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Epokio

Get a phone alert when the training run on your GPU box or server stalls, NaNs or finishes. No code changes: it reads the logs you already write. Works over SSH.

A run goes from Training to Stalled and an ntfy alert arrives on the phone

What it does better than Epokio What Epokio adds
TensorBoard Rich charts per step, images, histograms, embeddings and the profiler. Alerts when a run stalls, hits NaN or finishes, with no browser tab open; one list for many frameworks' logs; remote boxes over SSH with nothing installed.
W&B Team dashboards, history across machines, sweeps, artifacts and a model registry. No account, no logging calls, no upload; works without internet; reads W&B's local files, so you can keep both.
nvitop A live view of each GPU, its processes and memory. Knows about the run: epoch, time left, best score, stall and NaN detection, phone alerts.

There is no account and no cloud, and no logging code to add when your framework writes a supported file (Keras needs a CSVLogger or TensorBoard callback; a hand-written loop uses epokio.start, below). It also has a web page, a tray icon, a terminal view and a macOS menu bar app.

What it does

  • See every run on this machine or a remote GPU box: state, epoch, time left, best score, curves, plain-language notes ("recall is much higher than precision…") and the result images your framework saved.
  • Get told when a run finishes, fails (NaN loss), stalls or reaches a target score: on the Mac, in the tray, or as a phone push (ntfy, Slack, Discord, Telegram) sent by the helper while it runs.
  • Compare runs: only the settings that differ, a settings table for a whole sweep next to each run's main score, CSV export and Markdown reports.
  • Start and queue runs (Ultralytics YOLO) from the web page, one at a time on each GPU, with a data check before starting, a one-button Python setup (CUDA PyTorch on NVIDIA machines), resume from weights/last.pt, and failure causes in plain words.
  • AI assistants can read and queue runs through the MCP server (pip install "epokio[mcp]").

Reads Ultralytics (results.csv), Hugging Face Trainer (trainer_state.json), PyTorch Lightning (metrics.csv), Keras (CSVLogger) and TensorBoard event files (read without TensorFlow). The best score appears when a run logs a validation metric (or Hugging Face's best_metric); loss-only runs show none (Hugging Face runs that evaluate only eval_loss use the lowest eval_loss). A hand-written loop shows up with two lines:

import epokio
with epokio.start("runs/my-model", epochs=50, lr=1e-4) as run:
    for epoch in range(50):
        ...
        run.log(val_loss=vl, accuracy=acc)

Start

pip install epokio
epokio setup            # finds your runs, starts the helper, opens the page
epokio setup --lan      # also let your phone or another computer watch this machine

Other commands: epokio watch (terminal view, good over SSH), epokio tray (pip install "epokio[tray]"), epokio doctor (what Epokio sees, for a bug report), epokio agent --stop.

Viewing is open on this machine; anything that starts, stops or changes something needs the machine's token (epokio agent --show-token). The helper uses only the Python standard library.

MIT licence.

Metadata

Release files for epokio 0.7.0

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0.9.4

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0.9.3

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0.9.2

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0.9.1

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0.9.0

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0.8.0

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This release

0.7.0 This release

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0.6.1

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0.6.0

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0.5.4

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0.5.2

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0.5.1

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0.5.0

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0.4.4

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0.4.3

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0.4.2

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0.4.1

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0.4.0

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0.3.0

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