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Collect the settings that bring EOS from about 290 MB idle / 400-450 MB during a GENETIC run down to about 140 MB, with the measurements behind them: fitness cache, timezone override and SciPy-free self-consumption tables (#1376, #1377, #1378), autosave for SD/eMMC storage, and the glibc allocator and BLAS environment. Includes run times on a Raspberry Pi 4 and an x86 VM with data of a real installation. Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
117 lines
5.1 KiB
Markdown
117 lines
5.1 KiB
Markdown
% SPDX-License-Identifier: Apache-2.0
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(smalldevices-page)=
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# Running EOS on Small Devices
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EOS runs on single-board computers with 512 MB to 1 GB of RAM and on eMMC or SD storage.
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With the default configuration it needs about 290 MB at idle and 400–450 MB while a GENETIC
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run is in progress. The settings on this page bring that down to about 140 MB, without
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changing the optimization result.
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All figures below were measured with GENETIC at 300 individuals × 400 generations and
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15-minute slots; the individual measurements state their conditions.
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## Summary
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| Setting | Effect | Plan |
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|---|---|---|
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| `optimization.genetic.fitness_cache_max_entries: 0` [^pr1376] | −110 to −160 MB peak per run | identical |
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| `general.timezone_override` [^pr1377] | −28 MB at startup (no timezone lookup) | identical |
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| `optimization.self_consumption_interpolator: numpy` [^pr1378] | −37 MB after the first run (no SciPy) | identical |
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| `database.autosave_interval_sec: null` | storage writes from ~13 MB/min to ~1 KiB/min | identical, see notes |
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| `PYTHONMALLOC=malloc` (environment) | memory is returned after each run (glibc) | identical |
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| `OPENBLAS_NUM_THREADS=1` (environment) | no per-core BLAS buffers | identical |
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[^pr1376]: Added by [#1376](https://github.com/Akkudoktor-EOS/EOS/pull/1376).
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[^pr1377]: Added by [#1377](https://github.com/Akkudoktor-EOS/EOS/pull/1377), which also imports
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optional heavy packages on first use (−123 MB at startup without any configuration change).
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[^pr1378]: Added by [#1378](https://github.com/Akkudoktor-EOS/EOS/pull/1378).
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Example configuration:
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```json
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{
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"general": { "timezone_override": "Europe/Berlin" },
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"optimization": {
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"self_consumption_interpolator": "numpy",
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"genetic": { "fitness_cache_max_entries": 0 }
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},
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"database": { "autosave_interval_sec": null }
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}
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```
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Environment of the EOS process (for example in the systemd unit):
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```ini
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Environment=PYTHONMALLOC=malloc
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Environment=MALLOC_ARENA_MAX=2
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Environment=MALLOC_TRIM_THRESHOLD_=131072
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Environment=MALLOC_MMAP_THRESHOLD_=131072
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Environment=OPENBLAS_NUM_THREADS=1
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```
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## Measurements
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### Memory, all settings combined
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Linux arm64 (Debian 13, glibc, Python 3.13), one CPU per container, server-like process
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(EOSdash off), 48 h horizon, 77 kWh battery with direct marketing; "with EV" adds an
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electric vehicle and a dynamic tariff.
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| | Idle | Peak during a run | Peak with EV |
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|---|---|---|---|
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| `main` (default settings) | 282–288 MB | 401 MB | 451 MB |
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| all settings above | 136 MB | 142 MB | 144 MB |
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With the fitness cache disabled, the memory no longer depends on the number of planned
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devices: with an EV and five home appliances the peak was 144 MB (about 300 MB with the
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unbounded cache). More devices only cost run time.
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### Fitness cache
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Same platform, two consecutive runs, `MALLOC_ARENA_MAX=2`, measured together with the lazy
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imports (server baseline 136.5 MB before the first run):
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| Scenario | `fitness_cache_max_entries` | Peak RSS (anon) | Run time | Best fitness |
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|---|---|---|---|---|
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| base | `null` (unbounded) | 289.3 MB (231.3) | 89.5 s | −17.473689741050 |
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| base | `0` | 179.5 MB (121.1) | 88.7 s | −17.473689741050 |
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| with EV | `null` (unbounded) | 340.0 MB (282.0) | 131.6 s | −13.851295466546 |
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| with EV | `0` | 181.3 MB (123.3) | 133.2 s | −13.851295466546 |
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Only 6–15 % of the cache lookups hit. A Raspberry Pi 4 with real installation data (see below)
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showed the same picture: the unbounded cache made a run about 3.5 % faster and cost 100 MB.
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### Run time on real hardware
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Data of a real installation (77 kWh battery, direct marketing, 24 h control horizon at
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15 minutes), all settings above:
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| Device | Run time | Peak RSS |
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| Raspberry Pi 4 Model B (2 GB, Cortex-A72), fitness cache off | 623–636 s | 195 MB |
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| Raspberry Pi 4 Model B, fitness cache unbounded | 602–609 s | 294 MB |
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| x86 VM, 2 vCPU (container, with EV) | 173 s | 127 MB |
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On a Raspberry Pi 4 a run therefore takes about 10 minutes. With a 15-minute `ems.interval`
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the device is busy about 70 % of the time; a longer interval leaves more headroom.
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### Storage writes
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With the default `database.autosave_interval_sec` the measurement and prediction databases are
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written every few seconds. On an installation that pushes its forecasts and measurements from
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an external system this amounted to 12–14 MB per minute; with autosave disabled about
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1 KiB per minute remained. On SD cards and small eMMC this is the main source of wear.
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Disabling autosave means that data pushed to EOS is lost when EOS restarts. That is fine when an
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external system (an energy manager, Home Assistant, Node-RED) pushes forecasts and measurements
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again; it is not suitable when EOS fetches and keeps its own history.
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### Allocator
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On glibc, Python keeps memory freed after a run in its own pools. Measured on an x86 VM with an
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unbounded fitness cache, `PYTHONMALLOC=malloc` together with the trim thresholds above lowered
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the idle memory from about 295 MB to 238 MB, and after each run EOS fell back to about 260 MB
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instead of staying at its peak. With the fitness cache disabled the difference is small
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(about 5 MB), because the large per-run allocations no longer happen.
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