https://github.com/alexeyev/neurocuts-docker

NeuroCuts algorithm implementation -- with all dependencies handled

https://github.com/alexeyev/neurocuts-docker

Repository

NeuroCuts algorithm implementation -- with all dependencies handled

https://github.com/alexeyev/neurocuts-docker/blob/master/

# NeuroCuts

NeuroCuts is a deep RL algorithm for generating optimized packet classification trees. See the [preprint](https://arxiv.org/abs/1902.10319) 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](/context/requirements.txt)

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

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

To check that everything works, when inside, run
```bash
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:
```bash
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):

![stats](tensorboard.png)

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 `. 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`.

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