【错误解决】CondaHTTPError: HTTP 000 CONNECTION FAILED for url An HTTP error occurred when trying to retr

本文介绍了解决Anaconda使用过程中遇到的镜像源配置问题,通过修改.condarc文件,删除-defaults渠道,成功切换至清华镜像源,解决了HTTP连接超时错误。

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指定清华镜像

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes

然后再

conda create -n tensorflow_gpu python=3.6

程序报错

错误信息:

CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://repo.continuum.io/pkgs/main/win-64/repodata.json.bz2>
Elapsed: -


An HTTP error occurred when trying to retrieve this URL.
HTTP errors are often intermittent, and a simple retry will get you on your way.
ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='repo.continuum.io', port=443): Max retries exceeded with url: /pkgs/main/win-64/repodata.json.bz2 (Caused by ConnectTimeoutError(<urllib3.connection.VerifiedHTTPSConnection object at 0x000002AAC9D3FCF8>, 'Connection to repo.continuum.io timed out. (connect timeout=9.15)'))",),)

解决办法:

管理员权限打开Anaconda Prompt

输入命令:

conda config –show 

显示

再输入命令

conda --help

显示

找到file (C:\Users\cola\.condarc) by default.这行

按照自己的这个地址找到.condarc

用记事本打开,.condarc的内容为

channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
  - defaults
show_channel_urls: true

删掉- defaults,保存,问题解决。

CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/nvidia/win-64/libcusparse-dev-11.7.5.86-0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/nvidia/win-64/libcusparse-dev-11.7.5.86-0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/nvidia/win-64/libcublas-dev-11.11.3.6-0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/nvidia/win-64/libnpp-dev-11.8.0.86-0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/pytorch/win-64/pytorch-2.5.0-py3.12_cuda11.8_cudnn9_0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/nvidia/win-64/libcufft-dev-10.9.0.58-0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way. CondaHTTPError: HTTP 000 CONNECTION FAILED for url <https://conda.anaconda.org/nvidia/win-64/libcusolver-dev-11.4.1.48-0.tar.bz2> Elapsed: - An HTTP error occurred when trying to retrieve this URL. HTTP errors are often intermittent, and a simple retry will get you on your way.
最新发布
04-03
### 关于 CondaHTTPError解决方案 当遇到 `CondaHTTPError: HTTP 000 CONNECTION FAILED` 错误时,通常是因为网络连接问题或者 Anaconda 配置不当引起的。以下是针对该错误以及与 NVIDIA 和 PyTorch 相关包安装失败的具体解决方法。 #### 1. 修改镜像源 由于默认的 Anaconda 官方仓库可能在国内访问不稳定,建议切换到国内的镜像源(如清华大学开源软件镜像站)。可以通过以下命令修改配置文件: ```bash conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ conda config --set show_channel_urls yes ``` 如果需要恢复默认设置,则可以运行以下命令删除自定义通道: ```bash conda config --remove-key channels ``` 此操作能够显著改善因网络原因导致的下载失败问题[^4]。 #### 2. 清理缓存并重试 有时旧的元数据可能导致无法正常解析依赖关系或获取资源列表。清理本地缓存后再尝试更新索引是一个有效的办法: ```bash conda clean --all conda update conda ``` 上述两条命令分别用于清除不必要的临时文件和升级核心工具本身版本号至最新状态以便支持更多功能特性[^3]。 #### 3. 手动指定 URL 地址 对于特定库比如PyTorch,在某些情况下即使更改了全局channels也可能依旧存在个别package找不到的情况。此时可以直接通过完整路径来调用install函数完成安装过程: ```bash conda install pytorch torchvision torchaudio cpuonly -c pytorch-lts -c defaults ``` 这里特别注意的是最后两个参数 `-c` ,它们指定了额外查找位置从而绕过常规渠道限制达到目的[^2]。 另外考虑到题目提到Windows平台下64位架构环境构建需求,确保所选组件均兼容当前操作系统非常重要;同时也要留意显卡驱动程序是否满足最低要求以免引发其他关联性异常状况发生如"The NVIDIA driver on your system is too old"[^3]. --- ### 提供一段验证脚本作为辅助说明如何检测基本GPU可用情况 下面给出一个小例子用来简单测试NVIDIA GPU设备是否存在并且被成功识别出来: ```python import torch if torch.cuda.is_available(): print(f"CUDA Version: {torch.version.cuda}") device_count = torch.cuda.device_count() for i in range(device_count): print(f"Device {i}: {torch.cuda.get_device_name(i)}") else: print("No CUDA-enabled devices found.") ``` 执行以上Python代码片段可以帮助确认硬件设施准备就绪程度进而排查潜在隐患所在之处[^1]。 ---
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