coiled

library1.135.3pypypi✓ verified 23d ago

Coiled is a Python client library that simplifies scaling Python code and Dask clusters on the cloud (AWS, GCP, Azure). It handles cloud resource management, networking, and software environments, allowing data engineers and scientists to focus on their code. The library is actively maintained, with frequent updates to support new features and cloud capabilities. Its current version is 1.134.0, and it generally follows a continuous release cadence with frequent minor updates.

devops data aws gcp azure

pip install "coiled[dask]"

package ↗homepage ↗

INSTALL

IMPORT

SIG · COILED

coiled

devopspythonv1.135.3

Install

19.1s avg

Import

2578ms

Disk

202MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.135.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 19.1s · import 2.578s · 204MB

202MB installed

● package 202MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Cluster

✓ from coiled import Cluster

function

✓ from coiled import function

Used as a decorator for serverless functions.

This quickstart demonstrates how to create and use a Dask cluster and a serverless Python function with Coiled. Before running, ensure you have authenticated with `coiled login` and connected your cloud account using `coiled setup <aws|gcp|azure>` via the Coiled CLI. You can specify a software environment for consistency or Coiled will attempt to synchronize your local environment.

import coiled import dask.dataframe as dd import os # Ensure you are logged into Coiled via 'coiled login' in your terminal # and have connected your cloud account via 'coiled setup <aws|gcp|azure>'. # Quickstart for Dask Cluster try: cluster = coiled.Cluster( n_workers=5, software="dask-2023.12.0", # Specify a software environment or Coiled will try to sync your local env region=os.environ.get('COILED_REGION', 'us-east-1') # Use an environment variable for region ) client = cluster.get_client() print(f"Dask Dashboard link: {client.dashboard_link}") # Example Dask computation df = dd.read_csv("s3://dask-data/nyc-taxi/2015/yellow_tripdata_2015-01.csv", assume_missing=True) result = df.groupby('passenger_count').tip_amount.mean().compute() print("Dask Cluster computation result:") print(result) client.close() cluster.close() print("Dask Cluster closed.") except Exception as e: print(f"Error with Dask Cluster quickstart: {e}") print("Please ensure you have run 'coiled login' and 'coiled setup <cloud>' and set COILED_REGION if needed.") # Quickstart for Serverless Function # For serverless functions, you can also specify memory, cpu, region, etc. # e.g., @coiled.function(memory='512 GB', cpu=128, region='us-east-2') @coiled.function() def my_serverless_function(x): import time time.sleep(2) # Simulate work return x * 2 try: print("\nRunning serverless function...") future = my_serverless_function.submit(10) serverless_result = future.result() print(f"Serverless function result: {serverless_result}") except Exception as e: print(f"Error with Serverless Function quickstart: {e}") print("Serverless functions also require 'coiled login' and cloud setup.")

coiled --version

Debug

Known issues

gotchaEnvironment synchronization can be tricky. By default, Coiled attempts to replicate your local Python environment on the remote VMs. This can sometimes lead to discrepancies if local packages are not perfectly available or compatible in the cloud environment. Explicitly defining a software environment (e.g., using `software='my-env'` or a `container='my-docker-image'`) is often more robust, especially for production or complex setups.

fix

Use `coiled.Cluster(software='my-software-environment')` or `coiled.Cluster(container='daskdev/dask:latest')` to specify a managed environment or Docker image. For serverless functions, similar options apply to the `@coiled.function` decorator.

affects: All versions

gotchaAuthentication and cloud setup are prerequisites. Users must run `coiled login` and `coiled setup <aws|gcp|azure>` from their CLI before using the Python client to provision resources. Skipping these steps will result in authentication or permission errors.

fix

Run `pip install coiled` then `coiled login` and `coiled setup <your-cloud-provider>` in your terminal to authenticate and configure your cloud account.

affects: All versions

gotchaResource shutdown is not automatic by default for all resources. While Coiled clusters and functions have idle timeouts, explicitly closing `client` and `cluster` objects in your script (`client.close()`, `cluster.close()`) is a good practice to release resources promptly and avoid unexpected cloud costs, especially in interactive sessions or long-running scripts.

fix

Always include `client.close()` and `cluster.close()` in your scripts when you are done with the Dask cluster. For `coiled.function` with `keepalive` set, be mindful of the duration to prevent VMs from staying active longer than needed.

affects: All versions

gotchaSpecifying cloud regions is important for data locality and cost. Not setting a specific region might lead to clusters being provisioned in a default region that is geographically distant from your data sources, incurring higher data transfer costs and increased latency.

fix

Always specify the `region` parameter when creating a `coiled.Cluster` or using `@coiled.function` to match the location of your data (e.g., `coiled.Cluster(region='us-east-2')`).

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'coiled'

The 'coiled' Python package is not installed in the environment where the code is being executed.

fix

Install the coiled library using pip: `pip install coiled` or conda: `conda install -c coiled coiled`.

TypeError: TLS expects a ssl_context argument of type ssl.SSLContext

This error typically indicates an issue with the SSL/TLS configuration when connecting to a Coiled Dask cluster, often when the distributed client expects an SSL context but doesn't receive it, particularly when using `prefect-dask` or direct Dask client connections.

fix

Ensure proper SSL configuration is provided to your Dask client or task runner, often by explicitly setting a `security` parameter in the `coiled.Cluster` or `DaskTaskRunner` configuration, or by passing an `ssl_context` argument where expected.

Google auth failed to initialize error

This issue arises when the Coiled client cannot properly authenticate with Google services, often due to browser settings blocking cookies or other authentication flow interferences.

fix

Ensure your web browser allows all cookies, temporarily disable browser extensions like 'Brave Shields' if in use, or re-authenticate the Coiled client using the `coiled login` command.

AttributeError: No option 'environment' available

This error occurs when attempting to configure Dask options, specifically related to environment variables, in a manner or with a key name that is not recognized or supported by the current versions of Dask, distributed, or Coiled, especially when using `dask.config.set()` or `coiled.Cluster` configurations.

fix

Use the correct and supported method for setting environment variables for Dask workers within Coiled, which might involve using the `environment_vars` parameter directly in `coiled.Cluster` or passing a dictionary of Dask configurations via the `config` argument, e.g., `coiled.Cluster(config={'distributed.scheduler.worker-saturation': '1.0'})`.

Upgrade

Version history

1.135.3latest on PyPI · released Aug 14, 2026

Audit

Dependencies

daskoptionalRequired for Dask cluster functionality, which is a primary use case.

distributedoptionalDask's distributed scheduler, necessary for Dask clusters.

Agent activity

58 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/coiled/ ↗

homepagehttps://coiled.io ↗