adds uv support and toml file

.gitignore

@@ -1 +1,7 @@
 .ipynb_checkpoints/
+.idea/
+.venv/
+*.egg-info
+build/
+.python-version
+__pycache__/

README.md

@@ -47,11 +47,12 @@ The notebook `demo.ipynb` provides a full demo of training an 2-layer neural net
 For added convenience, the notebook `trace_graph.ipynb` produces graphviz visualizations. E.g. this one below is of a simple 2D neuron, arrived at by calling `draw_dot` on the code below, and it shows both the data (left number in each node) and the gradient (right number in each node).
 
 ```python
-from micrograd import nn
+from micrograd import nn , Value 
 n = nn.Neuron(2)
 x = [Value(1.0), Value(-2.0)]
 y = n(x)
-dot = draw_dot(y)
+# draw_dot function was defined in `trace_graph.ipynb` file. 
+# dot = draw_dot(y)
 ```
 
 ![2d neuron](gout.svg)

micrograd/__init__.py

@@ -0,0 +1,7 @@
+from micrograd.engine import Value
+from micrograd.graph import draw_dot
+
+__all__ = [
+    'Value',
+    'draw_dot'
+]

micrograd/graph.py

@@ -0,0 +1,35 @@
+from graphviz import Digraph
+
+def trace(root):
+    nodes, edges = set(), set()
+
+    def build(v):
+        if v not in nodes:
+            nodes.add(v)
+            for child in v._prev:
+                edges.add((child, v))
+                build(child)
+
+    build(root)
+    return nodes, edges
+
+
+def draw_dot(root, format='svg', rankdir='LR'):
+    """
+    format: png | svg | ...
+    rankdir: TB (top to bottom graph) | LR (left to right)
+    """
+    assert rankdir in ['LR', 'TB']
+    nodes, edges = trace(root)
+    dot = Digraph(format=format, graph_attr={'rankdir': rankdir})  # , node_attr={'rankdir': 'TB'})
+
+    for n in nodes:
+        dot.node(name=str(id(n)), label="{ data %.4f | grad %.4f }" % (n.data, n.grad), shape='record')
+        if n._op:
+            dot.node(name=str(id(n)) + n._op, label=n._op)
+            dot.edge(str(id(n)) + n._op, str(id(n)))
+
+    for n1, n2 in edges:
+        dot.edge(str(id(n1)), str(id(n2)) + n2._op)
+
+    return dot

pyproject.toml

@@ -0,0 +1,22 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "micrograd"
+version = "0.1.0"
+authors = [
+    { name = "Andrej Karpathy", email = "[email protected]" }
+]
+description = "A tiny scalar-valued autograd engine with a small PyTorch-like neural network library on top."
+readme = "README.md"
+license = { text = "MIT" }
+requires-python = ">=3.8"
+dependencies = [
+    "graphviz>=0.20.3",
+    "pytest>=8.3.4",
+    "torch>=2.5.1",
+]
+
+[project.urls]
+Repository = "https://github.com/karpathy/micrograd"

test/test_graph.py

@@ -0,0 +1,9 @@
+from micrograd import nn, Value, draw_dot
+
+
+def test_graph():
+    n = nn.Neuron(2)
+    x = [Value(1.0), Value(-2.0)]
+    y = n(x)
+    dot = draw_dot(y)
+    assert dot