Remote Data Access#

You’ll probably need your Dask workers to process private data (e.g. in S3), so you will need a way for those workers to authenticate.

In most cases, Coiled handles this for you and you can run the same code on your cluster that you do locally.


Suppose you have an object in an S3 bucket s3://david-auth-example/hello.txt that’s only accessible from your AWS account.

You might have code like this that you run locally to read some data from S3:

def read_object():
    s3 = boto3.client("s3")
    data = s3.get_object(Bucket='david-auth-example', Key="hello.txt")
    return data["Body"].read()

text = read_object()

When this code runs on your local machine, it’s using the AWS credentials you have in your local environment. What would you need to do to make this code work on a Coiled cluster?

(Easiest, automatic) Personal STS tokens#

Our goal is to make the transition from running locally to running in a cluster as seamless as possible, so by default Coiled uses your local credentials to create a temporary STS token (encrypted in transit) that we send to the cluster.

Coiled does this by setting environment variables AWS_SECRET_ACCESS_KEY and AWS_SESSION_TOKEN on the cluster. These variables do not contain your actual credentials, but rather the temporary STS token Coiled creates for you.

The identity we use for this is whichever one Boto on your local machine uses by default. For example, it could come from environment variables or a ~/.aws/credentials file.

If you want to check which identity Coiled will use for this by default, you can (if you have the AWS CLI installed) run aws sts get-caller-identity or in Python:

import boto3
sts = boto3.client("sts")

If the read_object function above worked locally, it will work on the cluster too.

You can turn off this default by specifying coiled.Cluster(..., credentials=None) when you start your cluster.

Instance Profile#

When you set up Coiled to run in your AWS account, we create an IAM policy named CoiledInstancePolicy. This policy is attached to the running instances in your cluster, and by default contains the minimum set of permissions needed for Coiled to work (just the ability to write logs to CloudWatch).

You can use this IAM policy to give your cluster additional permissions. For example to make the above example work even with no local AWS credentials, you could add the following permission:

    "Effect": "Allow",
    "Action": [
    "Resource": [

Note that if you use this option and also have local AWS credentials configured, the STS token we generate from them will take priority on the cluster and your code will use that instead of the Instance Profile. You can prevent this with coiled.Cluster(..., credentials=None).

Google Cloud#

When you configure Coiled to use your Google Cloud account (coiled setup gcp), we ask for two service accounts: one which Coiled will use to run instances on your behalf, and also the data access service account which will control what permissions those instances have.

You can give your workers permissions within Google Cloud by assigning permissions to that data access service account.

Service-agnostic authentication#

When you create your cluster, you can specify custom environment variables by passing a dictionary whose keys will become environment variables on the cluster:

cluster = coiled.Cluster(environ={"AUTH_TOKEN_FOR_CUSTOM_DATABASE": "some-token"})

If none of the above options work for you, you can use this to send a token to use directly in your code that runs on the cluster.