Can I use tqdm progress bar with map function to loop through dataframe/series rows? specifically, for the following case: defexample(x): x = x +2returnxif__name__ =='__main__': dframe = pd.DataFrame([{'a':1,'b':1}, {'a':2,'b':2}, {'a':3,'b':3}]) dframe['b...
We could chunk the files and usetqdmto follow the progress of the upload or the download of heavy files. Actual Behavior Just need to wait, no information can be retrieved concerning the current status when uploading or downloading. Specifications python-gitlab version: N/A Gitlab server versio...
在Jupyter Notebook或IPython环境中使用tqdm时,为了获得更好的显示效果,建议使用tqdm.notebook.tqdm而不是tqdm.tqdm_notebook。这是因为tqdm.tqdm_notebook是一个较旧的API,而在较新的版本中,tqdm库已经整合了自动检测环境的功能,因此推荐使用tqdm.notebook.tqdm。 以下是分点回答你的问题,并包含代码片段: 导入tqdm...
import gc # 垃圾回收接口 from tqdm import tqdm # 进度条库 import dask # 并行计算接口 from dask.diagnostics import ProgressBar import numpy as np import pandas as pd import matplotlib.pyplot as plt import time import dask.dataframe as dd # dask中的数表处理库 import sys # 外部参数获取接口 ...
from multiprocessing import Poolfrom tqdm import tqdmimport timedef myf(x): time.sleep(1) return x * xif __name__ == '__main__': value_x= range(200) P = Pool(processes=4) # 这里计算很快 res = [P.apply_async(func=myf, args=(i, )) for i in value_x] # 主要是看这里 resu...
tqdm more-itertools transformers>=4.19.0 tiktoken==0.3.1 ffmpeg-python==0.2.0 7 changes: 6 additions & 1 deletion 7 tests/test_transcribe.py Original file line numberDiff line numberDiff line change @@ -4,6 +4,7 @@ import torch import whisper from whisper.tokenizer import get_tokenizer...
importjsonimportosimportwebdatasetaswdsfromtqdmimporttqdm# Set the path to the LLaVA-Pretrain dataset directoryllava_pretrain_dir='<path_to_LLaVA-Pretrain>'# Paths to the dataset filesjson_file=os.path.join(llava_pretrain_dir,'blip_laion_cc_sbu_558k.json')output_path=os.path.join(ll...
pip install --upgrade tqdm pip install --upgrade metatrader5 pip install --upgrade onnx==1.12pip install --upgrade tf2onnx pip install --upgrade tensorflow==2.10.0 1.2. Checking TensorFlow version and GPU The below code checks the installed TensorFlow version and verifies whether it is possible...
tqdm(f, desc='task_idx'): prompt = jobj['prompt'] task_id = jobj['task_id'] gen_sents = generate_sample(model, tokenizer, prompt) gen_jobjs = {'task_id': task_id, "completion": gen_sents} output.write(gen_jobjs) f_output.close() 218 changes: 218 additions & 0 ...
importpandasaspdimportnumpyasnpimporttimefromtqdmimporttqdmfrommultiprocessingimportPool I am making two functions . Serial processing #SerialProcessing def process_serial(voters): result = {'by_user': [],'votes': []}foruserintqdm(voters,desc="Serial Processing"): ...