paddlepaddle简介
飞桨(PaddlePaddle)是百度于 2016 年 9 月开源的深度学习框架，旨在提供一款安全高效、灵活易用、可扩展的深度学习平台。
2018 年 10 月，飞桨团队发布 Paddle Fluid 1.0 版本，对神经网络描述、大规模分布式训练、高性能推理引擎等核心能力进行了全面升级。以工业界应用必需的分布式训练能力为例，在最新的 Paddle Fluid 1.5.2 版本中，飞桨支持数据并行、模型并行、流水线并行等多种并行模式，参数服务器架构和点对点同步训练架构全面支持在 CPU、GPU 等硬件资源设备上的大规模训练[1]。
paddlepaddle on valcano
在集群节点上传ctr-volcano.yaml，内容如下
apiVersion: batch.volcano.sh/v1alpha1
kind: Job
metadata:
name: ctr-volcano
spec:
minAvailable: 4
schedulerName: volcano
policies:
- event: PodEvicted
action: RestartJob
- event: PodFailed
action: RestartJob
tasks:
- replicas: 2
name: pserver
template:
metadata:
labels:
paddle-job-pserver: fluid-ctr
spec:
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/
type: ""
name: seqdata
containers:
- image: volcanosh/edlctr:v1
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: pserver
volumeMounts:
- mountPath: /mnt/seqdata
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 1Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
value: ""
- name: TRAINER_PACKAGE
value: /workspace
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: PADDLE_TRAINERS_NUM
value: "2"
- name: PADDLE_PSERVERS_NUM
value: "2"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: ENTRY
value: cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
- name: PADDLE_PORT
value: "30236"
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: PADDLE_TRAINING_ROLE
value: PSERVER
- name: TRAINING_ROLE
value: PSERVER
restartPolicy: OnFailure
- replicas: 2
policies:
- event: TaskCompleted
action: CompleteJob
name: trainer
template:
metadata:
labels:
paddle-job: fluid-ctr
spec:
imagePullSecrets:
- name: default-secret
volumes:
- hostPath:
path: /home/work/
type: ""
name: seqdata
containers:
- image: volcanosh/edlctr:v1
command:
- paddle_k8s
- start_fluid
imagePullPolicy: IfNotPresent
name: trainer
volumeMounts:
- mountPath: /mnt/seqdata
name: seqdata
resources:
limits:
cpu: 10
memory: 30Gi
ephemeral-storage: 10Gi
requests:
cpu: 1
memory: 100M
ephemeral-storage: 10Gi
env:
- name: GLOG_v
value: "0"
- name: GLOG_logtostderr
value: "1"
- name: TOPOLOGY
- name: TRAINER_PACKAGE
value: /workspace
- name: CPU_NUM
value: "2"
- name: NAMESPACE
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.namespace
- name: POD_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: POD_NAME
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: metadata.name
- name: PADDLE_CURRENT_IP
valueFrom:
fieldRef:
apiVersion: v1
fieldPath: status.podIP
- name: PADDLE_JOB_NAME
value: fluid-ctr
- name: PADDLE_IS_LOCAL
value: "0"
- name: FLAGS_rpc_deadline
value: "36000000"
- name: PADDLE_PORT
value: "30236"
- name: PADDLE_PSERVERS_NUM
value: "2"
- name: PADDLE_TRAINERS_NUM
value: "2"
- name: PADDLE_TRAINING_ROLE
value: TRAINER
- name: TRAINING_ROLE
value: TRAINER
- name: LD_LIBRARY_PATH
value: /usr/local/lib:/usr/local/nvidia/lib64:/usr/local/rdma/lib64:/usr/lib64/mlnx_ofed/valgrind
- name: ENTRY
value: cd /workspace/ctr && python train.py --is_local 0 --cloud_train 1
restartPolicy: OnFailure
在集群终端下部署。
kubectl apply -f ctr-volcano.yaml
查看作业运行情况。如果podgroup无法满足调度条件，请检查集群下的资源是充足。
kubectl get podgroup
kubectl describe podgroup ctr-volcano
kubectl get pods | grep ctr-volcano
可以选择一个PServer任务查看日志。
kubectl logs ctr-volcano-pserver-0
选择一个Tariner任务查看日志。
kubectl logs ctr-volcano-trainer-0
通过上述的训练过程，模型被保存在/workspace/ctr/models中，获取模型的方式有如下两种方式：
- 在 yaml 文件当中 trainer 部分的 spec 当中定义 volume，通过 docker 的 volume 映射容器路径和宿主机路径的机制，将/workspace/ctr/models 文件夹映射到宿主机的文件夹中。接下来通过 kubectl describe pod ctr-volcano-trainer-0，可以得知我们的模型所在的节点，接下来 ssh 登陆到对应的节点上，到宿主机被映射到路径下，就可以获取到训练出来到模型了。
- 如果需要更加灵活的，自动化的模型配送流程，可以在 K8S 集群上建立 File Server 和分布式文件系统，例如 GlusterFS。将 ctr-volcano-trainer-0 容器内部的/workspace/ctr/models 文件夹映射到 GlusterFS 的 PVC（Persistent Volume Claim）上。通过 ftp 的 wget/curl 操作命令就可以实现模型的获取和配送。