quick start and integrations guides for the docs (#47)

Authors:
  - Corey J. Nolet (https://github.com/cjnolet)

Approvers:
  - Ben Frederickson (https://github.com/benfred)
  - Jake Awe (https://github.com/AyodeAwe)

URL: #47

.gitignore

@@ -61,6 +61,7 @@ _xml
 # sphinx
 _html
 _text
+docs/source/_static/rust
 
 # clang tooling
 compile_commands.json

README.md

@@ -11,10 +11,10 @@
 
 ## Useful Resources
 
-- [cuVS Reference Documentation](https://docs.rapids.ai/api/cuvs/stable/): API Documentation.
-- [cuVS Getting Started](./docs/source/quick_start.md): Getting started with RAFT.
-- [Build and Install cuVS](./docs/source/build.md): Instructions for installing and building cuVS.
-- [Example Notebooks](./notebooks): Example jupyer notebooks
+- [Code Examples](https://github.com/rapidsai/cuvs/tree/HEAD/examples): Self-contained Code Examples.
+- [API Reference Documentation](https://docs.rapids.ai/api/cuvs/nightly/api_docs): API Documentation.
+- [Getting Started Guide](https://docs.rapids.ai/api/cuvs/nightly/getting_started): Getting started with RAFT.
+- [Build and Install Guide](https://docs.rapids.ai/api/cuvs/nightly/build): Instructions for installing and building cuVS.
 - [RAPIDS Community](https://rapids.ai/community.html): Get help, contribute, and collaborate.
 - [GitHub repository](https://github.com/rapidsai/cuvs): Download the cuVS source code.
 - [Issue tracker](https://github.com/rapidsai/cuvs/issues): Report issues or request features.

docs/source/api_docs.rst

@@ -1,5 +1,5 @@
-API Documentation
-=================
+API Reference
+=============
 
 .. toctree::
    :maxdepth: 1

docs/source/basics.rst

@@ -0,0 +1,90 @@
+cuVS API Basics
+===============
+
+- `Memory management`_
+- `Resource management`_
+
+Memory management
+-----------------
+
+Centralized memory management allows flexible configuration of allocation strategies, such as sharing the same CUDA memory pool across library boundaries. cuVS uses the [RMM](https://github.com/rapidsai/rmm) library, which eases the burden of configuring different allocation strategies globally across GPU-accelerated libraries.
+
+RMM currently has APIs for C++ and Python.
+
+C++
+^^^
+
+Here's an example of configuring RMM to use a pool allocator in C++ (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#example>`_):
+
+.. code-block:: c++
+
+    rmm::mr::cuda_memory_resource cuda_mr;
+    // Construct a resource that uses a coalescing best-fit pool allocator
+    // With the pool initially half of available device memory
+    auto initial_size = rmm::percent_of_free_device_memory(50);
+    rmm::mr::pool_memory_resource<rmm::mr::cuda_memory_resource> pool_mr{&cuda_mr, initial_size};
+    rmm::mr::set_current_device_resource(&pool_mr); // Updates the current device resource pointer to `pool_mr`
+    rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource(); // Points to `pool_mr`
+
+Python
+^^^^^^
+
+And the corresponding code in Python (derived from the RMM example `here <https://github.com/rapidsai/rmm?tab=readme-ov-file#memoryresource-objects>`_):
+
+.. code-block:: python
+
+    import rmm
+    pool = rmm.mr.PoolMemoryResource(
+      rmm.mr.CudaMemoryResource(),
+      initial_pool_size=2**30,
+      maximum_pool_size=2**32)
+    rmm.mr.set_current_device_resource(pool)
+
+
+Resource management
+-------------------
+
+cuVS uses an API from the `RAFT <https://github.com/rapidsai/raft>`_ library of ML and data mining primitives to centralize and reuse expensive resources, such as memory management. The below code examples demonstrate how to create these resources for use throughout this guide.
+
+See RAFT's `resource API documentation <https://docs.rapids.ai/api/raft/nightly/cpp_api/core_resources/>`_ for more information.
+
+C
+^
+
+.. code-block:: c
+
+    #include <cuda_runtime.h>
+    #include <cuvs/core/c_api.h>
+
+    cuvsResources_t res;
+    cuvsResourcesCreate(&res);
+
+    // ... do some processing ...
+
+    cuvsResourcesDestroy(res);
+
+C++
+^^^
+
+.. code-block:: c++
+
+    #include <raft/core/device_resources.hpp>
+
+    raft::device_resources res;
+
+Python
+^^^^^^
+
+.. code-block:: python
+
+    import pylibraft
+
+    res = pylibraft.common.DeviceResources()
+
+
+Rust
+^^^^
+
+.. code-block:: rust
+
+    let res = cuvs::Resources::new()?;