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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Blazing Eights - Colab GPU Training\n",
"\n",
"Clone repo → Train PPO agent on GPU → Push trained model back to GitHub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Setup: Clone repo & install deps"
]
},
{
"cell_type": "code",
"metadata": {},
"source": "# ====== CONFIG ======\nGITHUB_USERNAME = \"YurenHao0426\"\nREPO_NAME = \"blazing8\"\n# ====================\n\n!git clone https://github.com/{GITHUB_USERNAME}/{REPO_NAME}.git\n%cd {REPO_NAME}\n!pip install -q torch numpy tqdm",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"import torch\n",
"print(f\"PyTorch: {torch.__version__}\")\n",
"print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Train"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# 2-player training: GPU makes the PPO update faster\n",
"!python train.py --num_players 2 --episodes 200000 --save_path blazing_ppo_2p"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# (Optional) 3-player training\n",
"# !python train.py --num_players 3 --episodes 300000 --save_path blazing_ppo_3p"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# (Optional) 4-player training\n",
"# !python train.py --num_players 4 --episodes 400000 --lr 1e-4 --ent_coef 0.02 --save_path blazing_ppo_4p"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Download model locally (Option A)\n",
"Download .pt files directly from Colab to your machine."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"from google.colab import files\n",
"import glob\n",
"\n",
"# Download the final model\n",
"for f in glob.glob(\"*_final.pt\"):\n",
" print(f\"Downloading {f}...\")\n",
" files.download(f)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Push model to GitHub (Option B)\n",
"\n",
"Push trained .pt files to a `models/` directory in the repo.\n",
"\n",
"You'll need a **GitHub Personal Access Token** (PAT).\n",
"Create one at: https://github.com/settings/tokens → Generate new token (classic) → check `repo` scope."
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"from getpass import getpass\n",
"import os\n",
"\n",
"TOKEN = getpass(\"Enter your GitHub PAT: \")\n",
"\n",
"# Configure git\n",
"!git config user.email \"colab@training.ai\"\n",
"!git config user.name \"Colab Training\"\n",
"\n",
"# Create models dir, move .pt files there\n",
"os.makedirs(\"models\", exist_ok=True)\n",
"!mv *_final.pt models/\n",
"!ls -lh models/\n",
"\n",
"# Remove .pt from gitignore temporarily for models/ dir\n",
"with open(\".gitignore\", \"r\") as f:\n",
" lines = f.readlines()\n",
"with open(\".gitignore\", \"w\") as f:\n",
" for line in lines:\n",
" f.write(line)\n",
" f.write(\"\\n# Allow models dir\\n!models/\\n!models/*.pt\\n\")\n",
"\n",
"!git add models/ .gitignore\n",
"!git commit -m \"Add trained models from Colab GPU\"\n",
"!git push https://{TOKEN}@github.com/{GITHUB_USERNAME}/{REPO_NAME}.git main"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Quick evaluation"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"import sys\n",
"sys.path.insert(0, \".\")\n",
"from train import PolicyValueNet, evaluate_vs_random\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"model = PolicyValueNet().to(device)\n",
"\n",
"# Load the trained model\n",
"import glob\n",
"final_models = glob.glob(\"*_final.pt\") + glob.glob(\"models/*_final.pt\")\n",
"if final_models:\n",
" ckpt = torch.load(final_models[0], map_location=device, weights_only=True)\n",
" model.load_state_dict(ckpt[\"model\"])\n",
" model.eval()\n",
" print(f\"Loaded: {final_models[0]}\")\n",
" print(f\"Trained for {ckpt.get('episode', '?')} episodes\")\n",
" print()\n",
"\n",
" for n in [2, 3, 4]:\n",
" wr = evaluate_vs_random(model, num_players=n, num_games=2000, device=device)\n",
" print(f\" {n} players: win rate = {wr:.1%} (random baseline: {1/n:.1%})\")\n",
"else:\n",
" print(\"No model found. Train first!\")"
],
"execution_count": null,
"outputs": []
}
]
}
|
