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microsoft
GitHub Repository: microsoft/CyberBattleSim
 Path: blob/main/notebooks/run_quick.sh
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# Run all the Jupyter notebooks in quick test mode (small number of iteartions and episodes) and write the output to disk

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set -ex

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kernel=$1

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if [ -z "$kernel" ]; then

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    kernel=cybersim

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fi

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script_dir=$(dirname "$0")

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pushd "$script_dir/.."

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output_dir=notebooks/output/quick

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output_plot_dir=$output_dir/plots

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run () {

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    base=$1

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    suffix=$2

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    cat notebook notebooks/$base.py \

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        | jupytext --to ipynb  - \

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        | papermill --kernel $kernel $output_dir/$base$suffix.ipynb  "${@:3}"

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}

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jupyter kernelspec list

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mkdir $output_dir -p

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mkdir $output_plot_dir -p

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# run c2_interactive_interface '' # disabled: not deterministic and can fail

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# run  random_active_directory '' # disabled: not deterministic and can fail

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run toyctf-blank ''

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run toyctf-random ''

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run toyctf-solved ''

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run chainnetwork-optionwrapper ''

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run chainnetwork-random '' -y "

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    iterations: 100

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"

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run randomnetwork ''

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run notebook_benchmark '-chain' -y "

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    gymid: 'CyberBattleChain-v0'

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    iteration_count: 100

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    training_episode_count: 5

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    eval_episode_count: 3

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    maximum_node_count: 12

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    maximum_total_credentials: 7

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    plots_dir: $output_plot_dir

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"

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run notebook_benchmark '-toyctf' -y "

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    gymid: 'CyberBattleToyCtf-v0'

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    env_size: null

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    iteration_count: 100

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    training_episode_count: 3

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    eval_episode_count: 5

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    maximum_node_count: 12

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    maximum_total_credentials: 10

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    plots_dir: $output_plot_dir

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"

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run notebook_benchmark '-tiny' -y "

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    gymid: 'CyberBattleTiny-v0'

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    env_size: null

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    iteration_count: 100

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    training_episode_count: 4

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    eval_episode_count: 2

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    maximum_node_count: 5

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    maximum_total_credentials: 3

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    plots_dir: $output_plot_dir

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"

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run notebook_dql_transfer '' -y "

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    iteration_count: 500

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    training_episode_count: 5

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    eval_episode_count: 3

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    plots_dir: $output_plot_dir

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"

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run notebook_randlookups '' -y "

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    iteration_count: 500

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    training_episode_count: 5

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    eval_episode_count: 2

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    plots_dir: $output_plot_dir

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"

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run notebook_tabularq '' -y "

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    iteration_count: 200

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    training_episode_count: 5

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    eval_episode_count: 2

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    plots_dir: $output_plot_dir

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"

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run notebook_withdefender '' -y "

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    iteration_count: 100

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    training_episode_count: 3

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    plots_dir: $output_plot_dir

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"

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run dql_active_directory '' -y "

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    ngyms: 3

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    iteration_count: 50

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"

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popd

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