alexeyev / neurocuts-docker

NeuroCuts algorithm implementation -- with all dependencies handled

NeuroCuts

NeuroCuts is a deep RL algorithm for generating optimized packet classification trees. See the preprint for an overview.

What this fork is for

@alexeyev: It took me a while to make NeuroCuts mid-2019 code work in October 2022. This repository's purpose is to make the setup easier (e.g. for results reproduction or whatever).

You don't have to use Docker, you could just

1. install tensorflow:1.14.0,
2. take a look at requirements.txt

This code might work with other versions of libraries; my primary goal was to prepare a config that WORKS. No guarantees, though :)

cd neurocuts-docker
docker build -t neurocuts .
nvidia-docker run -it neurocuts:latest bash

To check that everything works, when inside, run

python run_neurocuts.py --rules=acl5_1k --fast --gpu --num-workers 20

Running NeuroCuts

You can train a NeuroCuts policy for the small acl5_1k rule set using the following command. This should converge to an memory access time of 9-10 within 50k timesteps:

python run_neurocuts.py --rules=acl5_1k --fast

To monitor training progress, open tensorboard --logdir=~/ray_results and navigate to the web UI. The important metrics to pay attention to are rules_remaining_min (this must reach zero before the policy starts generating "valid" trees), memory_access_valid_min (access time metric for valid trees), bytes_per_rule_valid_min (bytes per rule metric for valid trees), and vf_explained_var (explained variance of the value function, which approaches 1 as the policy converges):

To kick off a full-scale training run, pass in a comma separated list of rule file names from the classbench directory and overrides for other hyperparameters. Example:

python run_neurocuts.py --rules=acl1_10k,fw1_10k,ipc1_10k \
    --partition-mode=efficuts \
    --dump-dir=/tmp/trees --num-workers=8 --gpu

Inspecting trees

You can visualize and check the state of generated trees by running inspect_tree.py <tree.pkl>. This requires that you specify the --dump-dir option when running NeuroCuts training.

Running baselines

You can run the HiCuts, HyperCuts, EffiCuts, and CutSplit baselines using run_baselines.py.