This PR just applies the changes from the following PRs:
[Datasets] Automatically cast tensor columns when building Pandas blocks. #26684
reverted by Revert "[Datasets] Automatically cast tensor columns when building Pandas blocks." #26921
[AIR - Datasets] Fix TensorDtype construction from string and fix example. #26904
This fixes the test failures introduced in the originally reverted PRs.
Why are these changes needed?
Resubmitting #26869.
This PR was reverted due to failing tests; however, those failures were actually due to a dependency: #26950
This PR updates the Ray AIR/Tune ipynb examples to use the Tuner() API instead of tune.run().
Signed-off-by: Kai Fricke <kai@anyscale.com>
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Signed-off-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Signed-off-by: Kai Fricke <coding@kaifricke.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Xiaowei Jiang <xwjiang2010@gmail.com>
Signed-off-by: Kai Fricke coding@kaifricke.com
Why are these changes needed?
Splitting up #26884: This PR includes changes to use Tuner() instead of tune.run() for most docs files (rst and py), and a change to move reuse_actors to the TuneConfig
Why are these changes needed?
Consumers (e.g. Train) may expect generated batches to be of the same size. Prior to this change, the default behavior would be for each batch to be one block, which may be of different sizes.
Changes
Set default batch_size to 256. This was chosen to be a sensible default for training workloads, which is intentionally different from the existing default batch_size value for Dataset.map_batches.
Update docs for Dataset.iter_batches, Dataset.map_batches, and DatasetPipeline.iter_batches to be consistent.
Updated tests and examples to explicitly pass in batch_size=None as these tests were intentionally testing block iteration, and there are other tests that test explicit batch sizes.
ray.init() will currently start a new Ray instance even if one is already existing, which is very confusing if you are a new user trying to go from local development to a cluster. This PR changes it so that, when no address is specified, we first try to find an existing Ray cluster that was created through `ray start`. If none is found, we will start a new one.
This makes two changes to the ray.init() resolution order:
1. When `ray start` is called, the started cluster address was already written to a file called `/tmp/ray/ray_current_cluster`. For ray.init() and ray.init(address="auto"), we will first check this local file for an existing cluster address. The file is deleted on `ray stop`. If the file is empty, autodetect any running cluster (legacy behavior) if address="auto", or we will start a new local Ray instance if address=None.
2. When ray.init(address="local") is called, we will create a new local Ray instance, even if one is already existing. This behavior seems to be necessary mainly for `ray.client` use cases.
This also surfaces the logs about which Ray instance we are connecting to. Previously these were hidden because we didn't set up the log until after connecting to Ray. So now Ray will log one of the following messages during ray.init:
```
(Connecting to existing Ray cluster at address: <IP>...)
...connection...
(Started a local Ray cluster.| Connected to Ray Cluster.)( View the dashboard at <URL>)
```
Note that this changes the dashboard URL to be printed with `ray.init()` instead of when the dashboard is first started.
Co-authored-by: Eric Liang <ekhliang@gmail.com>
This PR adds .iter_torch_batches() and .iter_tf_batches() convenience APIs, which takes care of ML framework tensor conversion, the narrow tensor waste for the .iter_batches() call ("numpy" format), and unifies batch formats around two options: a single tensor for simple/pure-tensor/single-column datasets, and a dictionary of tensors for multi-column datasets.
I am surprised by the fact that `GetTimeoutError` is not a subclass of `TimeoutError`, which is counter-intuitive and may discourage users from trying the timeout feature in `ray.get`, because you have to "guess" the correct error type. For most people, I believe the first error type in their mind would be `TimeoutError`.
This PR fixes this.
We've supported namespace in c++ worker in https://github.com/ray-project/ray/pull/26327. Here we add doc for usage and also reinforce the documents of Java and Python, like adding explanation of specifying namespace while creating named actors.
- [x] add doc for basic c++ worker namespace usage
- [x] add explanation for specifying namespace while creating named actors, in Python, Java and C++
Add benchmark data for 4x4 GPU setup.
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Jimmy Yao <jiahaoyao.math@gmail.com>
Co-authored-by: Kai Fricke <kai@anyscale.com>
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
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>
