自定义 SageMaker 算法如何确定是否启用了检查点？

Question

【以下的问题经过翻译处理】 根据[SageMaker环境变量](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-container-environmental-variables-user-scripts.html)文档，算法应该将模型产物保存到由`SM_MODEL_DIR` 变量指定的文件夹中。

[SageMaker容器文档](https://github.com/aws/sagemaker-containers)描述了额外的环境变量，包括`SM_OUTPUT_DATA_DIR`用于写入非模型训练产物。

...但是算法如何确定是否已请求存储检查点？

[在Amazon SageMaker中使用检查点](https://docs.aws.amazon.com/sagemaker/latest/dg/model-checkpoints.html)文档只指定了一个默认本地保存路径，我找不到任何环境变量可以指示是否要保存检查点。我看到有一段代码检查默认本地路径是否存在，但我并不确定它是否有效（请求检查点时存在，不请求时不存在）。

将检查点参数化是件好事，可以避免在不需要的时候浪费EBS空间（和宝贵的IOPS）；根据其他I/O（如模型和数据文件夹）的惯例，我认为SageMaker有特定的机制来传递这个指令，而不仅仅是定义一个算法超参数？

Answer

【以下的回答经过翻译处理】 您好，

对于自定义 Sagemaker 容器或深度学习框架，我倾向于这样做。以下是我尝试过的pytorch的例子

- Entry point 文件:
```
# 1. Define a custom argument, say checkpointdir
 parser.add_argument("--checkpointdir", help="The checkpoint dir", type=str,
                     default=None)
# 2. You can additional params for checkpoint frequency etc

# 3. Code for checkpointing
if checkpointdir is not None:
   #TODO: save mode
 ```

- Jupyter 笔记本示例 Sagemaker estimator

```
# 1. Define local and remote variables for checkpoints
checkpoint_s3 = "s3://{}/{}t/".format(bucket, "checkpoints")
localcheckpoint_dir="/opt/ml/checkpoints/"

hyperparameters = {

"batchsize": "8",
    "epochs" : "1000",
    "learning_rate":.0001,
    "weight_decay":5e-5,
    "momentum":.9,
    "patience": 20,
    "log-level" : "INFO",
    "commit_id":commit_id,
    "model" :"FasterRcnnFactory",
    "accumulation_steps": 8,
# 2.  define hp for checkpoint dir
    "checkpointdir": localcheckpoint_dir
}

# In the Sagemaker estimator fit, specify the local and remote path
from sagemaker.pytorch import PyTorch

estimator = PyTorch(
     entry_point='experiment_train.py',
                    source_dir = 'src',
                    dependencies =['src/datasets', 'src/evaluators', 'src/models'],
                    role=role,
                    framework_version ="1.0.0",
                    py_version='py3',
                    git_config= git_config,
                    image_name= docker_repo,
                    train_instance_count=1,
                    train_instance_type=instance_type,
# 3. The entrypoint file will pick up the checkpoint location from here
                    hyperparameters =hyperparameters,
                    output_path=s3_output_path,
                    metric_definitions=metric_definitions,
                    train_use_spot_instances = use_spot,
                    train_max_run =  train_max_run_secs,
                    train_max_wait = max_wait_time_secs,   
                    base_job_name ="object-detection",
# 4. Sagemaker knows that the checkpoints will need to be periodically copied from the localcheckpoint_dir to s3 pointed to by checkpoint_s3
                    checkpoint_s3_uri=checkpoint_s3,
                    checkpoint_local_path=localcheckpoint_dir)
```

自定义 SageMaker 算法如何确定是否启用了检查点？

Relevanter Inhalt