Selecting Instance Types#

An instance is a server hosted by a cloud provider and instance types (or machine types) are defined by combinations of CPU, memory, storage, and networking capacity (see GCP supported instance types and AWS supported instance types for more details). When creating a cluster, Coiled will find all available instance types with the requirements that you specify for CPU, memory, and GPU. To allow for more fine-grain control of the type of cluster you create, you can provide a list of instance types for the scheduler and workers using the scheduler_vm_types and worker_vm_types keyword arguments. For example:

import coiled

cluster = coiled.Cluster(
    scheduler_vm_types=["t3.large", "t3.xlarge"],
    worker_vm_types=["m5n.large", "m5zn.large"],
)

It’s recommended you specify more than one instance type in your list to avoid instance availability issues in the cloud provider and region that you are using Coiled.

If you provide a list of specific instance types, Coiled will prioritize them in order of decreasing priority. If you instead specify the number of CPUs or memory, compatible instance types are prioritized in order of lowest to highest estimated cost.

Allowable Instance Types#

You can use coiled.list_instance_types() to see a list of all allowed instance types for your configured cloud provider. For example:

import coiled

coiled.list_instance_types()

For more details on supported instance types see

You can specify a single keyword argument or a combination (e.g. cores, memory and gpus) to filter the results.

import coiled

# Filter instances that have 4 cores only
coiled.list_instance_types(cores=4)

# Filter instances by cores and memory
coiled.list_instance_types(cores=2, memory="8 Gib")

You can also provide list_instance_types with a range of values, for example:

import coiled

coiled.list_instance_types(cores=[2, 8])

You might be also interested in the tutorial on Selecting GPU Types.

Setting Backend Options via Python API

Selecting GPU Types