英文:
Sagemaker batch transform job - Input data location
问题
我正在使用AWS Sagemaker中的自定义算法，并使用boto3的"create_training_job" API来训练模型。训练数据的"S3Uri"位置被传递给此API。然后，Sagemaker将此数据复制到docker镜像内部的文件夹"/opt/ml/input/data/"，我已根据此位置配置了训练逻辑 - /opt/ml/input/data/。
现在我需要使用"create_transform_job" API来生成推断。我将推断数据集的"S3Uri"传递给此API。推断数据集将被复制到docker的哪个位置？这对于配置代码中的位置是必需的。
英文:
I am using custom algorithm in AWS Sagemaker and used boto3 "create_training_job" API to train the model. The "S3Uri" location of the training data is passed to this API. Sagemaker in turn copies this data to the folder "/opt/ml/input/data/" inside the docker image and I have configured the training logic based on this location - /opt/ml/input/data/.
Now I need to generate inference using "create_transform_job" API. I am passing the "S3Uri" of the inference dataset, to this API. To which location inside the docker, will this inference dataset be copied over to? This is required for configuring the location in the code.
答案1
得分: 2
在运行SageMaker批量转换时，输入数据不会保存到计算实例的磁盘上。相反，数据通过HTTP请求流式传输到您的容器中。
有关批量转换功能的详细信息，请参阅：https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-batch.html
有关在运行批量转换作业时您的容器应如何提供请求的详细信息，请参阅：https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests
英文:
When running SageMaker Batch Transform, the input is not persisted to disk on the compute instance. Instead, the data is streamed to your container via HTTP requests.
For more details on how the Batch Transform feature works, see: https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-batch.html
For more details on how your container should serve requests when running Batch Transform jobs, see: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-batch-code.html#your-algorithms-batch-code-how-containe-serves-requests
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