Fork CUDA and ROCm guides into separate pages. (#15196)

Fixes #15115

These pages are very similar, but both stacks have undergone enough
development recently that they can now stand on their own. This PR
primarily forks the pages, but it also does some minor cleanup (fixing
dead links, adjusting formatting).

|  | |
|--------|--------|
| Current page |
https://www.iree.dev/guides/deployment-configurations/gpu-cuda-rocm/ |
| Preview of this PR - CUDA |
https://scotttodd.github.io/iree/guides/deployment-configurations/gpu-cuda
|
| Preview of this PR - ROCm |
https://scotttodd.github.io/iree/guides/deployment-configurations/gpu-rocm
|

docs/website/docs/guides/deployment-configurations/gpu-cuda.md

@@ -0,0 +1,143 @@
+---
+hide:
+  - tags
+tags:
+  - GPU
+  - CUDA
+icon: simple/nvidia
+---
+
+# GPU deployment using CUDA
+
+IREE can accelerate model execution on Nvidia GPUs using
+[CUDA](https://developer.nvidia.com/cuda-toolkit).
+
+## :octicons-download-16: Prerequisites
+
+In order to use CUDA to drive the GPU, you need to have a functional CUDA
+environment. It can be verified by the following steps:
+
+``` shell
+nvidia-smi | grep CUDA
+```
+
+If `nvidia-smi` does not exist, you will need to
+[install the latest CUDA Toolkit SDK](https://developer.nvidia.com/cuda-downloads).
+
+### Get the IREE compiler
+
+#### :octicons-package-16: Download the compiler from a release
+
+Python packages are regularly published to
+[PyPI](https://pypi.org/user/google-iree-pypi-deploy/). See the
+[Python Bindings](../../reference/bindings/python.md) page for more details.
+The core `iree-compiler` package includes the CUDA compiler:
+
+=== "Stable releases"
+
+    Stable release packages are
+    [published to PyPI](https://pypi.org/user/google-iree-pypi-deploy/).
+
+    ``` shell
+    python -m pip install iree-compiler
+    ```
+
+=== ":material-alert: Nightly releases"
+
+    Nightly releases are published on
+    [GitHub releases](https://github.com/openxla/iree/releases).
+
+    ``` shell
+    python -m pip install \
+      --find-links https://openxla.github.io/iree/pip-release-links.html \
+      --upgrade iree-compiler
+    ```
+
+!!! tip
+    `iree-compile` is installed to your python module installation path. If you
+    pip install with the user mode, it is under `${HOME}/.local/bin`, or
+    `%APPDATA%Python` on Windows. You may want to include the path in your
+    system's `PATH` environment variable:
+
+    ```shell
+    export PATH=${HOME}/.local/bin:${PATH}
+    ```
+
+#### :material-hammer-wrench: Build the compiler from source
+
+Please make sure you have followed the
+[Getting started](../../building-from-source/getting-started.md) page to build
+the IREE compiler, then enable the CUDA compiler target with the
+`IREE_TARGET_BACKEND_CUDA` option.
+
+!!! tip
+    `iree-compile` will be built under the `iree-build/tools/` directory. You
+    may want to include this path in your system's `PATH` environment variable.
+
+### Get the IREE runtime
+
+Next you will need to get an IREE runtime that includes the CUDA HAL driver.
+
+#### :material-hammer-wrench: Build the runtime from source
+
+Please make sure you have followed the
+[Getting started](../../building-from-source/getting-started.md) page to build
+IREE from source, then enable the CUDA HAL driver with the
+`IREE_HAL_DRIVER_CUDA` option.
+
+## Compile and run a program model
+
+With the compiler and runtime ready, we can now compile programs and run them
+on GPUs.
+
+### :octicons-file-code-16: Compile a program
+
+The IREE compiler transforms a model into its final deployable format in many
+sequential steps. A model authored with Python in an ML framework should use the
+corresponding framework's import tool to convert into a format (i.e.,
+[MLIR](https://mlir.llvm.org/)) expected by the IREE compiler first.
+
+Using MobileNet v2 as an example, you can download the SavedModel with trained
+weights from
+[TensorFlow Hub](https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification)
+and convert it using IREE's
+[TensorFlow importer](../ml-frameworks/tensorflow.md). Then run one of the
+following commands to compile:
+
+```shell hl_lines="2-3"
+iree-compile \
+    --iree-hal-target-backends=cuda \
+    --iree-hal-cuda-llvm-target-arch=<...> \
+    mobilenet_iree_input.mlir -o mobilenet_cuda.vmfb
+```
+
+Note that a cuda target architecture (`iree-hal-cuda-llvm-target-arch`) of
+the form `sm_<arch_number>` is needed to compile towards each GPU
+architecture. If no architecture is specified then we will default to
+`sm_35`.
+
+Here is a table of commonly used architectures:
+
+| CUDA GPU    | Target Architecture |
+| ----------- | ------------------- |
+| Nvidia K80  | `sm_35`             |
+| Nvidia P100 | `sm_60`             |
+| Nvidia V100 | `sm_70`             |
+| Nvidia A100 | `sm_80`             |
+
+### :octicons-terminal-16: Run a compiled program
+
+Run the following command:
+
+``` shell hl_lines="2"
+iree-run-module \
+    --device=cuda \
+    --module=mobilenet_cuda.vmfb \
+    --function=predict \
+    --input="1x224x224x3xf32=0"
+```
+
+The above assumes the exported function in the model is named as `predict` and
+it expects one 224x224 RGB image. We are feeding in an image with all 0 values
+here for brevity, see `iree-run-module --help` for the format to specify
+concrete values.