As a followup of #26619 (comment) and #26619 (comment), here we change from PermissionError to OSError, to be consistent as original error, and also change function name from _handle_read_s3_files_error to _handle_read_os_error, which is more general that we can handle other file systems such as GCS in the future.
Also change to hanlde any error message with pattern AWS Error [code xxx]: No response body as new issue with error code 100 is raised in #26672 .
This introduces an easy interface to retrieve the number of errored and terminated (non-errored) trials from the result grid.
Previously `tune.run(raise_on_failed_trial)` could be used to raise a TuneError if at least one trial failed. We've removed this option to make sure we always get a return value. `ResultGrid.num_errored` will make it easy for users to identify if trials failed and react to it instead of the old try-catch loop.
Signed-off-by: Kai Fricke <kai@anyscale.com>
I run several linters, including mypy, in my local environment.
This is a PR of style nits for autoscaler.py meant to silence my linters.
This PR also adds a mypy check for autoscaler.py
When submit task, GIL is not released due to this PR.
This cause a potential deadlock when actor died and got notified by GCS. In this case, the callback function submitted by GetAsync is going to execute some python function and the GIL is still hold by submit task. And submit task is blocking by a lock which is not released.
In the previous PR, it seems to fix some memory issue, but it's seems not there any more.
Signed-off-by: Yi Cheng <chengyidna@gmail.com>
The Tuner API is missing some arguments that tune.run() currently supports. This PR adds a number of them and adds a test to make sure they are correctly passed.
Signed-off-by: Kai Fricke <kai@anyscale.com>
In https://github.com/ray-project/ray/issues/19799, and https://github.com/ray-project/ray/issues/24184, we found when using Datasets to read S3 file, if file's credential is not set up right, the `read_xxx` API would throw confusing error message with `AWS Error [code 15]: No response body` like below:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/chengsu/ray/python/ray/data/read_api.py", line 758, in read_binary_files
return read_datasource(
File "/Users/chengsu/ray/python/ray/data/read_api.py", line 267, in read_datasource
requested_parallelism, min_safe_parallelism, read_tasks = ray.get(
File "/Users/chengsu/ray/python/ray/_private/client_mode_hook.py", line 105, in wrapper
return func(*args, **kwargs)
File "/Users/chengsu/ray/python/ray/_private/worker.py", line 2196, in get
raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(PermissionError): ray::_get_read_tasks() (pid=80200, ip=127.0.0.1)
File "pyarrow/_fs.pyx", line 439, in pyarrow._fs.FileSystem.get_file_info
File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 114, in pyarrow.lib.check_status
OSError: When getting information for key 'trainaasdasd' in bucket 'balajis-tiny-imagenet': AWS Error [code 15]: No response body.
```
The error message mentions nothing related to file credential, so it's quite confusing. This PR is to catch the error and give a better error message:
```
ray::_get_read_tasks() (pid=80200, ip=127.0.0.1)
File "/Users/chengsu/ray/python/ray/data/read_api.py", line 1127, in _get_read_tasks
reader = ds.create_reader(**kwargs)
File "/Users/chengsu/ray/python/ray/data/datasource/file_based_datasource.py", line 212, in create_reader
return _FileBasedDatasourceReader(self, **kwargs)
File "/Users/chengsu/ray/python/ray/data/datasource/file_based_datasource.py", line 350, in __init__
self._paths, self._file_sizes = meta_provider.expand_paths(
File "/Users/chengsu/ray/python/ray/data/datasource/file_meta_provider.py", line 173, in expand_paths
_handle_read_s3_files_error(e, path)
File "/Users/chengsu/ray/python/ray/data/datasource/file_meta_provider.py", line 342, in _handle_read_s3_files_error
raise PermissionError(
PermissionError: Failing to read AWS S3 file(s): "balajis-tiny-imagenet/trainaasdasd". Please check file exists and has proper AWS credential. See https://docs.ray.io/en/latest/data/creating-datasets.html#reading-from-remote-storage for more information.
```
- Stop using dot command to run ci.sh script: it doesn't fail the build if the command fails for windows and is generally dangerous since it will make unexpected changes to the current shell.
- Fix uncovered windows build issues.
Why are these changes needed?
Since locality_hints is an experimental feature, we stop promoting it in doc and don't enable it in AIR. See #26641 for more context
This PR replaces dataset.split(.., equal=True) implementation by dataset.split_at_indices() . My experiments (the script
) showed that dataset.split_at_indices() have more predictable performance than the dataset.split(…)
Concretely: on 10 m5.4xlarge nodes with 5000 iops disk
calling ds.split(81) on 200GB dataset with 400 blocks: the split takes 20-40 seconds, split_at_indices takes ~12 seconds.
calling ds.split(163) on 200GB dataset with 400 blocks, the split takes 40-100 seconds, split_at_indices takes ~24 seconds.
I don’t have much insight of dataset.split implementation, but with dataset.split_at_indices() we are just doing SPREAD to num_split_at_indices tasks, which yield much stable performance.
Note: clean up the usage of experimental locality_hints in #26647
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
Co-authored-by: Matthew Deng <matt@anyscale.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
My experiments (the script
) showed that dataset.split_at_indices() with SPREAD tasks have more predictable performance
Concretely: on 10 m5.4xlarge nodes with 5000 iops disk
calling ds.split_at_indices(81) on 200GB dataset with 400 blocks: the split_at_indices without this PR takes 7-19 seconds, split_at_indices with SPREAD takes 7-12 seconds.
Currently, it's not very easy to figure out why a DatasetPipeline may be underperforming. Add some warnings to help guide the user. As a next step, we can try to default to a good pipeline setting based on these constraints.
This is an experimental feature, so the following changes are added only to the WandbLoggerCallback. We are planning to collect feedback about usage and accordingly update or add these changes to the other W&B integration interfaces.
Allow reading the W&B project name and group name from environment variable if not already passed to callback
Add external hooks to fetch W&B API key, and to process any information about W&B run
Signed-off-by: Nikita Vemuri <nikitavemuri@gmail.com>
Make sure the OCR example is tested in documentation after we discovered that example notebooks are not tested in CI.
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
The current Dataset.split_at_indices() implementation suffers from O(n^2) memory usage in the small-split case (see issue) due to recursive splitting of the same blocks. This PR implements a split_at_indices() algorithm that minimizes the number of split tasks and data movement while ensuring that at most one block is used in each split task, for the sake of memory stability. Co-authored-by: scv119 <scv119@gmail.com>
The PR adds a new experimental flag to the placement group API to avoid placement group taking all cpus on each node. It is used internally by Air to avoid placement group (created by Tune) is using all CPU resources which are needed for dataset
This PR is to resolve#20888, where users have concern for the dataset-like methods used in dataset pipeline (such as map_batches, random_shuffle_each_window, etc). The reason is currently we define those dataset-like methods implicitly through Python setattr/getattr, to delegate the real work from dataset pipelien to dataset. This does not work very well with external developers/users if they want to navigate to the definition of method, or determine the method's return value data type.
So this PR is to explicitly define every dataset-like APIs in dataset pipeline class. This gives us a view of how much code we need to duplicate in upper bound. If we go with this direction, this means whenever we update or add a new method in Dataset, we need to update or add the same in DatasetPipeline.
