ray/release/data_processing_tests/README.rst
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[Test] Large scale dask on ray test (#14340)
* Add a test.

* Add a test.

* d

* Modify the release doc.

* Addressed code review.
2021-03-23 11:00:35 -07:00

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Running script
--------------
There are 2 workloads. Each workerload requires a different cluster.yaml.
Make sure to copy & paste both drivers.
Run `unset RAY_ADDRESS; python workloads/streaming_shuffle.py`. Use `cluster.yaml` for this release test.
Run `unset RAY_ADDRESS; python workloads/dask_on_ray_large_scale_test.py`. Use `dask_on_ray.yaml` for this release test.
Note that when you run `dask_on_ray.yaml`, you need to follow the below procedures.
```
ray up dask_on_ray.yaml -y # Start the ray cluster.
# Wait until the cluster nodes are up. Use `watch ray status` and wait until all worker nodes are up.
ray down dask_on_ray.yaml -y # After the cluster is up, you should call ray down.
ray up dask_on_ray.yaml -y
```
This process is required because ulimit is not permitted for images that we are using. Ulimit is necessary for large cluster testing like this.
Check out https://discuss.ray.io/t/setting-ulimits-on-ec2-instances/590/2 for more details
Success Criteria
----------------
For `streaming_shuffle.py`, make sure to include the output string to the release logs.
For `dask_on_ray_large_scale_test.py`, make sure the test runs for at least for an hour. This test should succeed, otherwise, it is a release blocker.
Check out https://github.com/ray-project/ray/pull/14340#discussion_r599271079 to learn the success condition of this test.