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Alluxio深度学习实战-1：体验在HDFS上运行PyTorch框架

日期：2020-04-23点击：394收藏

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-0

2.查看helm chart的状态

kubectl get all -l release=hdfs

3.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 2020

4.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.jar

5.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.cmd

6.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=mnist

2.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 alluxio

3.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 ~/.bashrc

2.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%)