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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyNOXLieRc+7DSEUOVtmG9NS",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/training/colab_training_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Boc3uL_qWUKy",
"outputId": "f337b98c-48b5-4e3f-d1da-b38b08aca48f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'ProteinMPNN'...\n",
"remote: Enumerating objects: 542, done.\u001b[K\n",
"remote: Counting objects: 100% (542/542), done.\u001b[K\n",
"remote: Compressing objects: 100% (297/297), done.\u001b[K\n",
"remote: Total 542 (delta 236), reused 508 (delta 226), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (542/542), 66.58 MiB | 13.73 MiB/s, done.\n",
"Resolving deltas: 100% (236/236), done.\n"
]
}
],
"source": [
"!git clone https://github.com/dauparas/ProteinMPNN.git"
]
},
{
"cell_type": "code",
"source": [
"!wget https://files.ipd.uw.edu/pub/training_sets/pdb_2021aug02_sample.tar.gz"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lPQopPm1WWrG",
"outputId": "9546d08c-9248-445e-81b0-b3ba82f4a801"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-10-26 03:43:33-- https://files.ipd.uw.edu/pub/training_sets/pdb_2021aug02_sample.tar.gz\n",
"Resolving files.ipd.uw.edu (files.ipd.uw.edu)... 198.48.92.75, 128.95.160.134, 128.95.160.135, ...\n",
"Connecting to files.ipd.uw.edu (files.ipd.uw.edu)|198.48.92.75|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 49690915 (47M) [application/octet-stream]\n",
"Saving to: ‘pdb_2021aug02_sample.tar.gz’\n",
"\n",
"pdb_2021aug02_sampl 100%[===================>] 47.39M 13.5MB/s in 3.7s \n",
"\n",
"2022-10-26 03:43:38 (12.9 MB/s) - ‘pdb_2021aug02_sample.tar.gz’ saved [49690915/49690915]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!tar xvf \"pdb_2021aug02_sample.tar.gz\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "21to7lSNWemb",
"outputId": "7a51e458-a4e4-4b79-d26b-81570e855d62"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"./pdb_2021aug02_sample/pdb/l3/7l3k_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l3c_L.pt\n",
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"./pdb_2021aug02_sample/pdb/l3/4l3c_Z.pt\n",
"./pdb_2021aug02_sample/pdb/l3/3l3d.pt\n",
"./pdb_2021aug02_sample/pdb/l3/2l31_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/3l3k_A.pt\n",
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"./pdb_2021aug02_sample/pdb/l3/6l31_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l32_D.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l3z.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l34.pt\n",
"./pdb_2021aug02_sample/pdb/l3/7l3n_E.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l3f_H.pt\n",
"./pdb_2021aug02_sample/pdb/l3/6l3x_M.pt\n",
"./pdb_2021aug02_sample/pdb/l3/5l3p_V.pt\n",
"./pdb_2021aug02_sample/pdb/l3/7l31_D.pt\n",
"./pdb_2021aug02_sample/pdb/l3/5l32_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/6l39_B.pt\n",
"./pdb_2021aug02_sample/pdb/l3/3l3v_A.pt\n",
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"./pdb_2021aug02_sample/pdb/l3/7l3v_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l3h_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/3l3s_E.pt\n",
"./pdb_2021aug02_sample/pdb/l3/7l3i.pt\n",
"./pdb_2021aug02_sample/pdb/l3/1l32_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/2l3w_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/1l3t.pt\n",
"./pdb_2021aug02_sample/pdb/l3/1l3b.pt\n",
"./pdb_2021aug02_sample/pdb/l3/5l38_I.pt\n",
"./pdb_2021aug02_sample/pdb/l3/4l33_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/1l3i_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/6l32.pt\n",
"./pdb_2021aug02_sample/pdb/l3/5l3i_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/6l3w_A.pt\n",
"./pdb_2021aug02_sample/pdb/l3/1l3l_E.pt\n",
"./pdb_2021aug02_sample/test_clusters.txt\n",
"./pdb_2021aug02_sample/valid_clusters.txt\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import sys"
],
"metadata": {
"id": "Ul9pmaQzXAg3"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sys.path.append(\"/content/ProteinMPNN/training\")"
],
"metadata": {
"id": "o-03W_vgWguT"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from training import main as run_training"
],
"metadata": {
"id": "6-xXsyM-W_7k"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n",
"\n",
"# argparser.add_argument(\"--path_for_training_data\", type=str, default=\"my_path/pdb_2021aug02\", help=\"path for loading training data\") \n",
"# argparser.add_argument(\"--path_for_outputs\", type=str, default=\"./test\", help=\"path for logs and model weights\")\n",
"# argparser.add_argument(\"--previous_checkpoint\", type=str, default=\"\", help=\"path for previous model weights, e.g. file.pt\")\n",
"# argparser.add_argument(\"--num_epochs\", type=int, default=200, help=\"number of epochs to train for\")\n",
"# argparser.add_argument(\"--save_model_every_n_epochs\", type=int, default=10, help=\"save model weights every n epochs\")\n",
"# argparser.add_argument(\"--reload_data_every_n_epochs\", type=int, default=2, help=\"reload training data every n epochs\")\n",
"# argparser.add_argument(\"--num_examples_per_epoch\", type=int, default=1000000, help=\"number of training example to load for one epoch\")\n",
"# argparser.add_argument(\"--batch_size\", type=int, default=10000, help=\"number of tokens for one batch\")\n",
"# argparser.add_argument(\"--max_protein_length\", type=int, default=10000, help=\"maximum length of the protein complext\")\n",
"# argparser.add_argument(\"--hidden_dim\", type=int, default=128, help=\"hidden model dimension\")\n",
"# argparser.add_argument(\"--num_encoder_layers\", type=int, default=3, help=\"number of encoder layers\") \n",
"# argparser.add_argument(\"--num_decoder_layers\", type=int, default=3, help=\"number of decoder layers\")\n",
"# argparser.add_argument(\"--num_neighbors\", type=int, default=48, help=\"number of neighbors for the sparse graph\") \n",
"# argparser.add_argument(\"--dropout\", type=float, default=0.1, help=\"dropout level; 0.0 means no dropout\")\n",
"# argparser.add_argument(\"--backbone_noise\", type=float, default=0.2, help=\"amount of noise added to backbone during training\") \n",
"# argparser.add_argument(\"--rescut\", type=float, default=3.5, help=\"PDB resolution cutoff\")\n",
"# argparser.add_argument(\"--debug\", type=bool, default=False, help=\"minimal data loading for debugging\")\n",
"# argparser.add_argument(\"--gradient_norm\", type=float, default=-1.0, help=\"clip gradient norm, set to negative to omit clipping\")\n",
"# argparser.add_argument(\"--mixed_precision\", type=bool, default=True, help=\"train with mixed precision\")\n",
"\n",
"# args = argparser.parse_args() \n",
"# main(args)\n",
"\n",
"class MyArgs(object):\n",
" def __init__(self):\n",
" self.path_for_training_data = \"/content/pdb_2021aug02_sample\"\n",
" self.path_for_outputs = \"/content/test\"\n",
" self.previous_checkpoint = \"\"\n",
" self.num_epochs = 50\n",
" self.save_model_every_n_epochs = 5\n",
" self.reload_data_every_n_epochs = 4\n",
" self.num_examples_per_epoch = 200\n",
" self.batch_size = 2000\n",
" self.max_protein_length = 2000\n",
" self.hidden_dim = 128\n",
" self.num_encoder_layers = 3\n",
" self.num_decoder_layers = 3\n",
" self.num_neighbors = 32\n",
" self.dropout = 0.1\n",
" self.backbone_noise = 0.1\n",
" self.rescut = 3.5\n",
" self.debug = False\n",
" self.gradient_norm = -1.0 #no norm\n",
" self.mixed_precision= True \n",
"\n",
"args = MyArgs()"
],
"metadata": {
"id": "mks1E1FTXGXL"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#epoch - number of times data is trained on\n",
"#step - number of optimizer steps\n",
"#time - time in seconds for one epoch to finish\n",
"#train - training perplexity = exp(average categorical cross entropy)\n",
"#valid - validation perplexity\n",
"#train_acc - training accuracy\n",
"#valid_acc - validation accuracy"
],
"metadata": {
"id": "6csB6zUrbcAH"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"run_training(args)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "68wFaZ0IXHzj",
"outputId": "52c84f9e-599b-4ca4-b46f-25170828a310"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
" cpuset_checked))\n",
"/usr/local/lib/python3.7/dist-packages/torch/utils/checkpoint.py:25: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"epoch: 1, step: 4, time: 1.3, train: 32.478, valid: 26.652, train_acc: 0.057, valid_acc: 0.060\n",
"epoch: 2, step: 8, time: 1.4, train: 31.076, valid: 24.790, train_acc: 0.056, valid_acc: 0.067\n",
"epoch: 3, step: 12, time: 0.9, train: 28.561, valid: 22.354, train_acc: 0.058, valid_acc: 0.075\n",
"epoch: 4, step: 16, time: 0.9, train: 25.958, valid: 20.493, train_acc: 0.063, valid_acc: 0.090\n",
"epoch: 5, step: 22, time: 1.6, train: 23.583, valid: 19.557, train_acc: 0.065, valid_acc: 0.114\n",
"epoch: 6, step: 28, time: 1.5, train: 21.809, valid: 19.653, train_acc: 0.069, valid_acc: 0.104\n",
"epoch: 7, step: 34, time: 1.5, train: 20.483, valid: 19.440, train_acc: 0.074, valid_acc: 0.105\n",
"epoch: 8, step: 40, time: 1.2, train: 19.474, valid: 19.000, train_acc: 0.097, valid_acc: 0.122\n",
"epoch: 9, step: 46, time: 2.0, train: 19.268, valid: 17.082, train_acc: 0.093, valid_acc: 0.116\n",
"epoch: 10, step: 52, time: 2.3, train: 18.658, valid: 16.812, train_acc: 0.097, valid_acc: 0.109\n",
"epoch: 11, step: 58, time: 2.3, train: 18.158, valid: 16.962, train_acc: 0.105, valid_acc: 0.120\n",
"epoch: 12, step: 64, time: 1.7, train: 17.705, valid: 16.780, train_acc: 0.109, valid_acc: 0.120\n",
"epoch: 13, step: 69, time: 32.2, train: 18.135, valid: 16.984, train_acc: 0.105, valid_acc: 0.116\n",
"epoch: 14, step: 74, time: 1.9, train: 18.008, valid: 17.287, train_acc: 0.106, valid_acc: 0.120\n",
"epoch: 15, step: 79, time: 2.0, train: 17.833, valid: 17.172, train_acc: 0.118, valid_acc: 0.111\n",
"epoch: 16, step: 84, time: 1.7, train: 17.451, valid: 16.884, train_acc: 0.119, valid_acc: 0.115\n",
"epoch: 17, step: 90, time: 3.0, train: 18.145, valid: 17.919, train_acc: 0.111, valid_acc: 0.129\n",
"epoch: 18, step: 96, time: 2.8, train: 17.651, valid: 17.883, train_acc: 0.103, valid_acc: 0.103\n",
"epoch: 19, step: 102, time: 2.0, train: 17.608, valid: 17.774, train_acc: 0.104, valid_acc: 0.126\n",
"epoch: 20, step: 108, time: 2.3, train: 17.374, valid: 18.082, train_acc: 0.114, valid_acc: 0.132\n",
"epoch: 21, step: 114, time: 31.8, train: 17.079, valid: 18.081, train_acc: 0.123, valid_acc: 0.117\n",
"epoch: 22, step: 120, time: 2.5, train: 16.938, valid: 18.005, train_acc: 0.130, valid_acc: 0.133\n",
"epoch: 23, step: 126, time: 1.6, train: 16.719, valid: 18.174, train_acc: 0.123, valid_acc: 0.116\n",
"epoch: 24, step: 132, time: 1.2, train: 16.580, valid: 17.844, train_acc: 0.129, valid_acc: 0.136\n",
"epoch: 25, step: 139, time: 2.6, train: 16.797, valid: 15.777, train_acc: 0.118, valid_acc: 0.125\n",
"epoch: 26, step: 146, time: 2.7, train: 16.433, valid: 15.730, train_acc: 0.125, valid_acc: 0.134\n",
"epoch: 27, step: 153, time: 1.8, train: 16.127, valid: 15.673, train_acc: 0.127, valid_acc: 0.135\n",
"epoch: 28, step: 160, time: 1.8, train: 15.798, valid: 15.235, train_acc: 0.124, valid_acc: 0.148\n",
"epoch: 29, step: 166, time: 31.4, train: 16.003, valid: 15.344, train_acc: 0.139, valid_acc: 0.146\n",
"epoch: 30, step: 172, time: 2.7, train: 15.639, valid: 15.524, train_acc: 0.146, valid_acc: 0.133\n",
"epoch: 31, step: 178, time: 1.9, train: 15.546, valid: 14.927, train_acc: 0.142, valid_acc: 0.153\n",
"epoch: 32, step: 184, time: 1.7, train: 15.215, valid: 15.001, train_acc: 0.155, valid_acc: 0.155\n",
"epoch: 33, step: 190, time: 4.9, train: 15.418, valid: 15.358, train_acc: 0.144, valid_acc: 0.137\n",
"epoch: 34, step: 196, time: 3.2, train: 15.255, valid: 14.912, train_acc: 0.144, valid_acc: 0.152\n",
"epoch: 35, step: 202, time: 1.9, train: 14.925, valid: 14.894, train_acc: 0.153, valid_acc: 0.156\n",
"epoch: 36, step: 208, time: 2.0, train: 14.736, valid: 14.729, train_acc: 0.154, valid_acc: 0.168\n",
"epoch: 37, step: 214, time: 32.7, train: 14.467, valid: 17.068, train_acc: 0.152, valid_acc: 0.140\n",
"epoch: 38, step: 220, time: 2.8, train: 14.410, valid: 16.987, train_acc: 0.158, valid_acc: 0.122\n",
"epoch: 39, step: 226, time: 2.0, train: 14.101, valid: 16.935, train_acc: 0.162, valid_acc: 0.123\n",
"epoch: 40, step: 232, time: 2.4, train: 14.034, valid: 17.452, train_acc: 0.168, valid_acc: 0.131\n",
"epoch: 41, step: 237, time: 4.0, train: 15.268, valid: 16.151, train_acc: 0.158, valid_acc: 0.147\n",
"epoch: 42, step: 242, time: 2.2, train: 15.403, valid: 16.473, train_acc: 0.147, valid_acc: 0.148\n",
"epoch: 43, step: 247, time: 1.6, train: 15.008, valid: 17.016, train_acc: 0.161, valid_acc: 0.129\n",
"epoch: 44, step: 252, time: 2.0, train: 14.492, valid: 16.040, train_acc: 0.168, valid_acc: 0.152\n",
"epoch: 45, step: 258, time: 32.4, train: 14.046, valid: 14.339, train_acc: 0.172, valid_acc: 0.161\n",
"epoch: 46, step: 264, time: 2.4, train: 13.564, valid: 13.959, train_acc: 0.179, valid_acc: 0.156\n",
"epoch: 47, step: 270, time: 1.8, train: 13.514, valid: 14.189, train_acc: 0.181, valid_acc: 0.151\n",
"epoch: 48, step: 276, time: 1.5, train: 13.257, valid: 13.961, train_acc: 0.180, valid_acc: 0.160\n",
"epoch: 49, step: 281, time: 4.1, train: 13.741, valid: 16.552, train_acc: 0.176, valid_acc: 0.154\n",
"epoch: 50, step: 286, time: 2.2, train: 13.157, valid: 17.389, train_acc: 0.180, valid_acc: 0.130\n"
]
}
]
}
]
} |