def main(_):
FLAGS.train_input = '../input/train.small'
FLAGS.valid_input = '../input/train.small'
FLAGS.batch_size = 4
FLAGS.feat_file_path = '../input/feature_index'
FLAGS.field_file_path = '../input/feat_fields.old'
melt.init()
dataset = Dataset('train')
#dataset = Dataset('valid')
iter = dataset.make_batch()
op = iter.get_next()
print('---batch_size', dataset.batch_size, FLAGS.batch_size)
sess = melt.get_session()
print('----sess', sess)
if not FLAGS.use_horovod:
for epoch in range(2):
for i in range(3):
batch = sess.run(op)
print(epoch, i, len(batch[0]['id']), batch[0]['id'])
else:
for epoch in range(2):
for i in range(3):
batch = sess.run(op)
print(epoch, i, len(batch[0]['id']), batch[0]['id'])
def main(_):
FLAGS.torch_only = True
#FLAGS.valid_input = None
melt.init()
fit = melt.get_fit()
FLAGS.eval_batch_size = 512 * FLAGS.valid_multiplier
model_name = FLAGS.model
model = getattr(base, model_name)()
loss_fn = nn.BCEWithLogitsLoss()
td = text_dataset.Dataset()
train_files = gezi.list_files(FLAGS.train_input)
train_ds = get_dataset(train_files, td)
## speed up a bit with pin_memory==True
## num_workers 1 is very slow especially for validation, seems 4 workers is enough, large number dangerous sometimes 12 ok sometimes hang, too much resource seems
#kwargs = {'num_workers': 12, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
#kwargs = {'num_workers': 6, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
kwargs = {
'num_workers': 8,
'pin_memory': True,
'collate_fn': lele.DictPadCollate()
}
## for 1 gpu, set > 8 might startup very slow
#num_workers = int(8 / hvd.size())
# num_workers = 0
# pin_memory = False
#kwargs = {'num_workers': num_workers, 'pin_memory': pin_memory, 'collate_fn': lele.DictPadCollate()}
train_dl = DataLoader(train_ds, FLAGS.batch_size, shuffle=True, **kwargs)
#kwargs['num_workers'] = max(1, num_workers)
#logging.info('num train examples', len(train_ds), len(train_dl))
if FLAGS.valid_input:
valid_files = gezi.list_files(FLAGS.valid_input)
valid_ds = get_dataset(valid_files, td)
valid_dl = DataLoader(valid_ds, FLAGS.eval_batch_size, **kwargs)
#kwargs['num_workers'] = max(1, num_workers)
valid_dl2 = DataLoader(valid_ds, FLAGS.batch_size, **kwargs)
#logging.info('num valid examples', len(valid_ds), len(valid_dl))
fit(
model,
loss_fn,
dataset=train_dl,
valid_dataset=valid_dl,
valid_dataset2=valid_dl2,
eval_fn=ev.evaluate,
valid_write_fn=ev.valid_write,
#write_valid=FLAGS.write_valid)
write_valid=False,
)
def main(_):
FLAGS.torch_only = True
melt.init()
fit = melt.get_fit()
FLAGS.eval_batch_size = 512 * FLAGS.valid_multiplier
model_name = FLAGS.model
model = getattr(base, model_name)()
model = model.cuda()
loss_fn = nn.BCEWithLogitsLoss()
td = text_dataset.Dataset()
train_files = gezi.list_files('../input/train/*')
train_ds = get_dataset(train_files, td)
#kwargs = {'num_workers': 4, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
#kwargs = {'num_workers': 0, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
#kwargs = {'num_workers': 4, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
num_workers = 1
kwargs = {'num_workers': num_workers, 'pin_memory': False, 'collate_fn': lele.DictPadCollate()}
train_sampler = train_ds
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_ds, num_replicas=hvd.size(), rank=hvd.rank())
train_dl = DataLoader(train_ds, FLAGS.batch_size, sampler=train_sampler, **kwargs)
valid_files = gezi.list_files('../input/valid/*')
valid_ds = get_dataset(valid_files, td)
kwargs['num_workers'] = 1
# support shuffle=False from version 1.2
valid_sampler = torch.utils.data.distributed.DistributedSampler(
valid_ds, num_replicas=hvd.size(), rank=hvd.rank(), shuffle=False)
kwargs['num_workers'] = 1
valid_sampler2 = torch.utils.data.distributed.DistributedSampler(
valid_ds, num_replicas=hvd.size(), rank=hvd.rank(), shuffle=False)
valid_dl = DataLoader(valid_ds, FLAGS.eval_batch_size, sampler=valid_sampler, **kwargs)
valid_dl2 = DataLoader(valid_ds, FLAGS.batch_size, sampler=valid_sampler2, **kwargs)
fit(model,
loss_fn,
dataset=train_dl,
valid_dataset=valid_dl,
valid_dataset2=valid_dl2,
eval_fn=ev.evaluate,
valid_write_fn=ev.valid_write,
#write_valid=FLAGS.write_valid)
write_valid=False,
)
def main(_):
FLAGS.torch_only = True
melt.init()
fit = melt.get_fit()
FLAGS.eval_batch_size = 512 * FLAGS.valid_multiplier
model_name = FLAGS.model
model = getattr(base, model_name)()
loss_fn = nn.BCEWithLogitsLoss()
td = TextDataset()
train_files = gezi.list_files('../input/train/*')
train_ds = get_dataset(train_files, td)
import multiprocessing
#--easy to be Killed .. if large workers
num_threads = int(multiprocessing.cpu_count() * 0.3)
logging.info('num_threads as multiprocessing.cpu_count', num_threads)
num_threads = 12
train_dl = DataLoader(train_ds,
FLAGS.batch_size,
shuffle=True,
num_workers=num_threads,
collate_fn=lele.DictPadCollate())
#logging.info('num train examples', len(train_ds), len(train_dl))
valid_files = gezi.list_files('../input/valid/*')
valid_ds = get_dataset(valid_files, td)
valid_dl = DataLoader(valid_ds,
FLAGS.eval_batch_size,
collate_fn=lele.DictPadCollate(),
num_workers=num_threads)
valid_dl2 = DataLoader(valid_ds,
FLAGS.batch_size,
collate_fn=lele.DictPadCollate(),
num_workers=num_threads)
#logging.info('num valid examples', len(valid_ds), len(valid_dl))
fit(
model,
loss_fn,
dataset=train_dl,
valid_dataset=valid_dl,
valid_dataset2=valid_dl2,
eval_fn=ev.evaluate,
valid_write_fn=ev.valid_write,
#write_valid=FLAGS.write_valid)
write_valid=False,
)
def main(_):
fit = mt.fit
config.init()
mt.init()
strategy = mt.distributed.get_strategy()
with strategy.scope():
model = getattr(base, FLAGS.model)()
loss_fn = model.get_loss()
# 如果用model.get_model 当前不能用model.get_loss 否则tf2 keras
# Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor 'Squeeze_6:0' shape=(None,) dtype=int64>, <tf.Tensor 'Squeeze_10:0' shape=(None,) dtype=int64>]
if not FLAGS.custom_loss:
loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)
# if tf.__version__ >= '2':
# # TODO FIXME has bug for tf1 non eager mode below but eager mode ok
# # TF2 may has problem eager input not ok...
# dataset = Dataset('train')
# dataset.make_batch()
# if not dataset.has_varlen_keys:
# inputs = dataset.get_inputs()
# model = model.get_model(inputs)
if not FLAGS.lm_target:
callbacks = []
#if tf.__version__ >= '2':
# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=FLAGS.log_dir, profile_batch=(5, 20))
# callbacks = [tensorboard_callback]
fit(
model,
loss_fn=loss_fn,
Dataset=Dataset,
eval_fn=ev.evaluate,
eval_keys=[
'uid_', 'did_', 'impression_id', 'uid_in_train', 'did_in_train'
], # used in evaluate.py for x
out_hook=out_hook,
infer_write_fn=infer_write,
valid_write_fn=valid_write,
callbacks=callbacks,
)
else:
fit(
model,
loss_fn=loss_fn,
Dataset=Dataset,
)
def main(_):
fit = melt.fit
config.init()
melt.init()
strategy = melt.distributed.get_strategy()
with strategy.scope():
model = getattr(base, FLAGS.model)()
# loss_fn = model.get_loss()
loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=True)
if tf.__version__ >= '2':
# TODO FIXME has bug for tf1 non eager mode below but eager mode ok
dataset = Dataset('train')
dataset.make_batch()
if not dataset.has_varlen_keys:
inputs = dataset.get_inputs()
model = model.get_model(inputs)
if not FLAGS.lm_target:
callbacks = []
# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=FLAGS.log_dir, profile_batch=(5, 20))
# callbacks = [tensorboard_callback]
fit(
model,
loss_fn=loss_fn,
Dataset=Dataset,
eval_fn=ev.evaluate,
eval_keys=[
'uid_', 'did_', 'impression_id', 'uid_in_train', 'did_in_train'
], # used in evaluate.py for x
out_hook=out_hook,
infer_write_fn=infer_write,
valid_write_fn=valid_write,
callbacks=callbacks,
)
else:
fit(
model,
loss_fn=loss_fn,
Dataset=Dataset,
)
def main(_):
FLAGS.torch_only = True
melt.init()
fit = melt.get_fit()
FLAGS.eval_batch_size = 512 * FLAGS.valid_multiplier
model_name = FLAGS.model
model = getattr(base, model_name)()
loss_fn = nn.BCEWithLogitsLoss()
td = TextDataset()
train_files = gezi.list_files('../input/train/*')
train_ds = get_dataset(train_files, td)
train_dl = DataLoader(train_ds,
FLAGS.batch_size,
shuffle=True,
num_workers=12)
logging.info('num train examples', len(train_ds), len(train_dl))
valid_files = gezi.list_files('../input/valid/*')
valid_ds = get_dataset(valid_files, td)
valid_dl = DataLoader(valid_ds, FLAGS.eval_batch_size)
valid_dl2 = DataLoader(valid_ds, FLAGS.batch_size)
logging.info('num valid examples', len(valid_ds), len(valid_dl))
print(dir(valid_dl))
fit(
model,
loss_fn,
dataset=train_dl,
valid_dataset=valid_dl,
valid_dataset2=valid_dl2,
eval_fn=ev.evaluate,
valid_write_fn=ev.valid_write,
#write_valid=FLAGS.write_valid)
write_valid=False,
)
def main(_):
FLAGS.torch = True
melt.init()
fit = melt.get_fit()
FLAGS.eval_batch_size = 512 * FLAGS.valid_multiplier
print('---------eval_batch_size', FLAGS.eval_batch_size)
model_name = FLAGS.model
model = getattr(base, model_name)()
Dataset = TextDataset if not 'tfrecord' in FLAGS.train_input else TFRecordDataset
loss_fn = nn.BCEWithLogitsLoss()
fit(
model,
loss_fn,
Dataset,
eval_fn=ev.evaluate,
valid_write_fn=ev.valid_write,
#write_valid=FLAGS.write_valid)
write_valid=False)
def main(_):
FLAGS.torch_only = True
melt.init()
#fit = melt.get_fit()
FLAGS.eval_batch_size = 512 * FLAGS.valid_multiplier
FLAGS.eval_batch_size = 512
model_name = FLAGS.model
model = getattr(base, model_name)()
model = model.cuda()
loss_fn = nn.BCEWithLogitsLoss()
td = text_dataset.Dataset()
train_files = gezi.list_files('../input/train/*')
train_ds = get_dataset(train_files, td)
#kwargs = {'num_workers': 4, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
#num_workers = int(16 / hvd.size())
num_workers = 1 # set to 1 2 min to start might just set to 0 for safe
num_workers = 0 # 设置0 速度比1慢很多 启动都需要1分多。。
# pin_memory 影响不大 单gpu提升速度一点点 多gpu 主要是 num_workers 影响资源占有。。有可能启动不起来
# 多gpu pin_memory = False 反而速度更快。。
#kwargs = {'num_workers': num_workers, 'pin_memory': True, 'collate_fn': lele.DictPadCollate()}
kwargs = {'num_workers': 1, 'pin_memory': False, 'collate_fn': lele.DictPadCollate()}
train_sampler = train_ds
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_ds, num_replicas=hvd.size(), rank=hvd.rank())
train_dl = DataLoader(train_ds, FLAGS.batch_size, sampler=train_sampler, **kwargs)
valid_files = gezi.list_files('../input/valid/*')
valid_ds = get_dataset(valid_files, td)
# support shuffle=False from version 1.2
valid_sampler = torch.utils.data.distributed.DistributedSampler(
valid_ds, num_replicas=hvd.size(), rank=hvd.rank(), shuffle=False)
# valid_sampler2 = torch.utils.data.distributed.DistributedSampler(
# valid_ds, num_replicas=hvd.size(), rank=hvd.rank(), shuffle=False)
valid_dl = DataLoader(valid_ds, FLAGS.eval_batch_size, sampler=valid_sampler, **kwargs)
#valid_dl2 = DataLoader(valid_ds, FLAGS.batch_size, sampler=valid_sampler2, **kwargs)
optimizer = optim.Adamax(model.parameters(), lr=0.1)
#optimizer = optim.SGD(model.parameters(), lr=0.1)
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer, root_rank=0)
optimizer = hvd.DistributedOptimizer(optimizer,
named_parameters=model.named_parameters())
for epoch in range(2):
train(epoch, model, loss_fn, train_dl, optimizer)
test(model, loss_fn, valid_dl)
from __future__ import division
from __future__ import print_function
import sys
import os
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
from torch.utils.data import DataLoader
import gezi
import lele
import melt
melt.init()
#import horovod.tensorflow as hvd
#import horovod.tensorflow as hvd
# from pyt.dataset import *
# from text_dataset import Dataset as TD
import numpy as np
#import horovod.tensorflow as hvd
import horovod.torch as hvd
hvd.init()
for i in range(5):
print(i)
