Skip to content

doc: Fixed typo with SM_MODEL_DIR, missing quotes #1287

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 12, 2020
Merged

doc: Fixed typo with SM_MODEL_DIR, missing quotes #1287

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
12e3357
doc: Fixed typo with SM_MODEL_DIR, missing quotes
ssmall41 Feb 12, 2020
89322b9
Merge branch 'master' into patch-1
laurenyu Feb 12, 2020
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
6 changes: 3 additions & 3 deletions doc/using_tf.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,7 @@ The training script is very similar to a training script you might run outside o

For the exhaustive list of available environment variables, see the `SageMaker Containers documentation <https://github.com/aws/sagemaker-containers#list-of-provided-environment-variables-by-sagemaker-containers>`_.

A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to ``SM_CHANNEL_TRAIN`` so that it can be deployed for inference later.
A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to ``SM_MODEL_DIR`` so that it can be deployed for inference later.
Hyperparameters are passed to your script as arguments and can be retrieved with an ``argparse.ArgumentParser`` instance.
For example, a training script might start with the following:

Expand Down Expand Up @@ -544,7 +544,7 @@ For example:

batch_output = 's3://{}/{}/results'.format(bucket, prefix) # The location to store the results

tf_transformer = tf_estimator.transformer(instance_count=1, instance_type='ml.m4.xlarge, output_path=batch_output)
tf_transformer = tf_estimator.transformer(instance_count=1, instance_type='ml.m4.xlarge', output_path=batch_output)

To use a model trained outside of SageMaker, you can package the model as a SageMaker model, and call the ``transformer`` method of the SageMaker model.

Expand All @@ -557,7 +557,7 @@ For example:

batch_output = 's3://{}/{}/results'.format(bucket, prefix) # The location to store the results

tf_transformer = tensorflow_serving_model.transformer(instance_count=1, instance_type='ml.m4.xlarge, output_path=batch_output)
tf_transformer = tensorflow_serving_model.transformer(instance_count=1, instance_type='ml.m4.xlarge', output_path=batch_output)

For information about how to package a model as a SageMaker model, see :ref:`overview:BYO Model`.
When you call the ``tranformer`` method, you specify the type and number of instances to use for the batch transform job, and the location where the results are stored in S3.
Expand Down