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# JSON (Amazon CLI)
可以通过 A [CreateTrainingJob](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTrainingJob.html)I AP SageMaker I 操作使用[DebugHookConfig](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_DebugHookConfig.html)、[DebugRuleConfiguration](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_DebugRuleConfiguration.html)、[ProfilerConfig](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_ProfilerConfig.html)和[ProfilerRuleConfiguration](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_ProfilerRuleConfiguration.html)对象为训练作业配置 Amazon D SageMaker ebugger 内置规则。您需要在`RuleEvaluatorImage`参数中指定正确的图片 URI，以下示例将引导您完成如何设置要请求的 JSON 字符串[CreateTrainingJob](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTrainingJob.html)。
以下代码显示了一个完整的 JSON 模板，用于使用所需设置和 Debugger 配置来运行训练作业。将模板另存为工作目录中的 JSON 文件，然后使用 Amazon CLI 运行训练作业。例如，将以下代码另存为 `debugger-training-job-cli.json`。
**注意**
确保使用正确的 Docker 容器映像。要查找 Amazon 深度学习容器镜像，请参阅[可用的 Deep Learning Containers 镜像](https://github.com/aws/deep-learning-containers/blob/master/available_images.md)。要查找使用 Debugger 规则时可用的 Docker 映像的完整列表，请参阅[用于 Debugger 规则的 Docker 映像](debugger-reference.md#debugger-docker-images-rules)。
```
{
"TrainingJobName": "{{debugger-aws-cli-test}}",
"RoleArn": "{{arn:aws:iam::111122223333:role/service-role/AmazonSageMaker-ExecutionRole-YYYYMMDDT123456}}",
"AlgorithmSpecification": {
// Specify a training Docker container image URI (Deep Learning Container or your own training container) to TrainingImage.
"TrainingImage": "{{763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training:2.4.1-gpu-py37-cu110-ubuntu18.04}}",
"TrainingInputMode": "{{File}}",
"EnableSageMakerMetricsTimeSeries": false
},
"HyperParameters": {
"sagemaker_program": "{{entry_point/tf-hvd-train.py}}",
"sagemaker_submit_directory": "{{s3://sagemaker-us-west-2-111122223333/debugger-boto3-profiling-test/source.tar.gz}}"
},
"OutputDataConfig": {
"S3OutputPath": "s3://{{sagemaker-us-west-2-111122223333/debugger-aws-cli-test}}/output"
},
"DebugHookConfig": {
"S3OutputPath": "s3://{{sagemaker-us-west-2-111122223333/debugger-aws-cli-test}}/debug-output",
"CollectionConfigurations": [
{
"CollectionName": "{{losses}}",
"CollectionParameters" : {
"train.save_interval": "{{50}}"
}
}
]
},
"DebugRuleConfigurations": [
{
"RuleConfigurationName": "{{LossNotDecreasing}}",
"RuleEvaluatorImage": "{{895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest}}",
"RuleParameters": {"rule_to_invoke": "{{LossNotDecreasing}}"}
}
],
"ProfilerConfig": {
"S3OutputPath": "s3://{{sagemaker-us-west-2-111122223333/debugger-aws-cli-test}}/profiler-output",
"ProfilingIntervalInMilliseconds": {{500}},
"ProfilingParameters": {
"DataloaderProfilingConfig": "{\"StartStep\": {{5}}, \"NumSteps\": {{3}}, \"MetricsRegex\": \".*\", }",
"DetailedProfilingConfig": "{\"StartStep\": {{5}}, \"NumSteps\": {{3}}, }",
"PythonProfilingConfig": "{\"StartStep\": {{5}}, \"NumSteps\": {{3}}, \"ProfilerName\": \"{{cprofile}}\", \"cProfileTimer\": \"{{total_time}}\"}",
"LocalPath": "/opt/ml/output/profiler/"
}
},
"ProfilerRuleConfigurations": [
{
"RuleConfigurationName": "ProfilerReport",
"RuleEvaluatorImage": "{{895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest}}",
"RuleParameters": {"rule_to_invoke": "ProfilerReport"}
}
],
"ResourceConfig": {
"InstanceType": "{{ml.p3.8xlarge}}",
"InstanceCount": {{1}},
"VolumeSizeInGB": 30
},
"StoppingCondition": {
"MaxRuntimeInSeconds": {{86400}}
}
}
```
保存 JSON 文件后，在终端中运行以下命令。（如果您使用 Jupyter 笔记本，则在行的开头使用 `!`。）
```
aws sagemaker create-training-job --cli-input-json file://debugger-training-job-cli.json
```
## 配置 Debugger 规则以调试模型参数
以下代码示例展示了如何使用此 SageMaker API 配置内置`VanishingGradient`规则。
**启用 Debugger 收集输出张量**
按如下方式指定 Debugger 钩子配置：
```
"DebugHookConfig": {
"S3OutputPath": "{{s3:////debug-output}}",
"CollectionConfigurations": [
{
"CollectionName": "{{gradients}}",
"CollectionParameters" : {
"save_interval": "{{500}}"
}
}
]
}
```
这将使训练作业按每 500 个步骤的 `save_interval` 保存一次 `gradients` 张量集合。要查找可用`CollectionName`值，请参阅*SMDebug 客户端库文档*中的[调试器内置集合](https://github.com/awslabs/sagemaker-debugger/blob/master/docs/api.md#built-in-collections)。要查找可用的`CollectionParameters`参数键和值，请参阅 *SageMaker Python SDK 文档*中的[https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.CollectionConfig](https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html#sagemaker.debugger.CollectionConfig)类。
**启用 Debugger 规则来调试输出张量**
以下`DebugRuleConfigurations` API 示例说明了如何对已保存的 `gradients` 集合运行内置 `VanishingGradient` 规则。
```
"DebugRuleConfigurations": [
{
"RuleConfigurationName": "{{VanishingGradient}}",
"RuleEvaluatorImage": "{{503895931360.dkr.ecr.us-east-1.amazonaws.com/sagemaker-debugger-rules:latest}}",
"RuleParameters": {
"rule_to_invoke": "{{VanishingGradient}}",
"threshold": "{{20.0}}"
}
}
]
```
通过类似于此示例中的配置，Debugger 使用 `VanishingGradient` 规则，在 `gradients` 张量的集合上为您的训练作业启动规则评估作业。要查找使用 Debugger 规则时可用的 Docker 映像的完整列表，请参阅[用于 Debugger 规则的 Docker 映像](debugger-reference.md#debugger-docker-images-rules)。要查找 `RuleParameters` 的键值对，请参阅 [Debugger 内置规则列表](debugger-built-in-rules.md)。
## 为分析系统和框架指标配置 Debugger 内置规则
以下示例代码演示如何指定 ProfilerConfig API 操作以启用收集系统和框架指标。
**启用 Debugger 分析以收集系统和框架指标**
------
#### [ Target Step ]
```
"ProfilerConfig": {
// Optional. Path to an S3 bucket to save profiling outputs
"S3OutputPath": "{{s3:////profiler-output}}",
// Available values for ProfilingIntervalInMilliseconds: 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds.
"ProfilingIntervalInMilliseconds": {{500}},
"ProfilingParameters": {
"DataloaderProfilingConfig": "{ \"StartStep\": {{5}}, \"NumSteps\": {{3}}, \"MetricsRegex\": \".*\" }",
"DetailedProfilingConfig": "{ \"StartStep\": {{5}}, \"NumSteps\": {{3}} }",
// For PythonProfilingConfig,
// available ProfilerName options: cProfile, Pyinstrument
// available cProfileTimer options only when using cProfile: cpu, off_cpu, total_time
"PythonProfilingConfig": "{ \"StartStep\": {{5}}, \"NumSteps\": {{3}}, \"ProfilerName\": \"{{cProfile}}\", \"cProfileTimer\": \"{{total_time}}\" }",
// Optional. Local path for profiling outputs
"LocalPath": "/opt/ml/output/profiler/"
}
}
```
------
#### [ Target Time Duration ]
```
"ProfilerConfig": {
// Optional. Path to an S3 bucket to save profiling outputs
"S3OutputPath": "{{s3:////profiler-output}}",
// Available values for ProfilingIntervalInMilliseconds: 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds.
"ProfilingIntervalInMilliseconds": {{500}},
"ProfilingParameters": {
"DataloaderProfilingConfig": "{ \"StartTimeInSecSinceEpoch\": {{12345567789}}, \"DurationInSeconds\": {{10}}, \"MetricsRegex\": \".*\" }",
"DetailedProfilingConfig": "{ \"StartTimeInSecSinceEpoch\": {{12345567789}}, \"DurationInSeconds\": {{10}} }",
// For PythonProfilingConfig,
// available ProfilerName options: cProfile, Pyinstrument
// available cProfileTimer options only when using cProfile: cpu, off_cpu, total_time
"PythonProfilingConfig": "{ \"StartTimeInSecSinceEpoch\": {{12345567789}}, \"DurationInSeconds\": {{10}}, \"ProfilerName\": \"{{cProfile}}\", \"cProfileTimer\": \"{{total_time}}\" }",
// Optional. Local path for profiling outputs
"LocalPath": "/opt/ml/output/profiler/"
}
}
```
------
**启用 Debugger 规则来分析指标**
以下示例代码显示了如何配置 `ProfilerReport` 规则。
```
"ProfilerRuleConfigurations": [
{
"RuleConfigurationName": "ProfilerReport",
"RuleEvaluatorImage": "{{895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest}}",
"RuleParameters": {
"rule_to_invoke": "ProfilerReport",
"CPUBottleneck_cpu_threshold": "{{90}}",
"IOBottleneck_threshold": "{{90}}"
}
}
]
```
要查找使用 Debugger 规则时可用的 Docker 映像的完整列表，请参阅[用于 Debugger 规则的 Docker 映像](debugger-reference.md#debugger-docker-images-rules)。要查找 `RuleParameters` 的键值对，请参阅 [Debugger 内置规则列表](debugger-built-in-rules.md)。
## 使用 `UpdateTrainingJob` API 更新 Debugger 剖析配置
在训练作业运行期间，可以使用 [UpdateTrainingJob](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_UpdateTrainingJob.html)API 操作更新调试器分析配置。配置新的[ProfilerConfig](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_ProfilerConfig.html)和[ProfilerRuleConfiguration](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_ProfilerRuleConfiguration.html)对象，并在`TrainingJobName`参数中指定训练作业名称。
```
{
"ProfilerConfig": {
"DisableProfiler": {{boolean}},
"ProfilingIntervalInMilliseconds": {{number}},
"ProfilingParameters": {
"{{string}}" : "{{string}}"
}
},
"ProfilerRuleConfigurations": [
{
"RuleConfigurationName": "{{string}}",
"RuleEvaluatorImage": "{{string}}",
"RuleParameters": {
"string" : "{{string}}"
}
}
],
"TrainingJobName": "{{your-training-job-name-YYYY-MM-DD-HH-MM-SS-SSS}}"
}
```
## 在 `CreateTrainingJob` API 中添加 Debugger 自定义规则配置
可以在 [ CreateTrainingJob](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateTrainingJob.html)API 操作中使用[ DebugHookConfig](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_DebugHookConfig.html)和[ DebugRuleConfiguration](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_DebugRuleConfiguration.html)对象为训练作业配置自定义规则。以下代码示例显示了如何使用此 SageMaker API 操作配置使用 *smdebug* 库编写的自定义`ImproperActivation`规则。此示例假定您已在 *custom\_rules.py* 文件中编写自定义规则，并将其上传到 Amazon S3 存储桶。该示例提供了预构建的 Docker 映像，您可以使用这些映像运行自定义规则。[URIs 适用于自定义规则评估者的 Amazon SageMaker 调试器图片](debugger-reference.md#debuger-custom-rule-registry-ids) 中列出了这些映像。您可以在 `RuleEvaluatorImage` 参数中为预构建的 Docker 映像指定 URL 注册表地址。
```
"DebugHookConfig": {
"S3OutputPath": "{{s3:////debug-output}}",
"CollectionConfigurations": [
{
"CollectionName": "{{relu_activations}}",
"CollectionParameters": {
"include_regex": "{{relu}}",
"save_interval": "{{500}}",
"end_step": "{{5000}}"
}
}
]
},
"DebugRulesConfigurations": [
{
"RuleConfigurationName": "{{improper_activation_job}}",
"RuleEvaluatorImage": "{{552407032007.dkr.ecr.ap-south-1.amazonaws.com/sagemaker-debugger-rule-evaluator:latest}}",
"InstanceType": "{{ml.c4.xlarge}}",
"VolumeSizeInGB": {{400}},
"RuleParameters": {
"source_s3_uri": "{{s3://bucket/custom_rules.py}}",
"rule_to_invoke": "{{ImproperActivation}}",
"collection_names": "{{relu_activations}}"
}
}
]
```
要查找使用 Debugger 规则时可用的 Docker 映像的完整列表，请参阅[用于 Debugger 规则的 Docker 映像](debugger-reference.md#debugger-docker-images-rules)。要查找 `RuleParameters` 的键值对，请参阅 [Debugger 内置规则列表](debugger-built-in-rules.md)。