wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-08-06 13:31:10 +08:00
Code Issues Packages Projects Releases Wiki Activity

[RaySGD] Rename PyTorch API endpoints to start with Torch (#7425)

Browse Source 
* Start renaming pytorch to torch

* Rename PyTorchTrainer to TorchTrainer

* Rename PyTorch runners to Torch runners

* Finish renaming API

* Rename to torch in tests

* Finish renaming docs + tests

* Run format + fix DeprecationWarning

* fix

* move tests up

* rename

Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
This commit is contained in:
Maksim Smolin
2020-03-03 16:44:42 -08:00
committed by GitHub
co-authored by Richard Liaw
parent f6883bf725
commit 3a134c7224
22 changed files with 222 additions and 218 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/examples/doc_code/raysgd_torch_signatures.py
+10 -10
View File
@@ -1,6 +1,6 @@
# flake8: noqa
"""
This file holds code for the Pytorch Trainer creator signatures.
This file holds code for the torch Trainer creator signatures.
It ignores yapf because yapf doesn't allow comments right after code blocks,
but we put comments right after code blocks to prevent large white spaces
@@ -18,7 +18,7 @@ def model_creator(config):
function to specify the optimization procedure for multiple models.
Args:
config (dict): Configuration dictionary passed into ``PyTorchTrainer``.
config (dict): Configuration dictionary passed into ``TorchTrainer``.
Returns:
One or more torch.nn.Module objects.
@@ -36,7 +36,7 @@ def optimizer_creator(model, config):
Args:
models: The return values from ``model_creator``. This can be one
or more torch nn modules.
config (dict): Configuration dictionary passed into ``PyTorchTrainer``.
config (dict): Configuration dictionary passed into ``TorchTrainer``.
Returns:
One or more Torch optimizer objects.
@@ -46,7 +46,7 @@ def optimizer_creator(model, config):
# __torch_data_start__
from ray.util.sgd.pytorch.examples.train_example import LinearDataset
from ray.util.sgd.torch.examples.train_example import LinearDataset
def data_creator(config):
"""Constructs torch.utils.data.Dataset objects.
@@ -55,7 +55,7 @@ def data_creator(config):
only one dataset will be used for training.
Args:
config: Configuration dictionary passed into ``PyTorchTrainer``
config: Configuration dictionary passed into ``TorchTrainer``
Returns:
One or Two Dataset objects. If only one Dataset object is provided,
@@ -71,10 +71,10 @@ def loss_creator(config):
"""Constructs the Torch Loss object.
Note that optionally, you can pass in a Torch Loss constructor directly
into the PyTorchTrainer (i.e., ``PyTorchTrainer(loss_creator=nn.BCELoss, ...)``).
into the TorchTrainer (i.e., ``TorchTrainer(loss_creator=nn.BCELoss, ...)``).
Args:
config: Configuration dictionary passed into ``PyTorchTrainer``
config: Configuration dictionary passed into ``TorchTrainer``
Returns:
Torch Loss object.
@@ -91,7 +91,7 @@ def scheduler_creator(optimizer, config):
Args:
optimizers: The return values from ``optimizer_creator``.
This can be one or more torch optimizer objects.
config: Configuration dictionary passed into ``PyTorchTrainer``
config: Configuration dictionary passed into ``TorchTrainer``
Returns:
One or more Torch scheduler objects.
@@ -108,9 +108,9 @@ ray.init()
# __torch_ray_end__
# __torch_trainer_start__
from ray.util.sgd import PyTorchTrainer
from ray.util.sgd import TorchTrainer
trainer = PyTorchTrainer(
trainer = TorchTrainer(
model_creator,
data_creator,
optimizer_creator,