my_logger.error(msg)
raise Exception(msg)
def handle_data(self, data):
""" ABC: Handle data """
self.parser.set_html_data(data)
pass
if __name__ == '__main__':
""" Main application processing for BookMarks """
my_logger.debug('INIT')
# initialization and setup
arg_parser = ArgParser()
config = CfgParser()
html_parser = MyHTMLParser()
# open bookmarks file and feed to the parser
bookmarks = None
try:
my_logger.info(f'Processing input file: {TheConfig.input_file}')
with open(TheConfig.input_file, mode='r', encoding='utf-8') as html:
bookmarks_html = html.read()
html_parser.feed(bookmarks_html)
bookmarks = html_parser.parser.bookmarks
except Exception as e:
my_logger.exception(f'Exception parsing file: {e}', exc_info=e)
# analyze bookmarks just parsed
```
python baseline.py directory --vgpu=1
```
"""
import os
import sys
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from config import ArgParser, get_eval_problem
from gpu_setup import create_distribute
args = ArgParser(sys.argv[1:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
cpu = args.pop_get("--cpu", default=False, dtype=bool)
gpus = args.pop_get("--gpus", default=None)
use_keras = args.pop_get("--keras", default=True, dtype=bool)
distribute = create_distribute(vgpus=vgpus, do_cpu=cpu, gpus=gpus)
problem = args.pop_get("--problem", "conv_train")
target = args.pop_get("--optimizer", "adam")
target_cfg = {
"adam": {
"class_name": "Adam",
"config": {
"learning_rate": 0.005,
"beta_1": 0.9,
"""Resume Training.
Run with
```
python resume.py directory --vgpu=1
```
"""
import os
import sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from config import ArgParser
from gpu_setup import create_distribute
args = ArgParser(sys.argv[2:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
distribute = create_distribute(vgpus=vgpus)
with distribute.scope():
strategy = l2o.strategy.build_from_config(sys.argv[1])
strategy.train()