mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
synced 2025-10-31 02:39:16 +08:00
270 lines
8.8 KiB
Plaintext
270 lines
8.8 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Proximal Policy Optimization - PPO",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AYV_dMVDxyc2"
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},
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"source": [
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"[](https://github.com/lab-ml/nn)\n",
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"[](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/fast_weights/experiment.ipynb) \n",
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"\n",
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"## Fast Weights Transformer\n",
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"\n",
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"This is an experiment training Shakespeare dataset with a Compressive Transformer model."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AahG_i2y5tY9"
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},
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"source": [
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"Install the `labml-nn` package"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ZCzmCrAIVg0L",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "028e759e-0c9f-472e-b4b8-fdcf3e4604ee"
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},
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"source": [
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"!pip install labml-nn"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Collecting labml-nn\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a4/07/d33ead6f84fad2a4e8ff31ccd42864ff7b942785ad9f80d7c98df1c20a02/labml_nn-0.4.94-py3-none-any.whl (171kB)\n",
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"\u001b[K |████████████████████████████████| 174kB 16.4MB/s \n",
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"\u001b[?25hCollecting labml>=0.4.110\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/eb/c8/98b18d0dda3811998838734f9a32e944397fdd6bb0597cef0ae2b57338e3/labml-0.4.110-py3-none-any.whl (106kB)\n",
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"\u001b[K |████████████████████████████████| 112kB 34.7MB/s \n",
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"\u001b[?25hCollecting labml-helpers>=0.4.76\n",
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" Downloading https://files.pythonhosted.org/packages/49/df/4d920a4a221acd3cfa384dddb909ed0691b08682c0d8aeaabeee2138624f/labml_helpers-0.4.76-py3-none-any.whl\n",
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"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from labml-nn) (1.19.5)\n",
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"Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from labml-nn) (1.8.0+cu101)\n",
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"Collecting einops\n",
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" Downloading https://files.pythonhosted.org/packages/5d/a0/9935e030634bf60ecd572c775f64ace82ceddf2f504a5fd3902438f07090/einops-0.3.0-py2.py3-none-any.whl\n",
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"Collecting gitpython\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a6/99/98019716955ba243657daedd1de8f3a88ca1f5b75057c38e959db22fb87b/GitPython-3.1.14-py3-none-any.whl (159kB)\n",
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"\u001b[K |████████████████████████████████| 163kB 47.1MB/s \n",
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"\u001b[?25hRequirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from labml>=0.4.110->labml-nn) (3.13)\n",
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"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->labml-nn) (3.7.4.3)\n",
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"Collecting gitdb<5,>=4.0.1\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ea/e8/f414d1a4f0bbc668ed441f74f44c116d9816833a48bf81d22b697090dba8/gitdb-4.0.7-py3-none-any.whl (63kB)\n",
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"\u001b[K |████████████████████████████████| 71kB 11.1MB/s \n",
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"\u001b[?25hCollecting smmap<5,>=3.0.1\n",
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" Downloading https://files.pythonhosted.org/packages/68/ee/d540eb5e5996eb81c26ceffac6ee49041d473bc5125f2aa995cf51ec1cf1/smmap-4.0.0-py2.py3-none-any.whl\n",
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"Installing collected packages: smmap, gitdb, gitpython, labml, labml-helpers, einops, labml-nn\n",
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"Successfully installed einops-0.3.0 gitdb-4.0.7 gitpython-3.1.14 labml-0.4.110 labml-helpers-0.4.76 labml-nn-0.4.94 smmap-4.0.0\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SE2VUQ6L5zxI"
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},
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"source": [
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"Imports"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0hJXx_g0wS2C"
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},
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"source": [
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"from labml import experiment\n",
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"from labml.configs import FloatDynamicHyperParam\n",
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"from labml_nn.rl.ppo.experiment import Trainer"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Lpggo0wM6qb-"
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},
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"source": [
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"Create an experiment"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bFcr9k-l4cAg"
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},
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"source": [
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"experiment.create(name=\"ppo\")"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "-OnHLi626tJt"
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},
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"source": [
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"Configurations"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Piz0c5f44hRo"
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},
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"source": [
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"configs = {\n",
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" # number of updates\n",
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" 'updates': 10000,\n",
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" # number of epochs to train the model with sampled data\n",
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" 'epochs': 4,\n",
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" # number of worker processes\n",
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" 'n_workers': 8,\n",
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" # number of steps to run on each process for a single update\n",
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" 'worker_steps': 128,\n",
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" # number of mini batches\n",
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" 'batches': 4,\n",
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" # Value loss coefficient\n",
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" 'value_loss_coef': FloatDynamicHyperParam(0.5),\n",
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" # Entropy bonus coefficient\n",
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" 'entropy_bonus_coef': FloatDynamicHyperParam(0.01),\n",
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" # Clip range\n",
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" 'clip_range': FloatDynamicHyperParam(0.1),\n",
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" # Learning rate\n",
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" 'learning_rate': FloatDynamicHyperParam(2.5e-4, (0, 1e-3)),\n",
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"}"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "wwMzCqpD6vkL"
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},
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"source": [
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"Set experiment configurations"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 17
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},
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"id": "e6hmQhTw4nks",
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"outputId": "0e978879-5dcd-4140-ec53-24a3fbd547de"
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},
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"source": [
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"experiment.configs(configs)"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<pre style=\"overflow-x: scroll;\"></pre>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {
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"tags": []
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qYQCFt_JYsjd"
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},
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"source": [
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"Create trainer"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "8LB7XVViYuPG"
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},
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"source": [
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"trainer = Trainer(\n",
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" updates=configs['updates'],\n",
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" epochs=configs['epochs'],\n",
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" n_workers=configs['n_workers'],\n",
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" worker_steps=configs['worker_steps'],\n",
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" batches=configs['batches'],\n",
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" value_loss_coef=configs['value_loss_coef'],\n",
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" entropy_bonus_coef=configs['entropy_bonus_coef'],\n",
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" clip_range=configs['clip_range'],\n",
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" learning_rate=configs['learning_rate'],\n",
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")"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KJZRf8527GxL"
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},
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"source": [
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"Start the experiment and run the training loop."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "aIAWo7Fw5DR8"
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},
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"source": [
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"with experiment.start():\n",
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" trainer.run_training_loop()"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "oBXXlP2b7XZO"
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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}
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]
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} |