def _new_logger(log_level: Optional[int] = None):
global _LOGGER
if _LOGGER is None:
_LOGGER = mp.get_logger()
if log_level is not None:
get_logger().setLevel(log_level)
return True
return False
def create_logger():
multiprocessing.log_to_stderr()
logger = multiprocessing.get_logger()
logger.setLevel(logging.INFO)
fh = logging.FileHandler("process.log")
fmt = '%(asctime)s - %(processName)s - %(levelname)s - %(funcName)s - %(message)s'
formatter = logging.Formatter(fmt)
fh.setFormatter(formatter)
logger.addHandler(fh)
return logger
from allennlp.data.token_indexers import PretrainedBertIndexer
from matchmaker.dataloaders.bling_fire_tokenizer import BlingFireTokenizer
from typing import Dict, Tuple, List
#
# Multiprocess input pipeline
# -------------------------------
#
# single epoch batch generators with multiple subprocesses, each subprocess works on its own file until the file is parsed completely
#
# - the processes have as little communication as possible (because it is prohibitly expensive in python)
# - the finished batches go into shared memory and then the queue to be picked up by the train/validaton loops
#
mp.get_logger().setLevel(
logging.WARNING) # ignore useless process start console logs
mp.set_sharing_strategy(
"file_system"
) # VERY MUCH needed for linux !! makes everything MUCH faster -> from 10 to 30+ batches/s
fasttext_vocab_cached_mapping = None
fasttext_vocab_cached_data = None
#
# we need to wrap the individual process queues, because they might be filled in different order
# now we make sure to always get the same training samples in the same order for all runs
#
class DeterministicQueue():
def __init__(self, distributed_queues):
self.distributed_queues = distributed_queues
import logging
import os
from queue import Empty
from typing import Iterable, Iterator, List, Optional
from torch.multiprocessing import JoinableQueue, Process, Queue, get_logger
from allennlp.common.checks import ConfigurationError
from allennlp.data.dataset import Batch
from allennlp.data.dataset_readers.multiprocess_dataset_reader import QIterable
from allennlp.data.instance import Instance
from allennlp.data.iterators.data_iterator import DataIterator, TensorDict
from allennlp.data.vocabulary import Vocabulary
logger = get_logger()
logger.setLevel(logging.INFO)
def _create_tensor_dicts_from_queue(input_queue: Queue, output_queue: Queue,
iterator: DataIterator, shuffle: bool,
index: int) -> None:
"""
Pulls instances from ``input_queue``, converts them into ``TensorDict``s
using ``iterator``, and puts them on the ``output_queue``.
"""
logger.info(f"Iterator worker: {index} PID: {os.getpid()}")
def instances() -> Iterator[Instance]:
instance = input_queue.get()
while instance is not None:
yield instance
from typing import Iterable, Iterator, List, Optional
import logging
from torch.multiprocessing import Manager, Process, Queue, get_logger
from allennlp.common.checks import ConfigurationError
from allennlp.data.instance import Instance
from allennlp.data.iterators.data_iterator import DataIterator, TensorDict
from allennlp.data.dataset import Batch
from allennlp.data.vocabulary import Vocabulary
logger = get_logger() # pylint: disable=invalid-name
logger.setLevel(logging.INFO)
def _create_tensor_dicts(input_queue: Queue,
output_queue: Queue,
iterator: DataIterator,
shuffle: bool,
index: int) -> None:
"""
Pulls at most ``max_instances_in_memory`` from the input_queue,
groups them into batches of size ``batch_size``, converts them
to ``TensorDict`` s, and puts them on the ``output_queue``.
"""
def instances() -> Iterator[Instance]:
instance = input_queue.get()
while instance is not None:
yield instance
instance = input_queue.get()
for tensor_dict in iterator(instances(), num_epochs=1, shuffle=shuffle):
import numpy as np import logging import torch.multiprocessing as mp from torch.multiprocessing import Process, Queue, Pipe, Lock import time import os import io from spiketag.view import wave_view from spiketag.utils import Timer from vispy import app, keys import torch info = mp.get_logger().info # Create two instances of MyRect, each using canvas.scene as their parent nCh = 160 #32 channel number # Number of cols and rows in the table. # nrows = 16 # ncols = 2 # Number of channels. # m = nrows*ncols # Number of samples per channel. npts = int(20000) # Generate the signals as a (nCh, npts) array. init_data = np.random.randn(npts, nCh).astype(np.float32) init_data[:, 4] = 1 wview = wave_view(data=init_data, fs=25e3, chs=np.arange(160), pagesize=npts) @wview.connect
