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docs/jlearch/setup.md

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# How to setup environment for experiments on Linux
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* Clone repository, go to root
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* Maybe you will need `chmod +x ./scripts/*` and `chmod +x gradlew`.
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* `chmod +x ./scripts/*` and `chmod +x gradlew`.
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* Set `Java 8` as default and set `JAVA_HOME` to this `Java`.
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For example
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* Go through [this](https://sdkman.io/install) until `Windows installation`
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* `pip install -r scripts/requirements.txt`
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* `./scripts/prepare.sh`
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* Change `scripts/prog_list` to run on smaller project or delete some classes from `contest_input/classes/<project>/list`.
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* Also you can reduce number of models in `models` variable in `scripts/train_iteratively.sh`
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# Default settings and how to change it
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* You can reduce number of models in `models` variable in `scripts/train_iteratively.sh`
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* You can change amount of required RAM in `run_contest_estimator.sh`: `16 gb` by default
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* You can change `batch_size` or `device` n `train.py`: `4096` and `gpu` by default
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* If you are completing setup on server, then you will need to uncomment tmp directory option in `run_contest_estimator.sh`
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# Continue setup
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* `scripts/train_iteratively.sh 30 2 models <your python3 command>`
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* In `models/` you should get models.
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* `mkdir eval/jacoco`
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* `./scripts/run_with_coverage.sh <any project (guava-26.0, for example)> 30 "NN_REWARD_GUIDED_SELECTOR path/to/model" some_alias`. `path/to/model` should be something like `models/nn32/0`.
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* `./scripts/run_with_coverage.sh <any project (guava-26.0, for example)> 30 "NN_REWARD_GUIDED_SELECTOR path/to/model" some_alias`. `path/to/model` should be something like `models/nn32/0`, where `nn32` is a type of model and `0` is the iteration number
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* You should get jacoco report in `eval/jacoco/guava-26.0/some_alias/`

scripts/run_contest_estimator.sh

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#JVM Flags and Options
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JVM_OPTS=""
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JVM_OPTS=$JVM_OPTS" -Xms512m"
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JVM_OPTS=$JVM_OPTS" -Xmx16384m"
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JVM_OPTS=$JVM_OPTS" -Xmx12288m"
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JVM_OPTS=$JVM_OPTS" -XX:+UseG1GC"
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JVM_OPTS=$JVM_OPTS" -verbose:gc"
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JVM_OPTS=$JVM_OPTS" -XX:+PrintGCDetails"

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