# How to read IOTA Train at Home training history and throughput

Read daily, weekly and monthly training trends, epoch tokens, throughput, contribution percentage and rank without confusing them with rewards.

- Canonical: https://iotahome.site/en/learn/training-history-and-metrics
- Publisher: IOTA Watch
- Published: 2026-10-01
- Updated: 2026-10-01

Open a saved device’s details, select a training run, then choose day, week or month. The chart and table show official historical samples for that selection. Training volume is not a token payout or a reward forecast.

## Which metrics can I compare?

- Tokens: reported training token count for an epoch

- Throughput: processing rate reported by the official history API

- Cumulative tokens: the cumulative training token count, separate from lifetime rewards

- Contribution: the reported fraction displayed as a percentage; 0.02 displays as 2%

- Rank and participants: the reported position and participant count for that epoch, when available

Switch the chart between tokens, throughput and cumulative tokens. The table preserves the individual samples so the trend can be checked against the numbers.

## Why can the chart have gaps?

The three history sources may contain different epochs. IOTA Watch joins values by epoch instead of assuming the array positions line up. A missing measurement is a gap, not an invented zero.

## Why is the selected run empty?

The official API may have no samples for this Miner ID in the chosen run or period. Check the task selection, try another period and inspect source warnings. An empty history does not prove that the device has failed.

For current activity, read [device status](https://iotahome.site/en/learn/device-status). For accounted tokens, read [how rewards work](https://iotahome.site/en/learn/how-rewards-work). [Data freshness](https://iotahome.site/en/learn/data-sources-and-freshness) explains why recent retrieval can still contain older samples.

## Sources and implementation

- [Macrocosmos · Train at Home](https://iota.macrocosmos.ai/)
- [Macrocosmos · TAH user guide](https://docs.macrocosmos.ai/product-and-services/tah/tah-user-guide)
- [IOTA Watch · source code](https://github.com/molimao/iota)