docs tweaks

Author: jjcmoon

README.md

@@ -5,11 +5,11 @@ _KLay is a Python library for evaluating sparse arithmetic circuits on the GPU._
 To get started, install KLay using pip and check out [the documentation](https://klaycircuits.readthedocs.io/en/latest/).
 
 ```bash
-pip install git+https://github.com/ML-KULeuven/klay
+pip install klaycircuits
 ```
 
 Features include:
-- Evaluation in Jax and PyTorch, on CPU and GPU.
+- Evaluation in Jax or PyTorch, on CPU or GPU.
 - Loading SDD and d-DNNF circuits compiled by PySDD or D4.
 - Evaluation in various semirings (e.g. real, log, tropical).
 - Propagating constants and merging duplicate nodes.

docs/index.rst

@@ -1,18 +1,17 @@
-.. KLay documentation master file, created by
-   sphinx-quickstart on Fri Mar 14 13:16:49 2025.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
 KLay Documentation
 ==================
 
-KLay is a Python library for accelerating inference on sparse arithmetic circuits.
+`KLay is a Python library for accelerating inference on sparse arithmetic circuits.`
+
+Get started using the :ref:`quickstart`!
+
+.. image:: _static/scatter_reduce.png
+  :width: 350
+  :align: center
 
 At its core, KLay transforms sparse directed acyclic graphs into layers that can be executed in parallel.
 The design of KLay is described in our paper `KLay: Accelerating Arithmetic Circuits for Neurosymbolic AI <https://arxiv.org/pdf/2410.11415>`_, published at ICLR 2025.
 
-.. image:: _static/scatter_reduce.png
-  :width: 400
 
 ----
 

docs/quickstart.rst

@@ -1,3 +1,5 @@
+.. _quickstart:
+
 Quick Start Guide
 =================
 
@@ -9,9 +11,9 @@ KLay supports Linux, Mac and Windows. Make sure you have installed Python, and i
 
 >>> pip install klaycircuits
 
-To build the latest version of KLay from source, download the repo and run:
+To install the latest development version of KLay, you can install from the Github repo.
 
->>> pip install .
+>>> pip install git+https://github.com/ML-KULeuven/klay
 
 
 Usage
@@ -22,27 +24,23 @@ For more information, check out the :ref:`circuit_construction`.
 
 .. code-block:: Python
 
-   from klaycircuits import Circuit
+   import klay
 
-   circuit = Circuit()
+   circuit = klay.Circuit()
    circuit.add_sdd(sdd_node)
 
 Now that we have the circuit, we can evaluate it. To do this, we first turn the circuit into a PyTorch module.
 
 .. code-block:: Python
 
-   import torch
-
    module = circuit.to_torch_module()
-   module = module.to("cuda:0")
 
-We can use our circuit as any other PyTorch module.
-The input should be a tensor with the weights for each literal, and the output is the result of evaluating circuit.
+The input to the module should be a tensor with the weights for each literal.
 For more details, see the :ref:`circuit_eval`.
 
 .. code-block:: Python
 
-   weights = torch.tensor([...], device="cuda:0")
+   weights = torch.tensor([...])
    result = module(weights)
    result.backward()