[2/n] Lightweight Ray AIR API refactor #37123
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pcmoritz
requested review from
richardliaw,
gjoliver,
krfricke,
xwjiang2010,
amogkam and
matthewdeng
as code owners
matthewdeng
approved these changes
Aug 2, 2023
| @@ -701,7 +700,7 @@ def train_func(config): | |||
| # Instantiate new Trainer in Trainable. | |||
| trainer = trainer_cls(**config) | |||
|
|
|||
| # Get the checkpoint from the Tune session, and use it to initialize | |||
| # Get the checkpoint from the train context, and use it to initialize | |||
There was a problem hiding this comment.
Should we be updating the code (under this comment) to use the new API as well? Or is that for a separate PR?
| @@ -222,15 +222,15 @@ def __init__( | |||
| raise ValueError( | |||
| "'checkpoint_at_end' cannot be used with a function trainable. " | |||
| "You should include one last call to " | |||
| "`ray.air.session.report(metrics=..., checkpoint=...)` at the end " | |||
| "`ray.train.session.report(metrics=..., checkpoint=...)` at the end " | |||
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Suggested change
| "`ray.train.session.report(metrics=..., checkpoint=...)` at the end " | |
| "`ray.train.report(metrics=..., checkpoint=...)` at the end " |
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
Signed-off-by: Philipp Moritz <pcmoritz@gmail.com>
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NripeshN
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Aug 15, 2023
This PR migrates all the train and tune examples and docstrings to the new API convention, see https://github.com/ray-project/enhancements/ Continuation of ray-project#36706 and ray-project#37906 Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: NripeshN <nn2012@hw.ac.uk>
NripeshN
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Aug 15, 2023
Continuation of ray-project#37123 Signed-off-by: NripeshN <nn2012@hw.ac.uk>
harborn
pushed a commit
to harborn/ray
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Aug 17, 2023
This PR removes some circularities in the Ray AIR import system so we can put the training related functions into `ray.train`. It introduces a training context and makes report, get_dataset_shard, Checkpoint, Result, and the following configs:
- CheckpointConfig
- DataConfig
- FailureConfig
- RunConfig
- ScalingConfig
available in `ray.train`. No user facing changes yet, the old APIs still work.
Going forward, it will be most consistent / symmetrical if these things are included in the following way:
```python
from ray import train, tune, serve # Pick the subset that is needed
# Include what you need from the following:
from ray.train import CheckpointConfig, DataConfig, FailureConfig, RunConfig, ScalingConfig
# ...
def train_func():
dataset_shard = train.get_dataset_shard("train")
world_size = train.get_context().get_world_size()
# ...
train.report(...)
trainer = train.torch.TorchTrainer(
train_func,
scaling_config=ScalingConfig(num_workers=2),
)
result = trainer.fit()
```
We have many examples in ray-project#37123 on how this looks like in actual code.
Signed-off-by: harborn <gangsheng.wu@intel.com>
harborn
pushed a commit
to harborn/ray
that referenced
this pull request
Aug 17, 2023
This PR migrates all the train and tune examples and docstrings to the new API convention, see https://github.com/ray-project/enhancements/ Continuation of ray-project#36706 and ray-project#37906 Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: harborn <gangsheng.wu@intel.com>
harborn
pushed a commit
to harborn/ray
that referenced
this pull request
Aug 17, 2023
Continuation of ray-project#37123 Signed-off-by: harborn <gangsheng.wu@intel.com>
harborn
pushed a commit
to harborn/ray
that referenced
this pull request
Aug 17, 2023
This PR removes some circularities in the Ray AIR import system so we can put the training related functions into `ray.train`. It introduces a training context and makes report, get_dataset_shard, Checkpoint, Result, and the following configs:
- CheckpointConfig
- DataConfig
- FailureConfig
- RunConfig
- ScalingConfig
available in `ray.train`. No user facing changes yet, the old APIs still work.
Going forward, it will be most consistent / symmetrical if these things are included in the following way:
```python
from ray import train, tune, serve # Pick the subset that is needed
# Include what you need from the following:
from ray.train import CheckpointConfig, DataConfig, FailureConfig, RunConfig, ScalingConfig
# ...
def train_func():
dataset_shard = train.get_dataset_shard("train")
world_size = train.get_context().get_world_size()
# ...
train.report(...)
trainer = train.torch.TorchTrainer(
train_func,
scaling_config=ScalingConfig(num_workers=2),
)
result = trainer.fit()
```
We have many examples in ray-project#37123 on how this looks like in actual code.
harborn
pushed a commit
to harborn/ray
that referenced
this pull request
Aug 17, 2023
This PR migrates all the train and tune examples and docstrings to the new API convention, see https://github.com/ray-project/enhancements/ Continuation of ray-project#36706 and ray-project#37906 Co-authored-by: matthewdeng <matthew.j.deng@gmail.com>
harborn
pushed a commit
to harborn/ray
that referenced
this pull request
Aug 17, 2023
arvind-chandra
pushed a commit
to lmco/ray
that referenced
this pull request
Aug 31, 2023
This PR removes some circularities in the Ray AIR import system so we can put the training related functions into `ray.train`. It introduces a training context and makes report, get_dataset_shard, Checkpoint, Result, and the following configs:
- CheckpointConfig
- DataConfig
- FailureConfig
- RunConfig
- ScalingConfig
available in `ray.train`. No user facing changes yet, the old APIs still work.
Going forward, it will be most consistent / symmetrical if these things are included in the following way:
```python
from ray import train, tune, serve # Pick the subset that is needed
# Include what you need from the following:
from ray.train import CheckpointConfig, DataConfig, FailureConfig, RunConfig, ScalingConfig
# ...
def train_func():
dataset_shard = train.get_dataset_shard("train")
world_size = train.get_context().get_world_size()
# ...
train.report(...)
trainer = train.torch.TorchTrainer(
train_func,
scaling_config=ScalingConfig(num_workers=2),
)
result = trainer.fit()
```
We have many examples in ray-project#37123 on how this looks like in actual code.
Signed-off-by: e428265 <arvind.chandramouli@lmco.com>
arvind-chandra
pushed a commit
to lmco/ray
that referenced
this pull request
Aug 31, 2023
This PR migrates all the train and tune examples and docstrings to the new API convention, see https://github.com/ray-project/enhancements/ Continuation of ray-project#36706 and ray-project#37906 Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: e428265 <arvind.chandramouli@lmco.com>
arvind-chandra
pushed a commit
to lmco/ray
that referenced
this pull request
Aug 31, 2023
Continuation of ray-project#37123 Signed-off-by: e428265 <arvind.chandramouli@lmco.com>
vymao
pushed a commit
to vymao/ray
that referenced
this pull request
Oct 11, 2023
This PR migrates all the train and tune examples and docstrings to the new API convention, see https://github.com/ray-project/enhancements/ Continuation of ray-project#36706 and ray-project#37906 Co-authored-by: matthewdeng <matthew.j.deng@gmail.com> Signed-off-by: Victor <vctr.y.m@example.com>
vymao
pushed a commit
to vymao/ray
that referenced
this pull request
Oct 11, 2023
Continuation of ray-project#37123 Signed-off-by: Victor <vctr.y.m@example.com>
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Why are these changes needed?
This PR migrates all the train and tune examples and docstrings to the new API convention, see https://github.com/ray-project/enhancements/
Continuation of #36706 and #37906
Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.