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Visualize Metrics

Watch your metrics update in real-time as training runs. Valohai automatically creates interactive visualizations from any JSON you print—no plotting code required.

Access Visualizations

During Training (Real-Time)

Metrics appear as soon as your code prints JSON. You don't need to wait for training to finish.

To view:

  1. Open your execution

  2. Click the Metadata tab (shows the count of logged metrics)

  3. Visualizations appear automatically

Graphs update live as new metrics are logged.

After Training

All visualizations remain available after the execution completes. Access them the same way: open the execution and click the Metadata tab.

Visualization Types

Time Series (Default)

Plot metrics over time—epochs, steps, or iterations. Watch loss decrease and accuracy improve as training progresses.

Best for:

  • Monitoring convergence

  • Detecting overfitting (train vs. validation curves)

  • Tracking learning rate schedules

Learn more →

Confusion Matrix

Visualize classification performance with an interactive confusion matrix. See where your model excels and where it struggles.

Best for:

  • Multi-class classification

  • Error analysis

  • Understanding misclassification patterns

Learn more →

Image Comparison

Stack output images from different runs and toggle between them. Use blend modes, side-by-side sliders, and color overlays to spot differences.

Best for:

  • Computer vision experiments

  • Quality control testing

  • Before/after comparisons

  • Medical imaging analysis

Learn more →

Quick Start

1. Log Metrics

Print JSON from your training code:

import json

print(json.dumps({
    "epoch": 1,
    "train_loss": 0.5,
    "val_loss": 0.6,
    "accuracy": 0.82
}))

2. Open Metadata Tab

Navigate to your execution and click the Metadata tab. You'll see:

  • Automatic time series graphs for all numeric metrics

  • Options to create additional visualization tabs

  • Controls to customize what's displayed

3. Customize Visualization

Choose your horizontal axis:

  • Select any metric (like epoch) from the dropdown

  • Default is _time (when the metric was logged)

Add metrics to plot:

  • Click Add/remove in the Vertical Axes section

  • Select which metrics to visualize

  • Create multiple graphs by adding more metrics

Adjust display:

  • Smoothing: Reduce noise with a smoothing slider

  • Vertical axis side: Place metrics on left or right axis

  • Logarithmic scale: Use log scale for vertical axis

  • Show legend: Toggle metric labels

  • One color per execution: Useful when comparing runs

Interactive Features (Powered by Plotly)

All graphs are interactive:

Zoom: Click and drag to zoom into specific regions Pan: Hold shift and drag to pan Reset: Double-click to reset view Hover: See exact values at any point Save image: Use the camera icon to download as PNG

Multiple Visualization Tabs

Create multiple visualization tabs to organize different metric groups:

  1. Click the + button next to the visualization tabs

  2. Name your new tab (e.g., "Loss Curves", "Accuracy Metrics")

  3. Configure each tab with different metrics and settings

Use cases:

  • Separate tab for loss metrics

  • Separate tab for accuracy metrics

  • Separate tab for learning rate schedule

  • Different time scales or smoothing per tab

Export Data

Download your metrics for external analysis:

From the Metadata tab:

  • Click Download raw data (top right)

  • Choose format: CSV or JSON

  • Get all metrics logged during the execution

Use the exported data for:

  • Custom visualizations in Python/R

  • Statistical analysis

  • Reporting and presentations

  • Sharing with stakeholders

Comparing Multiple Executions

View metrics from multiple executions on the same graph:

  1. Go to the Executions tab in your project

  2. Select multiple executions using checkboxes

  3. Click Compare

  4. All selected executions appear in the same visualization

This lets you directly compare:

  • Different hyperparameter settings

  • Different model architectures

  • Training runs with different data

Learn more about comparing executions →

When Metrics Don't Appear

Metadata Tab Grayed Out

Cause: No JSON printed yet

Solution: Make sure your code prints JSON:

import json
print(json.dumps({"epoch": 1, "loss": 0.5}))

Metrics Logged But Not Plotted

Cause: Non-numeric values can't be plotted

Solution: Only numeric values appear in graphs. String values appear in the data export but not in visualizations.

# Plotted
print(json.dumps({"epoch": 1, "loss": 0.5}))

# Not plotted (string value)
print(json.dumps({"model_name": "resnet50"}))

Graph Looks Wrong

Common issues:

Missing epoch counter:

# Hard to interpret
print(json.dumps({"loss": 0.5}))

# Clear progression
print(json.dumps({"epoch": 1, "loss": 0.5}))

Wrong horizontal axis:

  • Change from _time to your step counter (like epoch)

  • Click the Horizontal Axis dropdown and select your metric

Too noisy:

  • Use the Smoothing slider to reduce noise

  • Adjust the slider until trends are clear

Best Practices

Use Consistent Metric Names

Keep names identical across experiments:

# Good: Consistent
"train_loss"
"val_loss"

# Avoid: Inconsistent
"training_loss"
"validation_loss"

Include Step Counter

Always log a step or epoch number:

# Good
print(json.dumps({
    "epoch": epoch,
    "loss": loss
}))

# Avoid
print(json.dumps({
    "loss": loss  # No way to see progression
}))

Log Progressively

Print metrics throughout training, not just at the end:

# Good: See convergence in real-time
for epoch in range(100):
    loss = train_epoch()
    print(json.dumps({"epoch": epoch, "loss": loss}))

# Avoid: Only final result
# (No visibility into training progress)

Group Related Metrics

Log related metrics in the same JSON object:

# Good: All training metrics together
print(json.dumps({
    "epoch": epoch,
    "train_loss": train_loss,
    "train_accuracy": train_acc,
    "val_loss": val_loss,
    "val_accuracy": val_acc
}))

Next Steps

Learn specific visualization types:

Compare experiments:

Collect metrics:

PreviousCollect Metrics from Output Files (YOLO & Others)NextTime Series

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