subtitles/zh-CN/70_using-a-debugger-in-a-notebook.srt

1
00:00:05,400 --> 00:00:08,150
- [讲师] 在笔记本中使用 Python 调试器。
- [Instructor] Using the Python debugger in a notebook.

2
00:00:09,540 --> 00:00:12,330
在本视频中，我们将学习如何使用 Python 调试器
In this video, we'll learn how to use the Python debugger

3
00:00:12,330 --> 00:00:15,027
在 Jupyter Notebook 或 Colab 中。
in a Jupyter Notebook or a Colab.

4
00:00:15,027 --> 00:00:17,070
对于这个例子，我们正在运行代码
For this example, we are running code

5
00:00:17,070 --> 00:00:19,775
从令牌分类部分，
from the token classification section,

6
00:00:19,775 --> 00:00:21,513
下载 Conll 数据集，
downloading the Conll dataset,

7
00:00:23,670 --> 00:00:25,503
稍微看一下数据，
looking a little bit at data,

8
00:00:27,840 --> 00:00:29,250
在加载分词器之前
before loading a tokenizer

9
00:00:29,250 --> 00:00:31,173
预处理整个数据集。
to preprocess the whole dataset.

10
00:00:32,880 --> 00:00:34,740
查看下面链接的课程部分
Check out the section of the course linked below

11
00:00:34,740 --> 00:00:35,823
了解更多信息。
for more information.

12
00:00:37,080 --> 00:00:38,520
一旦完成，
Once this is done,

13
00:00:38,520 --> 00:00:41,580
我们尝试加载训练数据集的八个特征，
we try to load eight features of the training dataset,

14
00:00:41,580 --> 00:00:43,080
然后将它们批在一起，
and then batch them together,

15
00:00:43,080 --> 00:00:45,210
使用 tokenizer.pad，
using tokenizer.pad,

16
00:00:45,210 --> 00:00:46,760
我们得到以下错误。
and we get the following error.

17
00:00:48,090 --> 00:00:49,230
我们在这里使用 PyTorch，
We use PyTorch here,

18
00:00:49,230 --> 00:00:51,330
使用 return_tensors="pt"
with return_tensors="pt"

19
00:00:51,330 --> 00:00:53,273
但是你会在 TensorFlow 中遇到同样的错误。
but you will get the same error with TensorFlow.

20
00:00:54,120 --> 00:00:55,897
正如我们在 “如何调试错误？” 中看到的那样。视频，
As we have seen in the "How to debug an error?" video,

21
00:00:55,897 --> 00:00:59,160
错误消息在回溯的末尾。
the error message is at the end of the traceback.

22
00:00:59,160 --> 00:01:01,710
在这里，它表明我们应该使用填充，
Here, it indicates us we should use padding,

23
00:01:01,710 --> 00:01:04,290
我们实际上正在尝试这样做。
which we are actually trying to do.

24
00:01:04,290 --> 00:01:05,610
所以这根本没有用，
So this is not useful at all,

25
00:01:05,610 --> 00:01:06,990
我们需要更深入一点
and we will need to go a little deeper

26
00:01:06,990 --> 00:01:08,610
调试问题。
to debug the problem.

27
00:01:08,610 --> 00:01:10,650
幸运的是，你可以使用 Python 调试器
Fortunately, you can use the Python debugger

28
00:01:10,650 --> 00:01:13,170
任何时候你在 Jupyter Notebook 中遇到错误
at any time you get an error in a Jupyter Notebook

29
00:01:13,170 --> 00:01:16,350
通过在单元格中键入魔法命令 debug。
by typing the magic command, debug, in a cell.

30
00:01:16,350 --> 00:01:18,450
不要忘记开头的百分比。
Don't forget the percent at the beginning.

31
00:01:20,400 --> 00:01:21,870
执行该单元格时，
When executing that cell,

32
00:01:21,870 --> 00:01:23,910
你走到回溯的最底部
you go to the very bottom of the traceback

33
00:01:23,910 --> 00:01:25,320
你可以在其中键入命令
where you can type commands

34
00:01:25,320 --> 00:01:27,690
这将帮助你调试脚本。
that will help you debug your script.

35
00:01:27,690 --> 00:01:29,250
你应该学习的前两个命令，
The first two commands you should learn,

36
00:01:29,250 --> 00:01:32,040
是 u 和 d，代表向上和向下。
are u and d, for up and down.

37
00:01:32,040 --> 00:01:36,090
输入 u 和 enter 会让你更上一层楼
Typing u and enter will take you up one step

38
00:01:36,090 --> 00:01:38,910
在上一条指令的回溯中。
in the traceback to the previous instruction.

39
00:01:38,910 --> 00:01:41,190
输入 d 然后输入将带你
Typing d and then enter will take you

40
00:01:41,190 --> 00:01:43,023
在追溯中向下迈出一步。
one step down in the traceback.

41
00:01:44,130 --> 00:01:47,910
上升两次，我们到达了错误发生的地步。
Going up twice, we get to the point the error was reached.

42
00:01:47,910 --> 00:01:51,510
为调试器学习的第三个命令是 p，用于打印。
The third command to learn for the debugger is p, for print.

43
00:01:51,510 --> 00:01:54,780
它允许你打印任何你想要的值。
It allows you to print any value you want.

44
00:01:54,780 --> 00:01:58,740
例如，键入 p return_tensors 并输入，
For instance, typing p return_tensors and enter,

45
00:01:58,740 --> 00:02:02,893
我们看到传递给错误函数的值 pt。
we see the value pt that we pass to the bad function.

46
00:02:02,893 --> 00:02:05,370
我们还可以查看批处理输出
We can also have a look at the batch outputs

47
00:02:05,370 --> 00:02:07,353
此批处理行编码对象获取。
this batch line coding object gets.

48
00:02:09,480 --> 00:02:12,600
批量输出字典有点难以挖掘，
The batch outputs dictionary is a bit hard to dig in to,

49
00:02:12,600 --> 00:02:15,360
因此，让我们深入研究它的较小部分。
so let's dive into smaller pieces of it.

50
00:02:15,360 --> 00:02:18,390
在调试器内部，你不仅可以打印任何变量
Inside the debugger you can not only print any variable

51
00:02:18,390 --> 00:02:20,970
还要评估任何表达式，
but also evaluate any expression,

52
00:02:20,970 --> 00:02:23,610
例如，我们可以查看 input_ids 键
for instance, we can have a look at the input_ids keys

53
00:02:23,610 --> 00:02:25,203
这个 batch_outputs 对象。
this batch_outputs object.

54
00:02:27,600 --> 00:02:30,693
或者在这个 batch_outputs 对象的标签键处。
Or at the labels keys of this batch_outputs object.

55
00:02:35,730 --> 00:02:37,320
这些标签肯定很奇怪：
Those labels are definitely weird:

56
00:02:37,320 --> 00:02:38,970
它们大小不一，
they are of various sizes,

57
00:02:38,970 --> 00:02:41,340
我们实际上可以确认，如果我们愿意的话，
which we can actually confirm, if we want,

58
00:02:41,340 --> 00:02:43,983
通过以最小压缩打印尺寸。
by printing the size with the least compression.

59
00:02:52,290 --> 00:02:54,913
这是因为 tokenizer 的 pad 方法
This is because the pad method of the tokenizer

60
00:02:54,913 --> 00:02:57,090
只处理分词器输出：
only takes care of the tokenizer outputs:

61
00:02:57,090 --> 00:03:00,450
输入 ID、注意掩码和令牌类型 ID，
input IDs, attention mask, and token type IDs,

62
00:03:00,450 --> 00:03:02,340
所以我们必须自己填充标签
so we have to pad the labels ourselves

63
00:03:02,340 --> 00:03:05,310
在尝试用它们创建张量之前。
before trying to create a tensor with them.

64
00:03:05,310 --> 00:03:07,260
一旦你准备好退出 Python 调试器，
Once you are ready to exit the Python debugger,

65
00:03:07,260 --> 00:03:09,453
你可以按 q 并输入退出。
you can press q and enter for quit.

66
00:03:10,320 --> 00:03:11,670
修复错误的一种方法
One way to fix the error

67
00:03:11,670 --> 00:03:14,313
就是手动把 labels 补到最长。
is to manually pad the labels to the longest.

68
00:03:15,300 --> 00:03:17,400
另一种方法是使用数据整理器
Another way is to use a data collator

69
00:03:17,400 --> 00:03:19,863
专为令牌分类而设计。
specifically designed for token classification.

70
00:03:20,970 --> 00:03:22,950
你也可以直接使用 Python 调试器
You can also use a Python debugger directly

71
00:03:22,950 --> 00:03:23,850
在终端。
in the terminal.

72
00:03:23,850 --> 00:03:25,943
查看下面的视频链接以了解操作方法。
Check out the video link below to learn how.