Top 10 Examples of "tqdm in functional component" in Python
Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'tqdm' in functional components in Python. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
# set global_step to gobal_step of last saved checkpoint from model path
global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(
epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
)
set_seed(args) # Added here for reproductibility
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
if args.model_type != "distilbert":def download_data(dataset: Dict[str, str]) -> io.BytesIO:
from .. import session
f = session.get(dataset["url"], stream=True)
total = int(f.headers["Content-Length"])
buffer = io.BytesIO()
for chunk in tqdm(
f.iter_content(1024),
total=total // 1024 + 1 if total % 1024 > 0 else 0,
desc="download",
):
buffer.write(chunk)
buffer.seek(0)
compute_md5 = md5(buffer.getbuffer()).hexdigest()
if compute_md5 != dataset["md5sum"]:
raise RuntimeError(
f"Error in MD5 check: {compute_md5} instead of {dataset['md5sum']}"
)
return bufferaxis = 0
elif axis == 'columns':
axis = 1
# when axis=0, total is shape[axis1]
total = df.size // df.shape[axis]
# Init bar
if deprecated_t[0] is not None:
t = deprecated_t[0]
deprecated_t[0] = None
else:
t = tclass(*targs, total=total, **tkwargs)
if len(args) > 0:
# *args intentionally not supported (see #244, #299)
TqdmDeprecationWarning(
"Except func, normal arguments are intentionally" +
" not supported by" +
" `(DataFrame|Series|GroupBy).progress_apply`." +
" Use keyword arguments instead.",
fp_write=getattr(t.fp, 'write', sys.stderr.write))
try:
func = df._is_builtin_func(func)
except TypeError:
pass
# Define bar updating wrapper
def wrapper(*args, **kwargs):
# update tbar correctly
# it seems `pandas apply` calls `func` twice
# on the first column/row to decide whether it canaxis = 0
elif axis == 'columns':
axis = 1
# when axis=0, total is shape[axis1]
total = df.size // df.shape[axis]
# Init bar
if deprecated_t[0] is not None:
t = deprecated_t[0]
deprecated_t[0] = None
else:
t = tclass(*targs, total=total, **tkwargs)
if len(args) > 0:
# *args intentionally not supported (see #244, #299)
TqdmDeprecationWarning(
"Except func, normal arguments are intentionally" +
" not supported by" +
" `(DataFrame|Series|GroupBy).progress_apply`." +
" Use keyword arguments instead.",
fp_write=getattr(t.fp, 'write', sys.stderr.write))
# Define bar updating wrapper
def wrapper(*args, **kwargs):
# update tbar correctly
# it seems `pandas apply` calls `func` twice
# on the first column/row to decide whether it can
# take a fast or slow code path; so stop when t.total==t.n
t.update(n=1 if not t.total or t.n < t.total else 0)
return func(*args, **kwargs)
# Apply the provided function (in **kwargs)def create_labels(entityset,
min_training_data='28 days',
lead='7 days',
window='28 days',
reduce='sum',
binarize=None,
iterate_by=None):
label_cols = ['quantity', 'price']
time_index = "order_date"
index = "customer_id"
df = entityset['orders'].df.merge(
entityset['order_products'].df, how='outer')
tqdm.pandas(desc="Creating Labels", unit="customer")
# # Only use data after one of the label columns has been non-null
# for i, v in df[label_cols].iterrows():
# if v.dropna(how='all').shape[0] > 0:
# df = df.loc[slice(i, None), :]
# break
grouped = df.groupby(index, as_index=True)
project_cutoff_dates = grouped.progress_apply(
lambda df: make_labels_from_windows(
df,
cols=label_cols,
min_training_data=min_training_data,
lead=lead, window=window,
index_col=index,
date_col=time_index,def test_vectorized_math_applymap_on_large_dataframe(self):
LOG.info("test_vectorized_math_applymap_on_large_dataframe")
df = pd.DataFrame({"x": np.random.normal(size=1_000_000), "y": np.random.uniform(size=1_000_000)})
tqdm.pandas(desc="Pandas Vec math applymap ~ DF")
start_pd = time.time()
pd_val = df.progress_applymap(math_vec_square)
end_pd = time.time()
pd_time = end_pd - start_pd
start_swifter = time.time()
swifter_val = df.swifter.progress_bar(desc="Vec math applymap ~ DF").applymap(math_vec_square)
end_swifter = time.time()
swifter_time = end_swifter - start_swifter
self.assertEqual(pd_val, swifter_val) # equality test
if self.ncores > 1: # speed test
self.assertLess(swifter_time, pd_time)def test_nonvectorized_math_apply_on_large_series(self):
LOG.info("test_nonvectorized_math_apply_on_large_series")
df = pd.DataFrame({"x": np.random.normal(size=10_000_000)})
series = df["x"]
tqdm.pandas(desc="Pandas Nonvec math apply ~ Series")
start_pd = time.time()
pd_val = series.progress_apply(math_foo, compare_to=1)
end_pd = time.time()
pd_time = end_pd - start_pd
start_swifter = time.time()
swifter_val = series.swifter.progress_bar(desc="Nonvec math apply ~ Series").apply(math_foo, compare_to=1)
end_swifter = time.time()
swifter_time = end_swifter - start_swifter
self.assertEqual(pd_val, swifter_val) # equality test
if self.ncores > 1: # speed test
self.assertLess(swifter_time, pd_time)def test_vectorized_math_apply_on_large_dataframe(self):
LOG.info("test_vectorized_math_apply_on_large_dataframe")
df = pd.DataFrame({"x": np.random.normal(size=1_000_000), "y": np.random.uniform(size=1_000_000)})
tqdm.pandas(desc="Pandas Vec math apply ~ DF")
start_pd = time.time()
pd_val = df.progress_apply(math_vec_multiply, axis=1)
end_pd = time.time()
pd_time = end_pd - start_pd
start_swifter = time.time()
swifter_val = df.swifter.progress_bar(desc="Vec math apply ~ DF").apply(math_vec_multiply, axis=1)
end_swifter = time.time()
swifter_time = end_swifter - start_swifter
self.assertEqual(pd_val, swifter_val) # equality test
if self.ncores > 1: # speed test
self.assertLess(swifter_time, pd_time)def test_vectorized_math_apply_on_large_dataframe(self):
LOG.info("test_vectorized_math_apply_on_large_dataframe")
df = pd.DataFrame({"x": np.random.normal(size=1_000_000), "y": np.random.uniform(size=1_000_000)})
tqdm.pandas(desc="Pandas Vec math apply ~ DF")
start_pd = time.time()
pd_val = df.progress_apply(math_vec_multiply, axis=1)
end_pd = time.time()
pd_time = end_pd - start_pd
start_swifter = time.time()
swifter_val = df.swifter.progress_bar(desc="Vec math apply ~ DF").apply(math_vec_multiply, axis=1)
end_swifter = time.time()
swifter_time = end_swifter - start_swifter
self.assertEqual(pd_val, swifter_val) # equality test
if self.ncores > 1: # speed test
self.assertLess(swifter_time, pd_time)def test_nonvectorized_math_applymap_on_large_dataframe(self):
LOG.info("test_nonvectorized_math_applymap_on_large_dataframe")
df = pd.DataFrame({"x": np.random.normal(size=5_000_000), "y": np.random.uniform(size=5_000_000)})
tqdm.pandas(desc="Pandas Nonvec math applymap ~ DF")
start_pd = time.time()
pd_val = df.progress_applymap(math_foo)
end_pd = time.time()
pd_time = end_pd - start_pd
start_swifter = time.time()
swifter_val = df.swifter.progress_bar(desc="Nonvec math applymap ~ DF").applymap(math_foo)
end_swifter = time.time()
swifter_time = end_swifter - start_swifter
self.assertEqual(pd_val, swifter_val) # equality test
if self.ncores > 1: # speed test
self.assertLess(swifter_time, pd_time)