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tqdm | A Fast, Extensible Progress Bar for Python and CLI | Command Line Interface library

 by   tqdm Python Version: v4.63.2 License: Non-SPDX

 by   tqdm Python Version: v4.63.2 License: Non-SPDX

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kandi X-RAY | tqdm Summary

tqdm is a Python library typically used in Utilities, Command Line Interface applications. tqdm has no bugs, it has no vulnerabilities, it has build file available and it has high support. However tqdm has a Non-SPDX License. You can install using 'pip install tqdm' or download it from GitHub, PyPI.
A Fast, Extensible Progress Bar for Python and CLI
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Quality
Quality
Security
Security
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kandi-support Support

  • tqdm has a highly active ecosystem.
  • It has 21618 star(s) with 1113 fork(s). There are 202 watchers for this library.
  • There were 3 major release(s) in the last 6 months.
  • There are 274 open issues and 547 have been closed. On average issues are closed in 383 days. There are 65 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of tqdm is v4.63.2
tqdm Support
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Support
Best in #Command Line Interface
Average in #Command Line Interface

quality kandi Quality

  • tqdm has 0 bugs and 0 code smells.
tqdm Quality
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Quality
Best in #Command Line Interface
Average in #Command Line Interface

securitySecurity

  • tqdm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • tqdm code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
tqdm Security
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Security
Best in #Command Line Interface
Average in #Command Line Interface

license License

  • tqdm has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
tqdm License
Best in #Command Line Interface
Average in #Command Line Interface
tqdm License
Best in #Command Line Interface
Average in #Command Line Interface

buildReuse

  • tqdm releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • It has 5564 lines of code, 438 functions and 62 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
tqdm Reuse
Best in #Command Line Interface
Average in #Command Line Interface
tqdm Reuse
Best in #Command Line Interface
Average in #Command Line Interface
Top functions reviewed by kandi - BETA

kandi has reviewed tqdm and discovered the below as its top functions. This is intended to give you an instant insight into tqdm implemented functionality, and help decide if they suit your requirements.

  • Initialize progress bar .
  • Entry point for tqdm .
  • Wrapper for os . pipe
  • Redirect loggers to tqdm .
  • Wrapper for multiprocessing . map_map .
  • This function is called when tqdm progress bar .
  • Wrapper for multiprocessing . map .
  • A context manager for logging a tqdm progress bar .
  • Cast val to given typ .
  • Generate the product of a sequence of lists .

tqdm Key Features

A Fast, Extensible Progress Bar for Python and CLI

Get the postfix string in tqdm

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import numpy as np
from tqdm import tqdm

a = np.random.randint(0, 10, 10)
loop_obj = tqdm(np.arange(10))

for i in loop_obj:
    loop_obj.set_postfix_str(f"Current count: {i}")
    a = i*2/3  # Do some operations
    loop_obj.set_postfix_str(loop_obj.postfix + f" After processing: {a}")

BeautifulSoup and pd.read_html - how to save the links into separate column in the final dataframe?

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for i in table[:3]:
    df = pd.read_html(str(i))[0]
    df['address'] = link
    details.append(df)
final_df = pd.concat(details, ignore_index=True)
import pandas as pd

links = ['www.link1.com', 'www.link2.com','www.linkx.com']
details = []

for link in links:
    # page = requests.get(link)
    # sauce = BeautifulSoup(page.content, 'lxml')
    # table = sauce.find_all('table')
    table = ['<table><tr><td>table 1</td></tr></table>',
             '<table><tr><td>table 2</td></tr></table>',
             '<table><tr><td>table 3</td></tr></table>']
    # Only first 3 tables include data
    for i in table[:3]:
        df = pd.read_html(str(i))[0]
        df['address'] = link
        details.append(df)

final_df = pd.concat(details, ignore_index=True)
-----------------------
for i in table[:3]:
    df = pd.read_html(str(i))[0]
    df['address'] = link
    details.append(df)
final_df = pd.concat(details, ignore_index=True)
import pandas as pd

links = ['www.link1.com', 'www.link2.com','www.linkx.com']
details = []

for link in links:
    # page = requests.get(link)
    # sauce = BeautifulSoup(page.content, 'lxml')
    # table = sauce.find_all('table')
    table = ['<table><tr><td>table 1</td></tr></table>',
             '<table><tr><td>table 2</td></tr></table>',
             '<table><tr><td>table 3</td></tr></table>']
    # Only first 3 tables include data
    for i in table[:3]:
        df = pd.read_html(str(i))[0]
        df['address'] = link
        details.append(df)

final_df = pd.concat(details, ignore_index=True)
-----------------------
for i in table[:3]:
    df = pd.read_html(str(i))[0]
    df['address'] = link
    details.append(df)
final_df = pd.concat(details, ignore_index=True)
import pandas as pd

links = ['www.link1.com', 'www.link2.com','www.linkx.com']
details = []

for link in links:
    # page = requests.get(link)
    # sauce = BeautifulSoup(page.content, 'lxml')
    # table = sauce.find_all('table')
    table = ['<table><tr><td>table 1</td></tr></table>',
             '<table><tr><td>table 2</td></tr></table>',
             '<table><tr><td>table 3</td></tr></table>']
    # Only first 3 tables include data
    for i in table[:3]:
        df = pd.read_html(str(i))[0]
        df['address'] = link
        details.append(df)

final_df = pd.concat(details, ignore_index=True)

tf2.0: Gradient Tape returns None gradient in RNN model

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# Your imports
#-------
### 2. Simulated data and gradient computation:
batch_size = 100; input_length = 5
xtr_pad = tf.random.uniform((batch_size, input_length), maxval = 500, dtype=tf.int32)
ytr = tf.random.normal((batch_size, input_length, 200))


inp= Input(batch_shape= (batch_size, input_length), name= 'input') 
emb_out= Embedding(500, 100, input_length= input_length, trainable= False, name= 'embedding')(inp)
rnn= SimpleRNN(200, return_sequences= True, return_state= False, stateful= True, name= 'simpleRNN')

h0 = tf.Variable(tf.random.uniform((batch_size, 200)))

rnn_allstates= rnn(emb_out, initial_state=h0) 
model_rnn = Model(inputs=inp, outputs= rnn_allstates, name= 'model_rnn')
model_rnn.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])

ds = tf.data.Dataset.from_tensor_slices((xtr_pad, ytr)).batch(100)
embedding_layer = model_rnn.layers[1]
rnn_layer = model_rnn.layers[2]


@tf.function
def calculate_t_gradients(t, x, h0):
  return tf.gradients(model_rnn(x)[:,t,:], h0)

grads_allsteps= []
for b, (x_batch_train, y_batch_train) in enumerate(ds):
    for t in range(input_length):  
      grads_allsteps.append(calculate_t_gradients(t, x_batch_train, h0))
 
print(grads_allsteps) 
[[<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 1.2034059 , -0.46448404,  0.6272926 , ..., -0.40906236,
         0.07618493,  0.6338958 ],
       [ 1.2781916 , -0.20411322,  0.6174417 , ..., -0.31636393,
        -0.23417974,  0.67499626],
       [ 1.113218  , -0.65086263,  0.63425934, ..., -0.66614366,
        -0.07726163,  0.53647137],
       ...,
       [ 1.3399608 , -0.54088974,  0.6213518 , ...,  0.00831087,
        -0.14397278,  0.2614633 ],
       [ 1.213171  , -0.42787278,  0.60535026, ..., -0.56198204,
        -0.09142771,  0.6212783 ],
       [ 1.1901733 , -0.5743524 ,  0.36872283, ..., -0.42522985,
        -0.0861398 ,  0.495057  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 0.3487598 ,  1.2738569 , -0.48500937, ...,  0.6011117 ,
        -0.20381093,  0.45596513],
       [ 0.37931004,  1.2778724 , -0.8682532 , ...,  0.8170228 ,
         0.1456329 ,  0.23715591],
       [ 0.5984771 ,  0.92434835, -0.8879645 , ...,  0.38756457,
        -0.17436962,  0.47174054],
       ...,
       [ 0.61081064,  0.99631476, -0.5104377 , ...,  0.5042721 ,
         0.02844866,  0.34626445],
       [ 0.7126102 ,  1.0205276 , -0.60710275, ...,  0.49418694,
        -0.16092762,  0.41363668],
       [ 0.8581749 ,  1.1259711 , -0.5824491 , ...,  0.45388597,
        -0.16205123,  0.72434616]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 3.8507193e-01,  1.2925258e+00,  1.2027258e+00, ...,
         3.2430276e-01,  2.2319333e-01, -2.5218868e-01],
       [ 5.9262186e-01,  1.4497797e+00,  1.2479483e+00, ...,
         4.6175608e-01,  2.5466472e-01, -2.4279505e-01],
       [ 2.5734475e-01,  1.4562432e+00,  1.1020679e+00, ...,
         6.6081107e-01,  1.9841105e-01, -2.5595558e-01],
       ...,
       [ 5.1541841e-01,  1.6206543e+00,  9.6205616e-01, ...,
         7.2725344e-01,  2.5501373e-01, -7.7709556e-04],
       [ 4.4518453e-01,  1.6381552e+00,  1.0112666e+00, ...,
         5.5238277e-01,  2.4137528e-01, -2.6242572e-01],
       [ 6.6721851e-01,  1.5826726e+00,  1.1282607e+00, ...,
         3.2301426e-01,  2.2295776e-01,  1.1724380e-01]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 0.14262576,  0.578709  ,  0.1149607 , ...,  0.1229499 ,
        -0.42344815,  0.8837458 ],
       [-0.09711604,  0.04376438, -0.11737494, ...,  0.00389774,
         0.01737173,  0.17246482],
       [ 0.24414796,  0.30101255, -0.12234146, ..., -0.04850931,
        -0.31790918,  0.21326394],
       ...,
       [-0.20562285,  0.21999156,  0.02703794, ..., -0.03547464,
        -0.59052145,  0.04695258],
       [ 0.2087476 ,  0.46558812, -0.18172565, ..., -0.01167884,
        -0.20868361,  0.09055485],
       [-0.22442941,  0.16119067,  0.10854454, ...,  0.14752978,
        -0.32307786,  0.343314  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[-1.1414615 ,  0.37376842, -1.0230722 , ...,  0.60619426,
         0.22550163, -0.6948315 ],
       [-1.0124328 ,  0.27892357, -0.96915233, ...,  0.7048603 ,
        -0.15284726, -0.6734605 ],
       [-0.8542529 ,  0.25970122, -0.90076745, ...,  0.8825682 ,
        -0.02474228, -0.55014515],
       ...,
       [-0.89430666,  0.68327624, -1.0109956 , ...,  0.31722566,
        -0.23703958, -0.6766514 ],
       [-0.8633691 ,  0.28742114, -0.9896866 , ...,  0.98315084,
         0.0115847 , -0.55474746],
       [-0.7229766 ,  0.62417865, -1.2342371 , ...,  0.85149145,
        -0.04468453, -0.60606724]], dtype=float32)>]]
-----------------------
# Your imports
#-------
### 2. Simulated data and gradient computation:
batch_size = 100; input_length = 5
xtr_pad = tf.random.uniform((batch_size, input_length), maxval = 500, dtype=tf.int32)
ytr = tf.random.normal((batch_size, input_length, 200))


inp= Input(batch_shape= (batch_size, input_length), name= 'input') 
emb_out= Embedding(500, 100, input_length= input_length, trainable= False, name= 'embedding')(inp)
rnn= SimpleRNN(200, return_sequences= True, return_state= False, stateful= True, name= 'simpleRNN')

h0 = tf.Variable(tf.random.uniform((batch_size, 200)))

rnn_allstates= rnn(emb_out, initial_state=h0) 
model_rnn = Model(inputs=inp, outputs= rnn_allstates, name= 'model_rnn')
model_rnn.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])

ds = tf.data.Dataset.from_tensor_slices((xtr_pad, ytr)).batch(100)
embedding_layer = model_rnn.layers[1]
rnn_layer = model_rnn.layers[2]


@tf.function
def calculate_t_gradients(t, x, h0):
  return tf.gradients(model_rnn(x)[:,t,:], h0)

grads_allsteps= []
for b, (x_batch_train, y_batch_train) in enumerate(ds):
    for t in range(input_length):  
      grads_allsteps.append(calculate_t_gradients(t, x_batch_train, h0))
 
print(grads_allsteps) 
[[<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 1.2034059 , -0.46448404,  0.6272926 , ..., -0.40906236,
         0.07618493,  0.6338958 ],
       [ 1.2781916 , -0.20411322,  0.6174417 , ..., -0.31636393,
        -0.23417974,  0.67499626],
       [ 1.113218  , -0.65086263,  0.63425934, ..., -0.66614366,
        -0.07726163,  0.53647137],
       ...,
       [ 1.3399608 , -0.54088974,  0.6213518 , ...,  0.00831087,
        -0.14397278,  0.2614633 ],
       [ 1.213171  , -0.42787278,  0.60535026, ..., -0.56198204,
        -0.09142771,  0.6212783 ],
       [ 1.1901733 , -0.5743524 ,  0.36872283, ..., -0.42522985,
        -0.0861398 ,  0.495057  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 0.3487598 ,  1.2738569 , -0.48500937, ...,  0.6011117 ,
        -0.20381093,  0.45596513],
       [ 0.37931004,  1.2778724 , -0.8682532 , ...,  0.8170228 ,
         0.1456329 ,  0.23715591],
       [ 0.5984771 ,  0.92434835, -0.8879645 , ...,  0.38756457,
        -0.17436962,  0.47174054],
       ...,
       [ 0.61081064,  0.99631476, -0.5104377 , ...,  0.5042721 ,
         0.02844866,  0.34626445],
       [ 0.7126102 ,  1.0205276 , -0.60710275, ...,  0.49418694,
        -0.16092762,  0.41363668],
       [ 0.8581749 ,  1.1259711 , -0.5824491 , ...,  0.45388597,
        -0.16205123,  0.72434616]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 3.8507193e-01,  1.2925258e+00,  1.2027258e+00, ...,
         3.2430276e-01,  2.2319333e-01, -2.5218868e-01],
       [ 5.9262186e-01,  1.4497797e+00,  1.2479483e+00, ...,
         4.6175608e-01,  2.5466472e-01, -2.4279505e-01],
       [ 2.5734475e-01,  1.4562432e+00,  1.1020679e+00, ...,
         6.6081107e-01,  1.9841105e-01, -2.5595558e-01],
       ...,
       [ 5.1541841e-01,  1.6206543e+00,  9.6205616e-01, ...,
         7.2725344e-01,  2.5501373e-01, -7.7709556e-04],
       [ 4.4518453e-01,  1.6381552e+00,  1.0112666e+00, ...,
         5.5238277e-01,  2.4137528e-01, -2.6242572e-01],
       [ 6.6721851e-01,  1.5826726e+00,  1.1282607e+00, ...,
         3.2301426e-01,  2.2295776e-01,  1.1724380e-01]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[ 0.14262576,  0.578709  ,  0.1149607 , ...,  0.1229499 ,
        -0.42344815,  0.8837458 ],
       [-0.09711604,  0.04376438, -0.11737494, ...,  0.00389774,
         0.01737173,  0.17246482],
       [ 0.24414796,  0.30101255, -0.12234146, ..., -0.04850931,
        -0.31790918,  0.21326394],
       ...,
       [-0.20562285,  0.21999156,  0.02703794, ..., -0.03547464,
        -0.59052145,  0.04695258],
       [ 0.2087476 ,  0.46558812, -0.18172565, ..., -0.01167884,
        -0.20868361,  0.09055485],
       [-0.22442941,  0.16119067,  0.10854454, ...,  0.14752978,
        -0.32307786,  0.343314  ]], dtype=float32)>], [<tf.Tensor: shape=(100, 200), dtype=float32, numpy=
array([[-1.1414615 ,  0.37376842, -1.0230722 , ...,  0.60619426,
         0.22550163, -0.6948315 ],
       [-1.0124328 ,  0.27892357, -0.96915233, ...,  0.7048603 ,
        -0.15284726, -0.6734605 ],
       [-0.8542529 ,  0.25970122, -0.90076745, ...,  0.8825682 ,
        -0.02474228, -0.55014515],
       ...,
       [-0.89430666,  0.68327624, -1.0109956 , ...,  0.31722566,
        -0.23703958, -0.6766514 ],
       [-0.8633691 ,  0.28742114, -0.9896866 , ...,  0.98315084,
         0.0115847 , -0.55474746],
       [-0.7229766 ,  0.62417865, -1.2342371 , ...,  0.85149145,
        -0.04468453, -0.60606724]], dtype=float32)>]]
-----------------------
emb_layer= model_rnn.layers[1]; rnn_layer= model_rnn.layers[2]
n_steps = 40  

dhtdh0_rnn= []
for t in range(n_steps):
    with tf.GradientTape() as tape:
        tape.watch(h0)
        et= emb_layer(xtr_pad[:100])
        ht_all= rnn_layer(et, initial_state= [h0]) 
        ht= ht_all[:,t,:,]
        dhtdh0_t= tape.gradient(ht, h0)
        grad_agg= tf.reduce_mean(abs(dhtdh0_t), [0,1])
        print('step', t+1, 'done')
        dhtdh0_rnn.append(np.log(grad_agg))
        del tape

PIP failed to build package cytoolz

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python -m pip install --user cython
python -m pip install --user cytoolz
python -m pip install --user eth-brownie
-----------------------
STEP1: python -m pip install --user cython
STEP2: python -m pip install --user cytoolz
STEP3: python -m pip install --user eth-brownie
STEP4: python -m pip install --user pipx
STEP5: python -m pipx ensurepath
STEP6: RESTARTED TERMINAL
STEP7: pipx install eth-brownie

How does a gradient backpropagates through random samples?

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mu, std = out_RL[0]
dist = Normal(mu, std)
a = dist.sample()
log_p = dist.log_prob(a)
mu, std = out_RL[0]
dist = Normal(mu, std)
# a = dist.sample()
a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
log_p = dist.log_prob(a)
mu, std = out_RL[0].detach()
dist = Normal(mu, std)
log_p = dist.log_prob(a)
-----------------------
mu, std = out_RL[0]
dist = Normal(mu, std)
a = dist.sample()
log_p = dist.log_prob(a)
mu, std = out_RL[0]
dist = Normal(mu, std)
# a = dist.sample()
a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
log_p = dist.log_prob(a)
mu, std = out_RL[0].detach()
dist = Normal(mu, std)
log_p = dist.log_prob(a)
-----------------------
mu, std = out_RL[0]
dist = Normal(mu, std)
a = dist.sample()
log_p = dist.log_prob(a)
mu, std = out_RL[0]
dist = Normal(mu, std)
# a = dist.sample()
a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
log_p = dist.log_prob(a)
mu, std = out_RL[0].detach()
dist = Normal(mu, std)
log_p = dist.log_prob(a)
-----------------------
mu, std = out_RL[0]
dist = Normal(mu, std)
a = dist.sample()
log_p = dist.log_prob(a)
mu, std = out_RL[0]
dist = Normal(mu, std)
# a = dist.sample()
a = torch.tensor([1.23, 4.01, -1.2, ...], device='cuda')
log_p = dist.log_prob(a)
mu, std = out_RL[0].detach()
dist = Normal(mu, std)
log_p = dist.log_prob(a)

How to speed up async requests in Python

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async def fetch(start, end):
    # example
    url = "https://httpbin.org/anything/log?id="
    async with aiohttp.ClientSession() as session:
        post_tasks = []
        # prepare the coroutines that poat
        # use start and end arguments here!
        async for x in make_numbers(start, end):
            post_tasks.append(do_get(session, url, x))
        # now execute them all at once

        responses = [await f for f in
                     tqdm.tqdm(asyncio.as_completed(post_tasks), total=len(post_tasks))]
import concurrent.futures
from itertools import count

def one_executor(start, end):
    loop = asyncio.new_event_loop()
    try:
        loop.run_until_complete(fetch(start, end))
    except:
        print("error")


if __name__ == '__main__':

    s = time.perf_counter()
    # Change the value to the number of core you want to use.
    max_worker = 4
    length_by_executor = q // max_worker
    with concurrent.futures.ProcessPoolExecutor(max_workers=max_worker) as executor:
        for index_min in count(0, length_by_executor):
            # no matter with duplicated indexes due to the use of 
            # range in make_number function.
            index_max = min(index_min + length_by_executor, q)
            executor.submit(one_executor, index_min, index_max)
            if index_max == q:
                break

    elapsed = time.perf_counter() - s
    print(f"executed in {elapsed:0.2f} seconds.")
1 worker: executed in 13.90 seconds.
2 workers: executed in 7.24 seconds.
3 workers: executed in 6.82 seconds.
-----------------------
async def fetch(start, end):
    # example
    url = "https://httpbin.org/anything/log?id="
    async with aiohttp.ClientSession() as session:
        post_tasks = []
        # prepare the coroutines that poat
        # use start and end arguments here!
        async for x in make_numbers(start, end):
            post_tasks.append(do_get(session, url, x))
        # now execute them all at once

        responses = [await f for f in
                     tqdm.tqdm(asyncio.as_completed(post_tasks), total=len(post_tasks))]
import concurrent.futures
from itertools import count

def one_executor(start, end):
    loop = asyncio.new_event_loop()
    try:
        loop.run_until_complete(fetch(start, end))
    except:
        print("error")


if __name__ == '__main__':

    s = time.perf_counter()
    # Change the value to the number of core you want to use.
    max_worker = 4
    length_by_executor = q // max_worker
    with concurrent.futures.ProcessPoolExecutor(max_workers=max_worker) as executor:
        for index_min in count(0, length_by_executor):
            # no matter with duplicated indexes due to the use of 
            # range in make_number function.
            index_max = min(index_min + length_by_executor, q)
            executor.submit(one_executor, index_min, index_max)
            if index_max == q:
                break

    elapsed = time.perf_counter() - s
    print(f"executed in {elapsed:0.2f} seconds.")
1 worker: executed in 13.90 seconds.
2 workers: executed in 7.24 seconds.
3 workers: executed in 6.82 seconds.
-----------------------
async def fetch(start, end):
    # example
    url = "https://httpbin.org/anything/log?id="
    async with aiohttp.ClientSession() as session:
        post_tasks = []
        # prepare the coroutines that poat
        # use start and end arguments here!
        async for x in make_numbers(start, end):
            post_tasks.append(do_get(session, url, x))
        # now execute them all at once

        responses = [await f for f in
                     tqdm.tqdm(asyncio.as_completed(post_tasks), total=len(post_tasks))]
import concurrent.futures
from itertools import count

def one_executor(start, end):
    loop = asyncio.new_event_loop()
    try:
        loop.run_until_complete(fetch(start, end))
    except:
        print("error")


if __name__ == '__main__':

    s = time.perf_counter()
    # Change the value to the number of core you want to use.
    max_worker = 4
    length_by_executor = q // max_worker
    with concurrent.futures.ProcessPoolExecutor(max_workers=max_worker) as executor:
        for index_min in count(0, length_by_executor):
            # no matter with duplicated indexes due to the use of 
            # range in make_number function.
            index_max = min(index_min + length_by_executor, q)
            executor.submit(one_executor, index_min, index_max)
            if index_max == q:
                break

    elapsed = time.perf_counter() - s
    print(f"executed in {elapsed:0.2f} seconds.")
1 worker: executed in 13.90 seconds.
2 workers: executed in 7.24 seconds.
3 workers: executed in 6.82 seconds.
-----------------------
# async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
async def make_async_gen(f, n, q):
    async for x in make_numbers(n, q):
        yield f(x)
from asyncio import ensure_future, events
from asyncio.queues import Queue

def as_completed_for_async_gen(fs_async_gen, concurrency):
    done = Queue()
    loop = events.get_event_loop()
    # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
    todo = set()                                             # +

    def _on_completion(f):
        todo.remove(f)
        done.put_nowait(f)
        loop.create_task(_add_next())  # +

    async def _wait_for_one():
        f = await done.get()
        return f.result()

    async def _add_next():  # +
        try:
            f = await fs_async_gen.__anext__()
        except StopAsyncIteration:
            return
        f = ensure_future(f, loop=loop)
        f.add_done_callback(_on_completion)
        todo.add(f)

    # for f in todo:                           # -
    #     f.add_done_callback(_on_completion)  # -
    # for _ in range(len(todo)):               # -
    #     yield _wait_for_one()                # -
    for _ in range(concurrency):               # +
        loop.run_until_complete(_add_next())   # +
    while todo:                                # +
        yield _wait_for_one()                  # +
from functools import partial

CONCURRENCY = 200  # +

n = 0
q = 50_000_000

async def fetch():
    # example
    url = "https://httpbin.org/anything/log?id="

    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
        # post_tasks = []                                                # -
        # # prepare the coroutines that post                             # -
        # async for x in make_numbers(n, q):                             # -
        #     post_tasks.append(do_get(session, url, x))                 # -
        # Prepare the coroutines generator                               # +
        async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +

        # now execute them all at once                                                                         # -
        # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
        # Now execute them with a specified concurrency                                                        # +
        responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
# responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
    response = await f
    
    # Do something with response, such as writing to a local file
    # ...
async def do_get(session, url, x):
    headers = {
        'Content-Type': "application/x-www-form-urlencoded",
        'Access-Control-Allow-Origin': "*",
        'Accept-Encoding': "gzip, deflate",
        'Accept-Language': "en-US"
    }

    async with session.get(url + str(x), headers=headers) as response:
        data = await response.text()
        # print(data)  # -
        return data    # +
-----------------------
# async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
async def make_async_gen(f, n, q):
    async for x in make_numbers(n, q):
        yield f(x)
from asyncio import ensure_future, events
from asyncio.queues import Queue

def as_completed_for_async_gen(fs_async_gen, concurrency):
    done = Queue()
    loop = events.get_event_loop()
    # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
    todo = set()                                             # +

    def _on_completion(f):
        todo.remove(f)
        done.put_nowait(f)
        loop.create_task(_add_next())  # +

    async def _wait_for_one():
        f = await done.get()
        return f.result()

    async def _add_next():  # +
        try:
            f = await fs_async_gen.__anext__()
        except StopAsyncIteration:
            return
        f = ensure_future(f, loop=loop)
        f.add_done_callback(_on_completion)
        todo.add(f)

    # for f in todo:                           # -
    #     f.add_done_callback(_on_completion)  # -
    # for _ in range(len(todo)):               # -
    #     yield _wait_for_one()                # -
    for _ in range(concurrency):               # +
        loop.run_until_complete(_add_next())   # +
    while todo:                                # +
        yield _wait_for_one()                  # +
from functools import partial

CONCURRENCY = 200  # +

n = 0
q = 50_000_000

async def fetch():
    # example
    url = "https://httpbin.org/anything/log?id="

    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
        # post_tasks = []                                                # -
        # # prepare the coroutines that post                             # -
        # async for x in make_numbers(n, q):                             # -
        #     post_tasks.append(do_get(session, url, x))                 # -
        # Prepare the coroutines generator                               # +
        async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +

        # now execute them all at once                                                                         # -
        # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
        # Now execute them with a specified concurrency                                                        # +
        responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
# responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
    response = await f
    
    # Do something with response, such as writing to a local file
    # ...
async def do_get(session, url, x):
    headers = {
        'Content-Type': "application/x-www-form-urlencoded",
        'Access-Control-Allow-Origin': "*",
        'Accept-Encoding': "gzip, deflate",
        'Accept-Language': "en-US"
    }

    async with session.get(url + str(x), headers=headers) as response:
        data = await response.text()
        # print(data)  # -
        return data    # +
-----------------------
# async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
async def make_async_gen(f, n, q):
    async for x in make_numbers(n, q):
        yield f(x)
from asyncio import ensure_future, events
from asyncio.queues import Queue

def as_completed_for_async_gen(fs_async_gen, concurrency):
    done = Queue()
    loop = events.get_event_loop()
    # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
    todo = set()                                             # +

    def _on_completion(f):
        todo.remove(f)
        done.put_nowait(f)
        loop.create_task(_add_next())  # +

    async def _wait_for_one():
        f = await done.get()
        return f.result()

    async def _add_next():  # +
        try:
            f = await fs_async_gen.__anext__()
        except StopAsyncIteration:
            return
        f = ensure_future(f, loop=loop)
        f.add_done_callback(_on_completion)
        todo.add(f)

    # for f in todo:                           # -
    #     f.add_done_callback(_on_completion)  # -
    # for _ in range(len(todo)):               # -
    #     yield _wait_for_one()                # -
    for _ in range(concurrency):               # +
        loop.run_until_complete(_add_next())   # +
    while todo:                                # +
        yield _wait_for_one()                  # +
from functools import partial

CONCURRENCY = 200  # +

n = 0
q = 50_000_000

async def fetch():
    # example
    url = "https://httpbin.org/anything/log?id="

    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
        # post_tasks = []                                                # -
        # # prepare the coroutines that post                             # -
        # async for x in make_numbers(n, q):                             # -
        #     post_tasks.append(do_get(session, url, x))                 # -
        # Prepare the coroutines generator                               # +
        async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +

        # now execute them all at once                                                                         # -
        # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
        # Now execute them with a specified concurrency                                                        # +
        responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
# responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
    response = await f
    
    # Do something with response, such as writing to a local file
    # ...
async def do_get(session, url, x):
    headers = {
        'Content-Type': "application/x-www-form-urlencoded",
        'Access-Control-Allow-Origin': "*",
        'Accept-Encoding': "gzip, deflate",
        'Accept-Language': "en-US"
    }

    async with session.get(url + str(x), headers=headers) as response:
        data = await response.text()
        # print(data)  # -
        return data    # +
-----------------------
# async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
async def make_async_gen(f, n, q):
    async for x in make_numbers(n, q):
        yield f(x)
from asyncio import ensure_future, events
from asyncio.queues import Queue

def as_completed_for_async_gen(fs_async_gen, concurrency):
    done = Queue()
    loop = events.get_event_loop()
    # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
    todo = set()                                             # +

    def _on_completion(f):
        todo.remove(f)
        done.put_nowait(f)
        loop.create_task(_add_next())  # +

    async def _wait_for_one():
        f = await done.get()
        return f.result()

    async def _add_next():  # +
        try:
            f = await fs_async_gen.__anext__()
        except StopAsyncIteration:
            return
        f = ensure_future(f, loop=loop)
        f.add_done_callback(_on_completion)
        todo.add(f)

    # for f in todo:                           # -
    #     f.add_done_callback(_on_completion)  # -
    # for _ in range(len(todo)):               # -
    #     yield _wait_for_one()                # -
    for _ in range(concurrency):               # +
        loop.run_until_complete(_add_next())   # +
    while todo:                                # +
        yield _wait_for_one()                  # +
from functools import partial

CONCURRENCY = 200  # +

n = 0
q = 50_000_000

async def fetch():
    # example
    url = "https://httpbin.org/anything/log?id="

    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
        # post_tasks = []                                                # -
        # # prepare the coroutines that post                             # -
        # async for x in make_numbers(n, q):                             # -
        #     post_tasks.append(do_get(session, url, x))                 # -
        # Prepare the coroutines generator                               # +
        async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +

        # now execute them all at once                                                                         # -
        # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
        # Now execute them with a specified concurrency                                                        # +
        responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
# responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
    response = await f
    
    # Do something with response, such as writing to a local file
    # ...
async def do_get(session, url, x):
    headers = {
        'Content-Type': "application/x-www-form-urlencoded",
        'Access-Control-Allow-Origin': "*",
        'Accept-Encoding': "gzip, deflate",
        'Accept-Language': "en-US"
    }

    async with session.get(url + str(x), headers=headers) as response:
        data = await response.text()
        # print(data)  # -
        return data    # +
-----------------------
# async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
async def make_async_gen(f, n, q):
    async for x in make_numbers(n, q):
        yield f(x)
from asyncio import ensure_future, events
from asyncio.queues import Queue

def as_completed_for_async_gen(fs_async_gen, concurrency):
    done = Queue()
    loop = events.get_event_loop()
    # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
    todo = set()                                             # +

    def _on_completion(f):
        todo.remove(f)
        done.put_nowait(f)
        loop.create_task(_add_next())  # +

    async def _wait_for_one():
        f = await done.get()
        return f.result()

    async def _add_next():  # +
        try:
            f = await fs_async_gen.__anext__()
        except StopAsyncIteration:
            return
        f = ensure_future(f, loop=loop)
        f.add_done_callback(_on_completion)
        todo.add(f)

    # for f in todo:                           # -
    #     f.add_done_callback(_on_completion)  # -
    # for _ in range(len(todo)):               # -
    #     yield _wait_for_one()                # -
    for _ in range(concurrency):               # +
        loop.run_until_complete(_add_next())   # +
    while todo:                                # +
        yield _wait_for_one()                  # +
from functools import partial

CONCURRENCY = 200  # +

n = 0
q = 50_000_000

async def fetch():
    # example
    url = "https://httpbin.org/anything/log?id="

    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
        # post_tasks = []                                                # -
        # # prepare the coroutines that post                             # -
        # async for x in make_numbers(n, q):                             # -
        #     post_tasks.append(do_get(session, url, x))                 # -
        # Prepare the coroutines generator                               # +
        async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +

        # now execute them all at once                                                                         # -
        # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
        # Now execute them with a specified concurrency                                                        # +
        responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
# responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
    response = await f
    
    # Do something with response, such as writing to a local file
    # ...
async def do_get(session, url, x):
    headers = {
        'Content-Type': "application/x-www-form-urlencoded",
        'Access-Control-Allow-Origin': "*",
        'Accept-Encoding': "gzip, deflate",
        'Accept-Language': "en-US"
    }

    async with session.get(url + str(x), headers=headers) as response:
        data = await response.text()
        # print(data)  # -
        return data    # +
-----------------------
# async with aiohttp.ClientSession() as session:
async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=200)) as session:
async def make_async_gen(f, n, q):
    async for x in make_numbers(n, q):
        yield f(x)
from asyncio import ensure_future, events
from asyncio.queues import Queue

def as_completed_for_async_gen(fs_async_gen, concurrency):
    done = Queue()
    loop = events.get_event_loop()
    # todo = {ensure_future(f, loop=loop) for f in set(fs)}  # -
    todo = set()                                             # +

    def _on_completion(f):
        todo.remove(f)
        done.put_nowait(f)
        loop.create_task(_add_next())  # +

    async def _wait_for_one():
        f = await done.get()
        return f.result()

    async def _add_next():  # +
        try:
            f = await fs_async_gen.__anext__()
        except StopAsyncIteration:
            return
        f = ensure_future(f, loop=loop)
        f.add_done_callback(_on_completion)
        todo.add(f)

    # for f in todo:                           # -
    #     f.add_done_callback(_on_completion)  # -
    # for _ in range(len(todo)):               # -
    #     yield _wait_for_one()                # -
    for _ in range(concurrency):               # +
        loop.run_until_complete(_add_next())   # +
    while todo:                                # +
        yield _wait_for_one()                  # +
from functools import partial

CONCURRENCY = 200  # +

n = 0
q = 50_000_000

async def fetch():
    # example
    url = "https://httpbin.org/anything/log?id="

    async with aiohttp.ClientSession(connector=aiohttp.TCPConnector(limit=CONCURRENCY)) as session:
        # post_tasks = []                                                # -
        # # prepare the coroutines that post                             # -
        # async for x in make_numbers(n, q):                             # -
        #     post_tasks.append(do_get(session, url, x))                 # -
        # Prepare the coroutines generator                               # +
        async_gen = make_async_gen(partial(do_get, session, url), n, q)  # +

        # now execute them all at once                                                                         # -
        # responses = [await f for f in tqdm.asyncio.tqdm.as_completed(post_tasks, total=len(post_tasks))]     # -
        # Now execute them with a specified concurrency                                                        # +
        responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]  # +
# responses = [await f for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q)]
for f in tqdm.tqdm(as_completed_for_async_gen(async_gen, CONCURRENCY), total=q):
    response = await f
    
    # Do something with response, such as writing to a local file
    # ...
async def do_get(session, url, x):
    headers = {
        'Content-Type': "application/x-www-form-urlencoded",
        'Access-Control-Allow-Origin': "*",
        'Accept-Encoding': "gzip, deflate",
        'Accept-Language': "en-US"
    }

    async with session.get(url + str(x), headers=headers) as response:
        data = await response.text()
        # print(data)  # -
        return data    # +

Colab: (0) UNIMPLEMENTED: DNN library is not found

copy iconCopydownload iconDownload
!pip install tensorflow==2.7.0
-----------------------
'tensorflow==2.7.0',
'tf-models-official==2.7.0',
'tensorflow_io==0.23.1',

Cannot find conda info. Please verify your conda installation on EMR

copy iconCopydownload iconDownload
wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"
-----------------------
wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh  -O /home/hadoop/miniconda.sh \
    && /bin/bash ~/miniconda.sh -b -p $HOME/conda

echo -e '\n export PATH=$HOME/conda/bin:$PATH' >> $HOME/.bashrc && source $HOME/.bashrc


conda config --set always_yes yes --set changeps1 no
conda config -f --add channels conda-forge


conda create -n zoo python=3.7 # "zoo" is conda environment name
conda init bash
source activate zoo
conda install python 3.7.0 -c conda-forge orca 
sudo /home/hadoop/conda/envs/zoo/bin/python3.7 -m pip install virtualenv
“spark.pyspark.python": "/home/hadoop/conda/envs/zoo/bin/python3",
"spark.pyspark.virtualenv.enabled": "true",
"spark.pyspark.virtualenv.type":"native",
"spark.pyspark.virtualenv.bin.path":"/home/hadoop/conda/envs/zoo/bin/,
"zeppelin.pyspark.python" : "/home/hadoop/conda/bin/python",
"zeppelin.python": "/home/hadoop/conda/bin/python"

ERROR: Could not build wheels for pycairo, which is required to install pyproject.toml-based projects

copy iconCopydownload iconDownload
apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib  # or pip install manimlib
pip3 install manimce  # or pip install manimce
-----------------------
apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib  # or pip install manimlib
pip3 install manimce  # or pip install manimce

How to create a progress bar for iterations happening within installed modules

copy iconCopydownload iconDownload
class tqdm_array(np.ndarray):
    def __iter__(self):
        return iter(tqdm.tqdm(np.asarray(self)))
labels = np.array(segm_image.labels).view(tqdm_array)
-----------------------
class tqdm_array(np.ndarray):
    def __iter__(self):
        return iter(tqdm.tqdm(np.asarray(self)))
labels = np.array(segm_image.labels).view(tqdm_array)

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QUESTION

Get the postfix string in tqdm

Asked 2022-Apr-08 at 13:57

I have a tqdm progressbar. I set the postfix string using the method set_postfix_str in some part of my code. In another part, I need to append to this string. Here is an MWE.

import numpy as np
from tqdm import tqdm

a = np.random.randint(0, 10, 10)
loop_obj = tqdm(np.arange(10))

for i in loop_obj:
    loop_obj.set_postfix_str(f"Current count: {i}")
    a = i*2/3  # Do some operations
    loop_obj.set_postfix_str(f"After processing: {a}")  # clears the previous string
    
    # What I want
    loop_obj.set_postfix_str(f"Current count: {i}After processing: {a}")

Is there a way to append to the already set string using set_postfix_str?

ANSWER

Answered 2022-Apr-08 at 13:57

You could just append the new postfix to the old one like so:

import numpy as np
from tqdm import tqdm

a = np.random.randint(0, 10, 10)
loop_obj = tqdm(np.arange(10))

for i in loop_obj:
    loop_obj.set_postfix_str(f"Current count: {i}")
    a = i*2/3  # Do some operations
    loop_obj.set_postfix_str(loop_obj.postfix + f" After processing: {a}")

Source https://stackoverflow.com/questions/71797809

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tqdm

You can install using 'pip install tqdm' or download it from GitHub, PyPI.
You can use tqdm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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