nanotorch implementation

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+  "python.REPL.enableREPLSmartSend": false
+}

README.md

@@ -0,0 +1,28 @@
+
+
+
+
+Let's tidy up any small things
+
+
+
+Write a really nice README on what it does, how it does and how can one use it
+
+
+
+Make it ready for release (will push to pypi)
+
+
+
+
+
+Make a GitHub action for this actually, so I don't have to do this manually
+
+
+
+There's already a pkg on pypi named "nanotorch", so we might need to name it something else
+
+
+
+
+

examples/train_regression.py

@@ -0,0 +1,61 @@
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+from nanotorch.tensor import Tensor
+from nanotorch.nn import MLP, SGD
+
+# Generate synthetic regression data
+print("Generating data...")
+X, y = make_regression(n_samples=200, n_features=5, noise=10, random_state=42)
+y = y.reshape(-1, 1)  # Make y 2D: (200, 1)
+
+# Split into train/test
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+print(f"Training samples: {X_train.shape[0]}")
+print(f"Test samples: {X_test.shape[0]}")
+print(f"Features: {X_train.shape[1]}")
+
+# Create model
+model = MLP([5, 16, 16, 1])  # 5 inputs -> 16 -> 16 -> 1 output
+optimizer = SGD(model.parameters(), lr=0.001)
+
+# Training loop
+epochs = 100
+print("\nTraining...")
+
+for epoch in range(epochs):
+    # Convert to tensors
+    X_tensor = Tensor(X_train)
+    y_tensor = Tensor(y_train)
+    
+    # Forward pass
+    y_pred = model(X_tensor)
+    
+    # MSE loss
+    loss = ((y_pred - y_tensor) ** 2).mean()
+    
+    # Backward pass
+    optimizer.zero_grad()
+    loss.backward()
+    
+    # Update weights
+    optimizer.step()
+    
+    # Print progress
+    if (epoch + 1) % 10 == 0:
+        print(f"Epoch {epoch + 1}/{epochs}, Loss: {loss.data:.4f}")
+
+# Evaluate on test set
+print("\nEvaluating on test set...")
+X_test_tensor = Tensor(X_test)
+y_test_tensor = Tensor(y_test)
+
+y_pred_test = model(X_test_tensor)
+test_loss = ((y_pred_test - y_test_tensor) ** 2).mean()
+
+print(f"Test Loss (MSE): {test_loss.data:.4f}")
+
+# Show some predictions
+print("\nSample predictions:")
+for i in range(5):
+    print(f"True: {y_test[i, 0]:.2f}, Predicted: {y_pred_test.data[i, 0]:.2f}")

micrograd/engine.py

@@ -101,4 +101,4 @@ class Value:
 
     self.grad = 1.0
     for node in reversed(topo):
-      node._backward()
+      node._backward()

micrograd/nn.py

@@ -1,5 +1,5 @@
 import random
-from micrograd.engine import Value
+from engine import Value
 
 class Neuron:
   def __init__(self, n_inputs: int):

nanotorch/nn.py

@@ -0,0 +1,88 @@
+import numpy as np
+from nanotorch.tensor import Tensor
+
+class Linear:
+  def __init__(self, in_features, out_features):
+    """
+    A linear layer: y = x @ W + b
+
+    Args:
+      in_features: input dims
+      out_features: output dims
+    """
+    self.W = Tensor(np.random.randn(in_features, out_features) * 0.1)
+    self.b = Tensor(np.zeros(out_features))
+  
+  def __call__(self, x):
+    """
+    Forward pass: y = x @ W + b
+    
+    Args:
+      x: input tensor, shape = (batch_size, in_features)
+    
+    Returns:
+      output tensor, shape = (batch_size, out_features)
+    """
+    return x @ self.W + self.b
+  
+  def parameters(self):
+    """
+    Returns:
+      List of trainable parameters
+    """
+    return [self.W, self.b]
+
+class MLP:
+  """
+  An MLP is just stacked linear layers with activations: Input → Linear → ReLU → Linear → ReLU → Linear → Output
+  """
+  def __init__(self, layer_sizes):
+    """
+    MLP with ReLU activation
+
+    Args:
+      layer_sizes: list of layer dims [input, hidden1, hidden2, ..., output]
+        e.g. [2, 16, 16, 1] means:
+          - input: 2 features
+          - 2 hidden layers with 16 neurons each
+          - output: 1 value
+    """
+    self.layers = []
+    for i in range(len(layer_sizes) - 1):
+      self.layers.append(Linear(layer_sizes[i], layer_sizes[i + 1]))
+  
+  def __call__(self, x):
+    """
+    Forward pass with ReLU activation between layers.
+    No activation on the final layer (common for regression/raw logits).
+    """
+    for i, layer in enumerate(self.layers):
+      x = layer(x)
+      if i < len(self.layers) - 1:
+        x = x.relu()
+    
+    return x
+  
+  def parameters(self):
+    params = []
+    for layer in self.layers:
+      params.extend(layer.parameters())
+    return params
+
+class SGD:
+  def __init__(self, parameters, lr=0.01):
+    """
+    Args
+      parameters: list of Tensor objects to minimize
+      lr: learning rate
+    """
+    self.parameters = parameters
+    self.lr = lr
+  
+  def step(self):
+    for param in self.parameters:
+      param.data -= self.lr * param.grad
+  
+  def zero_grad(self):
+    for param in self.parameters:
+      param.grad = np.zeros_like(param.data, dtype=np.float64)

nanotorch/tensor.py

@@ -0,0 +1,189 @@
+import numpy as np
+
+class Tensor:
+  def __init__(self, data, _parents=(), _op=''):
+    self.data = np.array(data) if not isinstance(data, np.ndarray) else data
+    self._parents = _parents
+    self._op = _op
+
+    # gradient: same shape as data, init to zeros
+    self.grad = np.zeros_like(self.data, dtype=np.float64)
+    self._backward = lambda: None
+  
+  def __repr__(self) -> str:
+    return f"Tensor(data={self.data}, grad={self.grad})"
+  
+  def __neg__(self) -> 'Tensor':
+    return self * -1
+  
+  def __sub__(self, other: 'Tensor') -> 'Tensor':
+    return self + (-other)
+  
+  def __rsub__(self, other: 'Tensor') -> 'Tensor':
+    return Tensor(other) - self
+  
+  def __rmul__(self, other: 'Tensor') -> 'Tensor':
+    return self * other
+
+  def __radd__(self, other: 'Tensor') -> 'Tensor':
+    return self + other
+  
+  def _unbroadcast(self, grad, original_shape):
+    # sum over leading dimensions that were added
+    while len(grad.shape) > len(original_shape):
+      grad = grad.sum(axis=0)
+    
+    # sum over dims that were size 1 but got broadcasted
+    for i, (grad_dim, orig_dim) in enumerate(zip(grad.shape, original_shape)):
+      if orig_dim == 1 and grad_dim > 1:
+        grad = grad.sum(axis=i, keepdims=True)
+    
+    return grad
+
+  def __add__(self, other: 'Tensor') -> 'Tensor':
+    # handle scalar addition
+    other = other if isinstance(other, Tensor) else Tensor(other)
+    out = Tensor(self.data + other.data, (self, other), '+')
+
+    def _backward():
+      # undo broadcast by summing over broadcast dims
+      grad_self = self._unbroadcast(out.grad, self.data.shape)
+      grad_other = other._unbroadcast(out.grad, other.data.shape)
+
+      # accumulate grads
+      self.grad += grad_self
+      other.grad += grad_other
+    
+    out._backward = _backward
+    return out
+  
+  def __mul__(self, other: 'Tensor') -> 'Tensor':
+    other = other if isinstance(other, Tensor) else Tensor(other)
+    out = Tensor(self.data * other.data, (self, other), '*')
+
+    def _backward():
+      # local gradients with broadcasting
+      grad_self = out.grad * other.data
+      grad_other = out.grad * self.data
+      grad_self = self._unbroadcast(grad_self, self.data.shape)
+      grad_other = other._unbroadcast(grad_other, other.data.shape)
+
+      # accumulate grads
+      self.grad += grad_self
+      other.grad += grad_other
+    
+    out._backward = _backward
+    return out
+  
+  def __matmul__(self, other: 'Tensor') -> 'Tensor':
+    other = other if isinstance(other, Tensor) else Tensor(other)
+    out = Tensor(self.data @ other.data, (self, other), '@')
+
+    def _backward():
+      self_data = self.data
+      other_data = other.data
+
+      if other_data.ndim == 1:
+        grad_self = out.grad.reshape(-1, 1) @ other_data.reshape(1, -1)
+      else:
+        other_data_T = other_data.swapaxes(-2, -1)
+        grad_self = out.grad @ other_data_T
+      grad_self = self._unbroadcast(grad_self, self_data.shape)
+
+      if self_data.ndim == 1:
+        self_data_T = self_data.reshape(-1, 1)
+        grad_other = self_data_T @ out.grad.reshape(1, -1)
+      else:
+        self_data_T = self_data.swapaxes(-2, -1)
+        grad_other = self_data_T @ out.grad
+      grad_other = self._unbroadcast(grad_other, other_data.shape)
+
+      # accumulate grads
+      self.grad += grad_self
+      other.grad += grad_other
+    
+    out._backward = _backward
+    return out
+  
+  def __pow__(self, power) -> 'Tensor':
+    assert isinstance(power, (int, float)), "only support int/float powers"
+
+    out = Tensor(self.data ** power, (self, ), f'**{power}')
+
+    def _backward():
+      self.grad += power * (self.data ** (power - 1)) * out.grad
+    
+    out._backward = _backward
+    return out
+  
+  def sum(self, axis=None, keepdims=False) -> 'Tensor':
+    out = Tensor(self.data.sum(axis=axis, keepdims=keepdims), (self, ), 'sum')
+
+    def _backward():
+      grad = out.grad
+      if axis is not None and not keepdims:
+        grad = np.expand_dims(grad, axis=axis)
+      
+      self.grad += np.broadcast_to(grad, self.data.shape)
+    
+    out._backward = _backward
+    return out
+  
+  def mean(self, axis=None, keepdims=False):
+    if axis is None:
+      count = self.data.size
+    else:
+      count = self.data.shape[axis]
+    
+    out = Tensor(self.data.mean(axis=axis, keepdims=keepdims), (self, ), 'mean')
+
+    def _backward():
+      grad = out.grad / count
+      if axis is not None and not keepdims:
+        grad = np.expand_dims(grad, axis=axis)
+      
+      self.grad += np.broadcast_to(grad, self.data.shape)
+    
+    out._backward = _backward
+    return out
+  
+  def backward(self):
+    # build topological order
+    topo = []
+    visited = set()
+
+    def build_topo(tensor):
+      if tensor not in visited:
+        visited.add(tensor)
+        for parent in tensor._parents:
+          build_topo(parent)
+        topo.append(tensor)
+    
+    build_topo(self)
+
+    # init gradient of output to 1
+    self.grad = np.ones_like(self.data, dtype=np.float64)
+
+    # backprop
+    for node in reversed(topo):
+      node._backward()
+  
+  # activation functions
+  def relu(self) -> 'Tensor':
+    out = Tensor(np.maximum(0, self.data), (self, ), 'ReLU')
+
+    def _backward():
+      self.grad += (self.data > 0) * out.grad
+    
+    out._backward = _backward
+    return out
+  
+  def tanh(self) -> 'Tensor':
+    t = np.tanh(self.data)
+    out = Tensor(t, (self, ), 'tanh')
+
+    def _backward():
+      self.grad += (1 - t**2) * out.grad
+    
+    out._backward = _backward
+    return out

pyproject.toml

@@ -10,4 +10,14 @@ dependencies = [
     "matplotlib>=3.10.7",
     "mlx>=0.29.4",
     "nbconvert>=7.16.6",
+    "numpy>=2.3.4",
+    "scikit-learn>=1.7.2",
 ]
+
+[dependency-groups]
+dev = [
+    "pytest>=9.0.1",
+]
+
+[tool.setuptools]
+packages = ["nanotorch", "micrograd"]

tests/test_activations.py

@@ -0,0 +1,37 @@
+from nanotorch.tensor import Tensor
+
+# Test 1: ReLU
+print("Test 1 - ReLU:")
+x = Tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
+y = x.relu()
+
+y.backward()
+
+print(f"x.data: {x.data}")
+print(f"y.data: {y.data}")      # Should be [0, 0, 0, 1, 2]
+print(f"x.grad: {x.grad}")      # Should be [0, 0, 0, 1, 1]
+
+# Test 2: Tanh
+print("\nTest 2 - Tanh:")
+x = Tensor([0.0, 1.0, 2.0])
+y = x.tanh()
+
+y.backward()
+
+print(f"x.data: {x.data}")
+print(f"y.data: {y.data}")      # Should be [0, 0.76, 0.96] approx
+print(f"x.grad: {x.grad}")      # Should be [1, 0.42, 0.07] approx (1 - tanh²)
+
+# Test 3: ReLU in a computation graph
+print("\nTest 3 - ReLU in computation:")
+x = Tensor([[-1.0, 2.0],
+            [3.0, -4.0]])
+w = Tensor([[0.5, 0.5],
+            [0.5, 0.5]])
+z = (x @ w).relu()  # Linear layer + ReLU
+
+z.backward()
+
+print(f"z.data:\n{z.data}")
+print(f"x.grad:\n{x.grad}")
+print(f"w.grad:\n{w.grad}")

tests/test_add.py

@@ -0,0 +1,23 @@
+from nanotorch.tensor import Tensor
+
+# Test 1: Simple addition (no broadcasting)
+a = Tensor([1.0, 2.0, 3.0])
+b = Tensor([4.0, 5.0, 6.0])
+c = a + b
+
+c.backward()
+
+print("Test 1 - No broadcasting:")
+print(f"a.grad: {a.grad}")  # Should be [1, 1, 1]
+print(f"b.grad: {b.grad}")  # Should be [1, 1, 1]
+
+# Test 2: Broadcasting
+a = Tensor([[1.0, 2.0]])       # shape (1, 2)
+b = Tensor([[3.0], [4.0]])     # shape (2, 1)
+c = a + b                      # shape (2, 2)
+
+c.backward() 
+
+print("\nTest 2 - Broadcasting:")
+print(f"a.grad shape: {a.grad.shape}, values: {a.grad}")  # Should be (1,2) with [[2, 2]]
+print(f"b.grad shape: {b.grad.shape}, values: {b.grad}")  # Should be (2,1) with [[2], [2]]

tests/test_linear.py

@@ -0,0 +1,38 @@
+from nanotorch.nn import Linear
+from nanotorch.tensor import Tensor
+
+# Test 1: Single sample forward pass
+print("Test 1 - Single forward pass:")
+layer = Linear(3, 2)  # 3 inputs -> 2 outputs
+
+x = Tensor([1.0, 2.0, 3.0])  # shape (3,)
+y = layer(x)                  # shape (2,)
+
+print(f"x.shape: {x.data.shape}")
+print(f"y.shape: {y.data.shape}")
+print(f"y.data: {y.data}")
+
+# Test 2: Batch forward pass
+print("\nTest 2 - Batch forward pass:")
+x_batch = Tensor([[1.0, 2.0, 3.0],
+                  [4.0, 5.0, 6.0]])  # shape (2, 3) - batch of 2
+y_batch = layer(x_batch)             # shape (2, 2)
+
+print(f"x_batch.shape: {x_batch.data.shape}")
+print(f"y_batch.shape: {y_batch.data.shape}")
+
+# Test 3: Backward pass
+print("\nTest 3 - Backward pass:")
+x = Tensor([[1.0, 2.0]])  # shape (1, 2)
+layer = Linear(2, 3)       # 2 -> 3
+
+y = layer(x)
+loss = y.sum()  # Simple loss for testing
+
+loss.backward()
+
+print(f"W.grad shape: {layer.W.grad.shape}")  # Should be (2, 3)
+print(f"b.grad shape: {layer.b.grad.shape}")  # Should be (3,)
+print(f"x.grad shape: {x.grad.shape}")        # Should be (1, 2)
+print(f"W.grad:\n{layer.W.grad}")
+print(f"b.grad: {layer.b.grad}")

tests/test_matmul.py

@@ -0,0 +1,30 @@
+from nanotorch.tensor import Tensor
+
+# Test 1: Simple 2D matmul
+print("Test 1 - Simple 2D matmul:")
+x = Tensor([[1.0, 2.0, 3.0],
+            [4.0, 5.0, 6.0]])      # (2, 3)
+y = Tensor([[7.0, 8.0],
+            [9.0, 10.0],
+            [11.0, 12.0]])          # (3, 2)
+z = x @ y                           # (2, 2)
+
+z.backward()
+
+print(f"z.data:\n{z.data}")
+print(f"x.grad:\n{x.grad}")  # Should be z.grad @ y.T
+print(f"y.grad:\n{y.grad}")  # Should be x.T @ z.grad
+
+# Test 2: Vector-matrix multiplication
+print("\nTest 2 - Vector @ Matrix:")
+x = Tensor([1.0, 2.0, 3.0])        # (3,)
+y = Tensor([[4.0, 5.0],
+            [6.0, 7.0],
+            [8.0, 9.0]])            # (3, 2)
+z = x @ y                           # (2,)
+
+z.backward()
+
+print(f"z.data: {z.data}")
+print(f"x.grad: {x.grad}")
+print(f"y.grad:\n{y.grad}")

tests/test_mlp.py

@@ -0,0 +1,45 @@
+import numpy as np
+from nanotorch.tensor import Tensor
+from nanotorch.nn import MLP
+
+# Test 1: MLP forward pass
+print("Test 1 - MLP forward pass:")
+model = MLP([2, 4, 3, 1])  # 2 inputs -> 4 hidden -> 3 hidden -> 1 output
+
+x = Tensor([[1.0, 2.0]])   # Single sample, shape (1, 2)
+y = model(x)                # shape (1, 1)
+
+print(f"Input shape: {x.data.shape}")
+print(f"Output shape: {y.data.shape}")
+print(f"Output value: {y.data}")
+
+# Test 2: Batch processing
+print("\nTest 2 - Batch processing:")
+x_batch = Tensor([[1.0, 2.0],
+                  [3.0, 4.0],
+                  [5.0, 6.0]])  # 3 samples, shape (3, 2)
+y_batch = model(x_batch)        # shape (3, 1)
+
+print(f"Batch input shape: {x_batch.data.shape}")
+print(f"Batch output shape: {y_batch.data.shape}")
+
+# Test 3: Backward pass through entire network
+print("\nTest 3 - Full backward pass:")
+x = Tensor([[1.0, 2.0]])
+y_pred = model(x)
+y_true = Tensor([[5.0]])
+
+# MSE loss
+loss = ((y_pred - y_true) ** 2).mean()
+
+loss.backward()
+
+print(f"Loss: {loss.data}")
+print(f"Number of parameters: {len(model.parameters())}")
+print(f"First layer W.grad shape: {model.layers[0].W.grad.shape}")
+print(f"Last layer W.grad shape: {model.layers[-1].W.grad.shape}")
+
+# Verify gradients exist for all parameters
+all_have_grads = all(np.any(p.grad != 0) or p.grad.shape == () 
+                     for p in model.parameters())
+print(f"All parameters have gradients: {all_have_grads}")

tests/test_mul.py

@@ -0,0 +1,38 @@
+from nanotorch.tensor import Tensor
+
+# Test 1: Simple multiplication (no broadcasting)
+print("Test 1 - No broadcasting:")
+a = Tensor([2.0, 3.0, 4.0])
+b = Tensor([5.0, 6.0, 7.0])
+c = a * b  # [10, 18, 28]
+
+c.backward()
+
+print(f"c.data: {c.data}")
+print(f"a.grad: {a.grad}")  # Should be [5, 6, 7] (b's values)
+print(f"b.grad: {b.grad}")  # Should be [2, 3, 4] (a's values)
+
+# Test 2: Broadcasting case
+print("\nTest 2 - Broadcasting:")
+a = Tensor([[1.0, 2.0, 3.0]])  # shape (1, 3)
+b = Tensor([[2.0], [3.0]])      # shape (2, 1)
+c = a * b  # shape (2, 3)
+
+c.backward()
+
+print(f"c.data:\n{c.data}")
+print(f"a.grad shape: {a.grad.shape}, values: {a.grad}")  # Should be (1,3) with [[5, 5, 5]]
+print(f"b.grad shape: {b.grad.shape}, values: {b.grad}")  # Should be (2,1) with [[6], [6]]
+
+# Test 3: Chain rule test (addition + multiplication)
+print("\nTest 3 - Chain rule:")
+x = Tensor([2.0, 3.0])
+y = Tensor([4.0, 5.0])
+z = x * y       # [8, 15]
+w = z + z       # [16, 30]
+
+w.backward()
+
+print(f"w.data: {w.data}")
+print(f"x.grad: {x.grad}")  # Should be [8, 10] (2 * y, because w = 2*x*y)
+print(f"y.grad: {y.grad}")  # Should be [4, 6] (2 * x)

tests/test_reductions.py

@@ -0,0 +1,58 @@
+from nanotorch.tensor import Tensor
+
+# Test 1: Sum all elements
+print("Test 1 - Sum (all elements):")
+x = Tensor([[1.0, 2.0, 3.0],
+            [4.0, 5.0, 6.0]])  # shape (2, 3)
+y = x.sum()                     # 21.0, shape ()
+
+y.backward()
+
+print(f"y.data: {y.data}")
+print(f"x.grad:\n{x.grad}")  # Should be all 1s
+
+# Test 2: Sum along axis
+print("\nTest 2 - Sum (axis=1):")
+x = Tensor([[1.0, 2.0, 3.0],
+            [4.0, 5.0, 6.0]])  # shape (2, 3)
+y = x.sum(axis=1)               # [6, 15], shape (2,)
+
+y.backward()
+
+print(f"y.data: {y.data}")
+print(f"x.grad:\n{x.grad}")  # Should be all 1s
+
+# Test 3: Mean all elements
+print("\nTest 3 - Mean (all elements):")
+x = Tensor([[2.0, 4.0],
+            [6.0, 8.0]])  # shape (2, 2)
+y = x.mean()               # 5.0, shape ()
+
+y.backward()
+
+print(f"y.data: {y.data}")
+print(f"x.grad:\n{x.grad}")  # Should be all 0.25 (1/4)
+
+# Test 4: Mean along axis
+print("\nTest 4 - Mean (axis=0):")
+x = Tensor([[1.0, 2.0],
+            [3.0, 4.0]])  # shape (2, 2)
+y = x.mean(axis=0)         # [2, 3], shape (2,)
+
+y.backward()
+
+print(f"y.data: {y.data}")
+print(f"x.grad:\n{x.grad}")  # Should be all 0.5 (1/2)
+
+# Test 5: Chain rule with operations
+print("\nTest 5 - MSE Loss simulation:")
+pred = Tensor([1.0, 2.0, 3.0])
+target = Tensor([1.5, 2.5, 2.0])
+diff = pred - target
+squared = diff * diff
+loss = squared.mean()
+
+loss.backward()
+
+print(f"loss.data: {loss.data}")
+print(f"pred.grad: {pred.grad}")  # Should show gradient for each prediction

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+[[package]]
+name = "iniconfig"
+version = "2.3.0"
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+sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
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 version = "7.1.0"
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+[[package]]
+name = "joblib"
+version = "1.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e8/5d/447af5ea094b9e4c4054f82e223ada074c552335b9b4b2d14bd9b35a67c4/joblib-1.5.2.tar.gz", hash = "sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55", size = 331077, upload-time = "2025-08-27T12:15:46.575Z" }
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+]
+
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 version = "4.25.1"
@@ -463,6 +481,13 @@ dependencies = [
     { name = "matplotlib" },
     { name = "mlx" },
     { name = "nbconvert" },
+    { name = "numpy" },
+    { name = "scikit-learn" },
+]
+
+[package.dev-dependencies]
+dev = [
+    { name = "pytest" },
 ]
 
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@@ -472,8 +497,13 @@ requires-dist = [
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     { name = "mlx", specifier = ">=0.29.4" },
     { name = "nbconvert", specifier = ">=7.16.6" },
+    { name = "numpy", specifier = ">=2.3.4" },
+    { name = "scikit-learn", specifier = ">=1.7.2" },
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+[package.metadata.requires-dev]
+dev = [{ name = "pytest", specifier = ">=9.0.1" }]
+
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