edited general repo readme #351
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README.rst
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| @@ -36,16 +39,16 @@ Table of Contents | |||
| 11. `BYO Model <#byo-model>`__ | |||
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| Getting SageMaker Python SDK | |||
| Installing SageMaker Python SDK | |||
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should it be "the SageMaker Python SDK"? (I'm sure we're inconsistent throughout our docs about it...)
README.rst
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| pip install sagemaker | ||
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| You can install from source by cloning this repository and issuing a pip install command in the root directory of the repository. | ||
| You can install from the source by cloning this repository and running a pip install command in the root directory of the repository: |
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I think "from source" is an idiom here
README.rst
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| SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis(PCA), Linear Learner, Factorization Machines, Latent Dirichlet Allocation(LDA), Neural Topic Model(NTM) Random Cut Forest and k-nearest neighbors (k-NN) algorithms. | ||
| SageMaker Python SDK includes estimator wrappers for the AWS K-means, Principal Components Analysis(PCA), Linear Learner, Factorization Machines, | ||
| Latent Dirichlet Allocation(LDA), Neural Topic Model(NTM) Random Cut Forest and k-nearest neighbors (k-NN) algorithms. |
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should there be spaces before "(PCA)", "(LDA)", and "(NTM)"?
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| .. _AWS SageMaker Estimators and Models: src/sagemaker/amazon/README.rst | ||
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| BYO Docker Containers with SageMaker Estimators | ||
| ----------------------------------------------- | ||
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| When you want to use a Docker image prepared earlier and use SageMaker SDK for training the easiest way is to use dedicated ``Estimator`` class. You will be able to instantiate it with desired image and use it in same way as described in previous sections. | ||
| To use a Docker image that you created and use the SageMaker SDK for training, the easiest way is to use the dedicated ``Estimator`` class. | ||
| You can create an instance of the ``Estimator`` class with desired docker image and use it as described in previous sections. |
README.rst
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| This example shows a hyperparameter tuning job that creates up to 100 training jobs, running up to 10 at a time. | ||
| Each training job's learning rate will be a value between 0.05 and 0.06, but this value will differ between training jobs. | ||
| This example shows a hyperparameter tuning job that creates up to 100 training jobs, running up to 10 training jobs at a time. | ||
| Each training job's learning rate will is a value between 0.05 and 0.06, but this value will differ between training jobs. |
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remove "will" from "will is"
README.rst
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| @@ -511,4 +521,4 @@ After that, invoke the ``deploy()`` method on the ``Model``: | |||
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| This returns a predictor the same way an ``Estimator`` does when ``deploy()`` is called. You can now get inferences just like with any other model deployed on Amazon SageMaker. | |||
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| A full example is available in the `Amazon SageMaker examples repository <https://github.com/ragavvenkatesan/amazon-sagemaker-examples/tree/3c8394f21ee357da0b553b0ab024c5c5e425182a/advanced_functionality/mxnet_mnist_byom>`__. | |||
| A full example is available in the `Amazon SageMaker examples repository <https://github.com/ragavvenkatesan/amazon-sagemaker-examples/tree/3c8394f21ee357da0b553b0ab024c5c5e425182a/advanced_functionality/mxnet_mnist_byom>`__. | |||
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this link is to a fork of the examples repo - probably should link it to the real one?
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nadiaya
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Aug 15, 2018
laurenyu
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Added short note about validation channel
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Description of changes:
Edited the readme for clarity/style.
Please review, esp hyperparameter tuning and batch transform sections for technical accuracy.
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