# How to limit cpus used on each worker \[Autoscaler\]

**URL:** <https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802>\
**Category:** Ray Core\
**Created:** [February 9, 2021, 6:57pm UTC](https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802 "2021-02-09T18:57:18Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![jtm812](https://avatars.discourse-cdn.com/v4/letter/j/4af34b/32.png) [@jtm812](https://discuss.ray.io/u/jtm812)\
**Post date:** [February 9, 2021, 6:57pm UTC](https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802/1 "2021-02-09T18:57:18Z")

</div>

When I run locally on a single machine I can specify num\_cpus on ray.init(num\_cpus = 2). But i cant specify this on cluster

I want to limit the number of cpus being used per worker like this:

```
worker_node: 

             max_cpus: 3 

Head_node: 

              max_cpus: 4

```

Do you know how I would edit my yaml to do so, as what I have tried hasn’t worked?

The reason why is I run out of RAM on my ec2 when all cores are running on it.

* * *

```
cluster_name: autoscale

initial_workers: 5

min_workers: 5

max_workers: 5

initialization_commands:

    - aws configure set aws_access_key_id --------------------

    - aws configure set aws_secret_access_key -------------------

    - aws configure set default.region eu-west-2

    #access to docker login

    - eval $(aws ecr get-login --no-include-email --region eu-west-2)

   - sudo aws s3 cp s3:/ pipeline/ --recursive

docker:

    image: "/pipeline:ray"  

    container_name: "hello_ray_container"

    pull_before_run: True

    run_options:

        - "-v /home/ubuntu/pipeline/data:/opt/pipeline/data"

provider:

    type: aws

    region: eu-west-2

auth:

    ssh_user: ubuntu

head_node:

    InstanceType: c5.12xlarge

    ImageId: latest_dlami # Default Ubuntu 16.04 AMI

    BlockDeviceMappings:

        - DeviceName: /dev/sda1

          Ebs:

              VolumeSize: 200

worker_nodes:

    InstanceType: c5.12xlarge

    ImageId: latest_dlami # Default Ubuntu 16.04 AMI.

    BlockDeviceMappings:

        - DeviceName: /dev/sda1

          Ebs:

              VolumeSize: 200
```

---

<div class="post-metadata">

**Author:** ![Alex](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/alex/32/341_2.png) [@Alex](https://discuss.ray.io/u/Alex)\
**Post date:** [February 9, 2021, 8:08pm UTC](https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802/2 "2021-02-09T20:08:03Z")

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2 possibilities:

1. In theory, this is what the memory resource is for, so if you can estimate how much memory your tasks use, that’s ideal.

2. It’s sometimes difficult to estimate the memory usage of a task, so you can specify

```auto
head_node:
    resources: {"CPU": N}

```

to override ray’s CPU detection and manually set the number of CPUs that Ray will use on the machine.

---

<div class="post-metadata">

**Author:** ![jtm812](https://avatars.discourse-cdn.com/v4/letter/j/4af34b/32.png) [@jtm812](https://discuss.ray.io/u/jtm812)\
**Post date:** [February 10, 2021, 9:38am UTC](https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802/3 "2021-02-10T09:38:40Z")

</div>

Thanks for your response.

Unfortunately, I can’t get that to work. Is there a specific way to include it.

this is the error i get

```
raise ParamValidationError(report=report.generate_report())

```

botocore.exceptions.ParamValidationError: Parameter validation failed:  
Unknown parameter in input: “resources”, must be one of: BlockDeviceMappings, ImageId, InstanceType, Ipv6AddressCount, Ipv6Addresses, KernelId, KeyName, MaxCount, MinCount, Monitoring, Placement, RamdiskId, SecurityGroupIds, SecurityGroups, SubnetId, UserData, AdditionalInfo, ClientToken, DisableApiTermination, DryRun, EbsOptimized, IamInstanceProfile, InstanceInitiatedShutdownBehavior, NetworkInterfaces, PrivateIpAddress, ElasticGpuSpecification, ElasticInferenceAccelerators, TagSpecifications, LaunchTemplate, InstanceMarketOptions, CreditSpecification, CpuOptions, CapacityReservationSpecification, HibernationOptions, LicenseSpecifications, MetadataOptions, EnclaveOptions

```
head_node:
    resources: {"CPU": 4}

    InstanceType: c5.12xlarge

    ImageId: latest_dlami # Default Ubuntu 16.04 AMI

    BlockDeviceMappings:

        - DeviceName: /dev/sda1

          Ebs:

              VolumeSize: 200
```

---

<div class="post-metadata">

**Author:** ![jtm812](https://avatars.discourse-cdn.com/v4/letter/j/4af34b/32.png) [@jtm812](https://discuss.ray.io/u/jtm812)\
**Post date:** [February 10, 2021, 10:50am UTC](https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802/4 "2021-02-10T10:50:18Z")

</div>

solved it,

just added “–cpus=6” to docker input run options

Perhaps not the most elegant, but is simple.

---

<div class="post-metadata">

**Author:** ![Ameer\_Haj\_Ali](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/ameer_haj_ali/32/279_2.png) [@Ameer\_Haj\_Ali](https://discuss.ray.io/u/Ameer_Haj_Ali)\
**Post date:** [February 15, 2021, 6:47am UTC](https://discuss.ray.io/t/how-to-limit-cpus-used-on-each-worker-autoscaler/802/5 "2021-02-15T06:47:21Z")

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You should be able to add it under the `available_node_types` field with `resources` field.  
See this example:

> <https://github.com/ray-project/ray/blob/master/python/ray/autoscaler/aws/example-multi-node-type.yaml>