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
Uses the new AIR Train API for examples and tests.
The `Result` object gets a new attribute - `log_dir`, pointing to the Trial's `logdir` allowing users to access tensorboard logs and artifacts of other loggers.
This PR only deals with "low hanging fruit" - tests that need substantial rewriting or Train user guide are not touched. Those will be updated in followup PRs.
Tests and examples that concern deprecated features or which are duplicated in AIR have been removed or disabled.
Requires https://github.com/ray-project/ray/pull/25943 to be merged in first
This PR:
* Allows the user to set `keep_checkpoints_num` and `checkpoint_score_attr` in `RunConfig` using the `CheckpointStrategy` dataclass
* Adds two new fields to the `Result` object - `best_checkpoints` - a list of saved best checkpoints as determined by `CheckpointingConfig`.
As described in the related issue, using model_weight as the key throws an error.
This update points the user to use model as the key instead.
Co-authored-by: tamilflix <tamilflix30@gmail.com>
Ray SGD v1 has been denoted as a deprecated API for a while. This PR fully deprecates Ray SGD v1. An error will be raised if ray.util.sgd package is attempted to be imported.
Closes#16435
Adding a FAQ page. Currently has some basic questions that have come up in the past.
Explaining how to use Matplotlib due to threading in the distributed training function.
This PR adds a feature that allows user to make their training runs more reproducible. I've implemented this feature by following PyTorch's guide on how to limit sources of randomness (https://pytorch.org/docs/stable/notes/randomness.html).
These changes will make it easier for us to benchmark Ray Train, and also make it easier for users to reproduce their experiments.
Interface for DataParallelTrainer and updates to ScalingConfig definition.
Depends on #22986
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
The [original PR](https://github.com/ray-project/ray/pull/21864) was [reverted](https://github.com/ray-project/ray/pull/22117) because it caused `torch` (more specifically, `torch>=1.8.1`) to be required to use `ray.train`.
```
| File "ray_sgd_training.py", line 18, in <module>
| from ray import train
| File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/train/__init__.py", line 2, in <module>
| from ray.train.callbacks import TrainingCallback
| File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/train/callbacks/__init__.py", line 8, in <module>
| from ray.train.callbacks.profile import TorchTensorboardProfilerCallback
| File "/home/ray/anaconda3/lib/python3.7/site-packages/ray/train/callbacks/profile.py", line 6, in <module>
| from torch.profiler import profile
| ModuleNotFoundError: No module named 'torch.profiler'
```
A [minimal installation test suite](https://github.com/ray-project/ray/pull/22300) was added to detect this. Further, in this PR we make the following changes:
1. Move `TorchWorkerProfiler` to `ray.train.torch` so all torch imports are centralized.
2. Add import validation logic to `TorchWorkerProfiler.__init__` so an exception will only be raised if the user tries to initialize a `TorchWorkerProfiler` without having a valid version of `torch` installed:
```
>>> import ray
>>> import ray.train
>>> import ray.train.torch
>>> from ray.train.torch import TorchWorkerProfiler
>>> twp = TorchWorkerProfiler()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/matt/workspace/ray/python/ray/train/torch.py", line 365, in __init__
"Torch Profiler requires torch>=1.8.1. "
ImportError: Torch Profiler requires torch>=1.8.1. Run `pip install 'torch>=1.8.1'` to use TorchWorkerProfiler.
```
Continuing docs overhaul, tune now has:
- [x] better landing page
- [x] a getting started guide
- [x] user guide was cut down, partially merged with FAQ, and partially integrated with tutorials
- [x] the new user guide contains guides to tune features and practical integrations
- [x] we rewrote some of the feature guides for clarity
- [x] we got rid of sphinx-gallery for this sub-project (only data and core left), as it looks bad and is unnecessarily complicated anyway (plus, makes the build slower)
- [x] sphinx-gallery examples are now moved to markdown notebook, as started in #22030.
- [x] Examples are tested in the new framework, of course.
There's still a lot one can do, but this is already getting too large. Will follow up with more fine-tuning next week.
Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
Co-authored-by: Kai Fricke <krfricke@users.noreply.github.com>
Implement a TorchTensorboardProfilerCallback and corresponding TorchWorkerProfiler to support distributed PyTorch Profiler With TensorBoard integration.
Preview: [docs](https://ray--21931.org.readthedocs.build/en/21931/data/dataset.html)
The Ray Data project's docs now have a clearer structure and have partly been rewritten/modified. In particular we have
- [x] A Getting Started Guide
- [x] An explicit User / How-To Guide
- [x] A dedicated Key Concepts page
- [x] A consistent naming convention in `Ray Data` whenever is is referred to the project.
This surfaces quite clearly that, apart from the "Getting Started" sections, we really only have one real example. Once we have more, we can create an "Example" section like many other sub-projects have. This will be addressed in https://github.com/ray-project/ray/issues/21838.
