ray/release/rllib_tests/4gpus_64cpus.yaml
gjoliver 2c1fa459d4
[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

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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