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[air] update remaining dict scaling_configs (#26856)

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matthewdeng 2022-07-21 18:55:21 -07:00 • committed by GitHub
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69
doc/source/train/user_guide.rst
View file

@ -191,12 +191,13 @@ with one of the following:
.. code-block:: python
from ray.air import ScalingConfig
from ray.train.torch import TorchTrainer
# For GPU Training, set `use_gpu` to True.
use_gpu = False
trainer = TorchTrainer(
train_func,
scaling_config=dict(use_gpu=use_gpu, num_workers=2)
scaling_config=ScalingConfig(use_gpu=use_gpu, num_workers=2)
)
@ -204,24 +205,26 @@ with one of the following:
.. code-block:: python
from ray.air import ScalingConfig
from ray.train.tensorflow import TensorflowTrainer
# For GPU Training, set `use_gpu` to True.
use_gpu = False
trainer = TensorflowTrainer(
train_func,
scaling_config=dict(use_gpu=use_gpu, num_workers=2)
scaling_config=ScalingConfig(use_gpu=use_gpu, num_workers=2)
)
.. tabbed:: Horovod
.. code-block:: python
from ray.air import ScalingConfig
from ray.train.horovod import HorovodTrainer
# For GPU Training, set `use_gpu` to True.
use_gpu = False
trainer = HorovodTrainer(
train_func,
scaling_config=dict(use_gpu=use_gpu, num_workers=2)
scaling_config=ScalingConfig(use_gpu=use_gpu, num_workers=2)
)
To customize the backend setup, you can use a :ref:`train-api-backend-config` object.
@ -230,12 +233,13 @@ To customize the backend setup, you can use a :ref:`train-api-backend-config` ob
.. code-block:: python
from ray.air import ScalingConfig
from ray.train.torch import TorchTrainer, TorchConfig
trainer = TorchTrainer(
train_func,
torch_backend=TorchConfig(...),
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
@ -243,24 +247,26 @@ To customize the backend setup, you can use a :ref:`train-api-backend-config` ob
.. code-block:: python
from ray.air import ScalingConfig
from ray.train.tensorflow import TensorflowTrainer, TensorflowConfig
trainer = TensorflowTrainer(
train_func,
tensorflow_backend=TensorflowConfig(...),
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
.. tabbed:: Horovod
.. code-block:: python
from ray.air import ScalingConfig
from ray.train.horovod import HorovodTrainer, HorovodConfig
trainer = HorovodTrainer(
train_func,
tensorflow_backend=HorovodConfig(...),
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
For more configurability, please reference the :class:`BaseTrainer` API.
@ -295,7 +301,7 @@ Then, you can pass in the config dictionary as an argument to ``Trainer``:
trainer = TorchTrainer(
train_func,
+ train_loop_config=config,
scaling_config=dict(num_workers=2)
scaling_config=ScalingConfig(num_workers=2)
)
Putting this all together, you can run your training function with different
@ -303,7 +309,7 @@ configurations. As an example:
.. code-block:: python
from ray.air import session
from ray.air import session, ScalingConfig
from ray.train.torch import TorchTrainer
def train_func(config):
@ -313,7 +319,7 @@ configurations. As an example:
trainer = TorchTrainer(
train_func,
train_loop_config={"num_epochs": 2},
scaling_config=dict(num_workers=2)
scaling_config=ScalingConfig(num_workers=2)
)
result = trainer.fit()
print(result.metrics["num_epochs"])
@ -441,7 +447,7 @@ Using Ray Datasets is the recommended way for ingesting data into ``Trainer``\s
import ray
from ray import train
from ray.air import train_test_split
from ray.air import train_test_split, ScalingConfig
from ray.train.torch import TorchTrainer
def train_func(config):
@ -480,7 +486,7 @@ Using Ray Datasets is the recommended way for ingesting data into ``Trainer``\s
train_func,
train_loop_config={"worker_batch_size": 64, "num_epochs": 2},
datasets={"train": train_dataset, "validation": validation_dataset},
scaling_config=dict(num_workers=8),
scaling_config=ScalingConfig(num_workers=8),
)
dataset = ray.data.read_csv("...")
@ -564,7 +570,7 @@ appropriately in distributed training.
:emphasize-lines: 36, 37, 38, 39, 40, 41
import ray.train.torch
from ray.air import session, Checkpoint
from ray.air import session, Checkpoint, ScalingConfig
from ray.train.torch import TorchTrainer
import torch
@ -608,7 +614,7 @@ appropriately in distributed training.
trainer = TorchTrainer(
train_func,
train_loop_config={"num_epochs": 5},
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
result = trainer.fit()
@ -621,7 +627,7 @@ appropriately in distributed training.
.. code-block:: python
:emphasize-lines: 23
from ray.air import session, Checkpoint
from ray.air import session, Checkpoint, ScalingConfig
from ray.train.tensorflow import TensorflowTrainer
import numpy as np
@ -651,7 +657,7 @@ appropriately in distributed training.
trainer = TensorflowTrainer(
train_func,
train_loop_config={"num_epochs": 5},
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
result = trainer.fit()
@ -683,7 +689,7 @@ As an example, to completely disable writing checkpoints to disk:
:emphasize-lines: 9,14
from ray import train
from ray.air import RunConfig, CheckpointConfig
from ray.air import RunConfig, CheckpointConfig, ScalingConfig
from ray.train.torch import TorchTrainer
def train_func():
@ -694,7 +700,7 @@ As an example, to completely disable writing checkpoints to disk:
trainer = TorchTrainer(
train_func,
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
run_config=RunConfig(checkpoint_config=checkpoint_config)
)
trainer.fit()
@ -704,7 +710,7 @@ You may also config ``CheckpointConfig`` to keep the "N best" checkpoints persis
.. code-block:: python
from ray.air import session, Checkpoint, RunConfig, CheckpointConfig
from ray.air import session, Checkpoint, RunConfig, CheckpointConfig, ScalingConfig
from ray.train.torch import TorchTrainer
def train_func():
@ -724,7 +730,7 @@ You may also config ``CheckpointConfig`` to keep the "N best" checkpoints persis
trainer = TorchTrainer(
train_func,
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
run_config=RunConfig(checkpoint_config=checkpoint_config),
)
result = trainer.fit()
@ -751,7 +757,7 @@ Checkpoints can be loaded into the training function in 2 steps:
:emphasize-lines: 23, 25, 26, 29, 30, 31, 35
import ray.train.torch
from ray.air import session, Checkpoint
from ray.air import session, Checkpoint, ScalingConfig
from ray.train.torch import TorchTrainer
import torch
@ -802,7 +808,7 @@ Checkpoints can be loaded into the training function in 2 steps:
trainer = TorchTrainer(
train_func,
train_loop_config={"num_epochs": 2},
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
# save a checkpoint
result = trainer.fit()
@ -811,7 +817,7 @@ Checkpoints can be loaded into the training function in 2 steps:
trainer = TorchTrainer(
train_func,
train_loop_config={"num_epochs": 4},
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
resume_from_checkpoint=result.checkpoint,
)
result = trainer.fit()
@ -824,7 +830,7 @@ Checkpoints can be loaded into the training function in 2 steps:
.. code-block:: python
:emphasize-lines: 15, 21, 22, 25, 26, 27, 30
from ray.air import session, Checkpoint
from ray.air import session, Checkpoint, ScalingConfig
from ray.train.tensorflow import TensorflowTrainer
import numpy as np
@ -863,7 +869,7 @@ Checkpoints can be loaded into the training function in 2 steps:
trainer = TensorflowTrainer(
train_func,
train_loop_config={"num_epochs": 2},
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
# save a checkpoint
result = trainer.fit()
@ -872,7 +878,7 @@ Checkpoints can be loaded into the training function in 2 steps:
trainer = TensorflowTrainer(
train_func,
train_loop_config={"num_epochs": 5},
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
resume_from_checkpoint=result.checkpoint,
)
result = trainer.fit()
@ -925,8 +931,7 @@ A simple example for creating a callback that will print out results:
from typing import List, Dict
from ray.air import session
from ray.air.config import RunConfig
from ray.air import session, RunConfig, ScalingConfig
from ray.train.torch import TorchTrainer
from ray.tune.logger import LoggerCallback
@ -946,7 +951,7 @@ A simple example for creating a callback that will print out results:
trainer = TorchTrainer(
train_func,
run_config=RunConfig(callbacks=[callback]),
scaling_config=dict(num_workers=2),
scaling_config=ScalingConfig(num_workers=2),
)
trainer.fit()
@ -969,7 +974,7 @@ Here is an example:
.. code-block:: python
from typing import List, Dict
from ray.air import session
from ray.air import session, ScalingConfig
from ray.train.torch import TorchTrainer
import torch
@ -981,7 +986,7 @@ Here is an example:
accuracy = torchmetrics.functional.accuracy(preds, target).item()
session.report({"accuracy": accuracy})
trainer = TorchTrainer(train_func, scaling_config=dict(num_workers=2))
trainer = TorchTrainer(train_func, scaling_config=ScalingConfig(num_workers=2))
result = trainer.fit()
print(result.metrics["accuracy"])
# 0.20000000298023224
@ -1095,7 +1100,7 @@ pass it into a :class:`Tuner`.
.. code-block:: python
from ray import tune
from ray.air import session
from ray.air import session, ScalingConfig
from ray.train.torch import TorchTrainer
from ray.tune.tuner import Tuner, TuneConfig
@ -1105,7 +1110,7 @@ pass it into a :class:`Tuner`.
for _ in range(config["num_epochs"]):
session.report(dict(output=config["input"]))
trainer = TorchTrainer(train_func, scaling_config=dict(num_workers=2))
trainer = TorchTrainer(train_func, scaling_config=ScalingConfig(num_workers=2))
tuner = Tuner(
trainer,
param_space={

3
release/train_tests/horovod/train_horovod_multi_node_test.py
View file

@ -3,6 +3,7 @@ import os
import time
import ray
from ray.air import ScalingConfig
from ray.train.examples.horovod.horovod_example import (
train_func as horovod_torch_train_func,
)
@ -18,7 +19,7 @@ if __name__ == "__main__":
trainer = HorovodTrainer(
horovod_torch_train_func,
train_loop_config={"num_epochs": num_epochs, "lr": 1e-3},
scaling_config=dict(
scaling_config=ScalingConfig(
num_workers=num_workers,
trainer_resources={"CPU": 0},
),