Skip to content

documentation: fix ModelBuilder sample notebook links #4319

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.

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
Dec 19, 2023
Merged
Changes from all commits
Commits
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
22 changes: 11 additions & 11 deletions doc/overview.rst
Original file line number Diff line number Diff line change
Expand Up @@ -1034,7 +1034,7 @@ You can deploy the XGBoost model from the previous example to a SageMaker endpoi
initial_instance_count=1
)

For a sample notebook that demonstrates using ``ModelBuilder`` to build a XGBoost model, see `XGBoost example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-xgboost.ipynb>`_.
For a sample notebook that demonstrates using ``ModelBuilder`` to build a XGBoost model, see `XGBoost example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-xgboost.ipynb>`_.

**Triton models**.
You can use ``ModelBuilder`` to serve PyTorch models on a Triton inference server. Specify the ``model_server`` parameter as ``ModelServer.TRITON``, pass a model, and include a ``SchemaBuilder`` object which requires sample inputs and outputs from the model. The following snippet shows an example.
Expand All @@ -1056,7 +1056,7 @@ You can use ``ModelBuilder`` to serve PyTorch models on a Triton inference serve
initial_instance_count=1
)

For a sample notebook that demonstrates using ``ModelBuilder`` to build a Triton model, see `Triton example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-triton.ipynb>`_.
For a sample notebook that demonstrates using ``ModelBuilder`` to build a Triton model, see `Triton example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-triton.ipynb>`_.


**Hugging Face models**.
Expand Down Expand Up @@ -1101,7 +1101,7 @@ Create the ``ModelBuilder`` object and deploy the model onto a SageMaker endpoin
instance_type='ml.g5.2xlarge'
)

For a sample notebook that demonstrates using ``ModelBuilder`` to build a Hugging Face model, see `Hugging Face example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-huggingface.ipynb>`_.
For a sample notebook that demonstrates using ``ModelBuilder`` to build a Hugging Face model, see `Hugging Face example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-huggingface.ipynb>`_.


Deploy foundation models to SageMaker Endpoints
Expand Down Expand Up @@ -1135,7 +1135,7 @@ For gated models on Hugging Face Hub, request access and pass the associated key

A feature of ``ModelBuilder`` is the ability to run local tuning on the container when you use `LOCAL_CONTAINER` mode. In this case ``ModelBuilder`` tunes the parameter(s) for the underlying model server. This feature can be used by executing `tuned_model=model.tune()`. Before running `tune`, clean up other containers running locally or else you might see an "address already in use" error.

For a sample notebook that demonstrates using ``ModelBuilder`` to build a Hugging Face Hub model, see `Hugging Face Hub example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/model-builder-huggingface-llama2.ipynb>`_.
For a sample notebook that demonstrates using ``ModelBuilder`` to build a Hugging Face Hub model, see `Hugging Face Hub example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/model-builder-huggingface-llama2.ipynb>`_.


**JumpStart**. JumpStart also offers a number of pre-trained foundation models. Again, the model ID is required. Deploying a JumpStart model to a SageMaker endpoint is straightforward, as shown in the following example:
Expand All @@ -1154,24 +1154,24 @@ For a sample notebook that demonstrates using ``ModelBuilder`` to build a Huggin

For a list of available JumpStart model IDs, see `Built-in Algorithms with pre-trained Model Table <https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html>`_.

For a sample notebook that demonstrates using ``ModelBuilder`` to build a JumpStart model, see `JumpStart example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/foundation-models/model-builder-jumpstart-falcon.ipynb>`_.
For a sample notebook that demonstrates using ``ModelBuilder`` to build a JumpStart model, see `JumpStart example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/foundation-models/model-builder-jumpstart-falcon.ipynb>`_.

ModelBuilder examples
^^^^^^^^^^^^^^^^^^^^^

For example notebooks that demonstrate the use of ``ModelBuilder`` and its supporting classes, as well as model creation of traditional and foundation models, see the following links:

* `Pytorch example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-pytorch.ipynb>`_
* `Pytorch example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-pytorch.ipynb>`__

* `XGBoost example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-xgboost.ipynb>`_
* `XGBoost example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-xgboost.ipynb>`__

* `Triton example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-triton.ipynb>`_
* `Triton example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-triton.ipynb>`__

* `Hugging Face example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/traditional-models/model-builder-huggingface.ipynb>`_
* `Hugging Face example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/traditional-models/model-builder-huggingface.ipynb>`__

* `Hugging Face Hub example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/model-builder-huggingface-llama2.ipynb>`_
* `Hugging Face Hub example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/foundation-models/model-builder-huggingface-llama2.ipynb>`__

* `JumpStart example <https://github.com/aws-samples/sagemaker-hosting/SageMaker-Model-Builder/foundation-models/model-builder-jumpstart-falcon.ipynb>`_
* `JumpStart example <https://github.com/aws-samples/sagemaker-hosting/blob/main/SageMaker-Model-Builder/foundation-models/model-builder-jumpstart-falcon.ipynb>`__


Fine-tune a Model and Deploy to a SageMaker Endpoint
Expand Down