Raptor's questions

我安装了 Moodle 4.4.3。系统中添加了多个课程。我可以访问 Moodle API。我正在使用 API 调用core_course_get_contents我的其中一门课程的内容。以下是响应：

[
  {
    "id": 38,
    "name": "Section Title",
    "visible": 1,
    "summary": "",
    "summaryformat": 1,
    "section": 1,
    "hiddenbynumsections": 0,
    "uservisible": true,
    "modules": [
      {
        "id": 21,
        "url": "https://example.com/mod/wiki/view.php?id=21",
        "name": "Reading materials",
        "instance": 5,
        "contextid": 44,
        "visible": 1,
        "uservisible": true,
        "visibleoncoursepage": 1,
        "modicon": "https://example.com/theme/image.php/boost/wiki/1727859606/monologo?filtericon=1",
        "modname": "wiki",
        "purpose": "collaboration",
        "branded": false,
        "modplural": "Wikis",
        "availability": null,
        "indent": 0,
        "onclick": "",
        "afterlink": null,
        "customdata": "\"\"",
        "noviewlink": false,
        "completion": 0,
        "downloadcontent": 1,
        "dates": [],
        "groupmode": 0
      }
    ]
  }
]

课程页面包含 1 个添加了 Wiki 活动的条目。我了解到 ID=38 是课程页面上的条目“章节标题”，而 ID=21 是 Wiki ID。然后我使用 Moodle API 调用mod_wiki_get_page_contents尝试pageid=21获取 Wiki 活动的内容。但是响应并未显示 Wikipedia 的内容，而是显示其他页面的内容：

{
  "page": {
    "id": 21,
    "wikiid": 19,
    "subwikiid": 18,
    "groupid": 0,
    "userid": 0,
    "title": "Home",
    "cachedcontent": "(Wrong content here)",
    "contentformat": 1,
    "caneditpage": true,
    "version": 106,
    "tags": []
  },
  "warnings": []
}

在正确的 Wiki 页面的网页中，从编辑按钮的链接，我可以猜出 Wiki 的 ID 是7而不是21，但 ID=7 没有出现在任何响应中。当我使用mod_wiki_get_page_contents和 时pageid=7，它会显示正确的内容。

我的问题是：如何利用 Moodle API 获取 ID=7？

谢谢。

我使用以下命令在 Ubuntu 22.04 中安装了 CUDA Toolkit 12.6 Update 1（说明请参见此处）：

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.6.1/local_installers/cuda-repo-ubuntu2204-12-6-local_12.6.1-560.35.03-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204-12-6-local_12.6.1-560.35.03-1_amd64.deb
sudo cp /var/cuda-repo-ubuntu2204-12-6-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-6

安装成功。但是nvcc版本仍然是 11.5。下面是我安装的 2 个包。我以为安装 CUDA Toolkit 12.6 会取代 11.5，但 NVCC 仍然来自 11.5。

$ dpkg -l nvidia-cuda-toolkit
Desired=Unknown/Install/Remove/Purge/Hold
| Status=Not/Inst/Conf-files/Unpacked/halF-conf/Half-inst/trig-aWait/Trig-pend
|/ Err?=(none)/Reinst-required (Status,Err: uppercase=bad)
||/ Name                Version         Architecture Description
+++-===================-===============-============-=================================
ii  nvidia-cuda-toolkit 11.5.1-1ubuntu1 amd64        NVIDIA CUDA development toolkit

$ dpkg -l cuda-toolkit-12-6
Desired=Unknown/Install/Remove/Purge/Hold
| Status=Not/Inst/Conf-files/Unpacked/halF-conf/Half-inst/trig-aWait/Trig-pend
|/ Err?=(none)/Reinst-required (Status,Err: uppercase=bad)
||/ Name              Version      Architecture Description
+++-=================-============-============-=================================
ii  cuda-toolkit-12-6 12.6.1-1     amd64        CUDA Toolkit 12.6 meta-package

我如何将nvcc版本更新至 12.6？

以下是输出nvcc -V：

$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Thu_Nov_18_09:45:30_PST_2021
Cuda compilation tools, release 11.5, V11.5.119
Build cuda_11.5.r11.5/compiler.30672275_0

列出 的内容cuda-toolkit-12-6，其中不包括nvcc：

$ dpkg -L cuda-toolkit-12-6
/.
/usr
/usr/local
/usr/local/cuda-12.6
/usr/local/cuda-12.6/version.json
/usr/share
/usr/share/doc
/usr/share/doc/cuda-toolkit-12-6
/usr/share/doc/cuda-toolkit-12-6/changelog.Debian.gz

输出nvidia-smi：

$ nvidia-smi
Mon Sep 23 13:11:55 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01             Driver Version: 535.183.01   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  NVIDIA GeForce RTX 4090        Off | 00000000:81:00.0 Off |                  Off |
|  0%   24C    P8              21W / 450W |  10893MiB / 24564MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
|   1  NVIDIA GeForce RTX 4090        Off | 00000000:C1:00.0 Off |                  Off |
|  0%   24C    P8              32W / 450W |    818MiB / 24564MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|    0   N/A  N/A      1720      G   /usr/lib/xorg/Xorg                            4MiB |
|    0   N/A  N/A      2174      C   /usr/local/bin/ollama                       384MiB |
|    0   N/A  N/A   1808406      C   /opt/tljh/user/bin/python                 10486MiB |
|    1   N/A  N/A      1720      G   /usr/lib/xorg/Xorg                           15MiB |
|    1   N/A  N/A      1896      G   /usr/bin/gnome-shell                         10MiB |
|    1   N/A  N/A      2174      C   /usr/local/bin/ollama                       384MiB |
|    1   N/A  N/A   1808406      C   /opt/tljh/user/bin/python                   386MiB |
+---------------------------------------------------------------------------------------+

我有以下 Pandas 数据框：

import pandas as pd
from collections import Counter
print(sentences)

输出是（是的，列名是0）：

              0
0             A
1             B
2             C
3             D
4            EEE
...         ...
462064467    FGH
462064468    QRS
462064469    EEE
462064470   VWXYZ
462064471    !!!

[462064472 rows x 1 columns]

我有一个自定义函数来检查列中的内容的0长度是否大于 1（仅作为示例）：

def is_more_than_one_character(t):
    if len(t) > 1:
        return True
    else:
        return False

我像这样应用该函数：

counter = Counter(sentences.apply(is_more_than_one_character))

我希望计算每个长度 > 1 的字符串的出现次数。以下是示例输出print(counter)：

[(EEE, 2), (FGH, 1), (QRS, 1), (!!!, 1)...] 

但目前，输出是：

[(False, 460686058), (True, 1378414)]

我错过了什么？我想我已经接近答案了。提前致谢。