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Documentation Index

Fetch the complete documentation index at: https://docs.trainy.ai/llms.txt

Use this file to discover all available pages before exploring further.

Pluto allows you to save arbitrary files during your experiments. This is useful for saving model checkpoints, configuration files, output data, or any other files you want to associate with a run.

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Logging Files

Use pluto.File() with pluto.log() to save a file to your experiment: 
import json
import pluto

run = pluto.init(project="my-project", name="experiment")

# Write a JSON file
results = {"accuracy": 0.95, "loss": 0.05}
with open("results.json", "w") as f:
    json.dump(results, f)

# Log it to Pluto
pluto.log({"results": pluto.File("results.json")})

run.finish()

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Viewing Files

Files logged during an experiment can be viewed in the web UI. Navigate to your experiment, click the Summary tab, and visit the Files section. Screenshot 2026 01 21 At 2 14 08 PM
Plain text files will only render in dropdown if they are saved with a supported filename extension (i.e. .txt, .yaml, .py, etc.). For files that don’t follow this convention, the file won’t be viewable via the webUI but will still be downloadable.

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Use Cases

Common use cases for file logging include:
  • Configuration files: Track the exact configuration used for each run
  • Output data: Save predictions, embeddings, or other generated data
  • Logs and reports: Save text logs or generated reports
For logging specific media types with built-in visualization support, see:
  • Images - Log and visualize image data
  • Audio - Log and play back audio files
  • Video - Log and view video files