1. 单机多卡启动并行训练
飞桨2.0增加paddle.distributed.spawn函数来启动单机多卡训练，同时原有的paddle.distributed.launch的方式依然保留。
- paddle.distributed.launch通过指定启动的程序文件，以文件为单位启动多进程来实现多卡同步训练。以前在aistudio脚本任务说明里，就是推荐这种方法启动多卡任务。launch这种方式对进程管理要求较高。
- paddle.distributed.spawn是以function函数为单位启动多进程来实现多卡同步的，可以更好地控制进程，在日志打印、训练退出时更友好。这是当前推荐的用法。
下面分别介绍这两种方法。
1.1单机多卡启动方式1、launch启动
1.1.1使用高层API的场景
当调用paddle.Model高层API来实现训练时，想要启动单机多卡训练非常简单，代码不需要做任何修改，只需要在启动时增加一下参数-m paddle.distributed.launch。
代码语言：javascript复制 #单机单卡启动，默认使用第0号卡
$ python train.py
#单机多卡启动，默认使用当前可见的所有卡
$ python -m paddle.distributed.launch train.py
#单机多卡启动，设置当前使用的第0号和第1号卡
$ python -m paddle.distributed.launch --selected_gpus='0,1' train.py
#单机多卡启动，设置当前使用第0号和第1号卡
$ export CUDA_VISIABLE_DEVICES='0,1'
$ python -m paddle.distributed.launch train.py下面是一个高阶API的例子代码，直接执行cell代码框，就会在根目录生成hapitrain.py文件，然后就可以使用python来启动这个训练了。
代码语言：javascript复制%%writefile hapitrain.py
import paddle
from paddle.vision.transforms import ToTensor
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())
lenet = paddle.vision.models.LeNet()
# Mnist继承paddle.nn.Layer属于Net，model包含了训练功能
model = paddle.Model(lenet)
# 设置训练模型所需的optimizer, loss, metric
model.prepare(
paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()),
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 2))
)
# 启动训练
model.fit(train_dataset, epochs=1, batch_size=64, log_freq=400)
# 启动评估
model.evaluate(test_dataset, log_freq=100, batch_size=64)单机单卡启动，默认使用第0号卡
代码语言：javascript复制# 单机单卡启动，默认使用第0号卡
!python hapitrain.pyBegin to download
Download finished
Cache file /home/aistudio/.cache/paddle/dataset/mnist/train-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/train-labels-idx1-ubyte.gz
Begin to download
........
Download finished
Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-images-idx3-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-images-idx3-ubyte.gz
Begin to download
Download finished
Cache file /home/aistudio/.cache/paddle/dataset/mnist/t10k-labels-idx1-ubyte.gz not found, downloading https://dataset.bj.bcebos.com/mnist/t10k-labels-idx1-ubyte.gz
Begin to download
..
Download finished
W0628 15:25:11.488023 114 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0628 15:25:11.614305 114 device_context.cc:372] device: 0, cuDNN Version: 7.6.
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/1
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py:89: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if isinstance(slot[0], (np.ndarray, np.bool, numbers.Number)):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
return (isinstance(seq, collections.Sequence) and
step 400/938 - loss: 0.0555 - acc_top1: 0.9217 - acc_top2: 0.9649 - 50ms/step
step 800/938 - loss: 0.0300 - acc_top1: 0.9454 - acc_top2: 0.9782 - 39ms/step
step 938/938 - loss: 0.0213 - acc_top1: 0.9498 - acc_top2: 0.9803 - 38ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 100/157 - loss: 0.0057 - acc_top1: 0.9731 - acc_top2: 0.9927 - 28ms/step
step 157/157 - loss: 0.0013 - acc_top1: 0.9785 - acc_top2: 0.9945 - 28ms/step
Eval samples: 10000单机多卡启动，默认使用当前可见的所有卡
代码语言：javascript复制# 单机多卡启动，默认使用当前可见的所有卡
!python -m paddle.distributed.launch hapitrain.py/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
----------- Configuration Arguments -----------
gpus: None
heter_worker_num: None
heter_workers:
http_port: None
ips: 127.0.0.1
log_dir: log
nproc_per_node: None
server_num: None
servers:
training_script: hapitrain.py
training_script_args: []
worker_num: None
workers:
------------------------------------------------
WARNING 2021-06-28 15:26:17,473 launch.py:316] Not found distinct arguments and compiled with cuda. Default use collective mode
launch train in GPU mode
INFO 2021-06-28 15:26:17,475 launch_utils.py:471] Local start 1 processes. First process distributed environment info (Only For Debug):
=======================================================================================
| Distributed Envs Value |
---------------------------------------------------------------------------------------
| PADDLE_TRAINER_ID 0 |
| PADDLE_CURRENT_ENDPOINT 127.0.0.1:35079 |
| PADDLE_TRAINERS_NUM 1 |
| PADDLE_TRAINER_ENDPOINTS 127.0.0.1:35079 |
| FLAGS_selected_gpus 0 |
=======================================================================================
INFO 2021-06-28 15:26:17,475 launch_utils.py:475] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
W0628 15:26:24.305920 285 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0628 15:26:24.311555 285 device_context.cc:372] device: 0, cuDNN Version: 7.6.
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/1
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py:89: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if isinstance(slot[0], (np.ndarray, np.bool, numbers.Number)):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
return (isinstance(seq, collections.Sequence) and
step 400/938 - loss: 0.0586 - acc_top1: 0.9130 - acc_top2: 0.9611 - 38ms/step
step 800/938 - loss: 0.0288 - acc_top1: 0.9397 - acc_top2: 0.9759 - 39ms/step
step 938/938 - loss: 0.0545 - acc_top1: 0.9448 - acc_top2: 0.9785 - 40ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 100/157 - loss: 0.0035 - acc_top1: 0.9677 - acc_top2: 0.9911 - 36ms/step
step 157/157 - loss: 0.0057 - acc_top1: 0.9723 - acc_top2: 0.9929 - 36ms/step
Eval samples: 10000
INFO 2021-06-28 15:27:26,569 launch.py:240] Local processes completed.单机多卡启动，设置当前使用第0号和第1号卡 aistudio单卡也可以运行，可以看到launch的容错率较高
代码语言：javascript复制# 单机多卡启动，设置当前使用第0号和第1号卡 aistudio单卡也可以运行，可以看到launch的容错率较高
!CUDA_VISIABLE_DEVICES='0,1' && python -m paddle.distributed.launch hapitrain.py----------- Configuration Arguments -----------
gpus: None
heter_worker_num: None
heter_workers:
http_port: None
ips: 127.0.0.1
log_dir: log
nproc_per_node: None
server_num: None
servers:
training_script: hapitrain.py
training_script_args: []
worker_num: None
workers:
------------------------------------------------
WARNING 2021-06-28 15:28:10,632 launch.py:316] Not found distinct arguments and compiled with cuda. Default use collective mode
launch train in GPU mode
INFO 2021-06-28 15:28:10,637 launch_utils.py:471] Local start 1 processes. First process distributed environment info (Only For Debug):
=======================================================================================
| Distributed Envs Value |
---------------------------------------------------------------------------------------
| PADDLE_TRAINER_ID 0 |
| PADDLE_CURRENT_ENDPOINT 127.0.0.1:46909 |
| PADDLE_TRAINERS_NUM 1 |
| PADDLE_TRAINER_ENDPOINTS 127.0.0.1:46909 |
| FLAGS_selected_gpus 0 |
=======================================================================================
INFO 2021-06-28 15:28:10,637 launch_utils.py:475] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
W0628 15:28:19.819196 448 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0628 15:28:19.905493 448 device_context.cc:372] device: 0, cuDNN Version: 7.6.
The loss value printed in the log is the current step, and the metric is the average value of previous step.
Epoch 1/1
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py:89: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
if isinstance(slot[0], (np.ndarray, np.bool, numbers.Number)):
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:77: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
return (isinstance(seq, collections.Sequence) and
step 400/938 - loss: 0.0376 - acc_top1: 0.9136 - acc_top2: 0.9610 - 37ms/step
step 800/938 - loss: 0.0159 - acc_top1: 0.9423 - acc_top2: 0.9764 - 35ms/step
step 938/938 - loss: 0.0444 - acc_top1: 0.9479 - acc_top2: 0.9791 - 35ms/step
Eval begin...
The loss value printed in the log is the current batch, and the metric is the average value of previous step.
step 100/157 - loss: 0.0039 - acc_top1: 0.9767 - acc_top2: 0.9939 - 36ms/step
step 157/157 - loss: 0.0029 - acc_top1: 0.9815 - acc_top2: 0.9952 - 35ms/step
Eval samples: 10000
INFO 2021-06-28 15:29:19,766 launch.py:240] Local processes completed.1.1.2使用基础API场景
- 如果使用基础API的代码程序启动单机多卡训练，需要对单机单卡的代码进行3处修改，具体看下面未改变版本和改变版本的对比：
修改三处：
- 第1处改动，import库**
import paddle.distributed as dist
- 第2处改动，初始化并行环境**
dist.init_parallel_env()
- 第3处改动，增加paddle.DataParallel封装
net = paddle.DataParallel(paddle.vision.models.LeNet())
代码语言：javascript复制import paddle #未改动版本
from paddle.vision.transforms import ToTensor
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())
lenet = paddle.vision.models.LeNet()
# 加载训练集 batch_size 设为 64
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
def train():
epochs = 1
adam = paddle.optimizer.Adam(learning_rate=0.001, parameters=lenet.parameters())
# 用Adam作为优化函数
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data, y_data = data
predicts = lenet(x_data)
loss = paddle.nn.functional.cross_entropy(predicts, y_data, reduction='mean')
acc = paddle.metric.accuracy(predicts, y_data, k=1)
avg_acc = paddle.mean(acc)
loss.backward()
if batch_id % 400 == 0:
print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), avg_acc.numpy()))
adam.step()
adam.clear_grad()
# 启动训练
train()> epoch: 0, batch_id: 0, loss is: [2.7922328], acc is: [0.15625] epoch:
> 0, batch_id: 400, loss is: [0.10373791], acc is: [0.96875] epoch: 0,
> batch_id: 800, loss is: [0.01435608], acc is: [1.]这是有3处改动的基础API版本 还是先通过%%writefile normaltrain.py 命令将该文件存盘到根目录
代码语言：javascript复制%%writefile normaltrain.py
import paddle #这是有3处改动的版本
from paddle.vision.transforms import ToTensor
import paddle.distributed as dist #第1处改动，import库
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())
# 加载训练集 batch_size 设为 64
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
def train():
# 第2处改动，初始化并行环境
dist.init_parallel_env()
# 第3处改动，增加paddle.DataParallel封装
net = paddle.DataParallel(paddle.vision.models.LeNet()) #手册这里没有写全LeNet的库路径
epochs = 1
adam = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters())
# 用Adam作为优化函数
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = data[1]
predicts = net(x_data)
acc = paddle.metric.accuracy(predicts, y_data, k=2)
avg_acc = paddle.mean(acc)
loss = paddle.nn.functional.cross_entropy(predicts, y_data, reduction='mean')
loss.backward() #这里手册误写成了avg_loss
if batch_id % 400 == 0:
print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), avg_acc.numpy())) #这里手册误写成了avg_loss
adam.step()
adam.clear_grad()
# 启动训练
train()# 单机单卡启动，默认使用第0号卡 。这里单机单卡执行改后的代码会报错
# !python normaltrain.py
# 单机多卡启动，默认使用当前可见的所有卡
!python -m paddle.distributed.launch normaltrain.py
# 单机多卡启动，设置当前使用第0号和第1号卡 自动用当前所有的卡，只有单卡也不会报错
!CUDA_VISIABLE_DEVICES='0,1' && python -m paddle.distributed.launch normaltrain.py1.2 单机多卡启动方式2、spawn启动【推荐！！】
就像把物品放进盒子寄快递一样，只要将待并行计算的train函数体放入paddle.distributed.spawn里面就行了。命令为：
代码语言：javascript复制import paddle.distributed as dist
# 启动train多进程训练，默认使用所有可见的GPU卡
if __name__ == '__main__':
dist.spawn(train)
# 启动train函数2个进程训练，默认使用当前可见的前2张卡
if __name__ == '__main__':
dist.spawn(train, nprocs=2)
# 启动train函数2个进程训练，默认使用第4号和第5号卡
if __name__ == '__main__':
dist.spawn(train, nprocs=2, selelcted_gpus='4,5')- 基础API场景(不管是否像launch里面那样改代码) aistudio notebook里会报错，在实际多卡环境下正常。在aistudio 命令行下正常
- 高阶API场景 aistudio notebook里会报错，在aistudio 命令行下正常。
%%writefile normal3spawn.py
import paddle #这是有3处改动的版本
from paddle.vision.transforms import ToTensor
import paddle.distributed as dist #第1处改动，import库
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())
# 加载训练集 batch_size 设为 64
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
def train():
# 第2处改动，初始化并行环境
dist.init_parallel_env()
# 第3处改动，增加paddle.DataParallel封装
net = paddle.DataParallel(paddle.vision.models.LeNet()) #手册这里没有写全LeNet的库路径
epochs = 1
adam = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters())
# 用Adam作为优化函数
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = data[1]
predicts = net(x_data)
acc = paddle.metric.accuracy(predicts, y_data, k=2)
avg_acc = paddle.mean(acc)
loss = paddle.nn.functional.cross_entropy(predicts, y_data, reduction='mean')
loss.backward() #这里手册误写成了avg_loss
if batch_id % 400 == 0:
print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), avg_acc.numpy())) #这里手册误写成了avg_loss
adam.step()
adam.clear_grad()
# 启动train多进程训练，默认使用所有可见的GPU卡
import paddle.distributed as dist
if __name__ == '__main__':
dist.spawn(train)1.3单机多卡简要总结：
spawn方式下在notebook里报错的情况，猜测应该是notebook进程管理限制导致的。在命令行情况下或者cell里加叹号运行的时候，就没有问题。
spawn方式不需要去修改代码的内部部分，只是加上dist.spawn(train)这句，相当于给训练代码加了一个多进程的壳，简单方便，是推荐使用的单机多卡组网方式！
在不支持spawn的情况，再去考虑用launch方式启动单机多卡。
飞桨完备的并行模式：
- 数据并行：针对产业界最常用的数据并行模式，飞桨针对实际业务需求重点打磨多项技术，包括；飞桨提供集合通信架构和参数服务器架构两种方式，支持工业实践中常见的同步训练和异步训练的机制，并提供收敛效果有保障的分布式优化算法。
- 流水线并行：面向异构硬件，流水线并行能够将模型计算部分拆分到不同硬件并充分流水线化，从而大规模提升异构硬件的整体利用率。
- **模型并行：**对于超大规模分类问题，飞桨提供计算与存储同时并行的模型并行，解决单GPU无法解决的问题。
1.4使用fleetrun启动分布式任务
1.4.1 使用fleetrun启动分布式任务
Paddle提供命令行启动命令fleetrun，配合Paddle的分布式高级APIpaddle.distributed.fleet 即可轻松启动Paddle集合通信模式或参数服务器模式下的分布式任务。 fleetrun在静态图和动态图场景下均可使用。
注：目前paddle.distributed.fleet启动动态图分布式训练仅支持集合通信（Colletive Communication）模式，不支持参数服务器（Parameter-Server）模式。
- GPU单机多卡训练
若启动单机4卡的任务，只需通过–gpus指定空闲的4张卡即可。
代码语言：javascript复制 fleetrun --gpus=0,1,2,3 train.py注：如果指定了export CUDA_VISIBLE_DEVICES=0,1,2,3，则可以直接使用：
代码语言：javascript复制 export CUDA_VISIBLE_DEVICES=0,1,2,3
fleetrun train.py- GPU多机多卡训练
[示例一] 2机8卡 (每个节点4卡)
代码语言：javascript复制 fleetrun --ips="xx.xx.xx.xx,yy.yy.yy.yy" --gpus=0,1,2,3 train.py注：如果每台机器均指定了export CUDA_VISIBLE_DEVICES=0,1,2,3，则可以直接在每台节点上启动：
代码语言：javascript复制 export CUDA_VISIBLE_DEVICES=0,1,2,3
fleetrun --ips="xx.xx.xx.xx,yy.yy.yy.yy" train.py[示例二] 2机16卡（每个节点8卡，假设每台机器均有8卡可使用）
代码语言：javascript复制 fleetrun --ips="xx.xx.xx.xx,yy.yy.yy.yy" train.py1.4.2 Fleet单机多卡训练
使用Fleet接口进行动态图分布式训练其实非常的简单，基础API程序代码只需修改3个步骤：
导入paddle.distributed.fleet包
代码语言：javascript复制 from paddle.distributed import fleet初始化fleet环境
代码语言：javascript复制 fleet.init(is_collective=True)通过fleet获取分布式优化器和分布式模型
代码语言：javascript复制 strategy = fleet.DistributedStrategy()
adam = fleet.distributed_optimizer(adam, strategy=strategy)
dp_layer = fleet.distributed_model(layer)Fleet手册提供的例子
代码语言：javascript复制%%writefile train_fleet.py
# -*- coding: UTF-8 -*-
import paddle
import paddle.nn as nn
#分布式step 1: 导入paddle.distributed.fleet包
from paddle.distributed import fleet
# 定义全连接网络，需继承自nn.Layer
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
# 1.开启动态图模式
paddle.disable_static()
# 分布式step 2: 初始化fleet
fleet.init(is_collective=True)
# 2. 定义网络对象，损失函数和优化器
layer = LinearNet()
loss_fn = nn.MSELoss()
adam = paddle.optimizer.Adam(
learning_rate=0.001, parameters=layer.parameters())
# 分布式step 3: 通过fleet获取分布式优化器和分布式模型
strategy = fleet.DistributedStrategy()
adam = fleet.distributed_optimizer(adam, strategy=strategy)
dp_layer = fleet.distributed_model(layer)
for step in range(20):
# 3. 执行前向网络
inputs = paddle.randn([10, 10], 'float32')
outputs = dp_layer(inputs)
labels = paddle.randn([10, 1], 'float32')
loss = loss_fn(outputs, labels)
print("step:{}tloss:{}".format(step, loss.numpy()))
# 4. 执行反向计算和参数更新
loss.backward()
adam.step()
adam.clear_grad()!fleetrun --gpus=0 train_fleet.pyDeprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
----------- Configuration Arguments -----------
gpus: 0
heter_worker_num: None
heter_workers:
http_port: None
ips: 127.0.0.1
log_dir: log
nproc_per_node: None
server_num: None
servers:
training_script: train_fleet.py
training_script_args: []
worker_num: None
workers:
------------------------------------------------
WARNING 2021-06-28 15:56:16,986 launch.py:316] Not found distinct arguments and compiled with cuda. Default use collective mode
launch train in GPU mode
INFO 2021-06-28 15:56:16,990 launch_utils.py:471] Local start 1 processes. First process distributed environment info (Only For Debug):
=======================================================================================
| Distributed Envs Value |
---------------------------------------------------------------------------------------
| PADDLE_TRAINER_ID 0 |
| PADDLE_CURRENT_ENDPOINT 127.0.0.1:47263 |
| PADDLE_TRAINERS_NUM 1 |
| PADDLE_TRAINER_ENDPOINTS 127.0.0.1:47263 |
| FLAGS_selected_gpus 0 |
=======================================================================================
INFO 2021-06-28 15:56:16,991 launch_utils.py:475] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/layers/utils.py:26: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
def convert_to_list(value, n, name, dtype=np.int):
W0628 15:56:18.760403 1539 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1
W0628 15:56:18.826562 1539 device_context.cc:372] device: 0, cuDNN Version: 7.6.
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/distributed/fleet/base/fleet_base.py:633: UserWarning: It is recommended to use DistributedStrategy in fleet.init(). The strategy here is only for compatibility. If the strategy in fleet.distributed_optimizer() is not None, then it will overwrite the DistributedStrategy in fleet.init(), which will take effect in distributed training.
"It is recommended to use DistributedStrategy "
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/parallel.py:423: UserWarning: The program will return to single-card operation. Please check 1, whether you use spawn or fleetrun to start the program. 2, Whether it is a multi-card program. 3, Is the current environment multi-card.
warnings.warn("The program will return to single-card operation. "
step:0 loss:[2.747072]
step:1 loss:[3.9464068]
step:2 loss:[3.3363562]
step:3 loss:[1.7597802]
step:4 loss:[2.4984336]
step:5 loss:[1.3766874]
step:6 loss:[3.3678422]
step:7 loss:[1.8410085]
step:8 loss:[1.6417965]
step:9 loss:[4.009201]
step:10 loss:[1.7387416]
step:11 loss:[1.6013482]
step:12 loss:[1.6388085]
step:13 loss:[3.7573469]
step:14 loss:[0.9461777]
step:15 loss:[2.4906065]
step:16 loss:[2.613153]
step:17 loss:[2.8367076]
step:18 loss:[2.170548]
step:19 loss:[2.2705061]
INFO 2021-06-28 15:56:35,049 launch.py:240] Local processes completed.2.手写数字识别API Fleet多版本
2.1手写数字识别基础API Fleet版本
代码语言：javascript复制%%writefile normal_fleet.py
import paddle #这是有3处改动的版本
from paddle.vision.transforms import ToTensor
#分布式step 1: 导入paddle.distributed.fleet包
from paddle.distributed import fleet
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())
# 加载训练集 batch_size 设为 64
train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
# 分布式step 2: 初始化fleet
fleet.init(is_collective=True)
def train():
epochs = 1
net = paddle.vision.models.LeNet()
adam = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters())
# 分布式step 3: 通过fleet获取分布式优化器和分布式模型
strategy = fleet.DistributedStrategy()
adam = fleet.distributed_optimizer(adam, strategy=strategy)
net = fleet.distributed_model(net)
# 用Adam作为优化函数
for epoch in range(epochs):
for batch_id, data in enumerate(train_loader()):
x_data = data[0]
y_data = data[1]
predicts = net(x_data)
acc = paddle.metric.accuracy(predicts, y_data, k=2)
avg_acc = paddle.mean(acc)
loss = paddle.nn.functional.cross_entropy(predicts, y_data, reduction='mean')
loss.backward() #这里手册误写成了avg_loss
if batch_id % 400 == 0:
print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), avg_acc.numpy())) #这里手册误写成了avg_loss
adam.step()
adam.clear_grad()
if __name__ == '__main__':
train()!fleetrun --gpus=0 normal_fleet.py =======================================================================================
| Distributed Envs Value |
---------------------------------------------------------------------------------------
| PADDLE_TRAINER_ID 0 |
| PADDLE_CURRENT_ENDPOINT 127.0.0.1:42501 |
| PADDLE_TRAINERS_NUM 1 |
| PADDLE_TRAINER_ENDPOINTS 127.0.0.1:42501 |
| FLAGS_selected_gpus 0 |
=======================================================================================
epoch: 0, batch_id: 0, loss is: [2.5425684], acc is: [0.234375]
epoch: 0, batch_id: 400, loss is: [0.05207598], acc is: [1.]
epoch: 0, batch_id: 800, loss is: [0.04818164], acc is: [1.]2.2 手写数字识别高层API Fleet版本
代码语言：javascript复制%%writefile hapi_fleet.py
import paddle
from paddle.vision.transforms import ToTensor
import paddle.distributed as dist
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor())
test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor())
lenet = paddle.vision.models.LeNet()
# Mnist继承paddle.nn.Layer属于Net，model包含了训练功能
model = paddle.Model(lenet)
# 设置训练模型所需的optimizer, loss, metric
model.prepare(
paddle.optimizer.Adam(learning_rate=0.1, parameters=model.parameters()),
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 2))
)
def train():
# 启动训练
# 使用VisualDL 可视化
callback = paddle.callbacks.VisualDL(log_dir='visualdl_log')
model.fit(train_dataset, epochs=1, batch_size=64, callbacks=callback, log_freq=400)
# 未使用VisualDL 可视化
# model.fit(train_dataset, epochs=1, batch_size=64, log_freq=400)
# 启动评估
# model.evaluate(test_dataset, log_freq=20, batch_size=64)
if __name__ == '__main__':
train()!fleetrun hapi_fleet.py2.3 多机多卡手写数字识别
从单机多卡到多机多卡训练，在代码上并不需要做任何改动，只需修改启动命令，以2机4卡为例：
代码语言：javascript复制 fleetrun --ips="xx.xx.xx.xx,yy.yy.yy.yy" --gpus=0,1 dygraph_fleet.py在2台机器上分别运行以上启动命令，fleetrun将在后台分别启动2个多进程任务，执行分布式多机训练。 您将在ip为xx.xx.xx.xx的机器上看到命令台输出日志信息。
下面还是以aistudio为例子演示一下多机多卡，直接运行：
代码语言：javascript复制!fleetrun --ips="127.0.0.1" --gpus=0 normal_fleet.py3.飞桨2.0并行计算总结：
飞桨2.0在并行计算方面有着完备的解决方案，且是经过超大规模业务数据检验过的训练框架。并行计算，就是这么简单！
3.1 针对单机多卡的情况，优先推荐使用spawn方式
spawn的优点是：几乎不需要修改代码，只要导入spawn库，并在最后用spawn去调用训练函数即可。同时spawn方式可以更好地控制进程，在日志打印、训练退出时更友好
程序中只需要增加这两句：
代码语言：javascript复制 import paddle.distributed as dist
if __name__ == '__main__':
dist.spawn(train)然后直接用python train.py启动训练即可
3.2 针对多机多卡的情况，使用fleet方式。
普通API程序需要对应修改3个步骤：
导入paddle.distributed.fleet包
代码语言：javascript复制 from paddle.distributed import fleet初始化fleet环境
代码语言：javascript复制 fleet.init(is_collective=True)通过fleet获取分布式优化器和分布式模型
代码语言：javascript复制 strategy = fleet.DistributedStrategy()
adam = fleet.distributed_optimizer(adam, strategy=strategy)
dp_layer = fleet.distributed_model(layer)然后运行命令： fleetrun --ips=“xx.xx.xx.xx,yy.yy.yy.yy” --gpus=0,1 train.py
3.3 如果使用高层API代码，则程序不用修改，直接运行fleetrun命令即可。
4.利用VisualDL进行并行计算下的可视化
VisualDL是一个面向深度学习任务设计的可视化工具。VisualDL 利用了丰富的图表来展示数据，用户可以更直观、清晰地查看数据的特征与变化趋势，有助于分析数据、及时发现错误，进而改进神经网络模型的设计。喜欢的同学可以去star支持一下哦~
AI Studio Notebook 项目（Paddle1.8.0及以上版本）已经集成VisualDL工具以便于您的使用，可在可视化tab中启动VisualDL服务。
4.1 VisualDL可视化
在高层API程序中，只需要加上这句callback = paddle.callbacks.VisualDL(log_dir='visualdl_log')并在model.fit里面加上callbacks=callback参数即可，也就是这样：model.fit(train_dataset, epochs=1, batch_size=64, callbacks=callback, log_freq=400)
前面的hapi_fleet.py代码中已经加入了VisualDL语句支持，前面cell已经执行!fleetrun hapi_fleet.py现在直接就可以在AIStudio里面打开可视化了：
打开左侧标签栏 可视化->设置logdir->点击添加->选择 visualdl_log/ -> 点击启动VisualDL服务 -> 点击打开VisualDL，在打开的网页中，就能看到训练的loss/acc等统计了；
4.2 利用VisualDL-Service共享可视化结果
- 此功能是
VisualDL 2.0.4新添加的功能，需要安装 VisualDL 2.0.4 或者以上的版本，只需要一行代码visualdl service upload即可以将自己的log文件上传到远端， - 非常推荐这个功能，我们上传文件之后，就不再需要在本地保存这些文件，直接访问生成的链接就可以了，十分方便！
- 如果没有安装
VisualDL 2.0.4，需要使用命令pip install visualdl==2.0.5安装 - 执行下面的代码之后，访问生成的链接， 所有人都可以对训练过程进行查看分析
!pip install -U visualdl -q # ==2.0.5
!visualdl service upload --logdir visualdl_log
