> 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

TensorFlow tasks调度设计图

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：

场景2：将所有ps和所有worker都强制进行物理隔离。

测试用例

TensorFlow tasks调度设计图

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：

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。