension<=6.1.0.
credentials (Optional[str]) – Which credentials to use for Dask operations and forward to Dask clusters – options are “local”, or None. The default behavior is to use local credentials if available. NOTE: credential handling currently only works with AWS credentials.
credentials_duration_seconds (Optional[int]) – For “local” credentials shipped to cluster as STS token, set the duration of STS token. If not specified, the AWS default will be used.
timeout (Union[int, float, None]) – Timeout in seconds to wait for a cluster to start, will use default_cluster_timeout set on parent Cloud by default.
environ (Optional[Dict[str, str]]) – Dictionary of environment variables. Values will be transmitted to Coiled; for private environment variables (e.g., passwords or access keys you use for data access), send_private_envs() is recommended.
send_dask_config (bool) – Whether to send a frozen copy of local dask.config to the cluster.
backend_options (Union[AWSOptions, GCPOptions, None]) – Dictionary of backend specific options.
show_widget (bool) – Whether to use the rich-based widget display in IPython/Jupyter (ignored if not in those environments). For use cases involving multiple Clusters at once, show_widget=False is recommended. (Default: True)
custom_widget (Optional[ClusterWidget]) – Use the rich-based widget display outside of IPython/Jupyter (Default: False)
tags (Optional[Dict[str, str]]) – Dictionary of tags.
wait_for_workers (Union[int, float, bool, None]) – Whether to wait for a number of workers before returning control of the prompt back to the user. Usually, computations will run better if you wait for most workers before submitting tasks to the cluster. You can wait for all workers by passing True, or not wait for any by passing False. You can pass a fraction of the total number of workers requested as a float(like 0.6), or a fixed number of workers as an int (like 13). If None, the value from coiled.wait-for-workers in your Dask config will be used. Default: 0.3. If the requested number of workers don’t launch within 10 minutes, the cluster will be shut down, then a TimeoutError is raised.
package_sync (Union[bool, List[str]]) – Synchronize package versions between your local environment and the cluster. Cannot be used with the software option. Passing True will sync all packages (recommended). If software is not given, defaults to True. Passing specific packages as a list of strings will attempt to synchronize only those packages, use with caution. We recommend reading the additional documentation for this feature
package_sync_ignore (Optional[List[str]]) – A list of package names to exclude from the environment. Note their dependencies may still be installed, or they may be installed by another package that depends on them!
package_sync_strict (bool) – Only allow exact packages matches, not recommended unless your client platform/architecture matches the cluster platform/architecture
private_to_creator (Optional[bool]) – Only allow the cluster creator, not other members of team account, to connect to this cluster.
use_best_zone (bool) – Allow the cloud provider to pick the zone (in your specified region) that has best availability for your requested instances. We’ll keep the scheduler and workers all in a single zone in order to avoid any interzone network traffic (which would be billed).
spot_policy (Optional[Literal[‘on-demand’, ‘spot’, ‘spot_with_fallback’]]) – Purchase option to use for workers in your cluster, options are “on-demand”, “spot”, and “spot_with_fallback”; by default this is “on-demand”. (Google Cloud refers to this as “provisioning model” for your instances.) Spot instances are much cheaper, but can have more limited availability and may be terminated while you’re still using them if the cloud provider needs more capacity for other customers. On-demand instances have the best availability and are almost never terminated while still in use, but they’re significantly more expensive than spot instances. For most workloads, “spot_with_fallback” is likely to be a good choice: Coiled will try to get as many spot instances as we can, and if we get less than you requested, we’ll try to get the remaining instances as on-demand. For AWS, when we’re notified that an active spot instance is going to be terminated, we’ll attempt to get a replacement instance (spot if available, but could be on-demand if you’ve enabled “fallback”). Dask on the active instance will attempt a graceful shutdown before the instance is terminated so that computed results won’t be lost.
scheduler_port (Optional[int]) – Specify a port other than the default (8786) for communication with Dask scheduler; this is useful if your client is on a network that blocks 8786.
allow_ingress_from (Optional[str]) – Control the CIDR from which cluster firewall allows ingress to scheduler; by default this is open to any source address (0.0.0.0/0). You can specify CIDR, or “me” for just your IP address.
allow_ssh (Optional[bool]) – Allow connections to scheduler over port 22, used for SSH.
allow_spark (Optional[bool]) – Allow (secured) connections to scheduler on port 15003 used by Spark Connect. By default, this port is open.
jupyter (Optional[bool]) – Start a Jupyter server in the same process as Dask scheduler. The Jupyter server will be behind HTTPS with authentication (unless you disable use_dashboard_https, which we strongly recommend against). Note that jupyterlab will need to be installed in the software environment used on the cluster (or in your local environment if using package sync). Once the cluster is running, you can use jupyter_link to get link to access the Jupyter server.
region (Optional[str]) – The cloud provider region in which to run the cluster.
arm (Optional[bool]) – Use ARM instances for cluster; default is x86 (Intel) instances.

adapt(Adaptive=<class 'coiled.cluster.CoiledAdaptive'>, *, minimum=1, maximum=200, target_duration='3m', wait_count=24, interval='5s', **kwargs)[source]#

Dynamically scale the number of workers in the cluster based on scaling heuristics.

Parameters

minimum (int) – Minimum number of workers that the cluster should have while on low load, defaults to 1.
maximum (int) – Maximum numbers of workers that the cluster should have while on high load.
wait_count (int) – Number of consecutive times that a worker should be suggested for removal before the cluster removes it.
interval (timedelta or str) – Milliseconds between checks, defaults to 5000 ms.
target_duration (timedelta or str) – Amount of time we want a computation to take. This affects how aggressively the cluster scales up.

Return type

Adaptive

property asynchronous#: Are we running in the event loop?

close(force_shutdown=False)[source]#

Close the cluster.

Return type: Optional[Awaitable[None]]

get_client()[source]#

Return client for the cluster

If a client has already been initialized for the cluster, return that otherwise initialize a new client object.

get_logs(scheduler=True, workers=True)[source]#

Return logs for the scheduler and workers :type scheduler: bool :param scheduler: Whether or not to collect logs for the scheduler :type scheduler: boolean :type workers: bool :param workers: Whether or not to collect logs for the workers :type workers: boolean

Returns: logs – A dictionary of logs, with one item for the scheduler and one for the workers
Return type: Dict[str]

get_spark(block_till_ready=True)[source]#

Get a spark client. Experimental and subject to change without notice.

To use this, start the cluster with coiled.spark.get_spark_cluster.

async recommendations(target)[source]#

Make scale up/down recommendations based on current state and target.

Return a recommendation of the form - {“status”: “same”} - {“status”: “up”, “n”: <desired number of total workers>} - {“status”: “down”, “workers”: <list of workers to close>}

Return type: dict

scale(n)[source]#

Scale cluster to n workers

Parameters: n (int) – Number of workers to scale cluster size to.
Return type: Optional[Awaitable[None]]

async scale_up(n, reason=None)[source]#

Scales up to a target number of n workers

It’s documented that scale_up should scale up to a certain target, not scale up BY a certain amount:

https://github.com/dask/distributed/blob/main/distributed/deploy/adaptive_core.py#L60

Return type: None

send_credentials(automatic_refresh=False)[source]#

Manually trigger sending STS token to cluster.

Usually STS token is automatically sent and refreshed by default, this allows you to manually force a refresh in case that’s needed for any reason.

send_private_envs(env)[source]#

Send potentially private environment variables to be set on scheduler and all workers.

You can use this to send secrets (passwords, auth tokens) that you can use in code running on cluster. Unlike environment variables set with coiled.Cluster(environ=...), the values will be transmitted directly to your cluster without being transmitted to Coiled, logged, or written to disk.

The Dask scheduler will ensure that these environment variables are set on any new workers you add to the cluster.

set_keepalive(keepalive)[source]#

Set how long to keep cluster running if all the clients have disconnected.

This is a way to shut down no-longer-used cluster, in additional to dask idle timeout. With no keepalive set, cluster will not shut down on account of clients going away.

Parameters: keepalive – duration string like “30s” or “5m”

shutdown()[source]#

Shutdown the cluster; useful when shutdown_on_close is False.

Return type: Optional[Awaitable[None]]

sync(func, *args, asynchronous=None, callback_timeout=None, **kwargs)[source]#

Call func with args synchronously or asynchronously depending on the calling context

Return type: Union[TypeVar(_T), Coroutine[Any, Any, TypeVar(_T)]]

wait_for_workers(n_workers, timeout=None)[source]#

Blocking call to wait for n workers before continuing

Parameters

n_workers (int) – The number of workers
timeout (number, optional) – Time in seconds after which to raise a dask.distributed.TimeoutError

Return type

None

async workers_to_close(target)[source]#

Determine which, if any, workers should potentially be removed from the cluster.

Notes

Cluster.workers_to_close dispatches to Scheduler.workers_to_close(), but may be overridden in subclasses.

Return type: List of worker addresses to close, if any

Labs#

coiled.function(*, software=None, container=None, vm_type=None, cpu=None, memory=None, gpu=None, account=None, region=None, arm=None, disk_size=None, shutdown_on_close=True, spot_policy=None, idle_timeout='24 hours', keepalive='30 seconds', package_sync_ignore=None, environ=None, threads_per_worker=1, local=False, name=None)[source]#

Decorate a function to run on cloud infrastructure

This creates a Function object that executes its code on a remote cluster with the hardware and software specified in the arguments to the decorator. It can run either as a normal function, or it can return Dask Futures for parallel computing.

Parameters

software (Optional[str]) – Name of the software environment to use; this allows you to use and re-use existing Coiled software environments, and should not be used with package sync or when specifying a container to use for this specific cluster.
container (Optional[str]) – Name or URI of container image to use; when using a pre-made container image with Coiled, this allows you to skip the step of explicitly creating a Coiled software environment from that image. Note that this should not be used with package sync or when specifying an existing Coiled software environment.
vm_type (Union[str, list[str], None]) – Instance type, or list of instance types, that you would like to use. You can use coiled.list_instance_types() to see a list of allowed types.
cpu (Union[int, list[int], None]) – Number, or range, of CPUs requested. Specify a range by using a list of two elements, for example: cpu=[2, 8].
memory (Union[str, list[str], None]) – Amount of memory to request for each VM, Coiled will use a +/- 10% buffer from the memory that you specify. You may specify a range of memory by using a list of two elements, for example: memory=["2GiB", "4GiB"].
disk_size (Union[int, str, None]) – Size of persistent disk attached to each VM instance, specified as string with units or integer for GiB.
gpu (Optional[bool]) – Whether to attach a GPU; this would be a single NVIDIA T4.
region (Optional[str]) – The cloud provider region in which to run the cluster.
arm (Optional[bool]) – Whether to use ARM instances for cluster; default is x86 (Intel) instances.
keepalive – Keep your cluster running for the specified time, even if your Python session closes. Default is “30 seconds”.
spot_policy (Optional[str]) –
Purchase option to use for workers in your cluster, options are “on-demand”, “spot”, and “spot_with_fallback”; by default this is “spot_with_fallback” for Coiled Functions. (Google Cloud refers to this as “provisioning model” for your instances.) Note that even with this option, the first VM is always on-demand. This only applies to any additional VMs when running Coiled Functions in parallel across multiple VMs with the .map() and .submit() methods. When running on a single VM, an on-demand instance will be used.

Spot instances are much cheaper, but can have more limited availability and may be terminated while you’re still using them if the cloud provider needs more capacity for other customers. On-demand instances have the best availability and are almost never terminated while still in use, but they’re significantly more expensive than spot instances. For most workloads, “spot_with_fallback” is likely to be a good choice: Coiled will try to get as many spot instances as we can, and if we get less than you requested, we’ll try to get the remaining instances as on-demand. For AWS, when we’re notified that an active spot instance is going to be terminated, we’ll attempt to get a replacement instance (spot if available, but could be on-demand if you’ve enabled “fallback”). Dask on the active instance will attempt a graceful shutdown before the instance is terminated so that computed results won’t be lost.
idle_timeout (str) – Shut down the cluster after this duration if no activity has occurred. Default is “24 hours”.
package_sync_ignore (Optional[list[str]]) – A list of package names to exclude from the cloud VM environment. This is useful when you have large libraries installed locally that aren’t needed for the function being run. Note the packages listed here may still be installed by another package that depends on them.
environ (Optional[Dict[str, str]]) – Dictionary of environment variables to securely pass to the cloud VM environment.
threads_per_worker (Optional[int]) – Number of threads to run concurrent tasks in for each VM. -1 can be used to run as many concurrent tasks as there are CPU cores. Default is 1.
local (bool) – Whether or not to run this function locally or on cloud VMs. If True, this function will be run on your local machine, which can be useful for debugging or during development. Default is False.
name (Optional[str]) – Name for the Coiled cluster on which this function will run. If not specified, VM specification parameters like vm_type, disk_size, etc. will be used to produce a unique, deterministic name. Note that name is used for sharing cloud VMs among Coiled Functions with the same hardware and software specification, so please use this parameter with care. Default to None.

See the coiled.Cluster docstring for additional parameter descriptions.

Examples

>>> import coiled
>>> @coiled.function()
... def f(x):
...    return x + 1

>>> f(10)  # calling the function blocks until finished
11
>>> f.submit(10)  # immediately returns a future
<Future: pending, key=f-1234>
>>> f.submit(10).result()  # Call .result to get result
11

>>> futures = [f(i) for i in range(1000)]  # parallelize with a for loop
>>> [future.result() for future in futures]
...

Performance Reports#

coiled.list_performance_reports(account=None)[source]#

List performance reports stored on Coiled Cloud

Returns a list of dicts that contain information about Coiled Cloud hosted performance reports

Parameters: account – Associated account for which the user wishes to get reports from. If not specified, current / default account will be utilized.
Return type: List[Dict]

coiled.performance_report(filename='dask-report.html', private=False, account=None)[source]#

Generates a static performance report and saves it to Coiled Cloud

This context manager lightly wraps Dask’s performance_report. It generates a static performance report and uploads it to Coiled Cloud. After uploading, it prints out the url where the report is hosted. For a list of hosted performance reports, utilize coiled.list_performance_reports(). Note each user is limited to 5 hosted reports with each a maximum file size of 10mb.

The context manager yields an object that will have the url as an attribute, though the URL is not available inside the context but only after (see example).

Example:

with coiled.performance_report("filename") as perf_url:
    dask.compute(...)

assert isinstance(perf_url["url"], str)

Parameters

filename – The file name of the performance report file.
private – If set to True, the uploaded performance report is only accessible to logged in Coiled users who are members of the current / default or specified account.
account – Associated the account which user wishes to upload to. If not specified, current / default account will be utilized.

Return type

Generator[PerformanceReportURL, None, None]

Backend#

coiled.set_backend_options(account=None, backend='aws', ingress=None, firewall=None, network=None, aws_region='us-east-1', aws_access_key_id=None, aws_secret_access_key=None, gcp_service_creds_file=None, gcp_service_creds_dict=None, gcp_project_id=None, gcp_region=None, gcp_zone=None, instance_service_account=None, zone=None, registry_type='ecr', registry_namespace=None, registry_access_token=None, registry_uri='docker.io', registry_username=None, log_output=<_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>, **kwargs)[source]#

Configure account level settings for cloud provider and container registry.

This method configures account level backend settings for cloud providers, container registries, and setting up an account-level VPC for running clusters and other Coiled managed resources.

Parameters

account (Optional[str]) – Coiled account to configure if user has access. If not specified, current / default account will be utilized.
backend (Literal[‘aws’, ‘gcp’]) – Supported backends such as AWS VM (aws) and GCP VM (gcp).
ingress (Optional[List[Dict]]) – Specification of the ingress rules the firewall/security group that Coiled creates for the cluster scheduler. This is a list of ingress rules, each rule is a dictionary with a list of ports and a CIDR block from which to allow ingress on those ports to the scheduler. For example, [{"ports" [8787], "cidr": "0.0.0.0/0"}, {"ports" [8786], "cidr": "10.2.0.0/16"}] would allow the dashboard on 8787 to be accessed from any IP address, and the scheduler comm on 8786 to only be accessed from IP addresses in the 10.2.0.0/16 local network block.
firewall (Optional[Dict]) – A single ingress rule for the scheduler firewall/security group; this is deprecated and ingress rules should be specified with ingress instead.
network (Optional[Dict]) – Specification for your network/subnets, dictionary can take ID(s) for existing network and/or subnet(s).
aws_region (str) – The region which Coiled cloud resources will be deployed to and where other resources such as the docker registry are located or where a specified VPC will be created.
aws_access_key_id (Optional[str]) – For AWS support backend, this argument is required to create or use an existing Coiled managed VPC.
aws_secret_access_key (Optional[str]) – For AWS support backend, this argument is required to create or use an existing Coiled managed VPC.
use_scheduler_public_ip – Determines if the client connects to the Dask scheduler using it’s public or internal address.
gcp_service_creds_file (Optional[str]) – A string filepath to a Google Cloud Compute service account json credentials file used for creating and managing a Coiled VPC.
gcp_service_creds_dict (Optional[dict]) – A dictionary of the contents of a Google Cloud Compute service account json credentials file used for creating a VPC to host Coiled Cloud related assets.
gcp_project_id (Optional[str]) – The Google Cloud Compute project id in which a VPC will be created to host Coiled Cloud related assets.
gcp_region (Optional[str]) – The Google Cloud Compute region name in which a VPC will be created.
instance_service_account (Optional[str]) – Email for optional service account to attach to cluster instances; using this is the best practice for granting access to your data stored in Google Cloud services. This should be a scoped service instance with only the permissions needed to run your computations.
zone (Optional[str]) – Optional; used to specify zone to use for clusters (for either AWS or GCP).
registry_type (Literal[‘ecr’, ‘docker_hub’, ‘gar’]) – Custom software environments are stored in a docker container registry. By default, container images will be stored in AWS ECR. Users are able to store contains on a private registry by providing additional configuration registry_* arguments and specifying registry_type=’docker_hub’. To use Google Artifact Registry, pass registry_type=’gar’, gcp_project_id, gcp_region, and one of gcp_service_creds_dict or gcp_service_creds_file.
registry_uri (str) – The container registry URI. Defaults to docker.io. Only required if registry_type=’docker_hub’.
registry_username (Optional[str]) – A registry username (should be lowercased). Only required if registry_type=’docker_hub’.
registry_namespace (Optional[str]) – A namespace for storing the container images. Defaults to username if not specified. More information about docker namespaces can be found here: https://docs.docker.com/docker-hub/repos/create/. Only required if registry_type=’docker_hub’.
registry_access_token (Optional[str]) – A token to access registry images. More information about access tokens ca be found here: https://docs.docker.com/docker-hub/access-tokens/. Only required if registry_type=’docker_hub’.

Information#

coiled.diagnostics(account=None)[source]#

Run a diagnose check aimed to help support with any issues.

This command will call others to dump information that could help in troubleshooting issues. This command will return a json that will make it easier for you to share with the Coiled support team if needed.

Parameters: account (Optional[str]) – Name of the Coiled account to list core usage. If not provided, will use the coiled.account configuration value.
Return type: dict

coiled.get_billing_activity(account=None, cluster=None, cluster_id=None, start_time=None, end_time=None, kind=None, page=None)[source]#

Retrieve Billing information.

Parameters

account (Optional[str]) – The account to retrieve billing information from. If not provided, will default to Cloud.account configuration value.
cluster (Optional[str]) – Cluster name. Filter billing events to this cluster. Defaults to None.
cluster_id (Optional[int]) – Cluster id. Filter billing events to this cluster by id. Defaults to None.
start_time (Optional[str]) – Filter events after this datetime (isoformat). Defaults to None.
end_time (Optional[str]) – Filter events before this datetime (isoformat). Defaults to None.
kind (Optional[Literal[‘instance’, ‘monthly_grant’, ‘manual_adjustment’, ‘payg_payment’]]) – Filter events to this kind of event. Defaults to None.
page (Optional[int]) – Grab events from this page. Defaults to None.

Return type

Dict

coiled.list_local_versions()[source]#

Get information about local versions.

Returns the versions of Python, Coiled, Dask and Distributed that are installed locally. This information could be useful when troubleshooting issues.

Parameters: json – If set to True, it will return this list in json format instead of a table.
Return type: dict

coiled.list_user_information()[source]#

List information about your user.

This command will give you more information about your account, which teams you are part of and any limits that your account might have.

Return type: dict

Command Line API Reference#

coiled login#

Configure your Coiled account credentials

coiled login [OPTIONS]

Options

-t, --token <token>#: Coiled user token

-a, --account <account>#: Coiled account

--retry, --no-retry#: Whether or not to automatically ask for a new token if an invalid token is entered

--browser, --no-browser#: Open browser with page where you grant access

Deprecated since version 0.4.0: The following options for coiled env create have been deprecated: --post-build, --conda-env-name, --private, --environ. You can continue to use this feature by downgrading your Coiled version, but it will be removed in the future.

coiled env create#

Create a Coiled software environment

coiled env create [OPTIONS]

Options

-n, --name <name>#: Name of software environment, it must be lowercase.

--container <container>#: Base docker image to use.

--conda <conda>#: Conda environment file.

--pip <pip>#: Pip requirements file.

--force-rebuild#: Skip checks for an existing software environment build.

--account <account>#: Account to use for creating this software environment.

--gpu-enabled#

Set CUDA virtual package for Conda

Default: False

--architecture <architecture>#

CPU architecture to use for the software environment

Default: x86_64
Options: x86_64 | aarch64

--region-name <region_name>#: AWS or GCP region to use for storing this software environment.

coiled env delete#

Delete a Coiled software environment

coiled env delete [OPTIONS] NAME

Arguments

NAME#: Required argument

coiled env list#

List the Coiled software environments in an account

coiled env list [OPTIONS] [ACCOUNT]

Arguments

ACCOUNT#: Optional argument

coiled env inspect#

View the details of a Coiled software environment

coiled env inspect [OPTIONS] NAME

Arguments

NAME#: Required argument

coiled cluster ssh#

coiled cluster ssh [OPTIONS] [CLUSTER]

Options

--account <account>#: Coiled account (uses default account if not specified)

--private#: Use private IP address of scheduler (default is public IP address)

--worker <worker>#: Connect to worker with specified name or private IP address (default is to connect to scheduler)

--add-key#: Just add ssh key to local OpenSSH agent, no lifetime/expiration set

--delete-key#: Just delete ssh key from local OpenSSH agent

--dask#: Attach to shell in Dask container rather than shell on host machine.

Arguments

CLUSTER#: Optional argument

coiled cluster logs#

coiled cluster logs [OPTIONS] CLUSTER

Options

--account <account>#: Coiled account (uses default account if not specified)

--scheduler#: Get scheduler logs

--workers <workers>#: Get worker logs (‘any’, ‘all’, or comma-delimited list of names, states, or internal IP addresses)

--follow#: Passed directly to aws logs tail, see aws cli docs for details.

--filter <filter>#: Passed directly to aws logs tail, see aws cli docs for details.

--since <since>#: For follow, uses aws logs tail default (10m), otherwise defaults to start time of cluster.

--format <format>#: Passed directly to aws logs tail, see aws cli docs for details.

--profile <profile>#: Passed directly to aws logs tail, see aws cli docs for details.

Arguments

CLUSTER#: Required argument

coiled package-sync scan#

coiled package-sync scan [OPTIONS]

Options

--csv#: Output as CSV

-v, --verbose#: Output files that will end up in wheels

coiled package-sync debug#

coiled package-sync debug [OPTIONS]

Options

--csv#: Output as CSV

coiled notebook start#

Launch or re-open a notebook session, with optional file syncing.

If a notebook session with the same name already exists, it’s not re-created. If file sync was initially not enabled, running coiled notebook start --sync will begin file sync without re-launching the notebook.

coiled notebook start [OPTIONS]

Options

--name <name>#: Cluster name. If not given, defaults to a hash based on current working directory.

--account <account>#: Coiled account (uses default account if not specified)

--sync#: Sync the working directory with the filesystem on the notebook. Requires mutagen.

--software <software>#: Software environment name to use. If neither software nor container is specified, all the currently-installed Python packages are replicated on the VM using package sync.

--container <container>#: Container image to use. If neither software nor container is specified, all the currently-installed Python packages are replicated on the VM using package sync.

--vm-type <vm_type>#: VM type to use. Specify multiple times to provide multiple options.

--cpu <cpu>#: Number of CPUs requested for this notebook.

--memory <memory>#: Amount of memory for this notebook, Coiled will use a +/-10% buffer from the memory you specify.

--gpu#: Use GPU notebook server.

--region <region>#: The cloud provider region in which to run the notebook.

--open#: Whether to open the notebook in the default browser once it’s launched

--block, --no-block#: Whether to block while the notebook is running.

--idle-timeout <idle_timeout>#: Period of idleness after which to automatically shut down the notebook, e.g. ‘20 minutes’ or ‘1 hour’ or ‘10 days’ (default is ‘4 hours’). The notebook is considered active (not idle) if a browser is connected, even if no code is running.

coiled notebook stop#

Shut down a notebook session

coiled notebook stop [OPTIONS] NAME

Options

--account <account>#: Coiled account (uses default account if not specified)

Arguments

NAME#: Required argument

coiled run#

Run a command on the cloud.

coiled run [OPTIONS] [COMMAND]...

Options

--name <name>#: Run name. If not given, defaults to a unique name.

--account <account>#: Coiled account (uses default account if not specified)

--software <software>#: Software environment name to use. If neither software nor container is specified, all the currently-installed Python packages are replicated on the VM using package sync.

--container <container>#: Container image to use. If neither software nor container is specified, all the currently-installed Python packages are replicated on the VM using package sync.

--vm-type <vm_type>#: VM type to use. Specify multiple times to provide multiple options.

--gpu#: Have a GPU available.

--region <region>#: The cloud provider region in which to run the notebook.

--disk-size <disk_size>#: Use larger-than-default disk on VM, specified in GiB.

--keepalive <keepalive>#: Keep your VM running for the specified time, even after your command completes. In seconds (–keepalive 60) unless you specify units (–keepalive 3m for 3 minutes).Default to shutdown immediately after the command finishes.

-f, --file <file>#: Local files required to run command.

-e, --env <env>#: Environment variables securely transmitted to run command environment. Format is KEY=val, multiple vars can be set with separate –env for each.

-it, --interactive#: Open an interactive session, e.g., coiled run –interactive bash or coiled run –interactive python.

--detach#: Start the run, don’t wait for the results.

Arguments

COMMAND#: Optional argument(s)

Spark

Access Remote Data