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Copy file name to clipboardExpand all lines: docs/advanced-setup.md
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The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
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So, the following are the examples of possible addresses:
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* `"address": "/dev/log"` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
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* `"address": "/var/run/syslog"` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
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* `"address": "localhost:514"` -- log to local syslog using UDP socket, if it listens on port 514.
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* `"address": "<ip>:514"` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
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??? Info "Deprecated - configure syslog via command line"
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`--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
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`--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
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The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
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The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
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So, the following are the examples of possible usages:
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So, the following are the examples of possible usages:
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* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
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* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
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* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
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* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
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* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
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* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
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* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
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* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
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* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
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* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:stable_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. If you would like to use PyTorch or Reinforcement learning, you should use the torch or RL tags, `image: freqtradeorg/freqtrade:stable_freqaitorch`, `image: freqtradeorg/freqtrade:stable_freqairl`.
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!!! note "docker-compose-freqai.yml"
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We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
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We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
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### FreqAI position in open-source machine learning landscape
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