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iLampardiLampard
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fix tfb bug
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‎README.md

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@@ -218,7 +218,7 @@ To run the script, one should download the Taxi data following the above instruc
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```shell
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cd examples
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python benchmark_script.py
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python run_retweet.py
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```
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‎easy_tpp/config_factory/runner_config.py

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@@ -94,9 +94,6 @@ def ensure_valid_config(self):
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# during testing we dont do shuffle by default
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self.trainer_config.shuffle = False
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# during testing we dont apply tfb by default
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self.trainer_config.use_tfb = False
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return
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def update_config(self):

‎easy_tpp/torch_wrapper.py

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@@ -33,11 +33,10 @@ def __init__(self, model, base_config, model_config, trainer_config):
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self.opt = set_optimizer(optimizer, self.model.parameters(), self.learning_rate)
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# set up tensorboard
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self.use_tfb = self.trainer_config.use_tfb
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self.train_summary_writer, self.valid_summary_writer = None, None
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if self.use_tfb:
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self.train_summary_writer = SummaryWriter(log_dir=self.base_config.spec['tfb_train_dir'])
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self.valid_summary_writer = SummaryWriter(log_dir=self.base_config.spec['tfb_valid_dir'])
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if self.trainer_config.use_tfb:
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self.train_summary_writer = SummaryWriter(log_dir=self.base_config.specs['tfb_train_dir'])
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self.valid_summary_writer = SummaryWriter(log_dir=self.base_config.specs['tfb_valid_dir'])
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def restore(self, ckpt_dir):
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"""Load the checkpoint to restore the model.
@@ -64,7 +63,7 @@ def write_summary(self, epoch, kv_pairs, phase):
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kv_pairs (dict): metrics dict.
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phase (RunnerPhase): a const that defines the stage of model runner.
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"""
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if self.use_tfb:
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if self.trainer_config.use_tfb:
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summary_writer = None
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if phase == RunnerPhase.TRAIN:
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summary_writer = self.train_summary_writer

‎notebooks/easytpp_2_tfb_wb.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ant-research/EasyTemporalPointProcess/blob/main/notebooks/easytpp_2_tfb_wb.ipynb)\n",
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"\n",
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"\n",
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"# Tutorial 2: Tensorboard and Weights & Biases in EasyTPP\n",
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"\n",
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"EasyTPP provides built-in support for both Tensorboard and Weights & Biases (W&B) to help you track and visualize your model training. These tools allow you to monitor metrics, compare experiments, and debug your models effectively.\n",
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"\n",
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"\n",
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"## Example of using Tensorboard"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-02-03T02:24:56.584850Z",
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"start_time": "2025-02-03T02:24:56.580600Z"
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}
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},
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"outputs": [],
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"source": [
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"# As an illustrative example, we write the YAML content to a file\n",
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"yaml_content = \"\"\"\n",
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"pipeline_config_id: runner_config\n",
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"\n",
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"data:\n",
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" taxi:\n",
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" data_format: json\n",
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" train_dir: easytpp/taxi # ./data/taxi/train.json\n",
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" valid_dir: easytpp/taxi # ./data/taxi/dev.json\n",
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" test_dir: easytpp/taxi # ./data/taxi/test.json\n",
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" data_specs:\n",
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" num_event_types: 10\n",
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" pad_token_id: 10\n",
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" padding_side: right\n",
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"\n",
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"\n",
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"NHP_train:\n",
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" base_config:\n",
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" stage: train\n",
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" backend: torch\n",
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" dataset_id: taxi\n",
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" runner_id: std_tpp\n",
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" model_id: NHP # model name\n",
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" base_dir: './checkpoints/'\n",
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" trainer_config:\n",
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" batch_size: 256\n",
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" max_epoch: 2\n",
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" shuffle: False\n",
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" optimizer: adam\n",
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" learning_rate: 1.e-3\n",
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" valid_freq: 1\n",
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" use_tfb: True\n",
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" metrics: [ 'acc', 'rmse' ]\n",
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" seed: 2019\n",
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" gpu: -1\n",
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" model_config:\n",
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" hidden_size: 32\n",
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" loss_integral_num_sample_per_step: 20\n",
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" thinning:\n",
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" num_seq: 10\n",
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" num_sample: 1\n",
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" num_exp: 500 # number of i.i.d. Exp(intensity_bound) draws at one time in thinning algorithm\n",
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" look_ahead_time: 10\n",
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" patience_counter: 5 # the maximum iteration used in adaptive thinning\n",
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" over_sample_rate: 5\n",
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" num_samples_boundary: 5\n",
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" dtime_max: 5\n",
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" num_step_gen: 1\n",
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"\"\"\"\n",
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"\n",
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"# Save the content to a file named config.yaml\n",
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"with open(\"config.yaml\", \"w\") as file:\n",
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" file.write(yaml_content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Then we run the following command to train the model:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[31;1m2025-02-03 10:32:32,085 - config.py[pid:91053;line:34:build_from_yaml_file] - CRITICAL: Load pipeline config class RunnerConfig\u001b[0m\n",
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"\u001b[31;1m2025-02-03 10:32:32,089 - runner_config.py[pid:91053;line:161:update_config] - CRITICAL: train model NHP using CPU with torch backend\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:32,098 - runner_config.py[pid:91053;line:36:__init__] - INFO: Save the config to ./checkpoints/91053_8345177088_250203-103232/NHP_train_output.yaml\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:32,099 - base_runner.py[pid:91053;line:176:save_log] - INFO: Save the log to ./checkpoints/91053_8345177088_250203-103232/log\u001b[0m\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/miniconda3/envs/llm/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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"Downloading readme: 100%|██████████| 28.0/28.0 [00:00<00:00, 119B/s]\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.2244252199397379 0.29228809611195583\n",
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"min_dt: 0.000277777777777\n",
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"max_dt: 5.721388888888889\n",
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"\u001b[38;20m2025-02-03 10:32:38,267 - tpp_runner.py[pid:91053;line:60:_init_model] - INFO: Num of model parameters 15252\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:45,909 - base_runner.py[pid:91053;line:98:train] - INFO: Data 'taxi' loaded...\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:45,910 - base_runner.py[pid:91053;line:103:train] - INFO: Start NHP training...\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:46,425 - tpp_runner.py[pid:91053;line:96:_train_model] - INFO: [ Epoch 0 (train) ]: train loglike is -1.7553733776992408, num_events is 50454\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:47,128 - tpp_runner.py[pid:91053;line:107:_train_model] - INFO: [ Epoch 0 (valid) ]: valid loglike is -1.6691416010202664, num_events is 7204, acc is 0.4414214325374792, rmse is 0.3327808472052436\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:48,150 - tpp_runner.py[pid:91053;line:122:_train_model] - INFO: [ Epoch 0 (test) ]: test loglike is -1.6577474861303745, num_events is 14420, acc is 0.44667128987517335, rmse is 0.3408341129976238\u001b[0m\n",
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"\u001b[31;1m2025-02-03 10:32:48,150 - tpp_runner.py[pid:91053;line:124:_train_model] - CRITICAL: current best loglike on valid set is -1.6691 (updated at epoch-0), best updated at this epoch\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:48,487 - tpp_runner.py[pid:91053;line:96:_train_model] - INFO: [ Epoch 1 (train) ]: train loglike is -1.6284447180538213, num_events is 50454\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:48,995 - tpp_runner.py[pid:91053;line:107:_train_model] - INFO: [ Epoch 1 (valid) ]: valid loglike is -1.5259201159945863, num_events is 7204, acc is 0.4582176568573015, rmse is 0.33537458414488913\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:49,999 - tpp_runner.py[pid:91053;line:122:_train_model] - INFO: [ Epoch 1 (test) ]: test loglike is -1.5121817706527392, num_events is 14420, acc is 0.45977808599167824, rmse is 0.34166548827945314\u001b[0m\n",
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"\u001b[31;1m2025-02-03 10:32:50,000 - tpp_runner.py[pid:91053;line:124:_train_model] - CRITICAL: current best loglike on valid set is -1.5259 (updated at epoch-1), best updated at this epoch\u001b[0m\n",
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"\u001b[38;20m2025-02-03 10:32:50,000 - base_runner.py[pid:91053;line:110:train] - INFO: End NHP train! Cost time: 0.068m\u001b[0m\n"
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]
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}
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],
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"source": [
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"from easy_tpp.config_factory import Config\n",
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"from easy_tpp.runner import Runner\n",
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"\n",
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"config = Config.build_from_yaml_file('./config.yaml', experiment_id='NHP_train')\n",
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"\n",
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"model_runner = Runner.build_from_config(config)\n",
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"\n",
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"model_runner.run()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"source": [
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"After the training is done, we can see the tensorboard files in the `./checkpoints/` directory. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[34mcheckpoints\u001b[m\u001b[m easytpp_1_dataset.ipynb\n",
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"config.yaml easytpp_2_tfb_wb.ipynb\n",
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"\n",
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"./checkpoints:\n",
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"\u001b[34m91053_8345177088_250203-103232\u001b[m\u001b[m\n",
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"\n",
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"./checkpoints/91053_8345177088_250203-103232:\n",
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"NHP_train_output.yaml \u001b[34mmodels\u001b[m\u001b[m \u001b[34mtfb_valid\u001b[m\u001b[m\n",
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"log \u001b[34mtfb_train\u001b[m\u001b[m\n",
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"\n",
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"./checkpoints/91053_8345177088_250203-103232/models:\n",
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"saved_model\n",
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"\n",
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"./checkpoints/91053_8345177088_250203-103232/tfb_train:\n",
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"events.out.tfevents.1738549958.siqiaodeMacBook-Pro.local.91053.0\n",
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"\n",
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"./checkpoints/91053_8345177088_250203-103232/tfb_valid:\n",
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"events.out.tfevents.1738549958.siqiaodeMacBook-Pro.local.91053.1\n"
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]
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}
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],
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"source": [
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"!ls -R"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Then we can use the following script to visualize the training process:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"TensorFlow installation not found - running with reduced feature set.\n",
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"Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
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"TensorBoard 2.17.1 at http://localhost:6006/ (Press CTRL+C to quit)\n"
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]
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}
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],
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"source": [
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"! tensorboard --logdir \"./checkpoints/91053_8345177088_250203-103232/tfb_train/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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