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fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
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"Copyright (c) Microsoft Corporation. All rights reserved. \n",
"\n",
"Licensed under the MIT License.\n",
"\n",
"# FineTuning NLP Models with FLAML Library\n",
"\n",
"\n",
"## 1. Introduction\n",
"\n",
"FLAML is a Python library (https://github.com/microsoft/FLAML) designed to automatically produce accurate machine learning models \n",
"with low computational cost. It is fast and economical. The simple and lightweight design makes it easy to use and extend, such as adding new learners. FLAML can \n",
"- serve as an economical AutoML engine,\n",
"- be used as a fast hyperparameter tuning tool, or \n",
"- be embedded in self-tuning software that requires low latency & resource in repetitive\n",
" tuning tasks.\n",
"\n",
"In this notebook, we demonstrate how to use the FLAML library to fine tune an NLP language model with hyperparameter search. We have tested this notebook on a server with 4 NVidia V100 GPU (32GB) and 400GB CPU Ram.\n",
"\n",
support latest xgboost version (#599) * support latest xgboost version * Update test_classification.py * Update Exists problems when installing xgb1.6.1 in py3.6 * cleanup * xgboost version * remove time_budget_s in test * remove redundancy * stop support of python 3.6 Co-authored-by: zsk <shaokunzhang529@gmail.com> Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
2022-06-21 18:59:07 -07:00
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the `nlp,ray,notebook` and `blendsearch` option:\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
"```bash\n",
"pip install flaml[nlp,ray,notebook,blendsearch];\n",
"```"
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]
}
],
"source": [
Replaced !pip calls with %pip magic command. (#604) Closes #603.
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"%pip install flaml[nlp,ray,notebook,blendsearch]"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's run some examples. \n",
"\n",
"Note: throughout this notebook, you may see a few ModuleNotFoundErrors. As long as the cell successfully executes, you can ignore that error."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Sentiment Classification Example\n",
"### Load data and preprocess\n",
"\n",
"The Stanford Sentiment treebank (SST-2) dataset is a dataset for sentiment classification. First, let's load this dataset into pandas dataframes:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
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"Reusing dataset glue (/home/xliu127/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n",
"Reusing dataset glue (/home/xliu127/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n",
"Reusing dataset glue (/home/xliu127/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
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]
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
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"train_dataset = load_dataset(\"glue\", \"mrpc\", split=\"train\").to_pandas()\n",
"dev_dataset = load_dataset(\"glue\", \"mrpc\", split=\"validation\").to_pandas()\n",
"test_dataset = load_dataset(\"glue\", \"mrpc\", split=\"test\").to_pandas()"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Take a look at the first 5 examples of this dataset:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sentence</th>\n",
" <th>label</th>\n",
" <th>idx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>hide new secretions from the parental units</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>contains no wit , only labored gags</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>that loves its characters and communicates som...</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>remains utterly satisfied to remain the same t...</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>on the worst revenge-of-the-nerds clichés the ...</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sentence label idx\n",
"0 hide new secretions from the parental units 0 0\n",
"1 contains no wit , only labored gags 0 1\n",
"2 that loves its characters and communicates som... 1 2\n",
"3 remains utterly satisfied to remain the same t... 0 3\n",
"4 on the worst revenge-of-the-nerds clichés the ... 0 4"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Separate the data into X and y:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
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"custom_sent_keys = [\"sentence1\", \"sentence2\"] # specify the column names of the input sentences\n",
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"label_key = \"label\" # specify the column name of the label\n",
"\n",
"X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]\n",
"X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]\n",
"X_test = test_dataset[custom_sent_keys]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run FLAML"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"''' import AutoML class from flaml package '''\n",
"from flaml import AutoML\n",
"automl = AutoML()\n",
"\n",
"import ray\n",
"if not ray.is_initialized():\n",
" ray.init() "
]
},
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
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"TIME_BUDGET=1200\n",
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"automl_settings = {\n",
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" \"time_budget\": TIME_BUDGET, # setting the time budget\n",
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" \"task\": \"seq-classification\", # setting the task as seq-classification\n",
refactoring TransformersEstimator to support default and custom_hp (#511) * refactoring TransformersEstimator to support default and custom_hp * handling starting_points not in search space * addressing starting point more than max_iter * fixing upper < lower bug
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" \"fit_kwargs_by_estimator\": {\n",
" \"transformer\": {\n",
" \"output_dir\": \"data/output/\", # setting the output directory\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
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" \"model_path\": \"bert-base-uncased\", # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base\n",
refactoring TransformersEstimator to support default and custom_hp (#511) * refactoring TransformersEstimator to support default and custom_hp * handling starting_points not in search space * addressing starting point more than max_iter * fixing upper < lower bug
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" }\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
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" },\n",
" \"gpu_per_trial\": 1, # set to 0 if no GPU is available\n",
" \"log_file_name\": \"seqclass.log\", # set the file to save the log for HPO\n",
" \"log_type\": \"all\", # the log type for trials: \"all\" if logging all the trials, \"better\" if only keeping the better trials\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
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" \"use_ray\": {\"local_dir\": \"data/output/\"}, # set whether to use Ray\n",
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" \"n_concurrent_trials\": 4,\n",
" \"keep_search_state\": True, # keeping the search state\n",
"}"
]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"== Status ==<br>Current time: 2022-07-21 13:27:54 (running for 00:20:14.52)<br>Memory usage on this node: 22.9/376.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/96 CPUs, 0/4 GPUs, 0.0/252.58 GiB heap, 0.0/112.24 GiB objects (0.0/1.0 accelerator_type:V100)<br>Current best trial: a478b276 with val_loss=0.12009803921568629 and parameters={'learning_rate': 2.1872511767624938e-05, 'num_train_epochs': 3, 'per_device_train_batch_size': 4, 'seed': 7, 'global_max_steps': 9223372036854775807, 'learner': 'transformer'}<br>Result logdir: /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38<br>Number of trials: 33/1000000 (33 TERMINATED)<br><br>"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
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Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'loss': 0.3327, 'learning_rate': 1.0295123460382672e-05, 'epoch': 2.18}\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m {'loss': 0.4744, 'learning_rate': 3.8483947490148965e-06, 'epoch': 1.64}\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m {'loss': 0.1645, 'learning_rate': 4.8933330345560835e-06, 'epoch': 3.27}\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m {'loss': 0.5619, 'learning_rate': 1.3544721826857925e-05, 'epoch': 0.55}\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m {'eval_loss': 0.5415804386138916, 'eval_automl_metric': 0.13480392156862742, 'eval_runtime': 3.8874, 'eval_samples_per_second': 104.956, 'eval_steps_per_second': 104.956, 'epoch': 2.0}\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m {'train_runtime': 133.4439, 'train_samples_per_second': 54.974, 'train_steps_per_second': 13.744, 'train_loss': 0.4956582546754128, 'epoch': 2.0}\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'loss': 0.2012, 'learning_rate': 7.209055276814724e-06, 'epoch': 2.73}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m Num examples = 408\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m Batch size = 1\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_5c51b244_32_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-26-12/checkpoint-1834/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_5c51b244_32_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-26-12/checkpoint-1834/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_5c51b244_32_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-26-12/checkpoint-1834/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_5c51b244_32_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-26-12/checkpoint-1834/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=59407)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_5c51b244_32_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-26-12/checkpoint-1834/tokenizer_config.json\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m {'eval_loss': 0.5180040001869202, 'eval_automl_metric': 0.15931372549019607, 'eval_runtime': 3.7749, 'eval_samples_per_second': 108.082, 'eval_steps_per_second': 108.082, 'epoch': 1.0}\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m {'loss': 0.0951, 'learning_rate': 1.2306586074931466e-06, 'epoch': 3.82}\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m {'loss': 0.4703, 'learning_rate': 8.46798950794566e-06, 'epoch': 1.09}\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'eval_loss': 0.6077207326889038, 'eval_automl_metric': 0.11764705882352944, 'eval_runtime': 3.8401, 'eval_samples_per_second': 106.247, 'eval_steps_per_second': 106.247, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m {'eval_loss': 0.806081235408783, 'eval_automl_metric': 0.13480392156862742, 'eval_runtime': 3.7716, 'eval_samples_per_second': 108.177, 'eval_steps_per_second': 108.177, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m {'train_runtime': 256.0423, 'train_samples_per_second': 57.303, 'train_steps_per_second': 14.326, 'train_loss': 0.35705690737509027, 'epoch': 4.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m Num examples = 408\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m Batch size = 1\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_227f7ab0_29_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-24-35/checkpoint-3668/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_227f7ab0_29_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-24-35/checkpoint-3668/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_227f7ab0_29_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-24-35/checkpoint-3668/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_227f7ab0_29_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-24-35/checkpoint-3668/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=58929)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_227f7ab0_29_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-24-35/checkpoint-3668/tokenizer_config.json\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'loss': 0.1476, 'learning_rate': 4.122987093246777e-06, 'epoch': 3.27}\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m {'loss': 0.3574, 'learning_rate': 3.391257189033394e-06, 'epoch': 1.64}\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m {'eval_loss': 0.5781316757202148, 'eval_automl_metric': 0.1421568627450981, 'eval_runtime': 3.7277, 'eval_samples_per_second': 109.45, 'eval_steps_per_second': 109.45, 'epoch': 2.0}\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m {'train_runtime': 128.6158, 'train_samples_per_second': 57.038, 'train_steps_per_second': 14.26, 'train_loss': 0.4407762508745932, 'epoch': 2.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m Num examples = 408\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m Batch size = 1\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'loss': 0.1029, 'learning_rate': 1.03691890967883e-06, 'epoch': 3.82}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_89b0b9a7_33_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-27-29/checkpoint-1834/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_89b0b9a7_33_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-27-29/checkpoint-1834/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_89b0b9a7_33_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-27-29/checkpoint-1834/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_89b0b9a7_33_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-27-29/checkpoint-1834/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=59592)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_89b0b9a7_33_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=2,per_device_trai_2022-07-21_13-27-29/checkpoint-1834/tokenizer_config.json\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'eval_loss': 0.7132703065872192, 'eval_automl_metric': 0.12254901960784315, 'eval_runtime': 3.735, 'eval_samples_per_second': 109.237, 'eval_steps_per_second': 109.237, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m {'train_runtime': 264.9924, 'train_samples_per_second': 55.368, 'train_steps_per_second': 13.842, 'train_loss': 0.31339427247042223, 'epoch': 4.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m Num examples = 408\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m Batch size = 1\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_45d8f8ce_31_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-25-34/checkpoint-2751/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_45d8f8ce_31_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-25-34/checkpoint-2751/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_45d8f8ce_31_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-25-34/checkpoint-2751/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_45d8f8ce_31_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-25-34/checkpoint-2751/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=59250)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_13-07-38/train_45d8f8ce_31_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_13-25-34/checkpoint-2751/tokenizer_config.json\n",
"2022-07-21 13:30:17,073\tINFO tune.py:747 -- Total run time: 1358.29 seconds (1204.81 seconds for the tuning loop).\n",
"[flaml.automl: 07-21 13:30:20] {3314} INFO - selected model: None\n",
"/data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"{'loss': 0.405, 'learning_rate': 6.02286555920107e-06, 'epoch': 2.17}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"[flaml.automl: 07-21 13:32:01] {3457} INFO - retrain transformer for 101.3s\n",
"[flaml.automl: 07-21 13:32:01] {3464} INFO - retrained model: None\n",
"[flaml.automl: 07-21 13:32:01] {2742} INFO - fit succeeded\n",
"[flaml.automl: 07-21 13:32:01] {2743} INFO - Time taken to find the best model: 1019.1050026416779\n",
"[flaml.automl: 07-21 13:32:01] {2754} WARNING - Time taken to find the best model is 85% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"{'train_runtime': 93.2817, 'train_samples_per_second': 117.965, 'train_steps_per_second': 7.397, 'train_loss': 0.3405460468236951, 'epoch': 3.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
}
],
"source": [
"'''The main flaml automl API'''\n",
"automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"The best loss by FLAML: 0.12009803921568629\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
}
],
"source": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"print(\"The best loss by FLAML: {}\".format(automl.best_loss))"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Best model and metric"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best hyperparmeter config: {'learning_rate': 1.4736175808553141e-05, 'num_train_epochs': 7.623375372739029, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.21605876280261357, 'weight_decay': 0.11938244526496489, 'adam_epsilon': 7.353322403647365e-07, 'seed': 42, 'global_max_steps': 1878, 'learner': 'transformer'}\n",
"Best accuracy on validation data: 0.9404\n",
"Training duration of best run: 157.7 s\n"
]
}
],
"source": [
"'''retrieve best config and best learner'''\n",
"print('Best hyperparmeter config:', automl.best_config)\n",
"print('Best accuracy on validation data: {0:.4g}'.format(1-automl.best_loss))\n",
"print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.528, 'learning_rate': 8.898933352349567e-06, 'epoch': 1.0}\n",
"{'eval_loss': 0.2549280524253845, 'eval_automl_metric': 0.08600917431192656, 'eval_runtime': 1.0003, 'eval_samples_per_second': 871.751, 'eval_steps_per_second': 54.984, 'epoch': 1.0}\n",
"{'loss': 0.2278, 'learning_rate': 1.3880017803076292e-05, 'epoch': 2.0}\n",
"{'eval_loss': 0.24966619908809662, 'eval_automl_metric': 0.06766055045871555, 'eval_runtime': 1.0201, 'eval_samples_per_second': 854.778, 'eval_steps_per_second': 53.914, 'epoch': 2.0}\n",
"{'loss': 0.1455, 'learning_rate': 1.1410179501562432e-05, 'epoch': 3.0}\n",
"{'eval_loss': 0.23046882450580597, 'eval_automl_metric': 0.059633027522935755, 'eval_runtime': 1.0097, 'eval_samples_per_second': 863.6, 'eval_steps_per_second': 54.47, 'epoch': 3.0}\n",
"{'eval_loss': 0.23046882450580597, 'eval_automl_metric': 0.059633027522935755, 'eval_runtime': 0.9726, 'eval_samples_per_second': 896.568, 'eval_steps_per_second': 56.55, 'epoch': 3.0}\n",
"{'train_runtime': 146.7879, 'train_samples_per_second': 519.346, 'train_steps_per_second': 32.462, 'train_loss': 0.30043953600021217, 'epoch': 3.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using amp half precision backend\n",
"***** Running Prediction *****\n",
" Num examples = 872\n",
" Batch size = 64\n"
]
}
],
"source": [
"import pickle\n",
"automl.pickle(\"automl.pkl\")\n",
"\n",
"with open(\"automl.pkl\", \"rb\") as f:\n",
" automl = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 9,
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"metadata": {
"scrolled": true
},
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using amp half precision backend\n",
"***** Running Prediction *****\n",
" Num examples = 1821\n",
" Batch size = 4\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted labels [0 0 1 ... 1 1 1]\n"
]
}
],
"source": [
"'''compute predictions of testing dataset''' \n",
refactoring TransformersEstimator to support default and custom_hp (#511) * refactoring TransformersEstimator to support default and custom_hp * handling starting_points not in search space * addressing starting point more than max_iter * fixing upper < lower bug
2022-04-28 14:06:29 -04:00
"y_pred = automl.predict(X_test, **{\"per_device_eval_batch_size\": 1})\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
"print('Predicted labels', y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Log history"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 1.4211966209891772e-05, 'num_train_epochs': 0.22667066737595704, 'per_device_train_batch_size': 4, 'warmup_ratio': 0.0638407085008166, 'weight_decay': 0.24365576482793252, 'adam_epsilon': 1.2017005181798623e-08, 'seed': 44, 'global_max_steps': 567, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 0.00010969207855501012, 'num_train_epochs': 0.5341197058945968, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.00727467370977386, 'weight_decay': 0.2998042286577564, 'adam_epsilon': 2.5359832189101376e-08, 'seed': 44, 'global_max_steps': 168, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 0.00010969207855501012, 'num_train_epochs': 0.5341197058945968, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.00727467370977386, 'weight_decay': 0.2998042286577564, 'adam_epsilon': 2.5359832189101376e-08, 'seed': 44, 'global_max_steps': 168, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 6.105850155006292e-06, 'num_train_epochs': 0.5203392488419986, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.02982185620209511, 'weight_decay': 0.06967105125024514, 'adam_epsilon': 2.8808753274240916e-07, 'seed': 42, 'global_max_steps': 163, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 0.00010969207855501012, 'num_train_epochs': 0.5341197058945968, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.00727467370977386, 'weight_decay': 0.2998042286577564, 'adam_epsilon': 2.5359832189101376e-08, 'seed': 44, 'global_max_steps': 168, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 9.029571768192494e-05, 'num_train_epochs': 0.23081935882872334, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.05927331754619176, 'weight_decay': 0.06990187648448716, 'adam_epsilon': 2.6275569358808137e-08, 'seed': 41, 'global_max_steps': 145, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 0.00010969207855501012, 'num_train_epochs': 0.5341197058945968, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.00727467370977386, 'weight_decay': 0.2998042286577564, 'adam_epsilon': 2.5359832189101376e-08, 'seed': 44, 'global_max_steps': 168, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 0.00010208514406839324, 'num_train_epochs': 0.24916036398153807, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.28313267937361647, 'adam_epsilon': 1.5042842082223077e-08, 'seed': 44, 'global_max_steps': 78, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 0.00010969207855501012, 'num_train_epochs': 0.5341197058945968, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.00727467370977386, 'weight_decay': 0.2998042286577564, 'adam_epsilon': 2.5359832189101376e-08, 'seed': 44, 'global_max_steps': 168, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 0.00011786584823407102, 'num_train_epochs': 1.1449809097488275, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.015398649752204965, 'weight_decay': 0.3, 'adam_epsilon': 4.275263179285732e-08, 'seed': 43, 'global_max_steps': 313, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 0.00011786584823407102, 'num_train_epochs': 1.1449809097488275, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.015398649752204965, 'weight_decay': 0.3, 'adam_epsilon': 4.275263179285732e-08, 'seed': 43, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 3.59933640287326e-05, 'num_train_epochs': 1.7937390219164033, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.016745580745240057, 'weight_decay': 0.2892279950527897, 'adam_epsilon': 6.514301011066509e-08, 'seed': 44, 'global_max_steps': 313, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 3.59933640287326e-05, 'num_train_epochs': 1.7937390219164033, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.016745580745240057, 'weight_decay': 0.2892279950527897, 'adam_epsilon': 6.514301011066509e-08, 'seed': 44, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 1.4736175808553141e-05, 'num_train_epochs': 7.623375372739029, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.21605876280261357, 'weight_decay': 0.11938244526496489, 'adam_epsilon': 7.353322403647365e-07, 'seed': 42, 'global_max_steps': 1878, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.4736175808553141e-05, 'num_train_epochs': 7.623375372739029, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.21605876280261357, 'weight_decay': 0.11938244526496489, 'adam_epsilon': 7.353322403647365e-07, 'seed': 42, 'global_max_steps': 1878, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 0.0001178658482340711, 'num_train_epochs': 1.1449809097488262, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.015398649752204969, 'weight_decay': 0.3, 'adam_epsilon': 4.275263179285729e-08, 'seed': 43, 'global_max_steps': 313, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.4736175808553141e-05, 'num_train_epochs': 7.623375372739029, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.21605876280261357, 'weight_decay': 0.11938244526496489, 'adam_epsilon': 7.353322403647365e-07, 'seed': 42, 'global_max_steps': 1878, 'learner': 'transformer'}}\n"
]
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=automl_settings['log_file_name'], time_budget=3000)\n",
"for config in config_history:\n",
" print(config)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"plt.title('Learning Curve')\n",
"plt.xlabel('Wall Clock Time (s)')\n",
"plt.ylabel('Validation Accuracy')\n",
"print(len(valid_loss_history))\n",
"plt.scatter(time_history, 1 - np.array(valid_loss_history))\n",
"plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"## 3. Spooky-author-identification example"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"== Status ==<br>Current time: 2022-07-22 07:20:52 (running for 00:30:13.14)<br>Memory usage on this node: 19.0/376.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/4 CPUs, 0/4 GPUs, 0.0/252.65 GiB heap, 0.0/112.27 GiB objects (0.0/1.0 accelerator_type:V100)<br>Current best trial: 504afb96 with val_loss=0.11011235955056176 and parameters={'learning_rate': 4.567279255636039e-05, 'num_train_epochs': 5, 'per_device_train_batch_size': 32, 'seed': 37, 'global_max_steps': 9223372036854775807, 'learner': 'transformer'}<br>Result logdir: /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38<br>Number of trials: 12/1000000 (12 TERMINATED)<br><br>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m {'train_runtime': 675.882, 'train_samples_per_second': 108.628, 'train_steps_per_second': 6.791, 'train_loss': 0.15587145374491324, 'epoch': 5.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `BertForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m Batch size = 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m {'eval_loss': 0.47973549365997314, 'eval_automl_metric': 0.12134831460674156, 'eval_runtime': 42.4421, 'eval_samples_per_second': 115.334, 'eval_steps_per_second': 115.334, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m {'train_runtime': 482.2078, 'train_samples_per_second': 121.806, 'train_steps_per_second': 1.908, 'train_loss': 0.23729169679724652, 'epoch': 4.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `BertForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m Batch size = 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.1844, 'learning_rate': 2.7020242630660653e-06, 'epoch': 3.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_e041e7d6_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=5,per_device_trai_2022-07-22_07-09-19/checkpoint-4590/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_e041e7d6_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=5,per_device_trai_2022-07-22_07-09-19/checkpoint-4590/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_e041e7d6_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=5,per_device_trai_2022-07-22_07-09-19/checkpoint-4590/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_e041e7d6_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=5,per_device_trai_2022-07-22_07-09-19/checkpoint-4590/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=78542)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_e041e7d6_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=5,per_device_trai_2022-07-22_07-09-19/checkpoint-4590/tokenizer_config.json\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_567b6878_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-22_07-12-37/checkpoint-920/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_567b6878_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-22_07-12-37/checkpoint-920/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_567b6878_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-22_07-12-37/checkpoint-920/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_567b6878_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-22_07-12-37/checkpoint-920/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=78793)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_567b6878_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-22_07-12-37/checkpoint-920/tokenizer_config.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'eval_loss': 0.7813186049461365, 'eval_automl_metric': 0.13564862104187947, 'eval_runtime': 38.0461, 'eval_samples_per_second': 128.66, 'eval_steps_per_second': 128.66, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0829, 'learning_rate': 2.3353000145500413e-06, 'epoch': 3.13}\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m {'eval_loss': 0.5877022743225098, 'eval_automl_metric': 0.11848825331971402, 'eval_runtime': 40.6459, 'eval_samples_per_second': 120.43, 'eval_steps_per_second': 120.43, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0714, 'learning_rate': 1.9685757660340173e-06, 'epoch': 3.27}\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m {'loss': 0.0237, 'learning_rate': 3.91430499829805e-06, 'epoch': 4.36}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0584, 'learning_rate': 1.6018515175179931e-06, 'epoch': 3.41}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0836, 'learning_rate': 1.235127269001969e-06, 'epoch': 3.54}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0335, 'learning_rate': 8.684030204859451e-07, 'epoch': 3.68}\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m {'eval_loss': 0.6322397589683533, 'eval_automl_metric': 0.11664964249233911, 'eval_runtime': 39.775, 'eval_samples_per_second': 123.067, 'eval_steps_per_second': 123.067, 'epoch': 5.0}\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m {'train_runtime': 607.6324, 'train_samples_per_second': 120.83, 'train_steps_per_second': 3.777, 'train_loss': 0.17213530145699163, 'epoch': 5.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `BertForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m Batch size = 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0562, 'learning_rate': 5.01678771969921e-07, 'epoch': 3.81}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_9a34cd4a_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-22_07-14-32/checkpoint-2295/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_9a34cd4a_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-22_07-14-32/checkpoint-2295/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_9a34cd4a_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-22_07-14-32/checkpoint-2295/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_9a34cd4a_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-22_07-14-32/checkpoint-2295/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=79025)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_9a34cd4a_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-22_07-14-32/checkpoint-2295/tokenizer_config.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'loss': 0.0527, 'learning_rate': 1.3495452345389687e-07, 'epoch': 3.95}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'eval_loss': 0.8104404211044312, 'eval_automl_metric': 0.12625127681307458, 'eval_runtime': 37.4885, 'eval_samples_per_second': 130.573, 'eval_steps_per_second': 130.573, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m {'train_runtime': 1051.1951, 'train_samples_per_second': 55.875, 'train_steps_per_second': 13.969, 'train_loss': 0.2927411694969607, 'epoch': 4.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `BertForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m Batch size = 1\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_d0e0b7d6_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_train_2022-07-22_07-08-52/checkpoint-14684/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_d0e0b7d6_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_train_2022-07-22_07-08-52/checkpoint-14684/vocab.txt\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_d0e0b7d6_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_train_2022-07-22_07-08-52/checkpoint-14684/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_d0e0b7d6_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_train_2022-07-22_07-08-52/checkpoint-14684/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=78225)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-22_06-50-38/train_d0e0b7d6_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_train_2022-07-22_07-08-52/checkpoint-14684/tokenizer_config.json\n",
"2022-07-22 07:27:20,126\tINFO tune.py:747 -- Total run time: 2201.85 seconds (1801.97 seconds for the tuning loop).\n",
"[flaml.automl: 07-22 07:27:25] {3314} INFO - selected model: None\n",
"/data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.2317, 'learning_rate': 5.95732076822092e-06, 'epoch': 4.35}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[flaml.automl: 07-22 07:35:19] {3457} INFO - retrain transformer for 474.4s\n",
"[flaml.automl: 07-22 07:35:19] {3464} INFO - retrained model: None\n",
"[flaml.automl: 07-22 07:35:19] {2742} INFO - fit succeeded\n",
"[flaml.automl: 07-22 07:35:19] {2743} INFO - Time taken to find the best model: 1118.247492313385\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'train_runtime': 463.5873, 'train_samples_per_second': 158.374, 'train_steps_per_second': 1.24, 'train_loss': 0.20362980179164722, 'epoch': 5.0}\n"
]
}
],
"source": [
"from flaml import AutoML\n",
"import ray\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"ray.init(num_cpus=4, num_gpus=4, ignore_reinit_error=True)\n",
"\n",
"df = pd.read_csv('/data/xliu127/projects/hyperopt/FLAML/data/spooky-author-identification.csv')\n",
"X, y = df.drop('author', axis=1), df['author']\n",
"\n",
"X_train, X_val, y_train, y_val = train_test_split(X, y, random_state=123)\n",
"\n",
"\n",
"print(len(X_train), len(X_val))\n",
"automl_model = AutoML()\n",
"\n",
"automl_settings = {\n",
" \"time_budget\": 1800, \n",
" \"task\": \"seq-classification\", \n",
" \"fit_kwargs_by_estimator\": {\n",
" \"transformer\": {\n",
" \"output_dir\": \"data/output/\", \n",
" \"model_path\": \"bert-base-uncased\", \n",
" }\n",
" },\n",
" \"metric\": \"accuracy\",\n",
" \"gpu_per_trial\": 1, \n",
" \"log_file_name\": \"spooky_bert.log\", \n",
" \"log_type\": \"all\", \n",
" \"use_ray\": {\"local_dir\": \"data/output/\"}, # set whether to use Ray\n",
" \"n_concurrent_trials\": 4,\n",
" \"keep_search_state\": True, # keeping the search state\n",
"}\n",
"\n",
"automl_model.fit(X_train=X_train, y_train=y_train,X_val=X_val, y_val=y_val, **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the best loss for spooky author identification: 0.11133810010214507\n"
]
}
],
"source": [
"print(\"the best loss for spooky author identification: {}\".format(automl_model.best_loss))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"== Status ==<br>Current time: 2022-07-21 21:21:15 (running for 00:30:10.30)<br>Memory usage on this node: 20.5/376.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/4 CPUs, 0/4 GPUs, 0.0/252.62 GiB heap, 0.0/112.26 GiB objects (0.0/1.0 accelerator_type:V100)<br>Current best trial: 84d3be85 with val_loss=0.12951991828396325 and parameters={'learning_rate': 4.486769916716146e-05, 'num_train_epochs': 4, 'per_device_train_batch_size': 8, 'seed': 28, 'global_max_steps': 9223372036854775807, 'learner': 'transformer'}<br>Result logdir: /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05<br>Number of trials: 12/1000000 (12 TERMINATED)<br><br>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m {'eval_loss': 0.7418951392173767, 'eval_automl_metric': 0.1284984678243105, 'eval_runtime': 37.3935, 'eval_samples_per_second': 130.905, 'eval_steps_per_second': 130.905, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m {'train_runtime': 565.7729, 'train_samples_per_second': 103.816, 'train_steps_per_second': 6.49, 'train_loss': 0.2802804773409642, 'epoch': 4.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m Batch size = 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m {'eval_loss': 1.0893423557281494, 'eval_automl_metric': 0.6024514811031665, 'eval_runtime': 39.7178, 'eval_samples_per_second': 123.245, 'eval_steps_per_second': 123.245, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.2369, 'learning_rate': 1.4090340380281214e-05, 'epoch': 2.72}\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m {'train_runtime': 566.9953, 'train_samples_per_second': 77.694, 'train_steps_per_second': 9.714, 'train_loss': 1.0928592581461545, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'eval_loss': 1.092341661453247, 'eval_automl_metric': 0.6024514811031665, 'eval_runtime': 38.0057, 'eval_samples_per_second': 128.797, 'eval_steps_per_second': 128.797, 'epoch': 3.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m Batch size = 1\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_60247332_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_21-11-36/checkpoint-3672/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_60247332_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_21-11-36/checkpoint-3672/vocab.json\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_60247332_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_21-11-36/checkpoint-3672/merges.txt\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_60247332_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_21-11-36/checkpoint-3672/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_60247332_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_21-11-36/checkpoint-3672/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=50245)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_60247332_10_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=4,per_device_trai_2022-07-21_21-11-36/checkpoint-3672/tokenizer_config.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0896, 'learning_rate': 1.5104688589428795e-05, 'epoch': 3.13}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_6861ba34_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=3,per_device_trai_2022-07-21_21-11-51/checkpoint-3672/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_6861ba34_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=3,per_device_trai_2022-07-21_21-11-51/checkpoint-3672/vocab.json\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_6861ba34_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=3,per_device_trai_2022-07-21_21-11-51/checkpoint-3672/merges.txt\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_6861ba34_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=3,per_device_trai_2022-07-21_21-11-51/checkpoint-3672/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_6861ba34_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=3,per_device_trai_2022-07-21_21-11-51/checkpoint-3672/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=50412)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_6861ba34_11_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=3,per_device_trai_2022-07-21_21-11-51/checkpoint-3672/tokenizer_config.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.2195, 'learning_rate': 1.2404892966371977e-05, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0907, 'learning_rate': 1.2732721160184323e-05, 'epoch': 3.27}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.1252, 'learning_rate': 1.0719445552462741e-05, 'epoch': 3.27}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0926, 'learning_rate': 1.0360753730939852e-05, 'epoch': 3.41}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.1093, 'learning_rate': 9.033998138553504e-06, 'epoch': 3.54}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0908, 'learning_rate': 7.988786301695379e-06, 'epoch': 3.54}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.1166, 'learning_rate': 7.348550724644269e-06, 'epoch': 3.81}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0899, 'learning_rate': 5.616818872450909e-06, 'epoch': 3.68}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0923, 'learning_rate': 3.244851443206437e-06, 'epoch': 3.81}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'eval_loss': 0.7831101417541504, 'eval_automl_metric': 0.13462717058222673, 'eval_runtime': 37.9679, 'eval_samples_per_second': 128.925, 'eval_steps_per_second': 128.925, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'loss': 1.0862, 'learning_rate': 8.728840139619655e-07, 'epoch': 3.95}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.1164, 'learning_rate': 5.663103310735033e-06, 'epoch': 4.08}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'eval_loss': 1.0893481969833374, 'eval_automl_metric': 0.6024514811031665, 'eval_runtime': 36.2865, 'eval_samples_per_second': 134.899, 'eval_steps_per_second': 134.899, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m {'train_runtime': 1069.9104, 'train_samples_per_second': 54.898, 'train_steps_per_second': 13.725, 'train_loss': 1.0960205283875493, 'epoch': 4.0}\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m Batch size = 1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.0542, 'learning_rate': 3.977655896825797e-06, 'epoch': 4.36}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_ebe7d3ee_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=4,per_device_train_2022-07-21_21-08-22/checkpoint-11013/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_ebe7d3ee_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=4,per_device_train_2022-07-21_21-08-22/checkpoint-11013/vocab.json\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_ebe7d3ee_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=4,per_device_train_2022-07-21_21-08-22/checkpoint-11013/merges.txt\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_ebe7d3ee_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=4,per_device_train_2022-07-21_21-08-22/checkpoint-11013/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_ebe7d3ee_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=4,per_device_train_2022-07-21_21-08-22/checkpoint-11013/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=49988)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_ebe7d3ee_9_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0001,num_train_epochs=4,per_device_train_2022-07-21_21-08-22/checkpoint-11013/tokenizer_config.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.0618, 'learning_rate': 2.2922084829165607e-06, 'epoch': 4.63}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'loss': 0.0494, 'learning_rate': 6.06761069007325e-07, 'epoch': 4.9}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'eval_loss': 0.88468998670578, 'eval_automl_metric': 0.12972420837589382, 'eval_runtime': 37.9519, 'eval_samples_per_second': 128.979, 'eval_steps_per_second': 128.979, 'epoch': 5.0}\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m {'train_runtime': 873.0679, 'train_samples_per_second': 84.094, 'train_steps_per_second': 10.515, 'train_loss': 0.27977710040306475, 'epoch': 5.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForSequenceClassification.forward` and have been ignored: __index_level_0__. If __index_level_0__ are not expected by `RobertaForSequenceClassification.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m Num examples = 4895\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m Batch size = 1\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m Didn't find file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_bd71ed64_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-21_21-14-13/checkpoint-9180/added_tokens.json. We won't load it.\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_bd71ed64_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-21_21-14-13/checkpoint-9180/vocab.json\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_bd71ed64_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-21_21-14-13/checkpoint-9180/merges.txt\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_bd71ed64_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-21_21-14-13/checkpoint-9180/tokenizer.json\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m loading file None\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_bd71ed64_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-21_21-14-13/checkpoint-9180/special_tokens_map.json\n",
"\u001B[2m\u001B[36m(train pid=50658)\u001B[0m loading file /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-07-21_20-51-05/train_bd71ed64_12_global_max_steps=9223372036854775807,learner=transformer,learning_rate=0.0000,num_train_epochs=5,per_device_trai_2022-07-21_21-14-13/checkpoint-9180/tokenizer_config.json\n",
"2022-07-21 21:29:43,228\tINFO tune.py:747 -- Total run time: 2317.81 seconds (1801.93 seconds for the tuning loop).\n",
"[flaml.automl: 07-21 21:29:46] {3314} INFO - selected model: None\n",
"/data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.5742, 'learning_rate': 3.264882684494973e-05, 'epoch': 1.09}\n"
]
}
],
"source": [
"automl_settings[\"fit_kwargs_by_estimator\"][\"transformer\"][\"model_path\"] = \"roberta-base\"\n",
"automl_settings[\"log_file_name\"] = \"spooky_roberta.log\"\n",
"automl_model.fit(X_train=X_train, y_train=y_train,X_val=X_val, y_val=y_val, **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n",
"8\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"for each_file_name in ['bert', 'roberta']:\n",
" time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename='spooky_' + each_file_name + '.log', time_budget=3000)\n",
" print(len(valid_loss_history))\n",
" plt.scatter(time_history, 1 - np.array(valid_loss_history))\n",
" plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')\n",
"\n",
"plt.legend(['bert', 'roberta'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Other Tasks"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Besides sequence classification, FLAML currently also supports four other tasks (more tasks are to be supported, which can be found on FLAML's documentation website https://microsoft.github.io/FLAML/docs/Examples/AutoML-NLP):\n",
"\n",
"- sequence regression: predicting a float number from the input sequence, e.g., predicting the rating of a hotel review based on the text content;\n",
"- token classification: predicting the label of each token in a sequence, e.g., named entity recognition;\n",
"- multiple choice: predicting the best second half of a sentence that comes next to the first part of a sentence based on common sensen reasoning. An example is seen below;\n",
"- (abstractive) summarization: generating the textual summarization of an input paragraph;\n",
"\n",
"For each task, you only have to change the \"Load data and preprocess\" with the corresponding data loading process. For example:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"### 4.1 Multiple Choice Example"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multiple choice is a task of predicting the best second half of a sentence that follows the first half based on common sense reasoning. An example of multiple-choice classification problem is:\n",
"\n",
"On stage, a woman takes a seat at the piano. She\n",
"a) sits on a bench as her sister plays with the doll.\n",
"b) smiles with someone as the music plays.\n",
"c) is in the crowd, watching the dancers.\n",
"d) *nervously sets her fingers on the keys*."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No config specified, defaulting to: swag/regular\n",
"Reusing dataset swag (/home/xliu127/.cache/huggingface/datasets/swag/regular/0.0.0/9640de08cdba6a1469ed3834fcab4b8ad8e38caf5d1ba5e7436d8b1fd067ad4c)\n",
"No config specified, defaulting to: swag/regular\n",
"Reusing dataset swag (/home/xliu127/.cache/huggingface/datasets/swag/regular/0.0.0/9640de08cdba6a1469ed3834fcab4b8ad8e38caf5d1ba5e7436d8b1fd067ad4c)\n",
"No config specified, defaulting to: swag/regular\n",
"Reusing dataset swag (/home/xliu127/.cache/huggingface/datasets/swag/regular/0.0.0/9640de08cdba6a1469ed3834fcab4b8ad8e38caf5d1ba5e7436d8b1fd067ad4c)\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"train_dataset = load_dataset(\"swag\", split=\"train\").to_pandas().iloc[:10000]\n",
"dev_dataset = load_dataset(\"swag\", split=\"validation\").to_pandas().iloc[:10000]\n",
"test_dataset = load_dataset(\"swag\", split=\"test\").to_pandas()\n",
"\n",
"custom_sent_keys = [\n",
" \"sent1\",\n",
" \"sent2\",\n",
" \"ending0\",\n",
" \"ending1\",\n",
" \"ending2\",\n",
" \"ending3\",\n",
" \"gold-source\",\n",
" \"video-id\",\n",
" \"startphrase\",\n",
" \"fold-ind\",\n",
" ] # specify the column names of the input sentences\n",
"label_key = \"label\" # specify the column name of the label\n",
"\n",
"X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]\n",
"X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]\n",
"X_test = test_dataset[custom_sent_keys]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Members of the procession walk down the street holding small horn brass instruments.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset.iloc[0][\"sent1\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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"== Status ==<br>Current time: 2022-03-19 14:39:29 (running for 00:08:29.94)<br>Memory usage on this node: 33.0/376.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/96 CPUs, 0/4 GPUs, 0.0/250.17 GiB heap, 0.0/111.21 GiB objects (0.0/1.0 accelerator_type:V100)<br>Current best trial: de45e672 with val_loss=0.18300000000000005 and parameters={'learning_rate': 6.104513714676502e-06, 'num_train_epochs': 2.3743291981165893, 'per_device_train_batch_size': 8, 'warmup_ratio': 0.23610846764298543, 'weight_decay': 0.20205904544254147, 'adam_epsilon': 5.752964074991208e-08, 'seed': 41, 'global_max_steps': 9223372036854775807, 'learner': 'transformer'}<br>Result logdir: /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-03-19_14-30-59<br>Number of trials: 10/1000000 (10 TERMINATED)<br><br>"
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Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m {'eval_loss': 0.6315866112709045, 'eval_automl_metric': 0.18779999999999997, 'eval_runtime': 15.4883, 'eval_samples_per_second': 645.648, 'eval_steps_per_second': 40.353, 'epoch': 1.66}\n",
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m {'train_runtime': 190.7625, 'train_samples_per_second': 87.254, 'train_steps_per_second': 10.909, 'train_loss': 0.5091343906738046, 'epoch': 1.66}\n",
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m {'eval_loss': 1.2118068933486938, 'eval_automl_metric': 0.2015, 'eval_runtime': 15.2585, 'eval_samples_per_second': 655.374, 'eval_steps_per_second': 40.961, 'epoch': 2.87}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForMultipleChoice.forward` and have been ignored: ending3, ending1, video-id, sent1, ending0, sent2, fold-ind, ending2, startphrase, gold-source. If ending3, ending1, video-id, sent1, ending0, sent2, fold-ind, ending2, startphrase, gold-source are not expected by `RobertaForMultipleChoice.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m Num examples = 10000\n",
"\u001B[2m\u001B[36m(train pid=86157)\u001B[0m Batch size = 16\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
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"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m {'eval_loss': 1.2118068933486938, 'eval_automl_metric': 0.2015, 'eval_runtime': 15.1369, 'eval_samples_per_second': 660.639, 'eval_steps_per_second': 41.29, 'epoch': 2.87}\n",
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m {'train_runtime': 546.3809, 'train_samples_per_second': 156.658, 'train_steps_per_second': 39.165, 'train_loss': 0.5030154804349909, 'epoch': 2.87}\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'loss': 0.4854, 'learning_rate': 1.3592147782116173e-06, 'epoch': 2.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
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{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForMultipleChoice.forward` and have been ignored: fold-ind, sent2, gold-source, ending1, startphrase, sent1, ending0, video-id, ending2, ending3. If fold-ind, sent2, gold-source, ending1, startphrase, sent1, ending0, video-id, ending2, ending3 are not expected by `RobertaForMultipleChoice.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m Num examples = 10000\n",
"\u001B[2m\u001B[36m(train pid=86249)\u001B[0m Batch size = 16\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'eval_loss': 0.49709731340408325, 'eval_automl_metric': 0.17600000000000005, 'eval_runtime': 15.4983, 'eval_samples_per_second': 645.232, 'eval_steps_per_second': 40.327, 'epoch': 2.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 14:41:56,719\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'eval_loss': 0.5254333019256592, 'eval_automl_metric': 0.17800000000000005, 'eval_runtime': 15.45, 'eval_samples_per_second': 647.251, 'eval_steps_per_second': 40.453, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'loss': 0.3989, 'learning_rate': 3.8051750127352887e-07, 'epoch': 3.0}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 14:42:56,729\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'eval_loss': 0.5254867076873779, 'eval_automl_metric': 0.17789999999999995, 'eval_runtime': 15.424, 'eval_samples_per_second': 648.341, 'eval_steps_per_second': 40.521, 'epoch': 3.0}\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'eval_loss': 0.5332269072532654, 'eval_automl_metric': 0.17830000000000001, 'eval_runtime': 15.4452, 'eval_samples_per_second': 647.45, 'eval_steps_per_second': 40.466, 'epoch': 3.39}\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m {'train_runtime': 382.2827, 'train_samples_per_second': 88.597, 'train_steps_per_second': 11.076, 'train_loss': 0.5299136270370808, 'epoch': 3.39}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 14:43:56,739\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m The following columns in the test set don't have a corresponding argument in `RobertaForMultipleChoice.forward` and have been ignored: ending2, sent1, ending0, sent2, ending3, video-id, gold-source, ending1, startphrase, fold-ind. If ending2, sent1, ending0, sent2, ending3, video-id, gold-source, ending1, startphrase, fold-ind are not expected by `RobertaForMultipleChoice.forward`, you can safely ignore this message.\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m Num examples = 10000\n",
"\u001B[2m\u001B[36m(train pid=86195)\u001B[0m Batch size = 16\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
"2022-03-19 14:44:14,271\tINFO tune.py:639 -- Total run time: 795.18 seconds (504.18 seconds for the tuning loop).\n",
"[flaml.automl: 03-19 14:44:19] {2837} INFO - selected model: None\n",
"/data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'loss': 0.6603, 'learning_rate': 4.631567529441369e-06, 'epoch': 1.0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[flaml.automl: 03-19 14:46:08] {2947} INFO - retrain transformer for 109.2s\n",
"[flaml.automl: 03-19 14:46:08] {2954} INFO - retrained model: None\n",
"[flaml.automl: 03-19 14:46:08] {2283} INFO - fit succeeded\n",
"[flaml.automl: 03-19 14:46:08] {2284} INFO - Time taken to find the best model: 319.927033662796\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'train_runtime': 96.899, 'train_samples_per_second': 245.031, 'train_steps_per_second': 30.63, 'train_loss': 0.6602518278346073, 'epoch': 1.0}\n"
]
}
],
"source": [
"''' import AutoML class from flaml package '''\n",
"from flaml import AutoML\n",
"automl = AutoML()\n",
"\n",
"if not ray.is_initialized():\n",
" ray.init()\n",
"\n",
"automl_settings = {\n",
" \"time_budget\": 500, # setting the time budget\n",
" \"task\": \"multichoice-classification\", # setting the task as multiplechoice-classification\n",
refactoring TransformersEstimator to support default and custom_hp (#511) * refactoring TransformersEstimator to support default and custom_hp * handling starting_points not in search space * addressing starting point more than max_iter * fixing upper < lower bug
2022-04-28 14:06:29 -04:00
" \"fit_kwargs_by_estimator\": { # if model_path is not set, the default model is facebook/muppet-roberta-base: https://huggingface.co/facebook/muppet-roberta-base\n",
" \"transformer\": {\n",
" \"output_dir\": \"data/output/\", # setting the output directory\n",
" \"per_device_eval_batch_size\": 16, # the batch size for validation (inference)\n",
" }\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
" },\n",
" \"gpu_per_trial\": 1, # set to 0 if no GPU is available\n",
" \"log_file_name\": \"seqclass.log\", # set the file to save the log for HPO\n",
" \"log_type\": \"all\", # the log type for trials: \"all\" if logging all the trials, \"better\" if only keeping the better trials\n",
" \"use_ray\": {\"local_dir\": \"data/output/\"}, # set whether to use Ray\n",
" \"n_concurrent_trials\": 4\n",
"}\n",
"\n",
"'''The main flaml automl API'''\n",
"automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 0.00021956991427751982, 'num_train_epochs': 0.3549576494055084, 'per_device_train_batch_size': 8, 'warmup_ratio': 0.07425273520338253, 'weight_decay': 0.03879221030529465, 'adam_epsilon': 3.7880482987985576e-08, 'seed': 43, 'global_max_steps': 444, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 0.00021956991427751982, 'num_train_epochs': 0.3549576494055084, 'per_device_train_batch_size': 8, 'warmup_ratio': 0.07425273520338253, 'weight_decay': 0.03879221030529465, 'adam_epsilon': 3.7880482987985576e-08, 'seed': 43, 'global_max_steps': 444, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 1.3241899893349513e-06, 'num_train_epochs': 0.4379128434860086, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.257055208282222, 'weight_decay': 0.012652183020312091, 'adam_epsilon': 1.0189125195705357e-07, 'seed': 43, 'global_max_steps': 274, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 0.0002562922748967212, 'num_train_epochs': 0.1802995999606059, 'per_device_train_batch_size': 4, 'warmup_ratio': 0.1809477882684876, 'weight_decay': 0.10305626005953175, 'adam_epsilon': 5.536776887412208e-08, 'seed': 42, 'global_max_steps': 451, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 313, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 6.104513714676502e-06, 'num_train_epochs': 2.3743291981165893, 'per_device_train_batch_size': 8, 'warmup_ratio': 0.23610846764298543, 'weight_decay': 0.20205904544254147, 'adam_epsilon': 5.752964074991208e-08, 'seed': 41, 'global_max_steps': 1251, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 6.104513714676502e-06, 'num_train_epochs': 2.3743291981165893, 'per_device_train_batch_size': 8, 'warmup_ratio': 0.23610846764298543, 'weight_decay': 0.20205904544254147, 'adam_epsilon': 5.752964074991208e-08, 'seed': 41, 'global_max_steps': 1251, 'learner': 'transformer'}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 9.306519250357542e-06, 'num_train_epochs': 0.4664878701006166, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 5.931759315303309e-07, 'seed': 43, 'global_max_steps': 147, 'learner': 'transformer'}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 6.104513714676502e-06, 'num_train_epochs': 2.3743291981165893, 'per_device_train_batch_size': 8, 'warmup_ratio': 0.23610846764298543, 'weight_decay': 0.20205904544254147, 'adam_epsilon': 5.752964074991208e-08, 'seed': 41, 'global_max_steps': 1251, 'learner': 'transformer'}}\n",
"6\n"
]
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{
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from flaml.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=automl_settings['log_file_name'], time_budget=3000)\n",
"for config in config_history:\n",
" print(config)\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"plt.title('Learning Curve')\n",
"plt.xlabel('Wall Clock Time (s)')\n",
"plt.ylabel('Validation Accuracy')\n",
"print(len(valid_loss_history))\n",
"plt.scatter(time_history, 1 - np.array(valid_loss_history))\n",
"plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"### 4.2 Text Summarization Example"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The text summarization task summarizes a long text into a short sentence. For example:\n",
"\n",
"- Document: Army explosives experts were called out to deal with a suspect package at the offices on the Newtownards Road on Friday night. Roads were sealed off and traffic diverted as a controlled explosion was carried out. The premises, used by East Belfast MP Naomi Long, have been targeted a number of times. Most recently, petrol bomb attacks were carried out on the offices on consecutive nights in April and May. The attacks began following a Belfast City Council vote in December 2012 restricting the flying of the union flag at the City Hall. Condemning the latest hoax, Alliance MLA Chris Lyttle said: \"It is a serious incident for the local area, it causes serious disruption, it puts people's lives at risk, it can prevent emergency services reaching the area. \"Ultimately we need people with information to share that with the police in order for them to do their job and bring these people to justice.\n",
"\n",
"- Summary: A suspicious package left outside an Alliance Party office in east Belfast has been declared a hoax.\n",
"\n",
"In this example, we use FLAML to perform *abstractive summarization* using the t5-small language model, i.e., the summary is generated word-by-word. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration default\n",
"Reusing dataset xsum (/home/xliu127/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"204045\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration default\n",
"Reusing dataset xsum (/home/xliu127/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934)\n",
"Using custom data configuration default\n",
"Reusing dataset xsum (/home/xliu127/.cache/huggingface/datasets/xsum/default/1.2.0/32c23220eadddb1149b16ed2e9430a05293768cfffbdfd151058697d4c11f934)\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"train_dataset = load_dataset(\"xsum\", split=\"train\").to_pandas()\n",
"print(len(train_dataset))\n",
"dev_dataset = load_dataset(\"xsum\", split=\"validation\").to_pandas()\n",
"test_dataset = load_dataset(\"xsum\", split=\"test\").to_pandas()\n",
"\n",
"custom_sent_keys = [\"document\"] # specify the column names of the input sentences\n",
"label_key = \"summary\" # specify the column name of the label \n",
"\n",
"X_train, y_train = train_dataset[custom_sent_keys], train_dataset[label_key]\n",
"X_val, y_val = dev_dataset[custom_sent_keys], dev_dataset[label_key]\n",
"X_test = test_dataset[custom_sent_keys]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"== Status ==<br>Current time: 2022-03-19 14:55:00 (running for 00:08:31.38)<br>Memory usage on this node: 23.1/376.6 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/96 CPUs, 0/4 GPUs, 0.0/250.17 GiB heap, 0.0/111.21 GiB objects (0.0/1.0 accelerator_type:V100)<br>Current best trial: 08b6571c with val_loss=0.8569452656271894 and parameters={'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 9223372036854775807, 'learner': 'transformer', 'FLAML_sample_size': 10000}<br>Result logdir: /data/xliu127/projects/hyperopt/FLAML/notebook/data/output/train_2022-03-19_14-46-29<br>Number of trials: 8/1000000 (8 TERMINATED)<br><br>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
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"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m /data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m warnings.warn(\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m {'loss': 8.7635, 'learning_rate': 1.2308416834153697e-05, 'epoch': 0.11}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m /data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m warnings.warn(\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m /data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m warnings.warn(\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m /data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m warnings.warn(\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
"2022-03-19 14:56:00,679\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m {'eval_loss': 6.893245697021484, 'eval_automl_metric': 0.8537338408275918, 'eval_runtime': 102.2734, 'eval_samples_per_second': 110.801, 'eval_steps_per_second': 6.932, 'epoch': 0.11}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 14:57:00,687\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m {'eval_loss': 7.381210803985596, 'eval_automl_metric': 0.8475751825208984, 'eval_runtime': 107.4032, 'eval_samples_per_second': 105.509, 'eval_steps_per_second': 6.601, 'epoch': 0.16}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m {'eval_loss': 10.150897979736328, 'eval_automl_metric': 0.8566791839938478, 'eval_runtime': 108.2143, 'eval_samples_per_second': 104.718, 'eval_steps_per_second': 6.552, 'epoch': 0.36}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 14:58:00,697\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m {'eval_loss': 11.665904998779297, 'eval_automl_metric': 0.858011676038827, 'eval_runtime': 109.4667, 'eval_samples_per_second': 103.52, 'eval_steps_per_second': 6.477, 'epoch': 0.38}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
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{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m {'eval_loss': 6.893245697021484, 'eval_automl_metric': 0.8537338408275918, 'eval_runtime': 110.7246, 'eval_samples_per_second': 102.344, 'eval_steps_per_second': 6.403, 'epoch': 0.11}\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m {'train_runtime': 220.8946, 'train_samples_per_second': 4.648, 'train_steps_per_second': 0.149, 'train_loss': 8.763471198804451, 'epoch': 0.11}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 14:59:00,706\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m Num examples = 11332\n",
"\u001B[2m\u001B[36m(train pid=86232)\u001B[0m Batch size = 16\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m {'eval_loss': 7.381210803985596, 'eval_automl_metric': 0.8475751825208984, 'eval_runtime': 109.1975, 'eval_samples_per_second': 103.775, 'eval_steps_per_second': 6.493, 'epoch': 0.16}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m [nltk_data] Downloading package punkt to /home/xliu127/nltk_data...\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m [nltk_data] Package punkt is already up-to-date!\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m {'train_runtime': 232.9303, 'train_samples_per_second': 10.067, 'train_steps_per_second': 1.262, 'train_loss': 9.880440506280637, 'epoch': 0.16}\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m {'eval_loss': 10.150897979736328, 'eval_automl_metric': 0.8566791839938478, 'eval_runtime': 108.3182, 'eval_samples_per_second': 104.618, 'eval_steps_per_second': 6.546, 'epoch': 0.36}\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m {'train_runtime': 232.4568, 'train_samples_per_second': 92.218, 'train_steps_per_second': 2.887, 'train_loss': 11.215172903878349, 'epoch': 0.36}\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m {'eval_loss': 11.665904998779297, 'eval_automl_metric': 0.858011676038827, 'eval_runtime': 110.526, 'eval_samples_per_second': 102.528, 'eval_steps_per_second': 6.415, 'epoch': 0.38}\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m {'train_runtime': 236.6253, 'train_samples_per_second': 19.714, 'train_steps_per_second': 0.621, 'train_loss': 11.549961930614407, 'epoch': 0.38}\n"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-03-19 15:00:00,942\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m Num examples = 11332\n",
"\u001B[2m\u001B[36m(train pid=86184)\u001B[0m Batch size = 16\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m Num examples = 11332\n",
"\u001B[2m\u001B[36m(train pid=86160)\u001B[0m Batch size = 16\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m Using amp half precision backend\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m ***** Running Prediction *****\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m Num examples = 11332\n",
"\u001B[2m\u001B[36m(train pid=86225)\u001B[0m Batch size = 16\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
"2022-03-19 15:01:00,948\tWARNING ray_trial_executor.py:146 -- Skipping cleanup - trainable.stop did not return in time. Consider making `stop` a faster operation.\n",
"2022-03-19 15:02:20,150\tINFO tune.py:639 -- Total run time: 950.87 seconds (500.36 seconds for the tuning loop).\n",
"[flaml.automl: 03-19 15:02:25] {2837} INFO - selected model: None\n",
"/data/installation/anaconda3/envs/tmp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"[flaml.automl: 03-19 15:14:54] {2947} INFO - retrain transformer for 748.2s\n",
"[flaml.automl: 03-19 15:14:54] {2954} INFO - retrained model: None\n",
"[flaml.automl: 03-19 15:14:54] {2283} INFO - fit succeeded\n",
"[flaml.automl: 03-19 15:14:54] {2284} INFO - Time taken to find the best model: 472.3055913448334\n",
"[flaml.automl: 03-19 15:14:54] {2295} WARNING - Time taken to find the best model is 94% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'train_runtime': 14.6848, 'train_samples_per_second': 13894.959, 'train_steps_per_second': 434.258, 'train_loss': 10.199760437011719, 'epoch': 0.02}\n"
]
}
],
"source": [
"''' import AutoML class from flaml package '''\n",
"from flaml import AutoML\n",
"automl = AutoML()\n",
"\n",
"import ray\n",
"if not ray.is_initialized():\n",
" ray.init()\n",
"\n",
"automl_settings = {\n",
" \"time_budget\": 500, # setting the time budget\n",
fixing bug
2022-03-27 20:10:51 -04:00
" \"task\": \"summarization\", # setting the task as summarization\n",
refactoring TransformersEstimator to support default and custom_hp (#511) * refactoring TransformersEstimator to support default and custom_hp * handling starting_points not in search space * addressing starting point more than max_iter * fixing upper < lower bug
2022-04-28 14:06:29 -04:00
" \"fit_kwargs_by_estimator\": { # if model_path is not set, the default model is t5-small: https://huggingface.co/t5-small\n",
" \"transformer\": {\n",
" \"output_dir\": \"data/output/\", # setting the output directory\n",
" \"model_path\": \"t5-small\",\n",
" \"per_device_eval_batch_size\": 16, # the batch size for validation (inference)\n",
" }\n",
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
" },\n",
" \"gpu_per_trial\": 1, # set to 0 if no GPU is available\n",
" \"log_file_name\": \"seqclass.log\", # set the file to save the log for HPO\n",
" \"log_type\": \"all\", # the log type for trials: \"all\" if logging all the trials, \"better\" if only keeping the better trials\n",
" \"use_ray\": {\"local_dir\": \"data/output/\"}, # set whether to use Ray\n",
" \"metric\": \"rouge1\",\n",
" \"n_concurrent_trials\": 4, # sample: False # if the time is sufficient (e.g., longer than one trial's running time), you can set \n",
"}\n",
"\n",
"'''The main flaml automl API'''\n",
"automl.fit(X_train=X_train, y_train=y_train, X_val=X_val, y_val=y_val, **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 3.6439277745413994e-06, 'num_train_epochs': 0.454119690781029, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.04654549348562217, 'weight_decay': 0.06669806327326033, 'adam_epsilon': 2.5833461668835812e-08, 'seed': 42, 'global_max_steps': 125, 'learner': 'transformer', 'FLAML_sample_size': 10000}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 3.6439277745413994e-06, 'num_train_epochs': 0.454119690781029, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.04654549348562217, 'weight_decay': 0.06669806327326033, 'adam_epsilon': 2.5833461668835812e-08, 'seed': 42, 'global_max_steps': 125, 'learner': 'transformer', 'FLAML_sample_size': 10000}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 112, 'learner': 'transformer', 'FLAML_sample_size': 10000}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 112, 'learner': 'transformer', 'FLAML_sample_size': 10000}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 3.4236378229097798e-06, 'num_train_epochs': 8.919336644807531, 'per_device_train_batch_size': 4, 'warmup_ratio': 0.022492820063166875, 'weight_decay': 0.27013721375576616, 'adam_epsilon': 6.366959214432801e-08, 'seed': 43, 'global_max_steps': 180, 'learner': 'transformer', 'FLAML_sample_size': 10000}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 112, 'learner': 'transformer', 'FLAML_sample_size': 10000}}\n",
"{'Current Learner': 'transformer', 'Current Sample': 10000, 'Current Hyper-parameters': {'learning_rate': 2.83823390666728e-06, 'num_train_epochs': 1.6667827812145841, 'per_device_train_batch_size': 16, 'warmup_ratio': 0.04013366246992448, 'weight_decay': 0.2945152447208819, 'adam_epsilon': 4.694476379503266e-08, 'seed': 43, 'global_max_steps': 163, 'learner': 'transformer', 'FLAML_sample_size': 10000}, 'Best Learner': 'transformer', 'Best Hyper-parameters': {'learning_rate': 1.0000000000000003e-05, 'num_train_epochs': 1.0, 'per_device_train_batch_size': 32, 'warmup_ratio': 0.0, 'weight_decay': 0.0, 'adam_epsilon': 1e-06, 'seed': 42, 'global_max_steps': 112, 'learner': 'transformer', 'FLAML_sample_size': 10000}}\n",
"4\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"from flaml.data import get_output_from_log\n",
"time_history, best_valid_loss_history, valid_loss_history, config_history, metric_history = \\\n",
" get_output_from_log(filename=automl_settings['log_file_name'], time_budget=3000)\n",
"for config in config_history:\n",
" print(config)\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"plt.title('Learning Curve')\n",
"plt.xlabel('Wall Clock Time (s)')\n",
"plt.ylabel('Rouge 1')\n",
"print(len(valid_loss_history))\n",
"plt.scatter(time_history, 1 - np.array(valid_loss_history))\n",
"plt.step(time_history, 1 - np.array(best_valid_loss_history), where='post')\n",
"plt.show()"
]
}
],
"metadata": {
"interpreter": {
"hash": "e9d36fc5b7c3dd4177ff1b60184dd696c0acc18150a44682abca4d769811bd46"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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",
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
"version": "3.8.0"
fixing bug for ner (#463) * fixing bug for ner * removing global var * adding class for trial counter * adding notebook * adding use_ray dict * updating documentation for nlp
2022-03-20 22:03:02 -04:00
}
},
"nbformat": 4,
"nbformat_minor": 2
Fix HPO evaluation bug (#645) * fix eval automl metric bug on val_loss inconsistency * updating starting point search space to continuous * shortening notebok
2022-07-28 23:08:42 -04:00
}
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