mirror of
https://github.com/tencentmusic/cube-studio.git
synced 2024-12-15 06:09:57 +08:00
578 lines
22 KiB
Python
578 lines
22 KiB
Python
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import ray
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import re
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import os
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import sys
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import time
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from kubernetes import client, config, watch
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import json,datetime,time,os,sys
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sys.path.append(os.path.dirname(__file__))
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import argparse
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import datetime, time
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import pysnooper
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# print(os.environ)
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base_dir = os.path.split(os.path.realpath(__file__))[0]
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KFJ_NAMESPACE = os.getenv('KFJ_NAMESPACE', '')
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KFJ_TASK_ID = os.getenv('KFJ_TASK_ID', '')
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KFJ_TASK_NAME = os.getenv('KFJ_TASK_NAME', '')
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RAY_HOST='ray-header-'+KFJ_TASK_NAME
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os.system('set RAY_HOST %s'%RAY_HOST)
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os.environ['RAY_HOST']=RAY_HOST
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task_node_selectors = re.split(',|;|\n|\t', os.getenv('KFJ_TASK_NODE_SELECTOR', 'cpu=true,train=true'))
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KFJ_TASK_NODE_SELECTOR = {}
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for task_node_selector in task_node_selectors:
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KFJ_TASK_NODE_SELECTOR[task_node_selector.split('=')[0]] = task_node_selector.split('=')[1]
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KFJ_PIPELINE_ID = os.getenv('KFJ_PIPELINE_ID', '')
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KFJ_RUN_ID = os.getenv('KFJ_RUN_ID', '')
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KFJ_CREATOR = os.getenv('KFJ_CREATOR', '')
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KFJ_RUNNER = os.getenv('KFJ_RUNNER','')
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KFJ_PIPELINE_NAME = os.getenv('KFJ_PIPELINE_NAME', '')
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KFJ_TASK_IMAGES = os.getenv('KFJ_TASK_IMAGES', '')
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KFJ_TASK_VOLUME_MOUNT = os.getenv('KFJ_TASK_VOLUME_MOUNT', '')
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KFJ_TASK_RESOURCE_CPU = os.getenv('KFJ_TASK_RESOURCE_CPU', '')
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KFJ_TASK_RESOURCE_MEMORY = os.getenv('KFJ_TASK_RESOURCE_MEMORY', '')
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NUM_WORKER = 3
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HEADER_NAME = os.getenv('RAY_HOST', '')
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WORKER_NAME = HEADER_NAME.replace('header', 'worker')
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INIT_FILE=''
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def get_volume_mounts(volume_mount,username):
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k8s_volumes = []
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k8s_volume_mounts = []
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if volume_mount and ":" in volume_mount:
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volume_mount = volume_mount.strip()
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if volume_mount:
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volume_mounts_temp = re.split(',|;', volume_mount)
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volume_mounts_temp = [volume_mount_temp.strip() for volume_mount_temp in volume_mounts_temp if volume_mount_temp.strip()]
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for volume_mount in volume_mounts_temp:
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volume, mount = volume_mount.split(":")[0].strip(), volume_mount.split(":")[1].strip()
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if "(pvc)" in volume:
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pvc_name = volume.replace('(pvc)', '').replace(' ', '')
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volumn_name = pvc_name.replace('_', '-').lower()
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k8s_volumes.append({
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"name":volumn_name,
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"persistentVolumeClaim":{
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"claimName":pvc_name
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}
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})
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k8s_volume_mounts.append(
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{
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"name":volumn_name,
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"mountPath":os.path.join(mount, username),
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"subPath":username
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}
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)
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if "(hostpath)" in volume:
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hostpath_name = volume.replace('(hostpath)', '').replace(' ', '')
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temps = re.split('_|\.|/', hostpath_name)
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temps = [temp for temp in temps if temp]
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volumn_name = '-'.join(temps).lower() # hostpath_name.replace('_', '-').replace('/', '-').replace('.', '-')
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k8s_volumes.append(
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{
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"name":volumn_name,
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"hostPath":{
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"path":hostpath_name
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}
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}
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)
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k8s_volume_mounts.append({
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"name":volumn_name,
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"mountPath":mount
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})
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if "(configmap)" in volume:
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configmap_name = volume.replace('(configmap)', '').replace(' ', '')
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volumn_name = configmap_name.replace('_', '-').replace('/', '-').replace('.', '-').lower()
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k8s_volumes.append({
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"name":volumn_name,
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"configMap":{
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"name":configmap_name
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}
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})
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k8s_volume_mounts.append({
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"name":volumn_name,
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"mountPath":mount
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})
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return k8s_volumes,k8s_volume_mounts
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k8s_volumes, k8s_volume_mounts = get_volume_mounts(KFJ_TASK_VOLUME_MOUNT,KFJ_CREATOR)
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# k8s_volumes.append(
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# {
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# "name": "dshm",
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# "emptyDir": {
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# "medium": "Memory"
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# }
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# }
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# )
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k8s_volume_mounts.append(
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{
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"name":'tz-config',
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"mountPath":"/etc/localtime"
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}
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)
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k8s_volumes.append(
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{
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"name": "tz-config",
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"hostPath": {
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"path": '/usr/share/zoneinfo/Asia/Shanghai'
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}
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}
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)
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# k8s_volume_mounts.append(
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# {
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# "name":'dshm',
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# "mountPath":"/dev/shm"
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# }
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# )
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print(k8s_volumes)
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print(k8s_volume_mounts)
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GPU_TYPE= os.getenv('KFJ_GPU_TYPE', 'NVIDIA')
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GPU_RESOURCE= os.getenv('KFJ_TASK_RESOURCE_GPU', '0')
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print(GPU_TYPE,GPU_RESOURCE)
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def create_header_service(name):
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service_json = {
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"apiVersion": "v1",
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"kind": "Service",
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"metadata": {
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"namespace": KFJ_NAMESPACE,
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"name": name,
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"labels":{
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"run-id":os.getenv('KFJ_RUN_ID','unknown'),
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"run-rtx":os.getenv('KFJ_RUNNER','unknown'),
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"pipeline-rtx": os.getenv('KFJ_CREATOR', 'unknown'),
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"task-id":os.getenv('KFJ_TASK_ID','unknown'),
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"pipeline-id": os.getenv('KFJ_PIPELINE_ID', 'unknown')
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}
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},
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"spec": {
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"ports": [
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{
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"name": "client",
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"protocol": "TCP",
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"port": 10001,
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"targetPort": 10001
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},
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{
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"name": "dashboard",
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"protocol": "TCP",
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"port": 8265,
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"targetPort": 8265
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},
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{
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"name": "redis",
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"protocol": "TCP",
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"port": 6379,
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"targetPort": 6379
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}
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],
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"selector": {
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"component": name
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}
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}
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}
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return service_json
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# @pysnooper.snoop()
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def create_header_deploy(name):
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header_deploy = {
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"apiVersion": "apps/v1",
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"kind": "Deployment",
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"metadata": {
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"namespace": KFJ_NAMESPACE,
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"name": name,
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"labels":{
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"run-id":os.getenv('KFJ_RUN_ID','unknown'),
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"run-rtx":os.getenv('KFJ_RUNNER','unknown'),
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"pipeline-rtx": os.getenv('KFJ_CREATOR', 'unknown'),
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"task-id":os.getenv('KFJ_TASK_ID','unknown'),
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"pipeline-id": os.getenv('KFJ_PIPELINE_ID', 'unknown')
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}
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},
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"spec": {
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"replicas": 1,
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"selector": {
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"matchLabels": {
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"component": name,
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"type": "ray"
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}
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},
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"template": {
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"metadata": {
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"labels": {
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"pipeline-id": KFJ_PIPELINE_ID,
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"pipeline-name": KFJ_PIPELINE_NAME,
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"task-name": KFJ_TASK_NAME,
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'rtx-user': KFJ_RUNNER,
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"component": name,
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"type": "ray",
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"run-id": os.getenv('KFJ_RUN_ID', 'unknown'),
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}
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},
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"spec": {
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"restartPolicy": "Always",
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"volumes": k8s_volumes,
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# "imagePullSecrets": [
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# {
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# "name": "hubsecret"
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# }
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# ],
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"affinity": {
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"nodeAffinity": {
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"requiredDuringSchedulingIgnoredDuringExecution": {
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"nodeSelectorTerms": [
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{
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"matchExpressions": [
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{
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"key": node_selector_key,
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"operator": "In",
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"values": [
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KFJ_TASK_NODE_SELECTOR[node_selector_key]
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]
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} for node_selector_key in KFJ_TASK_NODE_SELECTOR
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]
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}
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]
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}
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},
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"podAntiAffinity": {
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"preferredDuringSchedulingIgnoredDuringExecution": [
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{
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"weight": 5,
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"podAffinityTerm": {
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"topologyKey": "kubernetes.io/hostname",
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"labelSelector": {
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"matchLabels": {
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"component": name
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}
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}
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}
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}
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]
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}
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},
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"containers": [
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{
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"name": "ray-head",
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"image": KFJ_TASK_IMAGES,
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"imagePullPolicy": "Always",
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"command": [
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"/bin/bash",
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"-c",
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"%s ray start --head --port=6379 --redis-shard-ports=6380,6381 --num-cpus=$MY_CPU_REQUEST --object-manager-port=12345 --node-manager-port=12346 --block"%INIT_FILE
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],
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"ports": [
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{
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"containerPort": 6379
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},
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{
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"containerPort": 10001
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},
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{
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"containerPort": 8265
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}
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],
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"volumeMounts": k8s_volume_mounts,
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"env": [
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{
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"name": "MY_CPU_REQUEST",
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"valueFrom": {
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"resourceFieldRef": {
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"resource": "requests.cpu"
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}
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}
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}
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],
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"resources": {
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"requests": {
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"cpu": KFJ_TASK_RESOURCE_CPU,
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"memory": KFJ_TASK_RESOURCE_MEMORY,
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},
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"limits": {
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"cpu": KFJ_TASK_RESOURCE_CPU,
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"memory": KFJ_TASK_RESOURCE_MEMORY
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}
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}
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}
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]
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}
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}
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}
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}
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if GPU_TYPE=='NVIDIA' and GPU_RESOURCE:
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header_deploy['spec']['template']['spec']['containers'][0]['resources']['requests']['nvidia.com/gpu'] = GPU_RESOURCE.split(',')[0]
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header_deploy['spec']['template']['spec']['containers'][0]['resources']['limits']['nvidia.com/gpu'] = GPU_RESOURCE.split(',')[0]
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return header_deploy
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def create_worker_deploy(header_name,worker_name):
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worker_deploy = {
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"apiVersion": "apps/v1",
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"kind": "Deployment",
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"metadata": {
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"namespace": KFJ_NAMESPACE,
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"name": worker_name,
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"labels": {
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"run-id":os.getenv('KFJ_RUN_ID','unknown'),
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"run-rtx":os.getenv('KFJ_RUNNER','unknown'),
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"pipeline-rtx": os.getenv('KFJ_CREATOR', 'unknown'),
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"task-id":os.getenv('KFJ_TASK_ID','unknown'),
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"pipeline-id": os.getenv('KFJ_PIPELINE_ID', 'unknown')
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}
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},
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"spec": {
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"replicas": NUM_WORKER,
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"selector": {
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"matchLabels": {
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"component": worker_name,
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"type": "ray"
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}
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},
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"template": {
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"metadata": {
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"labels": {
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"pipeline-id": KFJ_PIPELINE_ID,
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"pipeline-name": KFJ_PIPELINE_NAME,
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"task-name": KFJ_TASK_NAME,
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'rtx-user': KFJ_RUNNER,
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"component": worker_name,
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"type": "ray",
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"run-id": os.getenv('KFJ_RUN_ID', 'unknown'),
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}
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},
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"spec": {
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"affinity": {
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"nodeAffinity": {
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"requiredDuringSchedulingIgnoredDuringExecution": {
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"nodeSelectorTerms": [
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{
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"matchExpressions": [
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{
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"key": node_selector_key,
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"operator": "In",
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"values": [
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KFJ_TASK_NODE_SELECTOR[node_selector_key]
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]
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} for node_selector_key in KFJ_TASK_NODE_SELECTOR
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]
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}
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]
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}
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},
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"podAntiAffinity": {
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"preferredDuringSchedulingIgnoredDuringExecution": [
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{
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"weight": 5,
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"podAffinityTerm": {
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"topologyKey": "kubernetes.io/hostname",
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"labelSelector": {
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"matchLabels": {
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"component": worker_name
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}
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}
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}
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}
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]
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}
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},
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# "imagePullSecrets": [
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# {
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# "name": "hubsecret"
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# }
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# ],
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"restartPolicy": "Always",
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"volumes": k8s_volumes,
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"containers": [
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{
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"name": "ray-worker",
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"image": KFJ_TASK_IMAGES,
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"imagePullPolicy": "Always",
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"command": [
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"/bin/bash",
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"-c",
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"%s ray start --num-cpus=$MY_CPU_REQUEST --address=$RAY_HEAD_SERVICE_HOST:$RAY_HEAD_SERVICE_PORT_REDIS --object-manager-port=12345 --node-manager-port=12346 --block"%INIT_FILE
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],
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"volumeMounts": k8s_volume_mounts,
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"env": [
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{
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"name": "MY_CPU_REQUEST",
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"valueFrom": {
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"resourceFieldRef": {
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"resource": "requests.cpu"
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}
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}
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},
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{
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"name": "RAY_HEAD_SERVICE_HOST",
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"value": header_name
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},
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{
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"name": "RAY_HEAD_SERVICE_PORT_REDIS",
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"value": "6379"
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}
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],
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"resources": {
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"requests": {
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"cpu": KFJ_TASK_RESOURCE_CPU,
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"memory": KFJ_TASK_RESOURCE_MEMORY
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},
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"limits": {
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"cpu": KFJ_TASK_RESOURCE_CPU,
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"memory": KFJ_TASK_RESOURCE_MEMORY
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}
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}
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}
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]
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}
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}
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}
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}
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if GPU_TYPE=='NVIDIA' and GPU_RESOURCE:
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worker_deploy['spec']['template']['spec']['containers'][0]['resources']['requests']['nvidia.com/gpu'] = GPU_RESOURCE.split(',')[0]
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worker_deploy['spec']['template']['spec']['containers'][0]['resources']['limits']['nvidia.com/gpu'] = GPU_RESOURCE.split(',')[0]
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return worker_deploy
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# @pysnooper.snoop()
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def wait_for_nodes():
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# Wait for all nodes to join the cluster.
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while True:
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resources = ray.cluster_resources()
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node_keys = [key for key in resources if "node" in key]
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num_nodes = sum(resources[node_key] for node_key in node_keys)
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if num_nodes < NUM_WORKER:
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print("{} nodes have joined so far, waiting for {} more.".format(num_nodes, NUM_WORKER - num_nodes))
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sys.stdout.flush()
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time.sleep(1)
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else:
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break
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# @pysnooper.snoop()
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def launcher_cluster(file_path=None,deal=None):
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if file_path:
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config.kube_config.load_kube_config(config_file=file_path) # 使用kubeconfig文件名称获取访问权限，需要
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else:
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config.load_incluster_config() # 使用为pod配置的rbac访问集群
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# v1 = client.AppsV1Api()
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# v1.api_client.configuration.verify_ssl = False
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# 清理一下之前存在的
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try:
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print('begin delete old header service')
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client.CoreV1Api().delete_namespaced_service(HEADER_NAME, KFJ_NAMESPACE)
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except Exception as e1:
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pass
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# print(e1)
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try:
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print('begin delete old header deployment')
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client.AppsV1Api().delete_namespaced_deployment(HEADER_NAME, KFJ_NAMESPACE)
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except Exception as e1:
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pass
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# print(e1)
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try:
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print('begin delete old worker deployment')
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client.AppsV1Api().delete_namespaced_deployment(WORKER_NAME, KFJ_NAMESPACE)
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except Exception as e1:
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pass
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# print(e1)
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time.sleep(3)
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if deal=='create':
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header_service = create_header_service(HEADER_NAME)
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header_deploy = create_header_deploy(HEADER_NAME)
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worker_deploy = create_worker_deploy(HEADER_NAME,WORKER_NAME)
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try:
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print(KFJ_NAMESPACE,end='\n\n\n')
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print(json.dumps(header_service,indent=4,ensure_ascii=False),end='\n\n\n')
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print('begin create ray header service,%s ' % datetime.datetime.now())
|
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client.CoreV1Api().create_namespaced_service(KFJ_NAMESPACE, header_service, pretty='true')
|
||
print('begin create ray header deployment,%s ' % datetime.datetime.now())
|
||
print(json.dumps(header_deploy, indent=4, ensure_ascii=False),end='\n\n\n')
|
||
client.AppsV1Api().create_namespaced_deployment(KFJ_NAMESPACE, header_deploy, pretty='true')
|
||
print('begin create ray worker deployment,%s ' % datetime.datetime.now())
|
||
print(json.dumps(worker_deploy, indent=4, ensure_ascii=False),end='\n\n\n')
|
||
client.AppsV1Api().create_namespaced_deployment(KFJ_NAMESPACE, worker_deploy, pretty='true')
|
||
# 等待创建完成
|
||
time.sleep(20)
|
||
header_host = "%s:10001" % HEADER_NAME
|
||
print('begin connect ray cluster %s,%s ' % (header_host,datetime.datetime.now()),end='\n\n\n')
|
||
|
||
ray.util.connect(header_host,connection_retries=20)
|
||
wait_for_nodes()
|
||
print('ray cluster all node ready,%s ' % datetime.datetime.now(),end='\n\n\n')
|
||
|
||
except Exception as e:
|
||
print(e)
|
||
try:
|
||
print('begin delete error header service',end='\n\n\n')
|
||
client.CoreV1Api().delete_namespaced_service(HEADER_NAME, KFJ_NAMESPACE)
|
||
except Exception as e1:
|
||
pass
|
||
# print(e1)
|
||
try:
|
||
print('begin delete error header deployment',end='\n\n\n')
|
||
client.AppsV1Api().delete_namespaced_deployment(HEADER_NAME, KFJ_NAMESPACE)
|
||
except Exception as e1:
|
||
pass
|
||
# print(e1)
|
||
try:
|
||
print('begin delete error worker deployment',end='\n\n\n')
|
||
client.AppsV1Api().delete_namespaced_deployment(WORKER_NAME, KFJ_NAMESPACE)
|
||
except Exception as e1:
|
||
pass
|
||
print(e1)
|
||
# 如果出现错误，报错退出。不进行下一步代码
|
||
raise e
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
arg_parser = argparse.ArgumentParser(description="build component")
|
||
arg_parser.add_argument('--num_workers', type=int, required=False, help="workers的数量", default=3)
|
||
arg_parser.add_argument('--download_type', type=str, required=False, help="数据下载类型", default="url")
|
||
arg_parser.add_argument('--input_file', type=str, required=False, help="下载内容文件地址", default="/mnt/ray/url.txt")
|
||
|
||
args = arg_parser.parse_args()
|
||
print('NUM_WORKER',args.num_workers)
|
||
|
||
NUM_WORKER = int(args.num_workers)
|
||
|
||
|
||
if args.download_type=='url':
|
||
launcher_cluster(deal='create')
|
||
from download_url import main
|
||
main(src_file_path=args.input_file)
|
||
launcher_cluster()
|
||
|
||
|
||
|