Author: xidianwangtao@gmail.com

当前性能问题描述

  1. 增加worker数,一定范围内能带来较好的性能提升,但是继续增加worker数时,训练性能提升不明显;
  2. 增加ps数,一定范围内能带来较好的性能提升,但是继续增加ps数时,训练性能提升不明显;

可能原因:

  1. 与ps和worker的分布情况强相关:

    • 目前的调度策略,主要根据服务器的cpu和内存使用情况进行均衡调度,尽量使得集群中每台服务器的cpu和内存使用率相当。这种情况下,ps和worker的调度存在一定程度的随机性。
    • 如果调度时,每台包含worker的服务器都有对应一个ps,那么训练性能会更高?如果有,性能提升多少呢?
  2. K8S中的worker从HDFS集群中读取训练数据时存在IO瓶颈?可能网络上的或者是HDFS本身的配置,需要通过HDFS集群的监控来进一步排查。

下面,是针对第一种“可能原因:与ps和worker的分布情况强相关“ 设计的测试场景和用例:

场景1:将每个worker所在的服务器都有对应的ps。

测试用例

用例ID服务器数worker数ps数说明
11101一台服务器部署了10个worker和1个ps
255055台服务器分别部署了10个worker和1个p
3101001010台服务器分别部署了10个worker和1个p
4202002020台服务器分别部署了10个worker和1个p

TensorFlow tasks调度设计图

输入图片说明

调度实现

  • 场景1的TensorFlow对象模板***scene1.jinja***
# scene1.jinja —— 对象模板
{%- set name = "##NAME##" -%}
{%- set worker_replicas = ##WN## -%}
{%- set ps_replicas = ##PN## -%}
{%- set script = "##SCRIPT##" -%}
{%- set case = "##CASE##" -%}


{%- set port = 2222 -%}
{%- set log_host_dir = "/var/log/tensorflow" -%}
{%- set log_container_dir = "/var/log" -%}
{%- set image = "registry.vivo.xyz:4443/bigdata_release/tensorflow1.3.0" -%}
{%- set replicas = {"worker": worker_replicas, "ps": ps_replicas} -%}

{%- macro worker_hosts() -%}
  {%- for i in range(worker_replicas) -%}
    {%- if not loop.first -%},{%- endif -%}
    {{ name }}-worker-{{ i }}:{{ port }}
  {%- endfor -%}
{%- endmacro -%}

{%- macro ps_hosts() -%}
  {%- for i in range(ps_replicas) -%}
    {%- if not loop.first -%},{%- endif -%}
    {{ name }}-ps-{{ i }}:{{ port }}
  {%- endfor -%}
{%- endmacro -%}


{%- for i in range( begin_index, end_index ) -%}
{%- if task_type  == "worker" %}

---
kind: Service
apiVersion: v1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  clusterIP: None
  selector:
    name: {{ name }}
    job: {{ task_type }}
    task: "{{ i }}"
  ports:
  - port: {{ port }}
    targetPort: 2222
---
kind: Job
apiVersion: batch/v1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  template:
    metadata:
      labels:
        name: {{ name }}
        job: {{ task_type }}
        task: "{{ i }}"
    spec:
      imagePullSecrets:
      - name: harborsecret'
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: "CASE"
                  operator: In
                  values: 
                  - "{{ case }}"
                - key: "INDEX"
                  operator: In
                  values: 
                  - "{{ i // 10 }}"
                - key: "SCENCE"
                  operator: In
                  values: 
                  - "1"
      containers:
      - name: {{ name }}-{{ task_type }}-{{ i }}
        image: {{ image }}
        resources:
          requests:
            memory: "4Gi"
            cpu: "300m"
        ports:
        - containerPort: 2222
        command: ["/bin/sh", "-c", "export CLASSPATH=.:/usr/lib/jvm/java-1.8.0/lib/tools.jar:$(/usr/lib/hadoop-2.6.1/bin/hadoop classpath --glob); wget -r -nH  -np --cut-dir=1 -R 'index.html*,*gif'  {{ script }}; cd ./{{ name }}; sh ./run.sh {{ ps_hosts() }} {{ worker_hosts() }} {{ task_type }} {{ i }} {{ ps_replicas }} {{ worker_replicas }}"]
      restartPolicy: OnFailure

{%- endif -%}

{%- if task_type == "ps" -%}
---
kind: Service
apiVersion: v1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  clusterIP: None
  selector:
    name: {{ name }}
    job: {{ task_type }}
    task: "{{ i }}"
  ports:
  - port: {{ port }}
    targetPort: 2222
---
kind: Deployment
apiVersion: extensions/v1beta1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  replicas: 1
  template:
    metadata:
      labels:
        name: {{ name }}
        job: {{ task_type }}
        task: "{{ i }}"
    spec:
      imagePullSecrets:
      - name: harborsecret
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: "CASE"
                  operator: In
                  values: 
                  - "{{ case }}"
                - key: "INDEX"
                  operator: In
                  values: 
                  - "{{ i }}"
                - key: "SCENCE"
                  operator: In
                  values: 
                  - "1"
      containers:
      - name: {{ name }}-{{ task_type }}-{{ i }}
        image: {{ image }}
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
        ports:
        - containerPort: 2222
        command: ["/bin/sh", "-c","export CLASSPATH=.:/usr/lib/jvm/java-1.8.0/lib/tools.jar:$(/usr/lib/hadoop-2.6.1/bin/hadoop classpath --glob); wget -r -nH  -np --cut-dir=1 -R 'index.html*,*gif'  {{ script }}; cd ./{{ name }}; sh ./run.sh {{ ps_hosts() }} {{ worker_hosts() }} {{ task_type }} {{ i }} {{ ps_replicas }} {{ worker_replicas }}"]
      restartPolicy: Always
{%- endif -%}
{%- endfor -%}
  • Label Nodes

选择对应的节点打上对应的Label。

kubectl label node $node_name SCENCE=1 CASE=? INDEX=?

测试结果

用例2的测试截图:

输入图片说明

场景2:将所有ps和所有worker都强制进行物理隔离。

测试用例

用例ID服务器数worker数ps数说明
12101一台服务器部署10个worker,另外一台部署1个ps
2102055台服务器分别部署10个worker,5台服务器分别部署1个ps
320501010台服务器分别部署10个worker,10台服务器分别部署1个ps
4402002020台服务器分别部署10个worker,20台服务器分别部署1个ps

TensorFlow tasks调度设计图

输入图片说明

调度实现

  • 场景2的TensorFlow对象模板***scene2.jinja***
# scene2.jinja —— 对象模板
{%- set name = "##NAME##" -%}
{%- set worker_replicas = ##WN## -%}
{%- set ps_replicas = ##PN## -%}
{%- set script = "##SCRIPT##" -%}
{%- set case = "##CASE##" -%}


{%- set port = 2222 -%}
{%- set log_host_dir = "/var/log/tensorflow" -%}
{%- set log_container_dir = "/var/log" -%}
{%- set image = "registry.vivo.xyz:4443/bigdata_release/tensorflow1.3.0" -%}
{%- set replicas = {"worker": worker_replicas, "ps": ps_replicas} -%}

{%- macro worker_hosts() -%}
  {%- for i in range(worker_replicas) -%}
    {%- if not loop.first -%},{%- endif -%}
    {{ name }}-worker-{{ i }}:{{ port }}
  {%- endfor -%}
{%- endmacro -%}

{%- macro ps_hosts() -%}
  {%- for i in range(ps_replicas) -%}
    {%- if not loop.first -%},{%- endif -%}
    {{ name }}-ps-{{ i }}:{{ port }}
  {%- endfor -%}
{%- endmacro -%}


{%- for i in range( begin_index, end_index ) -%}
{%- if task_type  == "worker" %}

---
kind: Service
apiVersion: v1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  clusterIP: None
  selector:
    name: {{ name }}
    job: {{ task_type }}
    task: "{{ i }}"
  ports:
  - port: {{ port }}
    targetPort: 2222
---
kind: Job
apiVersion: batch/v1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  template:
    metadata:
      labels:
        name: {{ name }}
        job: {{ task_type }}
        task: "{{ i }}"
    spec:
      imagePullSecrets:
      - name: harborsecret'
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: "CASE"
                  operator: In
                  values: 
                  - "{{ case }}"
                - key: "INDEX"
                  operator: In
                  values: 
                  - "{{ i // 10 }}"
                - key: "SCENCE"
                  operator: In
                  values: 
                  - "2"
                - key: "TYPE"
                  operator: In
                  values: 
                  - "worker"
      containers:
      - name: {{ name }}-{{ task_type }}-{{ i }}
        image: {{ image }}
        resources:
          requests:
            memory: "4Gi"
            cpu: "300m"
        ports:
        - containerPort: 2222
        command: ["/bin/sh", "-c", "export CLASSPATH=.:/usr/lib/jvm/java-1.8.0/lib/tools.jar:$(/usr/lib/hadoop-2.6.1/bin/hadoop classpath --glob); wget -r -nH  -np --cut-dir=1 -R 'index.html*,*gif'  {{ script }}; cd ./{{ name }}; sh ./run.sh {{ ps_hosts() }} {{ worker_hosts() }} {{ task_type }} {{ i }} {{ ps_replicas }} {{ worker_replicas }}"]
      restartPolicy: OnFailure

{%- endif -%}

{%- if task_type == "ps" -%}
---
kind: Service
apiVersion: v1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  clusterIP: None
  selector:
    name: {{ name }}
    job: {{ task_type }}
    task: "{{ i }}"
  ports:
  - port: {{ port }}
    targetPort: 2222
---
kind: Deployment
apiVersion: extensions/v1beta1
metadata:
  name: {{ name }}-{{ task_type }}-{{ i }}
  namespace: {{ name }}
spec:
  replicas: 1
  template:
    metadata:
      labels:
        name: {{ name }}
        job: {{ task_type }}
        task: "{{ i }}"
    spec:
      imagePullSecrets:
      - name: harborsecret
      affinity:
        nodeAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
            nodeSelectorTerms:
              - matchExpressions:
                - key: "CASE"
                  operator: In
                  values: 
                  - "{{ case }}"
                - key: "INDEX"
                  operator: In
                  values: 
                  - "{{ i }}"
                - key: "SCENCE"
                  operator: In
                  values: 
                  - "2"
                - key: "TYPE"
                  operator: In
                  values: 
                  - "ps"
      containers:
      - name: {{ name }}-{{ task_type }}-{{ i }}
        image: {{ image }}
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
        ports:
        - containerPort: 2222
        command: ["/bin/sh", "-c","export CLASSPATH=.:/usr/lib/jvm/java-1.8.0/lib/tools.jar:$(/usr/lib/hadoop-2.6.1/bin/hadoop classpath --glob); wget -r -nH  -np --cut-dir=1 -R 'index.html*,*gif'  {{ script }}; cd ./{{ name }}; sh ./run.sh {{ ps_hosts() }} {{ worker_hosts() }} {{ task_type }} {{ i }} {{ ps_replicas }} {{ worker_replicas }}"]
      restartPolicy: Always
{%- endif -%}
{%- endfor -%}
  • Label Nodes

选择对应的节点打上对应的Label。

kubectl label node $node_name SCENCE=1 CASE=? INDEX=? TYPE=?

测试结果

用例2的测试截图:

输入图片说明

测试结论及思考

对比两种不同场景下用例2(5个ps,50个worker)的监控数据,发现如下现象:

  • 两种场景下,虽然创建了5个ps,但是实际上只有一个ps的负载比较高,其他的ps要么cpu usage在10%以下,要么甚至几乎为0。

  • 两种场景下同样的worker number和ps number,整个tensorflow cluster消耗的cpu和内存差别很小。

测试结论

  • 分布式tensorflow中,每个worker选择哪个ps作为自己的参数服务器跟我们如何强制分布ps和worker的布局无关,由分布式tensorflow内部自己控制(跟tf.train.replica_device_setter()设置的strategy有关)。

问题思考

  • 为什么这个训练中,多个ps中只有一个ps在工作?是算法只有一个Big参数?如果是,那么默认按照Round-Robin策略只会使用一个ps,就能解释这个问题了。这需要算法的兄弟进行确认。

  • 如果将Big参数拆分成众多Small参数,使用RR或LB或Partition策略之一,应该都能利用多个ps进行参数更新明显提升训练性能。

  • 通过这次折腾,也不是一无所获,至少发现我们对于Distributed TensorFlow的内部工作原理还不甚了解,非常有必要深入到源码进行分析。