Inheriting from `abc.ABC` is more readable than setting the meta class to `abc.ABCMeta`.
Relevant snippet from the Python 3.4 release notes:
> New class ABC has ABCMeta as its meta class. Using ABC as a base class has essentially the same effect as specifying metaclass=abc.ABCMeta, but is simpler to type and easier to read. (Contributed by Bruno Dupuis in bpo-16049.)
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Matthew Deng <matthew.j.deng@gmail.com>
If we use `os.environ` to set environment variables in tests, then our tests become coupled. By using `monkeypatch`, we can safely set environment variables while ensuring our tests remain decoupled.
For more information, see the [monkeypatching documentation](https://docs.pytest.org/en/6.2.x/monkeypatch.html#monkeypatching-environment-variables).
Expands the `to_torch` method for Datasets with:
* An ability to choose to output a list/dict of feature tensors instead of just one (through setting `feature_columns` to be a list of lists or a dict of lists)
* An ability to choose whether the label should be unsqueezed or not
* An ability to pass `None` as the label (for prediction).
Furthermore, this changes how the `feature_column_dtypes` argument works. Previously, it took a list of dtypes for each feature. However, as the tensor was concatenated in the end, only one dtype mattered (the biggest one). Now, this argument expects a single dtype which will be applied to the features tensor (or a list/dict if `feature_columns` is a list of list/dict of lists).
Unit tests for all cases are included.
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
When a list with mixed types is passed to tune.choice, they will be coerced to a single dtype during sampling (due to numpy.choice converting to an array internally). This behaviour is unintentional and surprising. This PR fixes this issue.
This PR contains most of the fixes @iycheng made in #21232, to make tests pass with GCS bootstrapping by supporting both Redis and GCS address as the bootstrap address. The main change is to use address_info["address"] to obtain the bootstrap address to pass to ray.init(), instead of using address_info["redis_address"]. In a subsequent PR, address_info["address"] will return the Redis or GCS address depending on whether using GCS to bootstrap.
This PR is introducing a canonical impl for stopping trials by collecting scattered logic from process_trial_result back into stop_trial. This way, we know what is expected (e.g. what callbacks are invoked and when they are invoked).
This PR will correct the current wrong logic that on_trial_complete callback is invoked before on_trial_checkpoint, which is the source of Syncer clean up issues.
This PR passes gcs address to worker and also update pubsub unit test.
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
Co-authored-by: Mingwei Tian <mwtian@anyscale.com>
This is part of redis removal. This PR enable global accessor to be able to start from gcs
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
Co-authored-by: Mingwei Tian <mwtian@anyscale.com>
This is part of redis removal. This PR enable log monitor and monitor to bootstrap from gcs
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
Co-authored-by: Mingwei Tian <mwtian@anyscale.com>
The run_dir argument in ray.train.backend.BackendExecutor.start_training isn't used but is causing the following error: if your host computer and job cluster use different OS, then you get a pathlib error because, for e.g., you can't instantiate a pathlib.WindowsPath in a Linux system.
Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com>
This is part of gcs ha project. This PR try to bootstrap dashboard with gcs address instead of redis.
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
This is part of #21129
This PR tries to cover the cpp/ray part of the bootstrap, some updates there:
remove the unused function/tests
some API updates
Co-authored-by: mwtian <81660174+mwtian@users.noreply.github.com>
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
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>
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.
