NeuroCuts is a deep RL algorithm for generating optimized packet classification trees. See the preprint for an overview.
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
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.
You can run the HiCuts, HyperCuts, EffiCuts, and CutSplit baselines using run_baselines.py.