是否可以重置/清空/释放 Google Colab 笔记本 GPU VRAM？ 无需重新启动会话

Question

目前我正在尝试在我的 Google Colab 上运行 Whisper。

它会引发 GPU 内存错误。 但是在抛出错误后，GPU 内存使用率仍然是最大的

我可能不知道 100 个问题，但给出的答案都不起作用

我需要一种无需重新启动会话即可释放 GPU 内存的方法，从而避免删除所有下载的数据

下面来看看笔记本目前的状态。 您看到错误抛出，但 GPU 内存仍然最大

这是 Google Colab 的全部代码

!pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
!pip install git+https://github.com/openai/whisper.git 

import os

# Add folders
checkContentFolder = os.path.exists("content")
checkDownLoadFolder = os.path.exists("download")
if not checkContentFolder:
  os.mkdir("content")
if not checkDownLoadFolder:
  os.mkdir("download")


import whisper
from pathlib import Path
from whisper.utils import write_srt
import pandas as pd

def main():

    # transcribe the audio
    #model = whisper.load_model("large")
    model = whisper.load_model("large-v1")
    #model = whisper.load_model("../input/whisper2/large-v1.pt")
    transcribe_name_begin="oop";
    sub_folder_name="/download/oop/"
    import os

    if not os.path.isdir(sub_folder_name):
        os.makedirs(sub_folder_name)
        
    _compression_ratio_threshold = 2.4
    for lectureId in range(142, 143):
        transcribePath=f"../content/"+transcribe_name_begin+str(lectureId)+".mp3";
        result = model.transcribe(transcribePath,
                                  language="en",
                                  beam_size=9,
                                  initial_prompt="Welcome to the Software Engineering Courses channel.",
                                  best_of=9,verbose=True,temperature=0.0,compression_ratio_threshold=_compression_ratio_threshold)
        #result = model.transcribe("../input/whisper2/lecture_"+str(lectureId)+".mp3",language="en",beam_size=5,initial_prompt="Welcome to the Software Engineering Courses channel.",best_of=5,verbose=True,temperature=0.0)

        # save SRT

        language = result["language"]
        sub_name = sub_folder_name+transcribe_name_begin+str(lectureId)+".srt"
        with open(sub_name, "w", encoding="utf-8") as srt:
            write_srt(result["segments"], file=srt)

        # Save output
        writing_lut = {
            '.txt': whisper.utils.write_txt,
            '.vtt': whisper.utils.write_vtt,
            '.srt': whisper.utils.write_txt,
        }

        output_type="All"

        if output_type == "All":
            for suffix, write_suffix in writing_lut.items():
                transcript_local_path =sub_folder_name+transcribe_name_begin+str(lectureId) +suffix
                with open(transcript_local_path, "w", encoding="utf-8") as f:
                    write_suffix(result["segments"], file=f)
                try:
                    transcript_drive_path =file_name
                except:
                    print(f"**Transcript file created: {transcript_local_path}**")
        else:
            transcript_local_path =sub_folder_name+transcribe_name_begin+str(lectureId) +output_type

            with open(transcript_local_path, "w", encoding="utf-8") as f:
                writing_lut[output_type](result["segments"], file=f)

if __name__ == "__main__":
    main()

Answer 1

有时这个问题的解决方案对我有用：

from numba import cuda 

device = cuda.get_current_device()
device.reset()

但有时会话在重置后崩溃了:(