2020-07-27 20:13:22 +00:00
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title = "Raspberry-Pi"
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2020-07-27 20:13:22 +00:00
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weight = 40
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+++
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2022-01-27 19:23:15 +00:00
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# Raspberry Pi
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2021-06-16 21:57:58 +00:00
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Both components can run next to each other on a raspberry pi or
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2020-07-27 20:13:22 +00:00
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similiar device.
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2020-11-14 20:48:52 +00:00
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There is a [project on
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github](https://github.com/docspell/rpi-scripts) that can help with
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setting up a raspberry pi with docspell.
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2021-06-16 21:57:58 +00:00
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The docker images are built for arm64 and armv7, these can also be
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used on the pi.
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2020-07-27 20:13:22 +00:00
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## REST Server
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The REST server component runs very well on the Raspberry Pi and
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similiar devices. It doesn't require much resources, because the heavy
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work is done by the joex components.
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## Joex
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Running the joex component on the Raspberry Pi is possible, but will
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2020-09-25 19:58:02 +00:00
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result in long processing times for OCR and text analysis. The board
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should provide 4G of RAM (like the current RPi4), especially if also a
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2021-01-20 20:35:54 +00:00
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database and solr are running next to it. The memory required by joex
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depends on the config and document language. Please pick a value that
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2021-01-25 07:50:46 +00:00
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suits your setup from [here](@/docs/configure/_index.md#memory-usage).
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2021-01-20 20:35:54 +00:00
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For boards like the RPi, it might be necessary to use
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`nlp.mode=basic`, rather than `nlp.mode=full`. You should also set the
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joex pool size to 1.
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2020-09-25 19:58:02 +00:00
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An example: on this [UP
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board](https://up-board.org/up/specifications/) with an Intel Atom
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2021-01-20 20:35:54 +00:00
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x5-Z8350 CPU (@1.44Ghz) and 4G RAM, a scanned (300dpi, in German) pdf
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file with 6 pages took *3:20 min* to process. This board also runs the
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SOLR and a postgresql database.
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2020-09-25 19:58:02 +00:00
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The same file was processed in 55s on a qemu virtual machine on my i7
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notebook, using 1 CPU and 4G RAM (and identical config for joex). The
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virtual machine only had to host docspell (joex and restserver, but
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the restserver is very lightweight).
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The learning task for text classification can also use high amount of
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memory, but this depends on the amount of data you have in docspell.
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If you encounter problems here, you can set the maximum amount of
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items to consider in the collective settings page.
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