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{
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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" : " Transformer" ,
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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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}
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},
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"source" : [
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- " Install the packages"
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+ " ### Install the packages"
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]
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},
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{
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"cell_type" : " code" ,
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+ "execution_count" : null ,
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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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+ "id" : " ZCzmCrAIVg0L" ,
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"outputId" : " cf107fb2-4d50-4c67-af34-367624553421" ,
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"pycharm" : {
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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+ "source" : [
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+ " !pip install labml-nn comet_ml --quiet"
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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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"source" : [
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- " !pip install labml-nn comet_ml"
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- ],
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+ " ### Enable [Comet](https://www.comet.ml)"
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+ ]
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+ },
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+ {
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+ "cell_type" : " code" ,
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"execution_count" : null ,
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- "outputs" : []
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+ "metadata" : {},
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+ "outputs" : [],
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+ "source" : [
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+ " #@markdown Select in order to enable logging this experiment to [Comet](https://www.comet.ml).\n " ,
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+ " use_comet = True #@param {type:\" boolean\" }\n " ,
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+ " \n " ,
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+ " if use_comet:\n " ,
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+ " import comet_ml\n " ,
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+ " comet_ml.init(project_name='transformer')"
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+ ]
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},
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{
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"cell_type" : " markdown" ,
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}
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},
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"source" : [
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- " Imports"
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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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+ "execution_count" : null ,
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"metadata" : {
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"id" : " 0hJXx_g0wS2C" ,
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"pycharm" : {
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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"source" : [
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" import torch\n " ,
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" import torch.nn as nn\n " ,
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" \n " ,
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" from labml import experiment\n " ,
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" from labml.configs import option\n " ,
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" from labml_nn.transformers.basic.autoregressive_experiment import 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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- {
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- "cell_type" : " markdown" ,
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- "source" : [
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- " Set [Comet](https://www.comet.ml) api-key and the workspace."
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- ],
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- "metadata" : {
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- "collapsed" : false ,
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- "pycharm" : {
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- "name" : " #%% md\n "
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- }
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- }
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- },
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- {
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- "cell_type" : " code" ,
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- "execution_count" : null ,
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- "outputs" : [],
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- "source" : [
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- " import os\n " ,
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- " \n " ,
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- " os.environ['COMET_API_KEY'] = \"\"\n " ,
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- " os.environ['COMET_WORKSPACE'] = \"\" "
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- ],
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- "metadata" : {
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- "collapsed" : false ,
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- "pycharm" : {
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- "name" : " #%%\n "
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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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}
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},
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"source" : [
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- " Create an experiment"
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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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+ "execution_count" : null ,
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"metadata" : {
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"id" : " bFcr9k-l4cAg" ,
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"pycharm" : {
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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"source" : [
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- " experiment.create(name=\" transformer\" , writers={'screen', 'web_api', 'comet'})"
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- ],
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- "execution_count" : null ,
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- "outputs" : []
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+ " experiment.create(name=\" transformer\" , writers={\" screen\" , \" comet\" } if use_comet else {'screen'})"
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+ ]
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},
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{
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"cell_type" : " markdown" ,
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}
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},
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"source" : [
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- " Initialize configurations "
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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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+ "execution_count" : null ,
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"metadata" : {
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"id" : " Piz0c5f44hRo" ,
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"pycharm" : {
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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"source" : [
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" conf = 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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},
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{
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"cell_type" : " markdown" ,
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},
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{
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"cell_type" : " code" ,
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+ "execution_count" : null ,
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"metadata" : {
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"colab" : {
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"base_uri" : " https://localhost:8080/" ,
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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"source" : [
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" experiment.configs(conf, {\n " ,
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" # Use character level tokenizer\n " ,
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" 'optimizer.optimizer': 'Noam',\n " ,
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" 'optimizer.learning_rate': 1.,\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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{
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"cell_type" : " markdown" ,
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},
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{
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"cell_type" : " code" ,
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+ "execution_count" : null ,
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"metadata" : {
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"colab" : {
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"base_uri" : " https://localhost:8080/" ,
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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"source" : [
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" experiment.add_pytorch_models({'model': conf.model})"
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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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{
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"cell_type" : " markdown" ,
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}
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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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+ " ### 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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+ "execution_count" : null ,
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"metadata" : {
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"colab" : {
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"base_uri" : " https://localhost:8080/" ,
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"name" : " #%%\n "
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}
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},
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+ "outputs" : [],
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"source" : [
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" # Start the experiment\n " ,
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" with experiment.start():\n " ,
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" conf.run()"
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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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{
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"cell_type" : " code" ,
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+ "execution_count" : null ,
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"metadata" : {
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"id" : " oBXXlP2b7XZO" ,
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"pycharm" : {
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"name" : " #%%\n "
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}
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},
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- "source" : [],
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- "execution_count" : null ,
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- "outputs" : []
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+ "outputs" : [],
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+ "source" : []
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}
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- ]
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- }
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+ ],
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+ "metadata" : {
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+ "accelerator" : " GPU" ,
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+ "colab" : {
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+ "collapsed_sections" : [],
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+ "name" : " Transformer" ,
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+ "provenance" : []
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+ },
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+ "kernelspec" : {
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+ "display_name" : " Python 3 (ipykernel)" ,
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+ "language" : " python" ,
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+ "name" : " python3"
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+ },
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+ "language_info" : {
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+ "codemirror_mode" : {
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+ "name" : " ipython" ,
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+ "version" : 3
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+ },
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+ "file_extension" : " .py" ,
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+ "mimetype" : " text/x-python" ,
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+ "name" : " python" ,
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+ "nbconvert_exporter" : " python" ,
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+ "pygments_lexer" : " ipython3" ,
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+ "version" : " 3.7.11"
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+ }
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+ },
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+ "nbformat" : 4 ,
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+ "nbformat_minor" : 4
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+ }
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