Optional: Data Parallelism

本教程介绍如何使用PyTorch实现多GPU数据并行。先导入模块并定义参数,创建虚拟数据集和简单模型,核心是创建模型并使用DataParallel包装,最后运行模型。展示了不同数量GPU(2、3、8)下的运行结果。

Optional: Data Parallelism

AuthorsSung Kim and Jenny Kang

In this tutorial, we will learn how to use multiple GPUs using DataParallel.

It’s very easy to use GPUs with PyTorch. You can put the model on a GPU:

device = torch.device("cuda:0")
model.to(device)

Then, you can copy all your tensors to the GPU:

mytensor = my_tensor.to(device)

Please note that just calling my_tensor.to(device) returns a new copy of my_tensor on GPU instead of rewriting my_tensor. You need to assign it to a new tensor and use that tensor on the GPU.

It’s natural to execute your forward, backward propagations on multiple GPUs. However, Pytorch will only use one GPU by default. You can easily run your operations on multiple GPUs by making your model run parallelly using DataParallel:

model = nn.DataParallel(model)

That’s the core behind this tutorial. We will explore it in more detail below.

Imports and parameters

Import PyTorch modules and define parameters.

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

# Parameters and DataLoaders
input_size = 5
output_size = 2

batch_size = 30
data_size = 100

Device

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

Dummy DataSet

Make a dummy (random) dataset. You just need to implement the getitem

class RandomDataset(Dataset):

    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len

rand_loader = DataLoader(dataset=RandomDataset(input_size, data_size),
                         batch_size=batch_size, shuffle=True)

Simple Model

For the demo, our model just gets an input, performs a linear operation, and gives an output. However, you can use DataParallel on any model (CNN, RNN, Capsule Net etc.)

We’ve placed a print statement inside the model to monitor the size of input and output tensors. Please pay attention to what is printed at batch rank 0.

class Model(nn.Module):
    # Our model

    def __init__(self, input_size, output_size):
        super(Model, self).__init__()
        self.fc = nn.Linear(input_size, output_size)

    def forward(self, input):
        output = self.fc(input)
        print("\tIn Model: input size", input.size(),
              "output size", output.size())

        return output

Create Model and DataParallel

This is the core part of the tutorial. First, we need to make a model instance and check if we have multiple GPUs. If we have multiple GPUs, we can wrap our model using nn.DataParallel. Then we can put our model on GPUs by model.to(device)

model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
  print("Let's use", torch.cuda.device_count(), "GPUs!")
  # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
  model = nn.DataParallel(model)

model.to(device)

Out:

Let's use 2 GPUs!

Run the Model

Now we can see the sizes of input and output tensors.

for data in rand_loader:
    input = data.to(device)
    output = model(input)
    print("Outside: input size", input.size(),
          "output_size", output.size())

Out:

In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
        In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
        In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
        In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
        In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
        In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
        In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
        In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])

Results

If you have no GPU or one GPU, when we batch 30 inputs and 30 outputs, the model gets 30 and outputs 30 as expected. But if you have multiple GPUs, then you can get results like this.

2 GPUs

If you have 2, you will see:

# on 2 GPUs
Let's use 2 GPUs!
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
    In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])

3 GPUs

If you have 3 GPUs, you will see:

Let's use 3 GPUs!
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])

8 GPUs

If you have 8, you will see:

Let's use 8 GPUs!
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])
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Starting Job Manager [ERROR] The execution result is empty. [ERROR] Could not get JVM parameters and dynamic configurations properly. [ERROR] Raw output from BashJavaUtils: INFO [] - Using standard YAML parser to load flink configuration file from /opt/flink/conf/config.yaml. ERROR [] - Failed to parse YAML configuration org.snakeyaml.engine.v2.exceptions.YamlEngineException: expected '<document start>', but found '<scalar>' in reader, line 1, column 9 at org.snakeyaml.engine.v2.parser.ParserImpl$ParseDocumentStart.produce(ParserImpl.java:493) ~[flink-dist-2.0.0.jar:2.0.0] at org.snakeyaml.engine.v2.parser.ParserImpl.lambda$produce$1(ParserImpl.java:232) ~[flink-dist-2.0.0.jar:2.0.0] at java.util.Optional.ifPresent(Unknown Source) ~[?:?] at org.snakeyaml.engine.v2.parser.ParserImpl.produce(ParserImpl.java:232) ~[flink-dist-2.0.0.jar:2.0.0] at org.snakeyaml.engine.v2.parser.ParserImpl.peekEvent(ParserImpl.java:206) ~[flink-dist-2.0.0.jar:2.0.0] at org.snakeyaml.engine.v2.parser.ParserImpl.checkEvent(ParserImpl.java:198) ~[flink-dist-2.0.0.jar:2.0.0] at org.snakeyaml.engine.v2.composer.Composer.getSingleNode(Composer.java:131) ~[flink-dist-2.0.0.jar:2.0.0] at org.snakeyaml.engine.v2.api.Load.loadOne(Load.java:110) ~[flink-dist-2.0.0.jar:2.0.0] at org.snakeyaml.engine.v2.api.Load.loadFromInputStream(Load.java:123) ~[flink-dist-2.0.0.jar:2.0.0] at org.apache.flink.configuration.YamlParserUtils.loadYamlFile(YamlParserUtils.java:100) [flink-dist-2.0.0.jar:2.0.0] at org.apache.flink.configuration.GlobalConfiguration.loadYAMLResource(GlobalConfiguration.java:252) [flink-dist-2.0.0.jar:2.0.0] at org.apache.flink.configuration.GlobalConfiguration.loadConfiguration(GlobalConfiguration.java:150) [flink-dist-2.0.0.jar:2.0.0] at org.apache.flink.runtime.util.ConfigurationParserUtils.loadCommonConfiguration(ConfigurationParserUtils.java:153) [flink-dist-2.0.0.jar:2.0.0] at org.apache.flink.runtime.util.bash.FlinkConfigLoader.loadConfiguration(FlinkConfigLoader.java:41) [flink-dist-2.0.0.jar:2.24.1] at org.apache.flink.runtime.util.bash.BashJavaUtils.runCommand(BashJavaUtils.java:66) [bash-java-utils.jar:2.24.1] at org.apache.flink.runtime.util.bash.BashJavaUtils.main(BashJavaUtils.java:54) [bash-java-utils.jar:2.24.1] Exception in thread "main" java.lang.RuntimeException: Error parsing YAML configuration. at org.apache.flink.configuration.GlobalConfiguration.loadYAMLResource(GlobalConfiguration.java:257) at org.apache.flink.configuration.GlobalConfiguration.loadConfiguration(GlobalConfiguration.java:150) at org.apache.flink.runtime.util.ConfigurationParserUtils.loadCommonConfiguration(ConfigurationParserUtils.java:153) at org.apache.flink.runtime.util.bash.FlinkConfigLoader.loadConfiguration(FlinkConfigLoader.java:41)--- services: kafka-0: image: apache/kafka:3.9.1 container_name: kafka-0 ports: - "19092:9092" - "19093:9093" environment: KAFKA_NODE_ID: 1 KAFKA_PROCESS_ROLES: broker,controller KAFKA_LISTENERS: PLAINTEXT://:9092,CONTROLLER://:19093 KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:19092,CONTROLLER://localhost:19093 KAFKA_CONTROLLER_LISTENER_NAMES: CONTROLLER KAFKA_LOG_DIRS: /var/lib/kafka/data KAFKA_AUTO_CREATE_TOPICS_ENABLE: "true" volumes: - ./kafka/conf/log4j.properties:/opt/kafka/config/log4j.properties - ./kafka/0/conf/kraft/server.properties:/opt/kafka/config/kraft/server.properties - ./kafka/0/data:/var/lib/kafka/data command: - sh - -c - > if [ ! -f /var/lib/kafka/data/meta.properties ]; then # 生成随机 UUID 并格式化存储(-c 指定配置文件路径) /opt/kafka/bin/kafka-storage.sh format \ -t $(/opt/kafka/bin/kafka-storage.sh random-uuid) \ -c /opt/kafka/config/kraft/server.properties fi exec /opt/kafka/bin/kafka-server-start.sh /opt/kafka/config/kraft/server.properties healthcheck: test: - CMD - kafka-broker-api-versions.sh - --bootstrap-server - localhost:19092 interval: 10s timeout: 10s retries: 5 networks: - datacamp-net flink-jobmanager-0: image: flink:2.0.0-java17 container_name: flink-jobmanager-0 ports: - "18081:8081" environment: FLINK_PROPERTIES: | jobmanager.rpc.address: flink-jobmanager-0 state.backend: filesystem state.checkpoints.dir: file:///tmp/flink-checkpoints heartbeat.interval: 1000 heartbeat.timeout: 5000 rest.flamegraph.enabled: true web.upload.dir: /opt/flink/usrlib volumes: - ./flink/jobmanager/conf:/opt/flink/conf - ./flink/jobmanager/0/flink-checkpoints:/tmp/flink-checkpoints - ./flink/jobmanager/0/usrlib:/opt/flink/usrlib command: jobmanager healthcheck: test: - CMD - curl - -f - http://localhost:8081 interval: 15s timeout: 5s retries: 10 networks: - datacamp-net flink-taskmanager-0: image: flink:2.0.0-java17 container_name: flink-taskmanager-0 environment: FLINK_PROPERTIES: | jobmanager.rpc.address: flink-jobmanager-0 taskmanager.numberOfTaskSlots: 2 state.backend: filesystem state.checkpoints.dir: file:///tmp/flink-checkpoints heartbeat.interval: 1000 heartbeat.timeout: 5000 volumes: - ./flink/taskmanager/conf:/opt/flink/conf - ./flink/taskmanager/0/flink-checkpoints:/tmp/flink-checkpoints - ./flink/taskmanager/0/usrlib:/opt/flink/usrlib command: taskmanager depends_on: flink-jobmanager-0: condition: service_healthy networks: - datacamp-net flink-taskmanager-1: image: flink:2.0.0-java17 container_name: flink-taskmanager-1 environment: FLINK_PROPERTIES: | jobmanager.rpc.address: flink-jobmanager-0 taskmanager.numberOfTaskSlots: 2 state.backend: filesystem state.checkpoints.dir: file:///tmp/flink-checkpoints heartbeat.interval: 1000 heartbeat.timeout: 5000 volumes: - ./flink/taskmanager/conf:/opt/flink/conf - ./flink/taskmanager/1/flink-checkpoints:/tmp/flink-checkpoints - ./flink/taskmanager/1/usrlib:/opt/flink/usrlib command: taskmanager depends_on: flink-jobmanager-0: condition: service_healthy networks: - datacamp-net networks: datacamp-net: driver: bridge
06-01
<think>好的,我现在需要帮助用户解决FlinkJobManager启动时遇到的YAML配置文件解析错误的问题。用户提到了YamlEngineException,具体是“expected '<document start>', butfound '<scalar>'”这个错误,发生在SnakeYAML解析过程中。我需要先分析可能的原因,然后逐步给出解决方案。首先,用户的问题可能出在YAML文件的格式上。YAML对缩进、冒号、特殊字符等有严格的要求。常见的错误包括缩进使用了Tab而不是空格,键名后缺少冒号后的空格,或者值中包含未转义的特殊字符。例如,用户可能在配置中写了类似`parallelism:10`而没有空格,或者路径中包含冒号未正确转义。接下来,我需要考虑JVM参数的配置是否正确。Flink的配置中,尤其是JVM参数部分,如果使用多行参数,需要用正确的YAML多行语法,比如使用`>-`来折叠多行内容,避免因为换行导致的解析错误。此外,用户可能错误地嵌套了配置项,比如将`java.opts.jobmanager`写成多级嵌套的结构,而实际上应该在同一层级下。 然后,动态配置部分,比如使用`configuration`节点下的参数,需要确保这些参数的键是正确的,并且值符合Flink的预期格式。例如,时间间隔参数需要加上单位,如`5min`,而数值参数则不需要引号,但涉及特殊字符时需要引号包裹。特殊字符的处理也是一个重点。比如在路径中出现的冒号,在Windows和Linux下可能需要不同的转义方式。用户可能在配置文件中直接写了包含冒号的路径而没有转义,导致解析器误认为是YAML的结构符号。 调试和验证方面,用户可能需要使用YAMLLint这样的工具来检查配置文件的基本语法。此外,启动Flink时添加调试参数,如`-Dyamllint.debug=true`,可以输出更详细的日志,帮助定位问题所在。最后,提供一个完整的正确配置示例会有助于用户对照检查自己的文件。例如,展示正确的缩进、JVM参数的多行写法、特殊字符的转义方法等,让用户能够直观地看到问题所在并进行修正。 在生成相关问题的时候,我需要围绕用户可能遇到的后续问题,比如如何验证配置生效、处理特殊符号密码、Kubernetes环境下的配置差异,以及监控JVM参数的方法。这些问题都是用户在解决当前错误后可能会进一步需要了解的,因此需要提前考虑并提供指引。</think>### Flink YAML配置文件解析错误的深度解决方案 #### 1. 基础语法错误排查 - **缩进问题**:YAML要求严格使用空格缩进,且每级缩进推荐2个空格[^1]。错误示例: ```yaml taskmanager: memory.process.size: 4096m # ❌使用Tab缩进 ``` 正确写法应使用空格: ```yaml taskmanager: memory.process.size: 4096m # ✅ ``` - **冒号处理规范**:键值对必须使用`key: value`格式,冒号后必须加空格。数学表达式参数需特别注意: ```yaml state.ttl: 3600000 # ✅正确 state.ttl:3.6e6 # ❌错误(冒号后无空格) ``` #### 2. JVM参数配置解析 - **多行参数的正确写法**: ```yaml env.java.opts.jobmanager: >- -Xmx$8g$ -XX:+UseG1GC -Dlog4j.configuration=file:///opt/flink/conf/log4j.properties ``` 其中`$8g$`表示数学表达式变量,实际配置应写具体值如`8g` - **错误嵌套结构**: ```yaml env: java: opts: jobmanager: "-Xmx4g" # ❌错误层级结构 ``` 正确结构应为: ```yaml env.java.opts.jobmanager: "-Xmx4g" # ✅ ``` #### 3. 动态配置数学表达式处理 当配置参数包含数学表达式时,需用$符号包裹: ```yaml taskmanager.memory.flink.size: $2048*2$m # ✅公式写法 taskmanager.memory.flink.size: 4096m # ✅最终值 ``` 但实际配置中应直接使用计算结果值,因为Flink不支持运行时计算表达式[^2] #### 4. 特殊结构验证方法 - **YAML层级验证工具**: ```bash python3 -c 'import yaml; yaml.safe_load(open("config.yaml"))' ``` 此命令可快速检测YAML结构错误 - **Flink配置预检命令**: ```bash ./bin/flink check /opt/flink/conf/config.yaml ``` 输出结果示例: ``` Validating configuration... [SUCCESS] YAML syntax verification passed [WARNING] Unrecognized configuration key: 'parallelsim.default' ``` #### 5. 完整配置示例(含数学表达式) ```yaml # config.yaml jobmanager.rpc.address: "flink-master" taskmanager: numberOfTaskSlots: $4*2$ # 实际应直接写8 memory: flink.size: 4096m managed.fraction: 0.7 parallelism.default: 8 state.backend: rocksdb state.checkpoints.dir: "file:///checkpoints" ``` #### 6. 调试日志分析 在启动命令中添加调试参数: ```bash ./bin/start-cluster.sh -Dlog4j.configuration=file:///conf/log4j-debug.xml ``` 关键日志标识: ``` DEBUG org.apache.flink.configuration.GlobalConfiguration - Loading configuration from: /opt/flink/conf/config.yaml ERROR org.apache.flink.runtime.entrypoint.ClusterEntrypoint - Failed to parse YAML configuration at line 23 ```
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