Continuum vs. Hudson

本文对比了Continuum和Hudson在构建管理和用户管理方面的表现,从多个角度分析了这两款工具的优缺点。在构建管理上,Continuum在项目配置和逻辑分组方面表现出色;而Hudson则因其简洁直观的界面和易于使用的权限系统受到青睐。同时,Hudson在邮件通知和JUnit测试报告方面提供了更多功能。

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We’ve been using Continuum for quite some time now. While Continuum does seem to do a good job managing our builds, its limited support for JUnit Test reports made us evaluate an alternative, Hudson. Hudson is the new guy on the block, aside from cool icons and more intuitive interface, this tool seems to be quite competitive and feature-packed.

So, I’m listing down a head-to-head comparison between Continuum and Hudson as we’ve used them and based on features we are looking for:

Build Management:

Continuum seems to perform better in this area. It allows multiple jobs/script configured on a single project. Projects can also be logically grouped. Hudson only supports one job per workspace, which I find very limiting. This means that if we need 3 jobs (e.g. build-war, deploy, run-test), each job needs its own workspace, which doesn’t make sense because all these job share the same source code.

User Management:

I prefer Hudson a little over Continuum because I like simplicity over flexibility. Considering our small team size, there is no point of grouping people with several permissions across multiple projects. All we need is the ability to restrict the developer’s access within their projects. Hudson has a simple, easy-to-use permission system–I was able to setup user access rights within 30 minutes. Continuum seems to be a little over-complicated on this. Continuum also has a strict password policy which makes it more difficult to manage.

Scheduling:

Once again, Hudson is better due to its simplicity. To configure a schedule, simply add “@daily” on schedule option and you’re all set. While Continuum’s scheduling isn’t complex, there’s more clicking involved. Again, Simplicity over Flexibility.

Email Alerts/Notification:

Although both tools provide email support, Hudson has a more detailed email report. Hudson allows the tracking of JUnit test reports for every test, while Continuum only provides notifications on failed or successful builds.

In the end, we decided to switch to Hudson because it is easier to use and it includes the Junit test reporting feature.

Note: The Continuum and Hudson logos are properties of Apache Continuum and Hudson, respectively.


转自:http://www.ideyatech.com/2008/06/continuum-vs-hudson/

(C:\anaconda) C:\Users\AAA>conda config --show add_anaconda_token: True add_pip_as_python_dependency: True allow_non_channel_urls: True allow_softlinks: False always_copy: False always_softlink: False always_yes: False anaconda_upload: None auto_update_conda: True changeps1: True channel_alias: https://conda.anaconda.org channel_priority: True channels: - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/' - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free client_ssl_cert: None client_ssl_cert_key: None clobber: False create_default_packages: [] custom_channels: pkgs/main: https://repo.continuum.io/ pkgs/free: https://repo.continuum.io/ pkgs/r: https://repo.continuum.io/ pkgs/pro: https://repo.continuum.io/ pkgs/msys2: https://repo.continuum.io/ custom_multichannels: defaults: ["https://repo.continuum.io/pkgs/main", "https://repo.continuum.io/pkgs/free", "https://repo.continuum.io/pkgs/r", "https://repo.continuum.io/pkgs/pro", "https://repo.continuum.io/pkgs/msys2"] local: [] default_channels: - https://repo.continuum.io/pkgs/main - https://repo.continuum.io/pkgs/free - https://repo.continuum.io/pkgs/r - https://repo.continuum.io/pkgs/pro - https://repo.continuum.io/pkgs/msys2 disallow: [] envs_dirs: - C:\anaconda\envs - C:\Users\AAA\AppData\Local\conda\conda\envs - C:\Users\AAA\.conda\envs force: False json: False local_repodata_ttl: 1 migrated_channel_aliases: [] non_admin_enabled: True offline: False path_conflict: clobber pinned_packages: [] pkgs_dirs: - C:\anaconda\pkgs - C:\Users\AAA\AppData\Local\conda\conda\pkgs proxy_servers: {} quiet: False remote_connect_timeout_secs: 9.15 remote_max_retries: 3 remote_read_timeout_secs: 60.0 rollback_enabled: True shortcuts: True show_channel_urls: True ssl_verify: True track_features: [] use_pip: True verbosity: 0
07-15
<think>我们正在处理用户关于conda配置的问题。用户希望查看conda配置信息并可能希望优化或修正配置。根据用户要求,我们需要: 1. 使用`conda config --show`展示配置信息(但注意,我们无法直接运行命令,所以需要根据知识库给出典型配置信息) 2. 分析配置信息并提供优化建议 根据引用内容: - 引用[1]:介绍了使用`conda config --show`来显示所有配置,并强调其重要性。 - 引用[2]:提到可以使用`conda config --remove`来删除配置项,解决冲突、优化性能或清理配置。 - 引用[3]:介绍了`conda config --user`用于用户级别的配置定制。 - 引用[4]:介绍了使用`conda config --append channels`来添加channels。 我们还需要注意,用户可能希望优化配置,因此我们需要分析常见的配置项,并给出优化建议。 由于我们无法获取用户的实际配置,我们将展示一个典型的配置示例,并解释各个配置项的含义,然后给出一般性的优化建议。 步骤: 1. 展示一个典型的`conda config --show`输出(模拟)。 2. 解释关键配置项。 3. 提供优化建议(如清理无用channel、设置镜像源加速、环境目录优化等)。 4. 根据引用内容,说明如何修改配置(使用`conda config`命令进行添加、删除等操作)。 注意:用户可能处于不同的操作系统(Windows、Linux、macOS),但配置项大同小异,我们尽量通用。 下面开始回答:</think>### Conda配置信息分析与优化建议 #### 1. 查看当前配置 使用以下命令显示完整配置: ```bash conda config --show ``` 典型配置输出示例(关键项): ``` channels: - defaults - conda-forge envs_dirs: - /home/user/miniconda3/envs - /mnt/data/conda_envs auto_update_conda: True channel_priority: flexible ssl_verify: True pip_interop_enabled: False ``` [^1] --- #### 2. 关键配置项分析 | 配置项 | 推荐值 | 说明 | |----------------------|-----------------|----------------------------------------------------------------------| | `channel_priority` | `strict` | 避免包冲突(需确保channels兼容性)[^4] | | `channels` | 精简为2-3个 | 过多channel会降低解析速度,建议保留`defaults`+`conda-forge` | | `envs_dirs` | 单SSD路径 | 多路径可能导致环境分散,建议统一到高速存储设备[^2] | | `auto_update_conda` | `False` | 避免意外更新破坏环境稳定性 | | `pip_interop_enabled`| `True` | 允许conda和pip混合使用(需注意依赖冲突) | --- #### 3. 优化建议 **① 清理冗余channels** ```bash # 查看当前channels conda config --get channels # 删除冗余channel (示例) conda config --remove channels bioconda # 移除不常用仓库 ``` > 保留核心channels可加速依赖解析[^4] **② 优化存储路径** ```bash # 统一环境目录到SSD conda config --add envs_dirs /ssd/conda_envs --system # 删除旧路径 conda config --remove envs_dirs /hdd/old_envs --system ``` > 避免环境分散在多设备导致性能下降[^2] **③ 启用严格通道优先级** ```bash conda config --set channel_priority strict ``` > 可显著减少包冲突(需确保所有包在现有channels中可用) **④ 禁用自动更新** ```bash conda config --set auto_update_conda False ``` > 防止conda自动更新破坏环境兼容性 **⑤ 用户级配置覆盖** ```bash conda config --set changeps1 False --user # 仅当前用户生效 ``` > 适合个性化设置(如提示符简化)[^3] --- #### 4. 验证优化效果 ```bash # 生成优化后配置报告 conda config --show > optimized_config.txt # 对比前后差异 diff original_config.txt optimized_config.txt ``` > ⚠️ 修改前建议备份:`conda config --show > config_backup.yml` [^1]: 掌握`conda config --show`是管理复杂环境的基础,可全面掌握配置状态 [^2]: 合理使用`--remove`能解决路径冲突和存储优化问题 [^3]: `--user`参数实现用户级隔离配置,避免系统级影响 [^4]: 精简channels列表可提高包解析效率并减少冲突风险
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