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quickstart_gpu.yaml
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apiVersion: batch/v1
kind: Job
metadata:
name: paddle-cluster-job-gpu
spec:
parallelism: 3
template:
metadata:
name: paddle-cluster-job-gpu
spec:
volumes:
- name: glusterfsvol
glusterfs:
endpoints: glusterfs-cluster
path: gfs_vol
- name: cuda-libs
hostPath:
path: /usr/local/nvidia/lib64/
containers:
- name: trainer
image: yancey1989/paddle_k8s_quickstart_gpu
imagePullPolicy: Always
command: ["/bin/bash", "/root/start.sh"]
resources:
requests:
cpu: 500m
memory: 1Gi
limits:
cpu: 2
memory: 5Gi
alpha.kubernetes.io/nvidia-gpu: 2
env:
- name: TRAINER_COUNT
value: "3"
- name: JOB_NAME
value: paddle-cluster-job-gpu
- name: JOB_PATH
value: /mnt/glusterfs/yanxu05
# using downward API to reference pod namespace
- name: JOB_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
- name: TRAINER_PACKAGE
value: /root/quick_start
- name: CONF_PADDLE_NIC
value: eth0
- name: CONF_PADDLE_PORT
value: "7164"
- name: CONF_PADDLE_PORTS_NUM
value: "2"
- name: CONF_PADDLE_PORTS_NUM_SPARSE
value: "2"
- name: CONF_PADDLE_GRADIENT_NUM
value: "3"
- name: TRAINER_COUNT
value: "3"
- name: TRAINER_THREAD
value: "2"
- name: LD_LIBRARY_PATH
value: "/usr/lib64:/usr/local/lib:/usr/local/lib64:/usr/local/nvidia/lib64"
- name: USE_GPU
value: "1"
volumeMounts:
- mountPath: /mnt/glusterfs
name: glusterfsvol
- mountPath: "/usr/local/nvidia/lib64"
name: cuda-libs
ports:
- name: jobport0
containerPort: 7164
- name: jobport1
containerPort: 7165
- name: jobport2
containerPort: 7166
- name: jobport3
containerPort: 7167
restartPolicy: Never