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Update Raspberry Pi Zero 2 W FAQ section with memory management tips

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- Expanded the guidance on running meshcore-bot on Raspberry Pi Zero 2 W, detailing two steps to optimize memory usage: disabling the web viewer and tuning the Mesh Graph settings.
- Included specific configuration examples to help users effectively manage resource constraints on the device.
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agessaman
2026-02-18 15:46:16 -08:00
parent de6580c231
commit 3afdfe6e01
1 changed files with 26 additions and 2 deletions
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@@ -32,7 +32,29 @@ See [Custom command reference website](command-reference-website.md): it explain
### How do I run meshcore-bot on a Raspberry Pi Zero 2 W?
The Pi Zero 2 W has 512 MB of RAM, which is enough to run the bot, but the Mesh Graph can grow large on a busy mesh. Add the following to the `[Path_Command]` section of your `config.ini` to keep memory usage in check:
The Pi Zero 2 W has 512 MB of RAM. The bot and the web viewer are two separate
Python processes; together they use roughly 300 MB on a busy mesh, which leaves
little headroom. Follow the two steps below to keep things comfortable.
#### Step 1 — Run the bot only (saves ~150 MB)
The web viewer is optional. If you don't need the browser-based dashboard on
the Pi itself, disable it and access it from another machine instead:
```ini
[Web_Viewer]
enabled = false
auto_start = false
```
The bot continues to work normally; the web viewer just won't start on the Pi.
If you still want the dashboard, run the viewer on a desktop or server that
shares the same database file (see [MeshCore Bot Data Viewer](web-viewer.md)).
#### Step 2 — Tune the Mesh Graph (saves another 50–100 MB on busy meshes)
Even with the web viewer off, the Mesh Graph can grow large. Add the following
to the `[Path_Command]` section of your `config.ini`:
```ini
[Path_Command]
@@ -51,4 +73,6 @@ graph_write_strategy = batched
# graph_capture_enabled = false
```
These settings do not affect path prediction accuracy: edges older than a few days carry negligible confidence due to the 48-hour recency half-life used by the scoring algorithm.
These settings do not affect path prediction accuracy: edges older than a few
days carry negligible confidence due to the 48-hour recency half-life used by
the scoring algorithm.