ray/release/rllib_tests/4gpus_64cpus.yaml

cloud_id: {{env["ANYSCALE_CLOUD_ID"]}}
region: us-west-2

max_workers: 0

head_node_type:
    name: head_node
    instance_type: g3.16xlarge

worker_node_types:
    - name: worker_node
      instance_type: m5.xlarge
      min_workers: 0
      max_workers: 0
      use_spot: false

aws:
    BlockDeviceMappings:
        - DeviceName: /dev/sda1
          Ebs:
            VolumeSize: 500
[RLlib] Expand machine for nightly multi-gpu learning tests. (#17955) 2021-08-19 22:27:30 +02:00			`cloud_id: {{env["ANYSCALE_CLOUD_ID"]}}`
			`region: us-west-2`

			`max_workers: 0`

			`head_node_type:`
			`name: head_node`
			`instance_type: g3.16xlarge`

			`worker_node_types:`
			`- name: worker_node`
			`instance_type: m5.xlarge`
			`min_workers: 0`
			`max_workers: 0`
			`use_spot: false`

[RLlib] Add an RLlib Tune experiment to UserTest suite. (#19807) * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * bump timeout * Write a more informational result dict. * Revert changes to compute config files that are not used. * add smoke test * update * reduce timeout * Reduce the # of env per worker to 1. * Small fix for getting trial_states * Trigger build * simply result dict * lint * more lint * fix smoke test Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com> 2021-11-03 17:04:27 -07:00			`aws:`
			`BlockDeviceMappings:`
			`- DeviceName: /dev/sda1`
			`Ebs:`
			`VolumeSize: 500`