背景介绍
谷歌的 Tensorflow 与 Facebook 的 PyTorch 一直是颇受社区欢迎的两种深度学习框架。虽然 PyTorch 仍然是款比较新的框架，但由于友好的开发体验，使它发展非常迅猛。但是PyTorch
默认并不支持在HDFS直接进行模型训练，这给许多将数据集存放在HDFS的用户带来了困难。他们需要将HDFS数据导出；或者修改PyTorch的源码支持HDFS协议才能进行训练。这给用户的使用造成极大的不变。
而使用Alluxio可以将HDFS的接口翻译成POSIX FileSystem接口，避免PyTorch的开发者在计算框架层面进行修改，可以大大提升开发效率。
本文为您介绍如何在Kubernetes的环境下，验证整个工作
准备HDFS 2.7.2环境
由于我并没有现成的HDFS集群，可以直接利用Helm Chart安装HDFS
1.安装Hadoop 2.7.2的helm chart
git clone https://github.com/cheyang/kubernetes-HDFS.git
kubectl label nodes cn-huhehaote.192.168.0.117 hdfs-namenode-selector=hdfs-namenode-0
#helm install -f values.yaml hdfs charts/hdfs-k8s
helm dependency build charts/hdfs-k8s
helm install hdfs charts/hdfs-k8s \
--set tags.ha=false \
--set tags.simple=true \
--set global.namenodeHAEnabled=false \
--set hdfs-simple-namenode-k8s.nodeSelector.hdfs-namenode-selector=hdfs-namenode-02.查看helm chart的状态
kubectl get all -l release=hdfs3.客户端访问hdfs
kubectl exec -it hdfs-client-f5bc448dd-rc28d bash
root@hdfs-client-f5bc448dd-rc28d:/# hdfs dfsadmin -report
Configured Capacity: 422481862656 (393.47 GB)
Present Capacity: 355748564992 (331.32 GB)
DFS Remaining: 355748515840 (331.32 GB)
DFS Used: 49152 (48 KB)
DFS Used%: 0.00%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0
Missing blocks (with replication factor 1): 0
-------------------------------------------------
Live datanodes (2):
Name: 172.31.136.180:50010 (172-31-136-180.node-exporter.arms-prom.svc.cluster.local)
Hostname: iZj6c7rzs9xaeczn47omzcZ
Decommission Status : Normal
Configured Capacity: 211240931328 (196.73 GB)
DFS Used: 24576 (24 KB)
Non DFS Used: 32051716096 (29.85 GB)
DFS Remaining: 179189190656 (166.88 GB)
DFS Used%: 0.00%
DFS Remaining%: 84.83%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Tue Mar 31 16:48:52 UTC 20204.HDFS 客户端配置
[root@iZj6c61fdnjcrcrc2sevsfZ kubernetes-HDFS]# kubectl exec -it hdfs-client-f5bc448dd-rc28d bash
root@hdfs-client-f5bc448dd-rc28d:/# cat /etc/hadoop-custom-conf
cat: /etc/hadoop-custom-conf: Is a directory
root@hdfs-client-f5bc448dd-rc28d:/# cd /etc/hadoop-custom-conf
root@hdfs-client-f5bc448dd-rc28d:/etc/hadoop-custom-conf# ls
core-site.xml hdfs-site.xml
root@hdfs-client-f5bc448dd-rc28d:/etc/hadoop-custom-conf# cat core-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://hdfs-namenode-0.hdfs-namenode.default.svc.cluster.local:8020</value>
</property>
</configuration>
root@hdfs-client-f5bc448dd-rc28d:/etc/hadoop-custom-conf# cat hdfs-site.xml
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:///hadoop/dfs/name</value>
</property>
<property>
<name>dfs.namenode.datanode.registration.ip-hostname-check</name>
<value>false</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>/hadoop/dfs/data/0</value>
</property>
</configuration>
root@hdfs-client-f5bc448dd-rc28d:/etc/hadoop-custom-conf# hadoop --version
Error: No command named `--version' was found. Perhaps you meant `hadoop -version'
root@hdfs-client-f5bc448dd-rc28d:/etc/hadoop-custom-conf# hadoop -version
Error: No command named `-version' was found. Perhaps you meant `hadoop version'
root@hdfs-client-f5bc448dd-rc28d:/etc/hadoop-custom-conf# hadoop version
Hadoop 2.7.2
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r b165c4fe8a74265c792ce23f546c64604acf0e41
Compiled by jenkins on 2016-01-26T00:08Z
Compiled with protoc 2.5.0
From source with checksum d0fda26633fa762bff87ec759ebe689c
This command was run using /opt/hadoop-2.7.2/share/hadoop/common/hadoop-common-2.7.2.jar5.实验HDFS基本文件操作
# hdfs dfs -ls /
Found 1 items
drwxr-xr-x - root supergroup 0 2020-03-31 16:51 /test
# hdfs dfs -mkdir /mytest
# hdfs dfs -copyFromLocal /etc/hadoop/hadoop-env.cmd /test/
# hdfs dfs -ls /test
Found 2 items
-rw-r--r-- 3 root supergroup 3670 2020-04-20 08:51 /test/hadoop-env.cmd6.下载数据
mkdir -p /data/MNIST/raw/
cd /data/MNIST/raw/
wget http://kubeflow.oss-cn-beijing.aliyuncs.com/mnist/train-images-idx3-ubyte.gz
wget http://kubeflow.oss-cn-beijing.aliyuncs.com/mnist/train-labels-idx1-ubyte.gz
wget http://kubeflow.oss-cn-beijing.aliyuncs.com/mnist/t10k-images-idx3-ubyte.gz
wget http://kubeflow.oss-cn-beijing.aliyuncs.com/mnist/t10k-labels-idx1-ubyte.gz
hdfs dfs -mkdir -p /data/MNIST/raw
hdfs dfs -copyFromLocal *.gz /data/MNIST/raw部署Alluxio
1.先选择指定节点，可以是一个或者多个
kubectl label nodes cn-huhehaote.192.168.0.117 dataset=mnist2.创建config.yaml, 其中要配置node selector指定节点
cat << EOF > config.yaml
image: registry.cn-huhehaote.aliyuncs.com/alluxio/alluxio
imageTag: "2.2.0-SNAPSHOT-b2c7e50"
nodeSelector:
dataset: mnist
properties:
alluxio.fuse.debug.enabled: "false"
alluxio.user.file.writetype.default: MUST_CACHE
alluxio.master.journal.folder: /journal
alluxio.master.journal.type: UFS
alluxio.master.mount.table.root.ufs: "hdfs://hdfs-namenode-0.hdfs-namenode.default.svc.cluster.local:8020"
worker:
jvmOptions: " -Xmx4G "
master:
jvmOptions: " -Xmx4G "
tieredstore:
levels:
- alias: MEM
level: 0
quota: 20GB
type: hostPath
path: /dev/shm
high: 0.99
low: 0.8
fuse:
image: registry.cn-huhehaote.aliyuncs.com/alluxio/alluxio-fuse
imageTag: "2.2.0-SNAPSHOT-b2c7e50"
jvmOptions: " -Xmx4G -Xms4G "
args:
- fuse
- --fuse-opts=direct_io
EOF
- 需要注意的是针对于不同的HDFS版本，需要在编译时刻指定。本例子提供了支持HDFS 2.7.2的容器镜像
- alluxio.master.mount.table.root.ufs用来指定HDFS地址
- quota用来指定缓存的上限， 具体配置可以参考https://docs.alluxio.io/os/user/stable/en/deploy/Running-Alluxio-On-Kubernetes.html
2.安装alluxio
wget http://kubeflow.oss-cn-beijing.aliyuncs.com/alluxio-0.12.0.tgz
tar -xvf alluxio-0.12.0.tgz
helm install alluxio -f config.yaml alluxio3.查看alluxio的状态, 等所有组件都处于ready状态
helm get manifest alluxio | kubectl get -f -
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/alluxio-master ClusterIP None <none> 19998/TCP,19999/TCP,20001/TCP,20002/TCP 14h
NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE
daemonset.apps/alluxio-fuse 4 4 4 4 4 <none> 14h
NAME DESIRED CURRENT READY UP-TO-DATE AVAILABLE NODE SELECTOR AGE
daemonset.apps/alluxio-worker 4 4 4 4 4 <none> 14h
NAME READY AGE
statefulset.apps/alluxio-master 1/1 14h准备PyTorch容器镜像
1.准备Dockerfile
创建目录，并且创建Dockerfile和PyTorch脚本
mkdir pytorch-mnist
cd pytorch-mnist
vim Dockerfile输入如下内容
FROM pytorch/pytorch:1.4-cuda10.1-cudnn7-devel
# pytorch/pytorch:1.4-cuda10.1-cudnn7-devel
ADD mnist.py /
CMD ["python", "/mnist.py"]2.准备测试代码mnist.py
cd pytorch-mnist
vim mnist.py输入如下内容
# -*- coding: utf-8 -*-
# @Author: cheyang
# @Date: 2020-04-18 22:41:12
# @Last Modified by: cheyang
# @Last Modified time: 2020-04-18 22:44:06
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += F.nll_loss(output,
target,
reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int,
default=1000,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=14, metavar='N',
help='number of epochs to train (default: 14)')
parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
help='learning rate (default: 1.0)')
parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
help='Learning rate step gamma (default: 0.7)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
help='For Saving the current Model')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
model = Net().to(device)
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
scheduler.step()
if args.save_model:
torch.save(model.state_dict(), "mnist_cnn.pt")
if __name__ == '__main__':
main()3.构建镜像
在同级目录下构建自定义镜像，本例子中的目标容器镜像为registry.cn-shanghai.aliyuncs.com/tensorflow-samples/mnist:pytorch-1.4-cuda10.1-cudnn7-devel
docker build -t \
registry.cn-shanghai.aliyuncs.com/tensorflow-samples/mnist:pytorch-1.4-cuda10.1-cudnn7-devel .4.将构建好的镜像 registry.cn-shanghai.aliyuncs.com/tensorflow-samples/mnist:pytorch-1.4-cuda10.1-cudnn7-devel 推送到之前在华东1区创建的镜像仓库中去。可以参考镜像基本操作。
提交PyTorch训练任务
1.安装arena
$ wget http://kubeflow.oss-cn-beijing.aliyuncs.com/arena-installer-0.3.3-332fcde-linux-amd64.tar.gz
$ tar -xvf arena-installer-0.3.3-332fcde-linux-amd64.tar.gz
$ cd arena-installer/
$ ./install.
$ yum install bash-completion -y
$ echo "source <(arena completion bash)" >> ~/.bashrc
$ chmod u+x ~/.bashrc2.利用arena提交训练任务，记得要选择selector是dataset=mnist
arena submit tf \
--name=alluxio-pytorch \
--selector=dataset=mnist \
--data-dir=/alluxio-fuse/data:/data \
--gpus=1 \
--image=registry.cn-shanghai.aliyuncs.com/tensorflow-samples/mnist:pytorch-1.4-cuda10.1-cudnn7-devel \
"python /mnist.py"3.并且通过arena查看训练日志
# arena logs --tail=20 alluxio-pytorch
Train Epoch: 12 [49280/60000 (82%)] Loss: 0.021669
Train Epoch: 12 [49920/60000 (83%)] Loss: 0.008180
Train Epoch: 12 [50560/60000 (84%)] Loss: 0.009288
Train Epoch: 12 [51200/60000 (85%)] Loss: 0.035657
Train Epoch: 12 [51840/60000 (86%)] Loss: 0.006190
Train Epoch: 12 [52480/60000 (87%)] Loss: 0.007776
Train Epoch: 12 [53120/60000 (88%)] Loss: 0.001990
Train Epoch: 12 [53760/60000 (90%)] Loss: 0.003609
Train Epoch: 12 [54400/60000 (91%)] Loss: 0.001943
Train Epoch: 12 [55040/60000 (92%)] Loss: 0.078825
Train Epoch: 12 [55680/60000 (93%)] Loss: 0.000925
Train Epoch: 12 [56320/60000 (94%)] Loss: 0.018071
Train Epoch: 12 [56960/60000 (95%)] Loss: 0.031451
Train Epoch: 12 [57600/60000 (96%)] Loss: 0.031353
Train Epoch: 12 [58240/60000 (97%)] Loss: 0.075761
Train Epoch: 12 [58880/60000 (98%)] Loss: 0.003975
Train Epoch: 12 [59520/60000 (99%)] Loss: 0.085389
Test set: Average loss: 0.0256, Accuracy: 9921/10000 (99%)总结
在HDFS上运行PyTorch程序本来需要用户修改PyTorch的适配器代码进行完成的工作，通过Alluxio，我们简化了适配工作，能够快速开展模型的开发和训练。而通过Kubernetes平台，这件事情变得非常简单，欢迎尝试。