Recommended Books

本文围绕WAP开发展开,介绍了WAP开发的相关技巧,如在不同浏览器和屏幕上测试应用、提供定时器替代方案、谨慎使用图像等,还推荐了多种开发工具,包括Waptor、MADK等,同时提及了WML文档验证、服务器托管等内容。

原文链接 :

http://www.techiwarehouse.com/cms/articles.php?cat=37


Recommended Books
   
 
 

Did You Know?
WML stands for Wireless Markup Language.

 
 

Tips
Check your content on different browsers.
Most of the current WAP browsers suffer from bugs and incompatibilities with the published specifications. This problem is particularly severe when things should work according to the specifications but fail mysteriously on a particular browser. (This situation will get better with time; there is now an official test suite available and a certification program. But until both are widely adopted, incompatibility remains a serious problem.) This WAP browser incompatibility means that, for the time being, you should test your WAP applications on as many different devices as possible. Don't assume that some technique you've used will work everywhere just because it works on one browser. Try it out on different devices, preferably on one from a different manufacturer.
Check it on different screens.(#)
Don't write your applications for one device and assume that everyone else has the same screen size. There are few things more irritating than having a nice new PDA with a big screen and having four words of text on each page. On the other hand, be aware that putting long essays onto WAP sites will cut out the huge portion of your audience accessing your site via a cell phone. If you have content that will benefit from a big screen, consider making two versions of your site: one with lots of small files for cell-phone users and one with a few big files for PDA users. Things like news stories and sports reports really benefit from this sort of treatment.
Also, don't rely on a screen's width to be a particular dimension. Some sites lay out tabulated information by relying on a particular cell phone to put line breaks in the right places. This type of layout is completely unreadable on most other cell phones, let alone PDA browsers.

Provide alternatives to timers.(#)
Many WAP sites use WML's timer facilities to display logos and splash screens before the main content loads. Very few sites provide any alternate means of entering the main page if the timer does not work as expected. If no alternative is provided, the user will be stranded on a page with no way to go forward or back.
The most common reason for a timer to fail is a name attribute on the timer referencing a variable that isn't initialized properly somewhere else. This works fine the first time, since the default value is used, but later visits to the same page will use the value "0" for the timer, which disables it. To be on the safe side, always provide a or element to perform the same action as the timer. This also improves the user's experience,since he or she can skip past the timed page, if the time-out is long.

Use images sparingly.(#)
WAP is not the Web. Screen sizes are small, and download times are long. Use an image only where a few words of text isn't enough. You can probably fit only one image on-screen at a time on most cell phones, so the complicated image-based layouts on so many Web pages are simply impossible on a WAP site. And don't use images as bullet points or other decorations. Not only does this slow down page display on most devices, but you cannot rely on the image appearing where you want it to on some browsers.
A direct call link in WML(#)
WML pages are designed to be viewed on a cellular telephone, and cellular telephones are designed to be used for calls. Combine these two uses by offering a link to your telephone number on your Wap site.Simply place a link of this type using the cardyou want on your Wap packet:
Callme!

Then replace what follows 0 by your telephone number (remember to delete the first 0 here).
When your visitors click on this link, their telephone will automatically dial your number to contact you. Practical isn't it?

Convert images into WAP ready ones.(#)
WAP Pictus is an image conversion package for turning full colour BMP,GIF or JPEG images in WAP ready WBMP images. The program is fairly simple and a single click will invoke the transformation on an imported image. Other features include image resize and dithering settings for adjusting the conversion quality.
What is a valid WML deck
A deck is referred to as a single collection of cards in WML, where each card contains structured content and navigation. A WML deck conforms to the WAP Forum WML specification; it is surrounded with tags, which contain the cards inside.
Every valid WML deck must contain an XML declaration as a valid XML document, as well as a document-type declaration, both of which are specified at the beginning of the WML deck.

Here is an example of a validation declaration in the header of a WML file:


Another example, using a different site:

WML is highly structured and very particular about syntax.Several current HTML browsers allow for "messy" code such as missing tags and other formatting snafus. Such mistakes are not allowed in WML; the mobile browser will complain and generally won't display the page.
WML is case sensitive.(#)
The tags and are treated as different tags, although they accomplish the same purpose (bold text). Therefore, you must be careful to match the case of your opening tags with your closing tags (for example, This isbold will not work as expected).
Many tags have required attributes. Developers accustomed toHTML may be used to including only attributes they need-in some WML tags, you must include a few attributes, even if they are blank or default.

WML's Events
WML supports the tag. The following example uses one of the supported events, the onenterforward event. This event occurs when the script enters the card in which this tag resides. In this particular case, it is a help card. Here is the code:

The currency rates were obtained from the Federal
Reserve Bank of New York on $(date).


We see here the tag, which calls the WMLScript function getInfodate(). This function loads the date variable with the date in the past on which the currency rates were obtained. It is invoked inside an onevent tag, with the event being entering the card, onenterforward.
The card itself includes two items. First is a free-text message, "The currency rates...." The second important item is the Back soft key.It is important to provide users with the key to go back from this Help card.

Tips for the best development tools
You can develop your WAP pages with Notepad, but as with all kind of development it's easier and faster if you have the right tool at hand.
Waptor 2.3 (736Kb) for Windows fromWAPDrive.A small and easy to use WML editor and viewer for Windows. Some knowledge of WML is necessary.

Mobile Application Development Kit (MADK) (20.6MB) for Windows from Motorola Motorolas free SDK for WAP developers.Includes a phone emulator and a WML development tool.

Wireless Companion (3.2MB) for Windows from yourwap.com.An combined HTML and WML browser.You must have Internet Explorer 4 or Netscape 4 or newer browser installed because the HTML browser uses their engine. You can choose one of the following phone skins: Ericsson 320, 380 or a Nokia 7110.

WAPman (181kb) for Win32 and Palm is a WML viewer for windows. This connects to a gateway, and if the gateway is unavailable then the program don't work.

WinWAP (1.2Mb) for Win32 is the fastest and easiest way to view WML pages without a WAP device. (except for theWapalizer of course). Even if it's version 2.2,it's still a little buggy. All WML decks will display correctly, but sometimes the links don't w ork.

Nokia WAP Toolkit (10.8MB).You must register to download the toolkit, but there is no other way to do this if you are serious about developing and testing WAP sites.

Ericssons WapIDE SDK (5.49MB).The Ericssons SDK are a collection of software for developing WML decks, device designer for creating visual layout of the devices, WML browser, Application Designer and Server Toolset containing perl libraries.

UP.SDK (5.8MB) Phone.com's SDK. By testing your WML decks with different development tools, your are able to filter out most possible errors. The downloadable from Nokia is sometime not enough. If you for example creates a WML deck to large for the gateway, no Nokia p roduct will tell you what's wrong. But this SDK will.

Photoshop/Paintshop WBMP plugin.This plugin is a must have for anyone working with images for WAP. This plugin let you open and save images in WBMP format.

Online WBMP converter. This service gives you the opportunity to upload and convert GIF's and JPG's to WBMP via the browser.

WBMP converter for Win32 Drag and drop converter for Windows. Let you easily converts GIF to WBMP (WAP images).Go to the Nokia web site at http://www.nokia.com/ and download the Nokia WAP toolkit. Then get a WAP browser to use on your computer from http://www.tucows.com

WBMP Wapaint.com allows you to makeyour own real time graphics for your WAP compatible cellular , with the WBMPformat.

Download wbmp (wab, wml, wireless images format) plug-in for Adobe photoshopThis plug-in allows photoshop (and compatible photoshop hosts) to read and write type 0 WBMP. This format is currently used for wireless devices.

Emulate a WAP phone on your Desktop.(#)
Emulate a WAP phone on your Desktop. This WAP browser goes the whole hog and not only gives you access to WAP Internet content but it also emulates a WAP phone, complete with mobile phone style fascia, working buttons and the navigation dial for scrolling up and down the page. On first impression it has a novelty appeal, but on the practical side it's the most realistic way to test your WAP pages without actually using a real WAP phone.
To set up the mime type in IIS: console management, actions, properties,http headers, new type, also is in HELP, but not very clear.

Content-type: text/vnd.wap.wml

To view files, use the following syntax:
file://c:/inetpub/wwwroot/394/hello.wml

equivalent to:
http://localhost/394/hello.wml

Or, assuming a mount of the CTILAB machine on drive E:
file://e:/ex/hello.wml

We had to associate .wml with the mime header in IIS, and had to do it in several places, not knowing which worked.
To check syntax
Save the .wml file as .xml and open in IE. Or, associate .wml files with internet explorer.
This will check the XML well-formedness, and provide some useful feedback if not correct.

In ASP the default mime type for response.write is text/html, so we have to specify that explicitly to be text/vnd.wap.wml

F9 is reload.Sometimes we have to restart the phone simulator to fix things.

URL has to be fully qualified in "dest" (destination). URL in form action statement in HTML, according to the spec, must be fully qualified. No one requires this however. BUT, it IS required for DEST in WML and HDML.

Tips for Server hosting
Free space for your WML pages: Wapdrive,Wappy, TagTag or Hoiley. These hosts are dedicated to host WML pages, but many host providers are now offering hosting of WML pages as well as HTML pages.
 

物联网通信协议测试是保障各类设备间实现可靠数据交互的核心环节。在众多适用于物联网的通信协议中,MQTT(消息队列遥测传输)以其设计简洁与低能耗的优势,获得了广泛应用。为确保MQTT客户端与服务端的实现严格遵循既定标准,并具备良好的互操作性,实施系统化的测试验证至关重要。 为此,采用TTCN-3(树表结合表示法第3版)这一国际标准化测试语言构建的自动化测试框架被引入。该语言擅长表达复杂的测试逻辑与数据结构,同时保持了代码的清晰度与可维护性。基于此框架开发的MQTT协议一致性验证套件,旨在自动化地检验MQTT实现是否完全符合协议规范,并验证其与Eclipse基金会及欧洲电信标准化协会(ETSI)所发布的相关标准的兼容性。这两个组织在物联网通信领域具有广泛影响力,其标准常被视为行业重要参考。 MQTT协议本身存在多个迭代版本,例如3.1、3.1.1以及功能更为丰富的5.0版。一套完备的测试工具必须能够覆盖对这些不同版本的验证,以确保基于各版本开发的设备与应用均能满足一致的质量与可靠性要求,这对于物联网生态的长期稳定运行具有基础性意义。 本资源包内包含核心测试框架文件、一份概述性介绍文档以及一份附加资源文档。这些材料共同提供了关于测试套件功能、应用方法及可能包含的扩展工具或示例的详细信息,旨在协助用户快速理解并部署该测试解决方案。 综上所述,一个基于TTCN-3的高效自动化测试框架,为执行全面、标准的MQTT协议一致性验证提供了理想的技术路径。通过此类专业测试套件,开发人员能够有效确保其MQTT实现的规范符合性与系统兼容性,从而为构建稳定、安全的物联网通信环境奠定坚实基础。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
import pandas as pd import numpy as np from sklearn.metrics.pairwise import cosine_similarity # 加载数据,这里假设数据存储在 CSV 文件中 data = pd.read_csv('library_borrowing_data.csv') # 创建用户 - 图书评分矩阵 user_book_matrix = data.pivot(index='user_id', columns='book_id', values='rating').fillna(0) # 计算用户之间的相似度 user_similarity = cosine_similarity(user_book_matrix) user_similarity_df = pd.DataFrame(user_similarity, index=user_book_matrix.index, columns=user_book_matrix.index) def get_similar_users(user_id, top_n=5): """ 获取与指定用户最相似的前 n 个用户 """ similar_users = user_similarity_df[user_id].sort_values(ascending=False) return similar_users[1:top_n + 1].index def recommend_books(user_id, top_n=5): """ 为指定用户推荐前 n 本潜在借阅图书 """ similar_users = get_similar_users(user_id) user_books = user_book_matrix.loc[user_id] unread_books = user_books[user_books == 0].index book_scores = {} for book in unread_books: score = 0 for similar_user in similar_users: similar_user_score = user_book_matrix.loc[similar_user, book] similarity = user_similarity_df.loc[user_id, similar_user] score += similar_user_score * similarity book_scores[book] = score recommended_books = pd.Series(book_scores).sort_values(ascending=False)[:top_n] return recommended_books.index # 示例:为用户 ID 为 1 的用户推荐图书 user_id = 1 recommended_books = recommend_books(user_id) print(f"为用户 {user_id} 推荐的图书 ID:{recommended_books}")帮我在这个代码上修改共提供 88378 条借阅记录用于模型训练和验证,其中包含 1451 个用户和 58549 全球校园人工智能算法精英大赛 算法挑战赛·基于高校图书馆借阅数据的用户潜在图书借阅结果预测推荐 本图书;测试集包含约 14510 条数据,用于最终结果评测。 该赛题的交互数据将根据用户分阶段发布,初赛阶段提供 600 个用户的完整交互 数据及全部用户信息和图书信息,复赛阶段新增 400 个新用户的交互数据,决赛阶段 释放剩余所有用户的交互数据,其中用户信息和图书信息仅在初赛阶段一次性完整提 供,后续阶段不再补充基础信息。数据集以 CSV 格式存储,共划分为 3 个文件 具体信息如下: 1. book.csv:图书信息,包含 book_id(图书 ID)、题名(书名)、作者、出版 社、一级分类、二级分类。 2. inter.csv:借阅交互记录,包含 inter_id(交互 ID)、user_id(用户 ID)、 book_id(图书 ID)、借阅时间、还书时间、续借时间、续借次数。 3. user.csv:用户信息,包含借阅人(用户 ID)、性别、DEPT(院系)、年级、 类型(本科/研究生等)。然后inter.csv文件内容被放在train.csv文件里面
11-04
<think>我们正在处理一个图书推荐系统的适配问题,需要根据全球校园人工智能算法精英大赛的数据集来修改代码。数据集包括三个CSV文件:book.csv、train.csv(包含借阅记录,即inter.csv的内容)、user.csv。数据分为训练验证集(88378条记录)和测试集(14510条记录)。我们需要修改现有的图书推荐代码以适配这个数据结构。 步骤: 1. 读取数据:我们需要读取三个CSV文件。 2. 数据预处理:将用户、图书和交互记录(借阅记录)进行处理,构建成适合推荐算法使用的格式。 3. 划分数据集:赛题已经划分了训练验证集(在train.csv中)和测试集(测试集应该是在另一个文件,但问题中没有给出文件名,我们假设测试集是单独发布的,可能在test.csv中?)。注意,问题中说明train.csv包含了训练和验证的借阅记录,而测试集是14510条数据。因此,我们可能需要将train.csv中的数据再划分为训练集和验证集(或者使用交叉验证),或者赛题后续会提供验证集?这里需要明确。 但是,根据问题描述,赛题数据分阶段发布,目前我们只有train.csv(包含训练和验证数据)和测试集(可能是后续发布)。因此,在训练阶段,我们可能需要自己从train.csv中划分一部分作为验证集。 4. 构建推荐模型:根据之前的图书推荐代码,可能是基于协同过滤、矩阵分解或其他方法。 5. 训练和验证:使用训练集训练模型,用验证集调整超参数,最后用测试集评估。 由于我们没有看到具体的“给定的图书推荐代码”,只能根据常见做法进行说明。下面是一个通用的修改步骤: 假设原代码是使用PyTorch或类似框架实现的,并且原代码可能是用于处理类似Movielens数据集的(通常是三个文件:用户、物品、评分)。我们需要修改数据加载部分。 具体修改建议: 1. 读取数据: - book.csv:包含图书的特征,如ISBN、书名、作者等。列名需要确认。 - user.csv:包含用户特征,如用户ID、学校、年级等。 - train.csv:包含借阅记录,即用户ID、图书ID、可能还有借阅时间、评分(如果有)等。但根据问题,借阅记录是隐式反馈(只有借阅行为,没有显式评分)。所以我们可以将每次借阅视为正样本。 2. 数据预处理: - 将用户和图书的ID进行重新编码(通常是从0开始的连续整数),以便于嵌入层使用。 - 对于用户和图书的特征,如果有类别特征,可以进行嵌入或独热编码;数值特征可以归一化。 - 由于是隐式反馈,我们需要构建用户-物品交互矩阵(二元矩阵,1表示借阅过,0表示未借阅)。 3. 数据集划分: - 赛题已经提供了train.csv(训练验证集)和后续的测试集(假设为test.csv)。因此,我们只需要读取train.csv作为训练和验证的数据源,然后自己划分(比如80%训练,20%验证)。注意,测试集是单独提供的,所以最终测试要用官方测试集。 4. 构建模型: - 根据原图书推荐代码,可能是矩阵分解(MF)或神经矩阵分解(NeuMF)等。我们只需要调整输入数据的维度和大小。 5. 训练和评估: - 损失函数:隐式反馈通常使用二元交叉熵损失(视为二分类问题:是否借阅)。 - 评估指标:常用的有准确率、召回率、F1、AUC、NDCG等。根据比赛要求选择。 代码结构修改示例(伪代码): 假设原代码的数据加载部分是这样的(以PyTorch为例): 原代码可能直接加载一个交互文件(如ratings.csv),现在我们需要改为加载三个文件,并合并处理。 修改后的数据加载步骤: ```python import pandas as pd from sklearn.model_selection import train_test_split # 读取数据 books = pd.read_csv('book.csv') users = pd.read_csv('user.csv') interactions = pd.read_csv('train.csv') # 注意:题目中train.csv包含了借阅记录(即inter.csv的内容) # 查看数据列名,假设interactions中有'user_id', 'book_id',可能还有'timestamp' # 如果没有显式评分,我们可以假设借阅行为代表正样本,因此可以设置一个虚拟评分1.0 # 构建交互数据:用户-图书-评分(1.0) # 重新编码用户ID和图书ID user_ids = interactions['user_id'].unique() book_ids = interactions['book_id'].unique() user_id_to_index = {user_id: idx for idx, user_id in enumerate(user_ids)} book_id_to_index = {book_id: idx for idx, book_id in enumerate(book_ids)} interactions['user_idx'] = interactions['user_id'].map(user_id_to_index) interactions['book_idx'] = interactions['book_id'].map(book_id_to_index) # 划分训练集和验证集(从train.csv中划分) train_interactions, val_interactions = train_test_split(interactions, test_size=0.2, random_state=42) # 构建训练数据和验证数据:通常需要正样本和负样本(负采样) # 在训练过程中,我们通常需要负采样(即用户没有借阅过的图书作为负样本) # 在验证和测试时,我们通常评估用户对一部分物品的预测得分(包括正样本和采样的负样本) # 然后,我们可以创建一个数据集类,用于生成训练样本(用户索引、图书索引、标签(1或0)) ``` 模型部分:根据原代码,可能是一个矩阵分解模型,如下: ```python import torch import torch.nn as nn import torch.nn.functional as F class MatrixFactorization(nn.Module): def __init__(self, num_users, num_items, embedding_dim=50): super().__init__() self.user_embedding = nn.Embedding(num_users, embedding_dim) self.item_embedding = nn.Embedding(num_items, embedding_dim) self.user_bias = nn.Embedding(num_users, 1) self.item_bias = nn.Embedding(num_items, 1) def forward(self, user, item): user_emb = self.user_embedding(user) item_emb = self.item_embedding(item) user_bias = self.user_bias(user).squeeze() item_bias = self.item_bias(item).squeeze() return (user_emb * item_emb).sum(dim=1) + user_bias + item_bias ``` 训练循环中,我们将用户和物品的索引输入模型,得到预测评分,然后用二元交叉熵损失。 注意:由于是隐式反馈,我们通常将问题视为二分类(借阅/未借阅)。因此,在训练时,我们不仅需要正样本(借阅记录),还需要负样本(用户未借阅的图书)。负采样通常是在训练过程中动态进行的。 最后,测试集的处理方式与训练集类似,但测试集的数据格式应该与train.csv相同(只是没有标签?或者测试集只有用户和图书对?需要看比赛要求)。如果是预测任务,可能需要输出用户对未借阅图书的预测评分,然后排序。 根据比赛要求,可能需要提交一个推荐列表(每个用户推荐N本图书)。 由于原问题没有给出具体的图书推荐代码,我们只能给出一般性的修改指导。如果原代码是基于协同过滤的,那么修改数据加载部分即可;如果原代码使用了额外的特征(如用户特征和图书特征),那么还需要从user.csv和book.csv中读取特征并整合。 总结修改步骤: 1. 替换数据加载部分,读取三个CSV文件。 2. 对用户和图书ID进行重新编码(映射到0~n-1的整数)。 3. 构建交互数据,并为训练集划分验证集(如果测试集尚未发布,则先不用管,后续单独加载测试集)。 4. 修改模型输入维度(用户数量和图书数量)。 5. 调整训练过程中的负采样策略(因为隐式反馈数据集中只有正样本)。 注意:测试集是单独发布的,所以在训练完成后,加载测试集(test.csv,假设文件名)并进行同样的预处理,然后用训练好的模型进行预测,并生成推荐结果。 相关问题: 1. 如何处理隐式反馈的推荐问题?需要负采样吗? 2. 如何评估隐式反馈推荐系统的性能? 3. 如何将用户和图书的特征(如用户信息、图书信息)整合到推荐模型中? 4. 全球校园人工智能算法精英大赛的测试集如何提交结果? 5. 如何优化模型以应对大规模数据(1451个用户和58549本图书)? 注意:由于图书数量很多(58549),而每个用户借阅的图书数量有限,这会导致用户-物品交互矩阵非常稀疏。因此,可能需要使用高效的负采样策略或使用基于深度学习的模型来捕获高阶交互。 引用说明:我们参考了比赛数据集的描述[^1]和训练代码的路径设置[^2]。 </think>以下是根据全球校园人工智能算法精英大赛赛题要求修改图书推荐代码的方案,结合您提供的引用信息和赛题数据特点: ### 修改方案 #### 1. CSV数据加载适配 ```python import pandas as pd # 加载赛题数据 books = pd.read_csv('book.csv') # 图书特征 [ISBN, 书名, 作者...] users = pd.read_csv('user.csv') # 用户特征 [学号, 学院, 专业...] interactions = pd.read_csv('train.csv') # 借阅记录 [用户ID, 图书ID, 借阅时间...] print(f"图书数量: {len(books)} | 用户数量: {len(users)} | 交互记录: {len(interactions)}") ``` #### 2. 数据预处理改进 ```python # 合并数据 data = pd.merge(interactions, users, on='user_id') data = pd.merge(data, books, on='book_id') # 处理稀疏性(示例) from scipy.sparse import csr_matrix ratings_matrix = csr_matrix( (data['rating'], (data['user_id'], data['book_id'])), shape=(users.max()+1, books.max()+1) ) # 特征工程(添加时间特征) data['borrow_year'] = pd.to_datetime(data['borrow_time']).dt.year ``` #### 3. 数据集划分适配方案 ```python from sklearn.model_selection import train_test_split # 按赛题划分训练验证集(88378)和测试集(14510) train_val = data[data['source'] == 'train'] # 含88378条 test = data[data['source'] == 'test'] # 含14510条 # 训练验证集二次划分 train, val = train_test_split( train_val, test_size=0.2, stratify=train_val['user_id'], # 保持用户分布 random_state=42 ) ``` #### 4. 模型训练接口修改 ```python # 示例:基于PyTorch的修改 def train_model(): model = NeuralCF(num_users=users.max()+1, num_items=books.max()+1, embedding_dim=64) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 训练循环 for epoch in range(10): for batch in train_loader: # 改为使用CSV字段名 user_ids = batch['user_id'] item_ids = batch['book_id'] ratings = batch['rating'] ... ``` #### 5. 分阶段数据发布处理 ```python # 增量训练设计 def update_model(new_data_path): new_data = pd.read_csv(new_data_path) # 数据预处理(同上) # 增量训练 model.partial_fit(new_data) # 更新用户/图书索引 model.expand_embeddings(new_users, new_books) ``` ### 关键适配点说明 1. **数据结构适配**: - 使用`book_id`替代通用`item_id` - 用户特征整合专业/学院等校园特有属性[^2] - 处理58549本图书的高稀疏性问题 2. **验证机制**: ```python val_loader = DataLoader(val_set, batch_size=1024) validate(model, val_loader) # 每轮训练后验证 ``` 3. **测试集处理**: ```python # 生成测试预测 test_preds = model.predict(test[['user_id', 'book_id']]) # 输出Top-K推荐 generate_recommendations(test_preds, k=10) ``` ### 优化建议 1. **特征工程**: - 添加图书类别特征(教材/小说/专业书等) - 构建用户-专业-图书的交叉特征 - 时间序列特征(学期/假期模式) 2. **处理冷启动**: ```python # 新书推荐策略 if book_id not in trained_items: return content_based_recommend(book_features) ``` 3. **分布式训练**: ```bash torchrun --nproc_per_node=4 train.py \ --csv_train path/to/train.csv \ --csv_val path/to/val.csv \ --csv_classes path/to/book_categories.csv ``` 参考PyTorch分布式训练最佳实践[^2] ###
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