Training on the Linux / RTX 3090 box

Remote-training workflow: run long training sessions on the Linux machine and watch the dashboard from any machine on the network. All training artifacts (checkpoints, TensorBoard logs, exported bots) are committed to git — no result ever depends on a single machine, and moving models between the box and the Mac is just git pull. General training concepts and the export/evaluate workflow live in TRAINING.md — this doc is only what differs on the Linux box.

Both workflows below are wrapped in idempotent scripts in training/ — re-running either is always safe.

One-time setup

GitHub auth first (git-over-HTTPS no longer accepts account passwords, so clone over SSH — this key also lets run_training.sh push results):

ssh-keygen -t ed25519        # accept the defaults
cat ~/.ssh/id_ed25519.pub    # add at github.com/settings/keys → "New SSH key"

cd ~/ai-training
git clone git@github.com:jcreek/CosmicClash.git
# (submodules are editor tooling only — training doesn't need them)

~/ai-training/CosmicClash/training/setup_linux.sh

setup_linux.sh is safe to re-run any time (after a Godot upgrade, a broken venv, a fresh clone — it checks each step before acting). It:

downloads the Godot 4.7.1 Linux binary to ~/ai-training/godot/ if missing (override the location by exporting GODOT_BIN);
creates training/.venv if missing and installs requirements;
verifies CUDA torch, reinstalling from the CUDA wheel index if the box got a CPU-only build;
runs the Godot import pass (fresh clones have no .godot/ cache, so class_name scripts aren't registered until the project imports once);
finishes with the headless smoke test — the game must boot without rendering.

Maximising throughput

Env stepping is CPU-bound (each --n-parallel instance is one headless Godot process simulating 2 agents); the 3090 only accelerates the PPO updates. So the two levers are instance count and in-engine speedup:

--n-parallel: start at nproc minus 2 (leave headroom for the trainer process itself). The Mac mini sustains 6; a many-core box should take considerably more. Each instance opens its own TCP port upward from --port (default 11008).
--speedup: in-engine physics time-scale. 16 is proven; try 24–32 and keep raising while time/fps in the console/TensorBoard still scales up. Back off if fps stops improving (CPU saturated) or physics glitches appear (ball tunnelling, ships escaping the arena — watch for respawn warnings in the Godot output).

Tune by watching time/fps: run a 2-minute smoke run per setting and keep the best. Reference: 6 instances × speedup 16 ≈ 1,385 steps/s on an M4 Mac mini — a 20M-step run in ~4 h. Doubling fps halves that.

Run training

run_training.sh <experiment> [train.py args...] wraps the whole cycle: git pull → train → export the policy JSON → commit and push checkpoints, logs, and the exported bot. Run it inside tmux/screen so an SSH disconnect doesn't kill training. Ctrl-C is safe: the trainer writes final.zip on the way out, and the script still exports, commits, and pushes what it has.

Fresh run:

cd ~/ai-training/CosmicClash/training
./run_training.sh run03 --timesteps 20000000 --n-parallel 14 --speedup 24

Resuming a previous policy (continues its timestep counter; --timesteps is additional steps). Lessons from run01/run02 hard-coded into flags:

./run_training.sh run03 --timesteps 20000000 --n-parallel 14 --speedup 24 \
    --resume checkpoints/run02/final.zip --ent-coef 0.001 --reset-std 0.3

--ent-coef — entropy bonus. 0.0001 collapsed the policy std to 0.075 by 20M steps (no exploration left); 0.005 blew it up to 3.0 (random play). 0.001 is the current middle. Healthy train/std drifts between ~0.2 and ~1.0 — check it 30–45 min in before committing to a long run.
--reset-std — on resume, restores exploration a collapsed checkpoint lost.

Results travel via git

run_training.sh commits and pushes everything a run produces:

training/checkpoints/<exp>/ — periodic checkpoints + final.zip, for future --resume, evaluation, and difficulty tiers (an early checkpoint is an easy bot);
training/logs/ — TensorBoard history;
Game/bots/<exp>.json — the exported policy, immediately playable (point Match or Spectate mode at res://bots/<exp>.json);
training/eval_history.json — if evaluations ran.

On the Mac (or anywhere), collecting the results is just git pull. A 20M-step run adds roughly 40 MB of checkpoints — acceptable growth for the guarantee that training is never lost with a machine.

Dashboard over the network

On the Linux box, bind TensorBoard to all interfaces instead of localhost:

cd ~/ai-training/CosmicClash/training
.venv/bin/tensorboard --logdir logs --host 0.0.0.0 --port 6006

Then from the Mac (or anything on the LAN): http://<linux-box-hostname>:6006.

If ufw is active on the box: sudo ufw allow 6006/tcp.
If you'd rather not open a port, tunnel instead: ssh -L 6006:localhost:6006 <linux-box> from the Mac, then browse http://localhost:6006.

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