tqdm | A Fast , Extensible Progress Bar for Python and CLI | Command Line Interface library
kandi X-RAY | tqdm Summary
kandi X-RAY | tqdm Summary
A Fast, Extensible Progress Bar for Python and CLI
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Top functions reviewed by kandi - BETA
- Process instances
- Get the lock
- Get instances of tqdm_cls
- Refresh the widget
- A context manager that allows you to change the bars
- Display the progress bar
- Close progress bar
- Format a time
- Format a time bar
- Run the progress bar
- Write text to chat
- Write text to file
- A parallel map function
- Decorator to return the shape of the screen
- Progress bar
- Convert docstring to rst
- Display the tqdm progress bar
- Set the description
- Writes a pipe to fout
- Edit the message text
- Returns a function that prints the status of the given file
- Reset the parameters
- Apply a function to a map
- A context manager that temporarily clears the bar
- Cast val to given typ
- Create a tqdm progress bar
- Initialize tqdm progress bar
tqdm Key Features
tqdm Examples and Code Snippets
Lines of Code : 0License : Non-SPDX (NOASSERTION)
Copyfrom tqdm import tqdm
for i in tqdm(range(10000)):
...
$ seq 9999999 | tqdm --bytes | wc -l
75.2MB [00:00, 217MB/s]
9999999
$ tar -zcf - docs/ | tqdm --bytes --total `du -sb docs/ | cut -f1` \
> backup.tgz
32%|██████████▍ from loguru import logger
from tqdm import tqdm
logger.remove()
logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True)
logger.info("Initializing")
for x in tqdm(range(100)):
logger.info("Iterating #{}", x)
time.sleep(0.1) # -*- coding: utf-8 -*-
"""Usage:
7zx.py [--help | options] ...
Options:
-h, --help Print this help and exit
-v, --version Print version and exit
-c, --compressed Use compressed (instead of uncompressed) file sizes
-s, --silent from __future__ import print_function
import sys
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from multiprocessing import Pool, RLock, freeze_support
from random import random
from threading import RLock as TRLock
"""
Inserting `tqdm` as a "pipe" in a chain of coroutines.
Not to be confused with `asyncio.coroutine`.
"""
from functools import wraps
from tqdm.auto import tqdm
def autonext(func):
@wraps(func)
def inner(*args, **kwargs):
res = f Community Discussions
Trending Discussions on tqdm
QUESTION
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.
ANSWER
Answered 2022-Apr-08 at 13:57You could just append the new postfix to the old one like so:
QUESTION
My question is somehow similiar to this one: How to save out in a new column the url which is reading pandas read_html() function?
I have a set of links that contain tables (4 tables each and I need only first three of them). The goal is to store the link of each table in the separate 'address' column.
...ANSWER
Answered 2022-Mar-31 at 13:28You are close to your goal - Add df['address'] in each iteration to your DataFrame before appending it to your list:
QUESTION
In a model with an embedding layer and SimpleRNN layer, I would like to compute the partial derivative dh_t/dh_0 for each step t.
The structure of my model, including imports and data preprocessing.
Toxic comment train data available: https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification/data?select=jigsaw-toxic-comment-train.csv
GloVe 6B 100d embeddings available: https://nlp.stanford.edu/projects/glove/
ANSWER
Answered 2022-Feb-18 at 14:02You could maybe try using tf.gradients. Also rather use tf.Variable for h0:
QUESTION
I'm trying to install eth-brownie using 'pipx install eth-brownie' but I get an error saying
...ANSWER
Answered 2022-Jan-02 at 09:59I used pip install eth-brownie and it worked fine, I didnt need to downgrade. Im new to this maybe I could be wrong but it worked fine with me.
QUESTION
I'm learning about policy gradients and I'm having hard time understanding how does the gradient passes through a random operation. From here: It is not possible to directly backpropagate through random samples. However, there are two main methods for creating surrogate functions that can be backpropagated through.
They have an example of the score function:
ANSWER
Answered 2021-Nov-30 at 05:48It is indeed true that sampling is not a differentiable operation per se. However, there exist two (broad) ways to mitigate this - [1] The REINFORCE way and [2] The reparameterization way. Since your example is related to [1], I will stick my answer to REINFORCE.
What REINFORCE does is it entirely gets rid of sampling operation in the computation graph. However, the sampling operation remains outside the graph. So, your statement
.. how does the gradient passes through a random operation ..
isn't correct. It does not pass through any random operation. Let's see your example
QUESTION
I want to download/scrape 50 million log records from a site. Instead of downloading 50 million in one go, I was trying to download it in parts like 10 million at a time using the following code but it's only handling 20,000 at a time (more than that throws an error) so it becomes time-consuming to download that much data. Currently, it takes 3-4 mins to download 20,000 records with the speed of 100%|██████████| 20000/20000 [03:48<00:00, 87.41it/s] so how to speed it up?
ANSWER
Answered 2022-Feb-27 at 14:37If it's not the bandwidth that limits you (but I cannot check this), there is a solution less complicated than the celery and rabbitmq but it is not as scalable as the celery and rabbitmq, it will be limited by your number of CPU.
Instead of splitting calls on celery workers, you split them on multiple processes.
I modified the fetch function like this:
QUESTION
Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.
Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.
ANSWER
Answered 2022-Feb-03 at 09:29I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the $PATH variable on EMR master node, it can identify conda. I want to use conda on Zeppelin.
I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.
...ANSWER
Answered 2022-Feb-05 at 00:17I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:
QUESTION
Error while installing manimce, I have been trying to install manimce library on windows subsystem for linux and after running
...ANSWER
Answered 2022-Jan-28 at 02:24apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib # or pip install manimlib
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install tqdm
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.
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