Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[xdoctest] reformat example code with google style No.186-190 #56166

Merged
merged 15 commits into from
Aug 22, 2023
Merged

[xdoctest] reformat example code with google style No.186-190 #56166

Show file tree
Hide file tree
Changes from 7 commits
Commits
Show all changes
15 commits
Select commit Hold shift + click to select a range
3a78436
fix: updated code examples.
PommesPeter Aug 10, 2023
f6b343b
fix: added paddle.seed
PommesPeter Aug 10, 2023
ebae2d0
fix: updated code style
PommesPeter Aug 11, 2023
fca95c7
Apply suggestions from code review
SigureMo Aug 14, 2023
9038d0b
refactor: refine detail of code examples
PommesPeter Aug 15, 2023
c5385f3
Merge branch 'xdoctest_186_190' of https://github.com/PommesPeter/Pad…
PommesPeter Aug 15, 2023
48f2577
Update python/paddle/distributed/auto_parallel/static/process_mesh_v2.py
SigureMo Aug 15, 2023
5b95064
fix: refine detail
PommesPeter Aug 16, 2023
7fe7cde
Merge branch 'xdoctest_186_190' of https://github.com/PommesPeter/Pad…
PommesPeter Aug 16, 2023
3629722
fix: refine detail
PommesPeter Aug 16, 2023
0360a8c
Update python/paddle/distributed/auto_parallel/static/process_mesh_v2.py
PommesPeter Aug 17, 2023
5ea2e2e
refactor: refine detail
PommesPeter Aug 17, 2023
888cfd5
Merge branch 'xdoctest_186_190' of https://github.com/PommesPeter/Pad…
PommesPeter Aug 17, 2023
3ce6fff
refactor: refine detail
PommesPeter Aug 17, 2023
09d384d
fix: refine doc
PommesPeter Aug 22, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
251 changes: 126 additions & 125 deletions python/paddle/distributed/auto_parallel/static/engine.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,39 +79,39 @@ class Engine:

.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.distributed.fleet import auto
from paddle.vision.datasets import MNIST

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = MNIST(mode='train', transform=transform)
valid_dataset = MNIST(mode='test', transform=transform)

model = paddle.vision.models.LeNet()
loss = paddle.nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
metrics = paddle.metric.Accuracy(topk=(1, 2))

engine = auto.Engine(model, loss, optimizer, metrics)
# fit
engine.fit(train_dataset,
epochs=2,
batch_size=64)
# evaluate
engine.evaluate(valid_dataset,
batch_size=64)
# predict
engine.predict(valid_dataset,
batch_size=64)
# save
engine.save("./my_model")
# load
engine.load("./my_model")
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.distributed.fleet import auto
>>> from paddle.vision.datasets import MNIST

>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
>>> ])
>>> train_dataset = MNIST(mode='train', transform=transform)
>>> valid_dataset = MNIST(mode='test', transform=transform)

>>> model = paddle.vision.models.LeNet()
>>> loss = paddle.nn.CrossEntropyLoss()
>>> optimizer = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> metrics = paddle.metric.Accuracy(topk=(1, 2))

>>> engine = auto.Engine(model, loss, optimizer, metrics)
>>> # fit
>>> engine.fit(train_dataset,
... epochs=2,
... batch_size=64)
>>> # evaluate
>>> engine.evaluate(valid_dataset,
... batch_size=64)
>>> # predict
>>> engine.predict(valid_dataset,
... batch_size=64)
>>> # save
>>> engine.save("./my_model")
>>> # load
>>> engine.load("./my_model")

"""

Expand Down Expand Up @@ -916,27 +916,27 @@ def fit(

.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.distributed.fleet import auto
from paddle.vision.datasets import MNIST

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = MNIST(mode='train', transform=transform)

model = paddle.vision.models.LeNet()
loss = paddle.nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
metrics = paddle.metric.Accuracy(topk=(1, 2))

engine = auto.Engine(model, loss, optimizer, metrics)
engine.fit(train_dataset,
epochs=2,
batch_size=64)
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.distributed.fleet import auto
>>> from paddle.vision.datasets import MNIST

>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
>>> ])
>>> train_dataset = MNIST(mode='train', transform=transform)

>>> model = paddle.vision.models.LeNet()
>>> loss = paddle.nn.CrossEntropyLoss()
>>> optimizer = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> metrics = paddle.metric.Accuracy(topk=(1, 2))

>>> engine = auto.Engine(model, loss, optimizer, metrics)
>>> engine.fit(train_dataset,
... epochs=2,
... batch_size=64)
"""
self._mode = 'train'
self._inputs_spec, self._labels_spec = self._prepare_data_spec(
Expand Down Expand Up @@ -1069,23 +1069,23 @@ def evaluate(

.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.distributed.fleet import auto
from paddle.vision.datasets import MNIST
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.distributed.fleet import auto
>>> from paddle.vision.datasets import MNIST

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
valid_dataset = MNIST(mode='test', transform=transform)
>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
>>> ])
>>> valid_dataset = MNIST(mode='test', transform=transform)

model = paddle.vision.models.LeNet()
loss = paddle.nn.CrossEntropyLoss()
metrics = paddle.metric.Accuracy(topk=(1, 2))
>>> model = paddle.vision.models.LeNet()
>>> loss = paddle.nn.CrossEntropyLoss()
>>> metrics = paddle.metric.Accuracy(topk=(1, 2))

engine = auto.Engine(model, loss, metrics=metrics)
engine.evaluate(valid_dataset, batch_size=64)
>>> engine = auto.Engine(model, loss, metrics=metrics)
>>> engine.evaluate(valid_dataset, batch_size=64)

"""
self._mode = 'eval'
Expand Down Expand Up @@ -1179,21 +1179,21 @@ def predict(

.. code-block:: python

import paddle
import paddle.vision.transforms as T
from paddle.distributed.fleet import auto
from paddle.vision.datasets import MNIST
>>> import paddle
>>> import paddle.vision.transforms as T
>>> from paddle.distributed.fleet import auto
>>> from paddle.vision.datasets import MNIST

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
valid_dataset = MNIST(mode='test', transform=transform)
>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
>>> ])
>>> valid_dataset = MNIST(mode='test', transform=transform)

model = paddle.vision.models.LeNet()
>>> model = paddle.vision.models.LeNet()

engine = auto.Engine(model)
engine.predict(valid_dataset, batch_size=64)
>>> engine = auto.Engine(model)
>>> engine.predict(valid_dataset, batch_size=64)
"""
self._mode = 'predict'
self._inputs_spec, self._labels_spec = self._prepare_data_spec(
Expand Down Expand Up @@ -1648,28 +1648,29 @@ def save(self, path, training=True):
Examples:

.. code-block:: python
import paddle
import paddle.vision.transforms as T
from paddle.distributed.fleet import auto
from paddle.vision.datasets import MNIST

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = MNIST(mode='train', transform=transform)

model = paddle.vision.models.LeNet()
loss = paddle.nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
metrics = paddle.metric.Accuracy(topk=(1, 2))

engine = auto.Engine(model, loss, optimizer, metrics)
engine.fit(train_dataset,
epochs=1,
batch_size=64)
engine.save("./my_model")

>>> import paddle
PommesPeter marked this conversation as resolved.
Show resolved Hide resolved
>>> import paddle.vision.transforms as T
>>> from paddle.distributed.fleet import auto
>>> from paddle.vision.datasets import MNIST

>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
>>> ])
>>> train_dataset = MNIST(mode='train', transform=transform)

>>> model = paddle.vision.models.LeNet()
>>> loss = paddle.nn.CrossEntropyLoss()
>>> optimizer = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> metrics = paddle.metric.Accuracy(topk=(1, 2))

>>> engine = auto.Engine(model, loss, optimizer, metrics)
>>> engine.fit(train_dataset,
... epochs=1,
... batch_size=64)
>>> engine.save("./my_model")

"""
if training:
Expand Down Expand Up @@ -1732,29 +1733,29 @@ def load(self, path, strict=True, load_optimizer=True):
Examples:

.. code-block:: python
import paddle
import paddle.vision.transforms as T
from paddle.distributed.fleet import auto
from paddle.vision.datasets import MNIST

transform = T.Compose([
T.Transpose(),
T.Normalize([127.5], [127.5])
])
train_dataset = MNIST(mode='train', transform=transform)

model = paddle.vision.models.LeNet()
loss = paddle.nn.CrossEntropyLoss()
optimizer = paddle.optimizer.Adam(
learning_rate=0.001, parameters=model.parameters())
metrics = paddle.metric.Accuracy(topk=(1, 2))

engine = auto.Engine(model, loss, optimizer, metrics)
engine.fit(train_dataset,
epochs=1,
batch_size=64)
engine.save("./my_model")
engine.load("./my_model")
>>> import paddle
PommesPeter marked this conversation as resolved.
Show resolved Hide resolved
>>> import paddle.vision.transforms as T
>>> from paddle.distributed.fleet import auto
>>> from paddle.vision.datasets import MNIST

>>> transform = T.Compose([
... T.Transpose(),
... T.Normalize([127.5], [127.5])
>>> ])
>>> train_dataset = MNIST(mode='train', transform=transform)

>>> model = paddle.vision.models.LeNet()
>>> loss = paddle.nn.CrossEntropyLoss()
>>> optimizer = paddle.optimizer.Adam(
... learning_rate=0.001, parameters=model.parameters())
>>> metrics = paddle.metric.Accuracy(topk=(1, 2))

>>> engine = auto.Engine(model, loss, optimizer, metrics)
>>> engine.fit(train_dataset,
... epochs=1,
... batch_size=64)
>>> engine.save("./my_model")
>>> engine.load("./my_model")

"""
self._strict = strict
Expand Down
13 changes: 6 additions & 7 deletions python/paddle/distributed/auto_parallel/static/process_mesh_v2.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,14 +33,13 @@ class ProcessMesh(core.ProcessMesh):
Examples:
.. code-block:: python
import paddle
import paddle.distributed as dist
>>> import paddle
>>> import paddle.distributed as dist
>>> paddle.enable_static()
paddle.enable_static()
mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]])
assert mesh.shape == [2, 3]
assert mesh.processe_ids == [2, 4, 5, 0, 1, 3]
>>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]])
>>> assert mesh.shape == [2, 3]
PommesPeter marked this conversation as resolved.
Show resolved Hide resolved
>>> assert mesh.process_ids == [2, 4, 5, 0, 1, 3]
"""

Expand Down
Loading