Histograms

Log binned distributions with pluto.Histogram.

Logging

Histogram logging lets you track how distributions of values (such as weight distributions, activation values, or gradient norms) change over training steps.

To log a histogram, instantiate the pluto.Histogram class and pass it to pluto.log:

histogram = pluto.Histogram(
    data=values,
    bins=64,
)
pluto.log({"layers/layer_0/weights": histogram}, step=epoch)

Parameter	Type	Description
data	Union[list, np.ndarray, torch.Tensor]	The values to build the histogram from.
bins	int	Number of bins for the histogram. Defaults to 64.

Examples

Logging Weight Distributions

import pluto
import torch

run = pluto.init(project="my-project")

model = MyModel()
for epoch in range(num_epochs):
    # ... training step ...

    # Log weight distributions for each layer
    for name, param in model.named_parameters():
        if "weight" in name:
            pluto.log({f"histograms/{name}": pluto.Histogram(param.data.cpu())}, step=epoch)

Logging Gradient Distributions

for name, param in model.named_parameters():
    if param.grad is not None:
        pluto.log({
            f"gradients/{name}": pluto.Histogram(param.grad.data.cpu(), bins=32)
        }, step=epoch)

Viewing

Histograms render in the Distributions widget, which also hosts categorical bar charts. To add one to a dashboard, open Add Widget, choose the Distributions tab, and search for your histogram log name (e.g. distributions/weights).

View modes

Each widget renders in one of three modes.

Step — a bar chart of the current step’s frequencies. Use the step slider below the widget to scrub through training; when several widgets share a section, their sliders can be linked via the lock icon so they move together.

Ridgeline — one density curve (joyplot) per step, stacked so you can watch the distribution evolve over training.

Heatmap — a density grid with frequency as color, the numeric bin on the X axis and step on the Y axis.

Steps on X

In Ridgeline and Heatmap modes, a Steps on X toggle in the settings popover transposes the chart so steps run along the X axis.

The same transpose applies in Heatmap mode:

Settings

Open the settings popover (the sliders icon, shown on hover) to adjust:

• X min / X max — pin the X axis to manual bounds instead of the per-run Auto range.
• Y max — manual Y bound (Step mode only).
• Ignore outliers — clamp the axis and frequency scale so one extreme step doesn’t flatten the rest (on by default).