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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple multi-processing demo using SubprocVecEnv from SB3\n",
+    "Based on a code example provided by Rachael Proctor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!primaite setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import packages and read config file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import multiprocessing as mp\n",
+    "mp.get_all_start_methods()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "from stable_baselines3 import PPO\n",
+    "from stable_baselines3.common.utils import set_random_seed\n",
+    "from stable_baselines3.common.vec_env import SubprocVecEnv\n",
+    "\n",
+    "from primaite.session.environment import PrimaiteGymEnv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "EPISODE_LEN = 128\n",
+    "NUM_EPISODES = 10\n",
+    "NO_STEPS = EPISODE_LEN * NUM_EPISODES\n",
+    "BATCH_SIZE = 32\n",
+    "LEARNING_RATE = 3e-4\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "with open(\"c:/projects/primaite/src/primaite/config/_package_data/data_manipulation.yaml\", \"r\") as f:\n",
+    "    cfg = yaml.safe_load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def make_env(rank: int, seed: int = 0) -> callable:\n",
+    "    \"\"\"Wrapper script for _init function.\"\"\"\n",
+    "\n",
+    "    def _init() -> PrimaiteGymEnv:\n",
+    "        env = PrimaiteGymEnv(env_config=cfg)\n",
+    "        env.reset(seed=seed + rank)\n",
+    "        model = PPO(\n",
+    "            \"MlpPolicy\",\n",
+    "            env,\n",
+    "            learning_rate=LEARNING_RATE,\n",
+    "            n_steps=NO_STEPS,\n",
+    "            batch_size=BATCH_SIZE,\n",
+    "            verbose=0,\n",
+    "            tensorboard_log=\"./PPO_UC2/\",\n",
+    "        )\n",
+    "        model.learn(total_timesteps=NO_STEPS)\n",
+    "        return env\n",
+    "\n",
+    "    set_random_seed(seed)\n",
+    "    return _init\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "n_procs = 4\n",
+    "train_env = SubprocVecEnv([make_env(i + n_procs) for i in range(n_procs)])\n",
+    "print(train_env)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}