This PR adds a user guide to AIR for using Ray Train. It provides a high level overview of the trainers and removes redundant sections.
The main file to review is here: doc/source/ray-air/trainer.rst.
Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
Signed-off-by: Richard Liaw <rliaw@berkeley.edu>
Signed-off-by: Kai Fricke <kai@anyscale.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Kai Fricke <kai@anyscale.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.
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>
1. Update `DummyTrainer` to take `num_epochs` instead of `runtime_seconds`.
1. Ray Train expects equal number of calls to `train.report()`. Different workers may run at different speeds and terminate after different epoch numbers, which causes an error.
2. Add `generate_epochs` to support `DatasetPipeline` when `use_stream_api` is True.
3. Update `__main__` code to support testing different configurations.
Follow up on our last discussion for supporting piecemeal fashion air users.
Only did for tensorflow for now, want to collect some feedback on API naming, package structure etc and I will add others.
This adds the following options to DatasetConfig, which can be used to enable streaming ingest.
```
# Whether the dataset should be streamed into memory using pipelined reads.
# When enabled, get_dataset_shard() returns DatasetPipeline instead of Dataset.
# The amount of memory to use is controlled by `stream_window_size`.
# False by default for all datasets.
use_stream_api: Optional[bool] = None
# Configure the streaming window size in bytes. A typical value is something like
# 20% of object store memory. If set to -1, then an infinite window size will be
# used (similar to bulk ingest). This only has an effect if use_stream_api is set.
# Set to 1.0 GiB by default.
stream_window_size: Optional[float] = None
# Whether to enable global shuffle (per pipeline window in streaming mode). Note
# that this is an expensive all-to-all operation, and most likely you want to use
# local shuffle instead.
# False by default for all datasets.
global_shuffle: Optional[bool] = None
```
This adds a per-dataset config object to DataParallelTrainer. These configs define how the Dataset should be read into the DataParallelTrainer. It configures the preprocessing, splitting, and ingest strategy per-dataset. DataParallelTrainers declare default DatasetConfigs for each dataset passed in the ``datasets`` argument. Users have the opportunity to selectively override these configs by passing the ``dataset_config`` argument. Trainers can also define user customizable values (e.g., XGBoostTrainer doesn't support streaming ingest).
This PR adds the minimal support for dataset configs. Future PRs will:
- Add support for streaming ingest
- Move this config from DataParallelTrainer to ml.Trainer
The package "ml" should be renamed to "air".
Main question: Keep a `ml.py` with `from ray.air import *` for some level of backwards compatibility?
I'd go for no to force people to use the new structure.
