Treats failures of provider.internal_ip during node drain as non-fatal.
For example, if a node is deleted by a third party between the time it's scheduled for termination and drained, there will now be no error on GCP.
Closes#21151
In some compiler, the static ray runtime in ray runtime holder maybe a new un-init instance in dynamic library,
so we need to init ray time holder in dynamic library to make sure the new instance valid.
We fix the issue that it's unable to specify the concurrency group name of an actor task at runtime with the following usage:
```python
a.f2.options(concurrency_group="compute").remote()
```
This PR adds documentation for Workflow Metadata, which we recently added support in https://github.com/ray-project/ray/pull/19372.
Co-authored-by: Yi Cheng <74173148+iycheng@users.noreply.github.com>
I added memory monitor to the scalability tests. This broke the tests because creating a memory monitor requires the node resources (to be scheduled on a head node), and that broke "resource leak" check. Ideally, this resource leak check should be more robust, but I fix the issue in an easier way for now. In the sooner future, memory monitor will become a fixture, and in that case, we should fix resource leak function code.
This reverts commit 968f08607b.
It is breaking e2e tests where worker nodes cannot start. e.g.
```
Traceback (most recent call last):
File "/home/ray/anaconda3/bin/ray", line 8, in <module>
sys.exit(main())
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/scripts/scripts.py", line 1961, in main
return cli()
File "/home/ray/anaconda3/lib/python3.7/site-packages/click/core.py", line 1128, in __call__
return self.main(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/click/core.py", line 1053, in main
rv = self.invoke(ctx)
File "/home/ray/anaconda3/lib/python3.7/site-packages/click/core.py", line 1659, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/home/ray/anaconda3/lib/python3.7/site-packages/click/core.py", line 1395, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/home/ray/anaconda3/lib/python3.7/site-packages/click/core.py", line 754, in invoke
return __callback(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/autoscaler/_private/cli_logger.py", line 808, in wrapper
return f(*args, **kwargs)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/scripts/scripts.py", line 733, in start
address_ip, password=redis_password)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/_private/services.py", line 593, in create_redis_client
_, redis_ip_address, redis_port = validate_bootstrap_address(redis_address)
File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/_private/services.py", line 494, in validate_bootstrap_address
raise ValueError("Malformed address. Expected '<host>:<port>'.")
ValueError: Malformed address. Expected '<host>:<port>'.
```
This PR unreverts #21115, fixing the handling of an `"auto"` address in the `RAY_ADDRESS` environment variable.
Co-authored-by: Mingwei Tian <mwtian@anyscale.com>
Dask default's to a disk-based shuffle even thought we're using a distributed scheduler, which appears to be resulting in dropped data since the filesystem isn't shared across nodes. Dask Distributed manually sets the shuffle algorithm in the global config to the task-based shuffle, which the Dask-on-Ray scheduler should probably do as well.
This PR adds a Dask config helper, `enable_dask_on_ray`, that sets Dask-on-Ray as the default scheduler along with changing the default shuffle to a task-based shuffle. The shuffle method can still be overridden by the user by manually specifying `df.set_index(shuffle="disk")`.
This change adds support for parsing `--address` as bootstrap address, and treating `--port` as GCS port, when using GCS for bootstrapping.
Not launching Redis in GCS bootstrapping mode, and using GCS to fetch initial cluster information, will be implemented in a subsequent change.
Also made some cleanups.
* updating azure autoscaler versions and backwards compatibility, and moving to azure-identity based authentication
* adding azure sdk rqmts for tests
* updating azure test requirements and adding wrapper function for azure sdk function resolution
* adding docstring to get_azure_sdk_function
Co-authored-by: Scott Graham <scgraham@microsoft.com>
Currently, the logic of uri reference in raylet is:
- For job level, add uri reference when job started and remove uri reference when job finished.
- For actor level, add and remove uri reference for detached actor only.
In this PR, the logic is optimized to:
- For job level, check if runtime env should be installed eagerly first. If true, add or remove uri reference.
- For actor level
* First, add uri reference for starting worker process to avoid that runtime env is gcd before worker registered.
* Second, add uri reference for echo worker thread of worker process. We will remove reference when worker disconnected.
- Besides, we move the instance of `RuntimeEnvManager` from `node_manager` to `worker_pool`.
- Enable the test `test_actor_level_gc` and add some tests in python and worker pool test.
GcsClient accepts only redis before. To make it work without redis, we need to be able to pass gcs address to gcs client as well.
In this PR, we add GCS related into into GcsClientOptions so that we can connect to the gcs directly with gcs address.
This PR is part of GCS bootstrap. In the following PR, we'll add functionality to set the correct GcsClientOptions based on flags.
