Skip to content

documentation: update doc for Elastic Inference MXNet 1.7.0 #2168

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Feb 22, 2021
Merged

documentation: update doc for Elastic Inference MXNet 1.7.0 #2168

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
26b502a
documentation: update doc for Elastic Inference MXNet 1.7.0
Feb 20, 2021
8bfd620
Merge branch 'master' into eimx-docs
ahsan-z-khan Feb 22, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 5 additions & 4 deletions doc/frameworks/mxnet/using_mxnet.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -377,7 +377,7 @@ It loads the model parameters from a ``model.params`` file in the SageMaker mode
return net

MXNet on Amazon SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`__, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance.
In order to load and serve your MXNet model through Amazon Elastic Inference, the MXNet context passed to your MXNet Symbol or Module object within your ``model_fn`` needs to be set to ``eia``, as shown `here <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html#ei-mxnet>`__.
In order to load and serve your MXNet model through Amazon Elastic Inference, import the ``eimx`` Python package and make one change in the code to partition your model and optimize it for the ``EIA`` back end, as shown `here <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html#ei-mxnet>`__.

Based on the example above, the following code-snippet shows an example custom ``model_fn`` implementation, which enables loading and serving our MXNet model through Amazon Elastic Inference.

Expand All @@ -392,11 +392,12 @@ Based on the example above, the following code-snippet shows an example custom `
Returns:
mxnet.gluon.nn.Block: a Gluon network (for this example)
"""
net = models.get_model('resnet34_v2', ctx=mx.eia(), pretrained=False, classes=10)
net.load_params('%s/model.params' % model_dir, ctx=mx.eia())
net = models.get_model('resnet34_v2', ctx=mx.cpu(), pretrained=False, classes=10)
net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
net.hybridize(backend='EIA', static_alloc=True, static_shape=True)
return net

The `default_model_fn <https://github.com/aws/sagemaker-mxnet-container/pull/55/files#diff-aabf018d906ed282a3c738377d19a8deR71>`__ loads and serve your model through Elastic Inference, if applicable, within the Amazon SageMaker MXNet containers.
If you are using MXNet 1.5.1 and earlier, the `default_model_fn <https://github.com/aws/sagemaker-mxnet-container/pull/55/files#diff-aabf018d906ed282a3c738377d19a8deR71>`__ loads and serve your model through Elastic Inference, if applicable, within the Amazon SageMaker MXNet containers.

For more information on how to enable MXNet to interact with Amazon Elastic Inference, see `Use Elastic Inference with MXNet <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html>`__.

Expand Down