def main():
print()
TorchConfig.init()
logging.basicConfig(level=logging.WARN)
logging.getLogger('zensols.deeplearn.model').setLevel(logging.WARN)
#logging.getLogger('zensols.config.meta').setLevel(logging.DEBUG)
logger.setLevel(logging.INFO)
run = [3, 4, 5, 6, 7, 8]
res = None
if run == [0]:
res = find_leaks()
elif run is None:
load()
res = end()
else:
fac = create_facade()
fac.progress_bar = False
fac.epochs = 50
for r in run:
res = {
0: fac.dataset,
1: lambda: fac.write(include_object_graph=True),
2: fac.debug,
3: fac.train,
4: fac.test,
5: fac.persist_result,
6: fac.write_result,
7: load,
8: fac.deallocate,
9: end
}[r]()
return res
def create_facade(self, *args) -> ModelFacade:
"""Create and return a facade. This deallocates and cleans up state from any
previous facade creation as a side effect.
:param args: given to the :obj:`cli_args_fn` function to create
arguments passed to the CLI
"""
if len(self.config_overwrites) > 0:
dconf = DictionaryConfig(self.config_overwrites)
app_args = {'config_overwrites': dconf}
else:
app_args = None
self.deallocate()
# reclaim memory running GC and GPU cache clear
self.cleanup()
try:
# reset random state for consistency of each new test
if self.reset_torch:
TorchConfig.init()
# create a factory that instantiates Python objects
cli_args_fn = self.cli_args_fn(*args)
# create the facade used for this instance
self._facade: ModelFacade = self._create_facade(
cli_args_fn, app_args)
return self._facade
except Exception as e:
try:
# recover the best we can
self.cleanup(quiet=True)
self._facade = None
except Exception:
pass
raise DeepLearnError(f'Could not create facade: {e}') from e
def init():
from zensols.deeplearn import TorchConfig
# reset random state for consistency before any other packages are
# imported
TorchConfig.init()
from zensols import deepnlp
# initialize the NLP system
deepnlp.init()
def main():
print()
TorchConfig.init()
logging.basicConfig(level=logging.WARN)
logger.setLevel(logging.INFO)
run = [2, 3, 4]
res = None
for r in run:
res = {1: dataset, 2: train_model, 3: test_model, 4: load_results}[r]()
return res
def main():
print()
TorchConfig.init()
logging.basicConfig(level=logging.WARN)
logging.getLogger('zensols.deeplearn.model').setLevel(logging.WARN)
run = 5
{
0: dataset,
1: dataframe,
2: metadata,
3: stash_info,
4: batch,
5: model,
6: tmp
}[run]()
def __init__(self, app_root_dir: str = '..', deepnlp_path: str = '..'):
"""Set up the interpreter environment so we can import local packages.
:param app_root_dir: the application root directory
:param deepnlp_path: the path to the DeepNLP source code
"""
import sys
from pathlib import Path
self.app_root_dir = Path(app_root_dir)
# add the example to the Python library path
sys.path.append(str(self.app_root_dir / 'cb'))
# add the deepnlp path
sys.path.append(deepnlp_path)
# reset random state for consistency before any other packages are
# imported
from zensols.deeplearn import TorchConfig
TorchConfig.init()
# initialize the NLP system
from zensols.deepnlp import init
init()
#!/usr/bin/env python
from typing import List
import sys
from pathlib import Path
import logging
from zensols.deeplearn import TorchConfig
from zensols import deepnlp
# reset random state for consistency before any other packages are
# imported
TorchConfig.init()
# initialize the NLP system
deepnlp.init()
class CliHarness(object):
"""A utility class to automate the creation of execution of the model from
either the command line or a Python REPL.
"""
def __init__(self, args: List[str] = sys.argv, src_dir_name: str = 'src'):
"""Configure the Python interpreter and this run class.
:param args: the command line arguments
:param src_dir_name: the directory add the Python path containing the
source for the application
"""
self.args = args[1:]
def setUp(self):
TorchConfig.init()
self.recreate_factory()
targ = Path('target')
if targ.exists() and targ.is_dir():
shutil.rmtree(targ)
def setUp(self):
TorchConfig.init()
config = AppConfig('test-resources/iris/iris.conf',
env={'app_root': '.'})
self.config = config
self.fac = ImportConfigFactory(config, shared=True, reload=False)