diff --git a/doc/overview.rst b/doc/overview.rst index 244f0b0b68..319560b5ff 100644 --- a/doc/overview.rst +++ b/doc/overview.rst @@ -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 `_. +For a sample notebook that demonstrates using ``ModelBuilder`` to build a XGBoost model, see `XGBoost example `_. **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. @@ -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 `_. +For a sample notebook that demonstrates using ``ModelBuilder`` to build a Triton model, see `Triton example `_. **Hugging Face models**. @@ -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 `_. +For a sample notebook that demonstrates using ``ModelBuilder`` to build a Hugging Face model, see `Hugging Face example `_. Deploy foundation models to SageMaker Endpoints @@ -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 `_. +For a sample notebook that demonstrates using ``ModelBuilder`` to build a Hugging Face Hub model, see `Hugging Face Hub example `_. **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: @@ -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 `_. -For a sample notebook that demonstrates using ``ModelBuilder`` to build a JumpStart model, see `JumpStart example `_. +For a sample notebook that demonstrates using ``ModelBuilder`` to build a JumpStart model, see `JumpStart example `_. 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 `_ + * `Pytorch example `__ - * `XGBoost example `_ + * `XGBoost example `__ - * `Triton example `_ + * `Triton example `__ - * `Hugging Face example `_ + * `Hugging Face example `__ - * `Hugging Face Hub example `_ + * `Hugging Face Hub example `__ - * `JumpStart example `_ + * `JumpStart example `__ Fine-tune a Model and Deploy to a SageMaker Endpoint