File size: 4,721 Bytes
5c3cdcb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"machine_shape": "hm",
"authorship_tag": "ABX9TyMmemQnx6G7GOnn6XBdjgxY",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"gpuClass": "standard",
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
"## Parameters\n",
"\n",
"\n",
"1. Wandb\n",
"\n"
],
"metadata": {
"id": "S-tXDWP8WTLc"
}
},
{
"cell_type": "code",
"source": [
"from getpass import getpass\n",
"import os\n",
"os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
],
"metadata": {
"id": "1ZtdYgxWNGwZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"2. train run parameters"
],
"metadata": {
"id": "ao0nAh3MOdN7"
}
},
{
"cell_type": "code",
"source": [
"ALGO = \"ppo\"\n",
"ENV = \"CartPole-v1\"\n",
"SEED = 1"
],
"metadata": {
"id": "jKL_NFhVOjSc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Setup\n",
"Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
],
"metadata": {
"id": "bsG35Io0hmKG"
}
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
],
"metadata": {
"id": "k5ynTV25hdAf"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Installing the correct packages:\n",
"\n",
"While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
],
"metadata": {
"id": "jKxGok-ElYQ7"
}
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"!apt install python-opengl\n",
"!apt install ffmpeg\n",
"!apt install xvfb\n",
"!apt install swig"
],
"metadata": {
"id": "nn6EETTc2Ewf"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"%cd /content/rl-algo-impls\n",
"python -m pip install ."
],
"metadata": {
"id": "AfZh9rH3yQii"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Run Once Per Runtime"
],
"metadata": {
"id": "4o5HOLjc4wq7"
}
},
{
"cell_type": "code",
"source": [
"import wandb\n",
"wandb.login()"
],
"metadata": {
"id": "PCXa5tdS2qFX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Restart Session beteween runs"
],
"metadata": {
"id": "AZBZfSUV43JQ"
}
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"from pyvirtualdisplay import Display\n",
"\n",
"virtual_display = Display(visible=0, size=(1400, 900))\n",
"virtual_display.start()"
],
"metadata": {
"id": "VzemeQJP2NO9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%cd /content/rl-algo-impls\n",
"!python train.py --algo {ALGO} --env {ENV} --seed {SEED}"
],
"metadata": {
"id": "07aHYFH1zfXa"
},
"execution_count": null,
"outputs": []
}
]
} |