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Inline Visualization

Simplegrad includes inline visualisation utilities for Jupyter notebooks. graph renders the full computation graph of a tensor using Graphviz, showing each operation as a node and the data-flow edges between them. plot and scatter produce quick training curves directly in the notebook without needing a separate plotting library.

import simplegrad as sg

x = sg.Tensor([1.0, 2.0], requires_grad=True)
y = (x * x).sum()
sg.graph(y)           # renders the computation graph inline

losses = [0.9, 0.7, 0.5, 0.3]
sg.plot(losses, title="Training loss")

Computation Graph

graph(tensor: Tensor, path: str | None = None) -> graphviz.Digraph

Render the computation graph of a tensor as an SVG diagram.

Functions decorated with @compound_op are enclosed in a labelled black-border rectangle. The internal intermediate tensors and the final operation node of the compound op are inside the rectangle; the output tensor node itself sits outside, connecting to the cluster via an edge.

Node colors
  • Green (#34b87e): leaf tensors (inputs / parameters)
  • Yellow (#feba14): intermediate tensors
  • Light grey (#e8e8e8): operation nodes

Parameters:

  • tensor (Tensor) –

    The output tensor whose computation graph to visualize.

  • path (str | None, default: None ) –

    If provided, save the SVG to this file path (without extension).

Returns:

  • Digraph –

    A graphviz.Digraph object. Displays inline in Jupyter notebooks.

Training Plots

plot(results: dict[str, list[RecordInfo]], selected: list[str] | None = None, num_cols: int = 2, cell_w: int = 8, cell_h: int = 5, path: Path | None = None, color: str | None = None)

Plot training metrics as line charts.

Parameters:

  • results (dict[str, list[RecordInfo]]) –

    Mapping of metric name to list of RecordInfo data points.

  • selected (list[str] | None, default: None ) –

    Subset of metric names to plot. Plots all if None.

  • num_cols (int, default: 2 ) –

    Number of subplot columns. Defaults to 2.

  • cell_w (int, default: 8 ) –

    Width of each subplot cell in inches. Defaults to 8.

  • cell_h (int, default: 5 ) –

    Height of each subplot cell in inches. Defaults to 5.

  • path (Path | None, default: None ) –

    If provided, save the figure to this path.

  • color (str | None, default: None ) –

    Fixed color for all lines. Random if None.

scatter(results: dict[str, list[RecordInfo]], selected: list[str] | None = None, num_cols: int = 2, cell_w: int = 8, cell_h: int = 5, path: Path | None = None, color: str | None = None)

Plot training metrics as scatter charts.

Parameters:

  • results (dict[str, list[RecordInfo]]) –

    Mapping of metric name to list of RecordInfo data points.

  • selected (list[str] | None, default: None ) –

    Subset of metric names to plot. Plots all if None.

  • num_cols (int, default: 2 ) –

    Number of subplot columns. Defaults to 2.

  • cell_w (int, default: 8 ) –

    Width of each subplot cell in inches. Defaults to 8.

  • cell_h (int, default: 5 ) –

    Height of each subplot cell in inches. Defaults to 5.

  • path (Path | None, default: None ) –

    If provided, save the figure to this path.

  • color (str | None, default: None ) –

    Fixed color for all points. Random if None.