AWS SageMaker - 在本地训练但部署到 AWS? [英] AWS SageMaker - training locally but deploying to AWS?

问题描述

我在使用 SageMaker 时遇到了以下挑战:

I have a the following challenge with SageMaker:

• 我下载了其中一本教程笔记本(https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb)

• 我通过修改以下行在本地(成功)运行了培训:

• I've downloaded one of the tutorial notebooks (https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/tensorflow_abalone_age_predictor_using_keras.ipynb)

• I ran the training locally (successfully) with the modifying the following line:

abalone_estimator = TensorFlow(entry_point='abalone.py',
                       role=role,
                       training_steps= 100,
                       evaluation_steps= 100,                                 
                       hyperparameters={'learning_rate': 0.001},
                       train_instance_count=1,
                       **train_instance_type='local'**)

abalone_estimator.fit(inputs)

然后我想使用以下行将我的模型部署到 AWS，但 SDK 似乎是在本地部署它(它没有失败，我只是看到它在我的机器上运行)

I then wanted to deploy my model to AWS with the following line but it seems the SDK deploys it locally (it doesn't fail, I just see it running on my machine)

abalone_predictor = abalone_estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')

有关如何修复它以便将其部署到 AWS 或重新加载我的训练模型并从头开始部署到 AWS 的任何提示?

Any tips on how to either fix it so it gets deployed to AWS or alternatively re-load my training model and deploy it to AWS from scratch?

非常感谢，斯蒂芬

推荐答案

在 SageMaker 上再次运行培训更容易.否则，以下是您必须执行的步骤.

Its easier to run the training again on SageMaker. Otherwise, here are the steps that you would have to do.

1. 将训练期间生成的检查点文件转换为 tensorflow 服务模型.
2. 以特定格式压缩它们并上传到 S3
3. 然后像上面所做的那样创建估算器并进行推理.

如果您想了解上述每个具体步骤的详细信息，请告诉我，但如果您的数据集不是太大，我会说只需在 SageMaker 上重新训练即可.

If you want details on each of the specific steps above do let me know, but if your dataset is not too big, I would say just retrain on SageMaker.

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