doc: Add ECR URI Estimator example - #1688

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Added example of creating an Estimator using an ECR URI

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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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@laurenyulaurenyu changed the title [docs] Added ECR URI Estimator exampledoc: ADd ECR URI Estimator exampleJul 8, 2020
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Comment threaddoc/frameworks/tensorflow/using_tf.rst Outdated
Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers

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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.

Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.

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Update using_tf.rst
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removed trailing whitespace
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@laurenyulaurenyu changed the title doc: ADd ECR URI Estimator exampledoc: Add ECR URI Estimator exampleJul 8, 2020
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
Comment threaddoc/frameworks/tensorflow/using_tf.rst Outdated
Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.

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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.

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Comment threaddoc/frameworks/tensorflow/using_tf.rst Outdated
Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.

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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?

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I'm restructuring those docs to make them more explicitly about Docker containers.

Comment threaddoc/frameworks/tensorflow/using_tf.rst Outdated
Comment threaddoc/frameworks/tensorflow/using_tf.rst Outdated
- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:

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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.

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Fixed

Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Update using_tf.rst
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@laurenyu Made fixed to address your feedback

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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?

Comment threaddoc/frameworks/tensorflow/using_tf.rst Outdated
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Update doc/frameworks/tensorflow/using_tf.rst
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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laurenyu merged commit 6cdc977 into aws:masterJul 21, 2020
Evan-W-ang added a commit to Evan-W-ang/sagemaker-python-sdk that referenced this pull request Jun 8, 2026
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doc: Add ECR URI Estimator example (aws#1688)
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Added example of creating an Estimator using an ECR URI
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By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
@laurenyulaurenyuJul 8, 2020

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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@laurenyu Made fixed to address your feedback
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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doc: Add ECR URI Estimator example (aws#1688)
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Added example of creating an Estimator using an ECR URI
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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doc: ADd ECR URI Estimator exampledoc: Add ECR URI Estimator exampleJul 8, 2020
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@laurenyu Made fixed to address your feedback
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
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Update doc/frameworks/tensorflow/using_tf.rst
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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doc: Add ECR URI Estimator example (aws#1688)
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Added example of creating an Estimator using an ECR URI
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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[docs] Added ECR URI Estimator exampledoc: ADd ECR URI Estimator exampleJul 8, 2020
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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doc: ADd ECR URI Estimator exampledoc: Add ECR URI Estimator exampleJul 8, 2020
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@laurenyu Made fixed to address your feedback
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
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Update doc/frameworks/tensorflow/using_tf.rst
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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doc: Add ECR URI Estimator example (aws#1688)
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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doc: Add ECR URI Estimator example - #1688
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Added example of creating an Estimator using an ECR URI
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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Update using_tf.rst
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removed trailing whitespace
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doc: ADd ECR URI Estimator exampledoc: Add ECR URI Estimator exampleJul 8, 2020
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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doc: Add ECR URI Estimator example (aws#1688)
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Added example of creating an Estimator using an ECR URI
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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[docs] Added ECR URI Estimator exampledoc: ADd ECR URI Estimator exampleJul 8, 2020
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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Update using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Suggested change

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@laurenyu Made fixed to address your feedback
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
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Update doc/frameworks/tensorflow/using_tf.rst
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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doc: Add ECR URI Estimator example (aws#1688)
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
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var __m = "*";
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doc: Add ECR URI Estimator example - #1688
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Added example of creating an Estimator using an ECR URI
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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Update using_tf.rst
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removed trailing whitespace
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@laurenyu Made fixed to address your feedback
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Update doc/frameworks/tensorflow/using_tf.rst
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Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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doc: Add ECR URI Estimator example (aws#1688)
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doc: Add ECR URI Estimator example - #1688
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Added example of creating an Estimator using an ECR URI
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Update using_tf.rst
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Added example of creating an Estimator using an ECR URI
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[docs] Added ECR URI Estimator exampledoc: ADd ECR URI Estimator exampleJul 8, 2020
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Where the S3 url is a path to your training data within Amazon S3.
The constructor keyword arguments define how SageMaker runs your training script.

Create an Estimator using Docker containers
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I think this sounds a bit misleading - the estimator isn't being created by way of somehow running a Docker container - this is just describing how to specify a custom Docker image with an estimator.
Also, what's the motivation for putting this in the TF doc as opposed to a more general section? The information included in this change is more broad than TF.
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Update using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
You can also specify a Docker image when creating an Estimator by directly specifying the ECR URI instead of the Python and framework version. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Comment threaddoc/frameworks/tensorflow/using_tf.rst
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Specify a Docker image using an Estimator
-----------------------------------------

You can also specify a Docker image when creating an Estimator by specifying the ECR URI for the Python and framework version directly. For a full list of available container URIs, see `Available Deep Learning Containers Images <https://github.com/aws/deep-learning-containers/blob/master/available_images.md>`__ For more information on using Docker containers, see `Use Your Own Algorithms or Models with Amazon SageMaker <https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html>`__.
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Half of the use cases in Use Your Own Algorithms or Models with Amazon SageMaker (the first two) don't require the user to have to do anything themselves with Docker. Docker Container Basics seems like it's probably more about using Docker containers?
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I'm restructuring those docs to make them more explicitly about Docker containers.
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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- ``py_version=''``
- ``framework_version=''``

The following example uses the ``image_name='763104351884.dkr.ecr.us-east-1.amazonaws.com/tensorflow-training:1.15.2-cpu-py37-ubuntu18.04'`` arg to specify the container image, Python version, and framework version:
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I think it would be better to put an obviously fake placeholder image URI and direct to the doc page with the updated URIs. While the example URI is the latest TF 1.x version right now, it's less likely that this will get updated as future versions of TF are released.
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Fixed
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@laurenyu Made fixed to address your feedback
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just one small comment for lining up the args. I assume similar changes for inference and other frameworks will come in separate PRs?
Comment threaddoc/frameworks/tensorflow/using_tf.rst
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@IvyBazan@laurenyu
Update doc/frameworks/tensorflow/using_tf.rst
6cbf3d7
Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>
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@laurenyu
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merged commit 6cdc977
into
aws:masterJul 21, 2020
Evan-W-ang added a commit
to Evan-W-ang/sagemaker-python-sdk
that referenced
this pull request
Jun 8, 2026
@Evan-W-ang
doc: Add ECR URI Estimator example (aws#1688)
69a9f8a
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@IvyBazan@sagemaker-bot@laurenyu@chuyang-deng