> For the complete documentation index, see [llms.txt](https://docs.macrocosmos.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.macrocosmos.ai/product-and-services/tah/tah-user-guide.md).

# TAH User Guide

The Training at Home (TAH) application allows you to connect and partake in our decentralized AI training platform [IOTA](/subnets/subnet-9-iota.md). You can plug in any hardware you have access to and earn rewards for powering decentralized AI model training. 

## Installation

To start training at home, visit <https://iota.macrocosmos.ai/> and click "Download" in the Train at Home section. Currently TAH only supports MacOS, with Linux support coming soon. Look at [Hardware & OS Requirements](/product-and-services/tah/hardware-requirements.md) for more details.

If you already have access, you can double click a `.dmg` file to install the app.&#x20;

Video below demonstrates installation process:

{% embed url="<https://drive.google.com/file/d/1KEEYE37jqd3Wgxuds-JQSaQVe9gkSc3e/view?usp=drive_link>" %}

## Operating TAH

Once the app is installed you should be able to see the main dashboard:

![TAH dashboard showing Start training and Connect controls](/files/dYXR6ovbY3gWD0Z6l8dk)

You’ll see metrics for your model, and in the center there will be a visualization of all the other participants in the training.

#### Main controls (top right of the screen)

<figure><img src="/files/9OoNKNQ29gfiQj04DBE3" alt=""><figcaption></figcaption></figure>

* **Start training**: begins contributing your GPU/CPU to the current run. Click again to stop/exit the run.
* **Connect**: optionally paste your wallet coldkey to receive rewards to that address. You can train without connecting; if you connect later, rewards accrue to the provided coldkey from that point forward.
* **Status pill**: shows whether your hardware is ready (e.g., “Connected • Your GPU is ready to contribute”). Resolve any warnings before starting.

#### Start a session

1. Launch the app and wait for the status pill to read **Connected**.
2. (Optional) Click **Connect**, paste your wallet coldkey, and save.
3. Click **Start training**. The run ID, model size, and layer count populate in the right panel; progress and tokens/hour graphs begin updating.
4. To stop, click **Start training** again (it toggles off) or quit the app.&#x20;
5. A TAH tray icon is also available that allows you to stop or start training and to quit the app:

<figure><img src="/files/1CPrYhqfhkviBZwfB0VT" alt=""><figcaption></figcaption></figure>

#### Monitor while training

* **Run panel**: shows run ID, model size, layers, cumulative progress, and tokens/hour graph.

<figure><img src="/files/vRioekWxnoMGZ3WJmt4e" alt=""><figcaption></figcaption></figure>

***

* **Network view**: visualizes peers and traffic; switch tabs (Network/Layer/Miner) for different views.

<figure><img src="/files/J3WI2wwf228ZbkGEbCVn" alt=""><figcaption></figcaption></figure>

***

* **Loss chart**: training loss over time; toggle Log/Linear and tokens/time axes.

<figure><img src="/files/JPXByENQFnOKV2CDjkIl" alt=""><figcaption></figcaption></figure>

***

* **Run Log** (bottom right): per-epoch/step logs; lock icon indicates read-only when idle.

<figure><img src="/files/vKAOmScoXnRZU3HdC71z" alt=""><figcaption></figcaption></figure>

#### Resource behavior

* The app uses available GPU/CPU once training starts. If you need to pause, toggle **Start training** off.
* Keep the laptop powered and on a stable network for steady rewards. Thermal throttling may reduce contribution; use a cooler or lower-power profile if needed.

#### Updates

* If an update is available, install the latest build before starting a new session for compatibility with the active run.
  * If you do not update, you will not be able to join the current run.

## Rewards

Earnings will be calculated and paid as described below.

#### How Rewards Are Calculated

Rewards are primarily based on:

* The amount of tokens you contribute during training.
* How many times you contribute your model weights back to the network.
* Additional factors such as uptime, quality, and current network parameters.

Note: Exact weighting can evolve as the system updates.

#### Payout Cadence

* Payouts occur approximately every 24 hours; this window may be extended in the future.
* Minimum payout is subject to a variable network threshold (approximately 0.4 alpha currently). Balances below the threshold roll over to the next payout. For details see the [FAQs](/product-and-services/tah/faqs.md) "Earnings and Payouts" section.
* Payouts may be frozen if the active run is not improving. For details see the [FAQs](/product-and-services/tah/faqs.md) "Earnings and Payouts" section.
* Network transfer costs are deducted from the amount you receive.
* To receive payouts, optionally connect a wallet by pasting your public coldkey (via the **Connect** button in the app). If you don’t connect, training still runs but rewards won’t be delivered to you; unconnected earnings are not paid retroactively.

#### Viewing Earnings

* In the app, click the **Miner** button (top left) to view your current earnings and contribution stats.

<figure><img src="/files/Idbj6pnFxV4wXi0W7PjT" alt="" width="375"><figcaption></figcaption></figure>

* *Total Earned*: Total amount credited to you, including both completed and pending payments.
* *Total Paid:* The amount paid to your wallet.
* *Total Pending:* Pending amount credited to you, but not yet paid.

## Accessing Log data for T\@H

To document how the Train at Home app is running, operation logs are stored on your computer. You can access these via the following steps:

1. Open the Terminal app.
2. Navigate to the T\@H logging directory by typing `cd ~/Library/Logs/IOTA\ Train\ at\ Home` in the Terminal window, then press "enter".
3. Type `ls` and press "enter". This will show you your current log files. The file which provides diagnostic data on your T\@H activity is named `YYYY-MM-DD-cli.log`.
4. Choose the relevant date and open the file by typing `cat YYYY-MM-DD-cli.log` and pressing "enter". This will show the file contents in the terminal, allowing you to scroll through the T\@H events.
5. You can copy this log to another directory using `cp YYYY-MM-DD-cli.log /Users/<your-user-name>/Documents/logs` for example.

<figure><img src="/files/gT00ZkkdoMQiAvMWSgon" alt=""><figcaption><p>Commands used to access the T@H logs, with a print out of initial logs</p></figcaption></figure>

#### Taxes & Compliance

* You are responsible for complying with local tax and regulatory requirements related to rewards.

{% columns %}
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For more questions use [FAQs](/product-and-services/tah/faqs.md)
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If you have any issues use [TAH Support](broken://pages/sOuXmk7Tjf1tKjUMyPCT)
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