本篇内容介绍了“TensorFlow on Kubernetes性能怎么理解”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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当前性能问题描述
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；
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：
与ps和worker的分布情况强相关：
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 | | 1 | 1 | 10 | 1 | 一台服务器部署了10个worker和1个ps |
| 2 | 5 | 50 | 5 | 5台服务器分别部署了10个worker和1个p |
| 3 | 10 | 100 | 10 | 10台服务器分别部署了10个worker和1个p |
| 4 | 20 | 200 | 20 | 20台服务器分别部署了10个worker和1个p |
TensorFlow tasks调度设计图
调度实现
# 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 -%}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
| 用例ID | 服务器数 | worker数 | ps数 | 说明 | | 1 | 2 | 10 | 1 | 一台服务器部署10个worker，另外一台部署1个ps |
| 2 | 10 | 20 | 5 | 5台服务器分别部署10个worker，5台服务器分别部署1个ps |
| 3 | 20 | 50 | 10 | 10台服务器分别部署10个worker，10台服务器分别部署1个ps |
| 4 | 40 | 200 | 20 | 20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
调度实现
# 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 -%}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：
测试结论
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。
“TensorFlow on Kubernetes性能怎么理解”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站，小编将为大家输出更多高质量的实用文章！
名称栏目：TensorFlowonKubernetes性能怎么理解
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