Collecting cmake Using cached https://files.pythonhosted.org/packages/e7/90/39fc3e549f434ae8e9e9a8af96337de6556c82447321b4bc0e39bc68b55d/cmake-3.28.4.tar.gz Complete output from command python setup.py egg_info: Traceback (most recent call last): File "<string>", line 1, in <module> File "/tmp/pip-build-0dszm7fs/cmake/setup.py", line 8, in <module> from skbuild import setup ModuleNotFoundError: No module named 'skbuild' ---------------------------------------- Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-0dszm7fs/cmake/ robot@WP:~$ pip3

时间: 2025-07-23 15:04:12 AIGC 浏览: 27
<think>我们正在解决安装CMake时出现的依赖错误,具体是缺少skbuild模块。根据引用内容,安装opencv-python时也出现过类似问题,解决方案是先安装scikit-build(即skbuild模块),然后再安装CMake。因此,我们可以按照类似的步骤来解决当前问题。 步骤: 1. 首先安装scikit-build模块。我们可以使用pip安装,并指定清华镜像源以加速下载。 2. 安装完scikit-build后,再安装CMake。 3. 最后,如果需要,再安装其他依赖(如opencv-python等)。 注意:根据引用[1]和[2],安装scikit-build是解决该问题的关键。 具体命令如下: 步骤1:安装scikit-build pip install -i https://pypi.tuna.tsinghua.edu.cn/simple scikit-build 步骤2:安装CMake pip install -i https://pypi.tuna.tsinghua.edu.cn/simple cmake 如果安装过程中遇到其他问题,可以尝试更新pip或检查Python环境。 另外,根据引用[3]和[4],如果安装过程中出现其他错误,可能需要安装其他依赖(如ffmpeg)或者重新安装有问题的模块(如hashlib)。但当前问题主要是缺少skbuild,所以先按照上述步骤解决。 如果上述步骤后问题仍然存在,可以尝试: - 升级pip: `pip install --upgrade pip` - 检查Python环境,确保没有多个Python版本冲突。 根据引用[5],虽然内容未显示,但我们可以推断出类似问题都是通过安装scikit-build来解决的。 因此,总结解决方案:先安装scikit-build,再安装CMake。</think>### 解决安装 CMake 时出现 "ModuleNotFoundError: No module named 'skbuild'" 错误 当在 Python 环境中安装 CMake 时出现该错误,表明系统缺少 `scikit-build` 模块(提供 `skbuild`)。以下是完整解决方案: #### 解决步骤: 1. **安装 scikit-build** 先安装缺失的核心依赖: ```bash pip install -i https://pypi.tuna.tsinghua.edu.cn/simple scikit-build ``` > 使用清华镜像源加速下载[^1][^2] 2. **安装 CMake** 解决依赖后重新安装 CMake: ```bash pip install -i https://pypi.tuna.tsinghua.edu.cn/simple cmake ``` 3. **验证安装** 检查是否安装成功: ```bash python -c "import cmake; print(cmake.__version__)" ``` #### 常见问题处理: - **权限问题**:在 Linux/macOS 上添加 `--user` 参数: ```bash pip install --user scikit-build ``` - **环境冲突**:建议使用虚拟环境: ```bash python -m venv myenv source myenv/bin/activate # Linux/macOS # 或 myenv\Scripts\activate (Windows) pip install scikit-build cmake ``` - **安装超时**:添加超时参数: ```bash pip --default-timeout=1000 install scikit-build ``` #### 根本原因: 该错误通常发生在安装需要编译的 Python 包(如 CMake、OpenCV)时。`scikit-build` 是基于 `setuptools` 的构建工具,提供 CMake 集成能力,缺失时会导致构建流程中断[^1][^2][^3]。 > 注:此方案同样适用于解决安装 `opencv-python` 等包时的同类错误[^1][^3]
阅读全文

相关推荐

(tensorflow) C:\Users\lky>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 Looking in indexes: https://download.pytorch.org/whl/cu118 Collecting torch Using cached https://download.pytorch.org/whl/cu118/torch-2.6.0%2Bcu118-cp39-cp39-win_amd64.whl.metadata (28 kB) Collecting torchvision Using cached https://download.pytorch.org/whl/cu118/torchvision-0.21.0%2Bcu118-cp39-cp39-win_amd64.whl.metadata (6.3 kB) Collecting torchaudio Using cached https://download.pytorch.org/whl/cu118/torchaudio-2.6.0%2Bcu118-cp39-cp39-win_amd64.whl.metadata (6.8 kB) Collecting filelock (from torch) Using cached https://download.pytorch.org/whl/filelock-3.13.1-py3-none-any.whl.metadata (2.8 kB) Requirement already satisfied: typing-extensions>=4.10.0 in c:\users\lky\.conda\envs\tensorflow\lib\site-packages (from torch) (4.12.2) Collecting networkx (from torch) Using cached https://download.pytorch.org/whl/networkx-3.3-py3-none-any.whl.metadata (5.1 kB) Requirement already satisfied: jinja2 in c:\users\lky\.conda\envs\tensorflow\lib\site-packages (from torch) (3.1.6) Collecting fsspec (from torch) Using cached https://download.pytorch.org/whl/fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB) Collecting sympy==1.13.1 (from torch) Using cached https://download.pytorch.org/whl/sympy-1.13.1-py3-none-any.whl (6.2 MB) Collecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch) Using cached https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB) Requirement already satisfied: numpy in c:\users\lky\.conda\envs\tensorflow\lib\site-packages (from torchvision) (1.20.0) Collecting pillow!=8.3.*,>=5.3.0 (from torchvision) Using cached https://download.pytorch.org/whl/pillow-11.0.0-cp39-cp39-win_amd64.whl.metadata (9.3 kB) Requirement already satisfied: MarkupSafe>=2.0 in c:\users\lky\.conda\envs\tensorflow\lib\site-packages (from jinja2->torch) (3.0.2) INFO: pip is looking at multiple versions of networkx to determine which versi

python版本是3.7为啥在这里卡住了 (autolabel) abot@abot:~/AutoLabelImg-master$ pip install -r requirements.txt Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting natsort Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ef/82/7a9d0550484a62c6da82858ee9419f3dd1ccc9aa1c26a1e43da3ecd20b0d/natsort-8.4.0-py3-none-any.whl (38 kB) Collecting strsimpy Using cached https://pypi.tuna.tsinghua.edu.cn/packages/fc/90/bd55a4b18f4b75a76e32f444975d2c869d692eb23897d116d47122f88d1a/strsimpy-0.2.1-py3-none-any.whl (45 kB) Collecting easygui Using cached https://pypi.tuna.tsinghua.edu.cn/packages/8e/a7/b276ff776533b423710a285c8168b52551cb2ab0855443131fdc7fd8c16f/easygui-0.98.3-py2.py3-none-any.whl (92 kB) Collecting pyautogui Using cached https://pypi.tuna.tsinghua.edu.cn/packages/65/ff/cdae0a8c2118a0de74b6cf4cbcdcaf8fd25857e6c3f205ce4b1794b27814/PyAutoGUI-0.9.54.tar.gz (61 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting pyqt5>=5.14.1 Using cached https://pypi.tuna.tsinghua.edu.cn/packages/4d/5d/b8b6e26956ec113ad3f566e02abd12ac3a56b103fcc7e0735e27ee4a1df3/PyQt5-5.15.10.tar.gz (3.2 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... -

Looking in indexes: https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple Collecting paddlespeech Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/93/0d/d4a84fd48b79ee6eae33ef3dce027ec24953d2f6ee31f9cb890ea0fb6c32/paddlespeech-1.4.2-py3-none-any.whl (1.6 MB) Collecting braceexpand (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/fa/93/e8c04e80e82391a6e51f218ca49720f64236bc824e92152a2633b74cf7ab/braceexpand-0.1.7-py2.py3-none-any.whl (5.9 kB) Collecting editdistance (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/ea/2a/6b823e71cef694d6f070a1d82be2842706fa193541aab8856a8f42044cd0/editdistance-0.8.1-cp312-cp312-macosx_11_0_arm64.whl (79 kB) Collecting g2p-en (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/d7/d9/b77dc634a7a0c0c97716ba97dd0a28cbfa6267c96f359c4f27ed71cbd284/g2p_en-2.1.0-py3-none-any.whl (3.1 MB) Collecting g2pM (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/af/21/dc5b497f09a94a9605e0b8a94ad0e01ae73a2b65109bf5bd325b0814b6a8/g2pM-0.1.2.5-py3-none-any.whl (1.7 MB) Collecting h5py (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/10/13/cc1cb7231399617d9951233eb12fddd396ff5d4f7f057ee5d2b1ca0ee7e7/h5py-3.13.0-cp312-cp312-macosx_11_0_arm64.whl (2.9 MB) Collecting hyperpyyaml (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/33/c9/751b6401887f4b50f9307cc1e53d287b3dc77c375c126aeb6335aff73ccb/HyperPyYAML-1.2.2-py3-none-any.whl (16 kB) Collecting inflect (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/8a/eb/427ed2b20a38a4ee29f24dbe4ae2dafab198674fe9a85e3d6adf9e5f5f41/inflect-7.5.0-py3-none-any.whl (35 kB) Collecting jsonlines (from paddlespeech) Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/f8/62/d9ba6323b9202dd2fe166beab8a86d29465c41a028

pip install maixpy3 -U Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple, https://mirrors.aliyun.com/pypi/simple/, https://mirror.baidu.com/pypi/simple Collecting maixpy3 Using cached https://mirrors.aliyun.com/pypi/packages/dd/f5/b5a29b1cfff0ab9f198d1217f9ec821634aea391aa72eebb8c21a059ac9f/maixpy3-0.5.3.tar.gz (74.5 MB) Preparing metadata (setup.py) ... done Requirement already satisfied: Pillow in e:\python\lib\site-packages (from maixpy3) (10.4.0) Requirement already satisfied: numpy in e:\python\lib\site-packages (from maixpy3) (2.1.2) INFO: pip is looking at multiple versions of maixpy3 to determine which version is compatible with other requirements. This could take a while. Using cached https://mirrors.aliyun.com/pypi/packages/50/c8/73166b1a8caa5a1c04847c61f5c174583400492174b30cc7d94433b5f59d/maixpy3-0.5.2.tar.gz (74.5 MB) Preparing metadata (setup.py) ... done Using cached https://mirrors.aliyun.com/pypi/packages/f8/49/6ec3b06ac904b59c3d52bdb482820dcdd2f4665aa142ea676d4bc4b5090a/maixpy3-0.5.1.tar.gz (74.4 MB) Preparing metadata (setup.py) ... done Using cached https://mirrors.aliyun.com/pypi/packages/81/c2/e1a9d1e4f0051988c9c55839d3d82a993fe653ae6a7f2dde31405f30e95a/maixpy3-0.5.0.tar.gz (74.4 MB) Preparing metadata (setup.py) ... done Using cached https://mirrors.aliyun.com/pypi/packages/55/2f/695a209630f842a1ef55b9b00a118bfea2e75288d213bd86d90de760cbe6/maixpy3-0.4.9.tar.gz (74.4 MB) Preparing metadata (setup.py) ... done Using cached https://mirrors.aliyun.com/pypi/packages/cc/7d/5d148d81f4f18e3e8efd7c23b36dd3ce8ffc785988d192568a407c1ee819/maixpy3-0.4.8.tar.gz (74.4 MB) Preparing metadata (setup.py) ... done Using cached https://mirrors.aliyun.com/pypi/packages/fa/d1/85fc38dd53d406e852f0f0ec92d27ed20fe92bb2d52e3e1753205958cd78/maixpy3-0.4.7.tar.gz (74.1 MB) Preparing metadata (setup.py) ... done Using cached https://mirrors.aliyun.com/pypi/packages/4c/ee/31dffaf29586a85ea3b1ff4ebed77b356a12c09b598abc591c722d7fe5a7/ma

transformers在pycharm中安装报错:PS D:\Python\PythonProject3> conda activate pytorch-gpu PS D:\Python\PythonProject3> pip show transformers WARNING: Package(s) not found: transformers PS D:\Python\PythonProject3> pip install transformers Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting transformers Using cached https://pypi.tuna.tsinghua.edu.cn/packages/5b/0b/e45d26ccd28568013523e04f325432ea88a442b4e3020b757cf4361f0120/transformers-4.30.2-py3-none-any.whl (7.2 MB) Requirement already satisfied: filelock in d:\anaconda3\lib\site-packages (from transformers) (3.0.8) Collecting huggingface-hub<1.0,>=0.14.1 (from transformers) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7f/c4/adcbe9a696c135578cabcbdd7331332daad4d49b7c43688bc2d36b3a47d2/huggingface_hub-0.16.4-py3-none-any.whl (268 kB) Requirement already satisfied: numpy>=1.17 in d:\anaconda3\lib\site-packages (from transformers) (1.21.6) Collecting packaging>=20.0 (from transformers) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl (53 kB) Collecting pyyaml>=5.1 (from transformers) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/1e/ae/964ccb88a938f20ece5754878f182cfbd846924930d02d29d06af8d4c69e/PyYAML-6.0.1-cp37-cp37m-win_amd64.whl (153 kB) Collecting regex!=2019.12.17 (from transformers) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/9c/a9/e482ecfb6b229cc0b55c5360674280adddea26327a4ecdc258bc09d1fbd3/regex-2024.4.16-cp37-cp37m-win_amd64.whl (269 kB) Requirement already satisfied: requests in d:\anaconda3\lib\site-packages (from transformers) (2.19.1) Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/90/06/1f3a3a6fb57bf3e72f63cbf0ae0991540065dd6a13393b89761b38634cb0/tokenizers-0.13.3-cp37-cp37m-win_amd64.whl (3.5 MB) Collecting safetensors>=0.3.1 (from transformers) Using cached https://py

Using cached https://mirrors.ustc.edu.cn/pypi/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl (134 kB) Collecting fsspec (from torch==2.1.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/44/4b/e0cfc1a6f17e990f3e64b7d941ddc4acdc7b19d6edd51abf495f32b1a9e4/fsspec-2025.3.2-py3-none-any.whl (194 kB) Collecting numpy (from torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/b6/f5/467ca8675c7e6c567f571d8db942cc10a87588bd9e20a909d8af4171edda/numpy-2.2.5-cp310-cp310-win_amd64.whl (12.9 MB) Collecting requests (from torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl (64 kB) Collecting pillow!=8.3.*,>=5.3.0 (from torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/ed/3c/9831da3edea527c2ed9a09f31a2c04e77cd705847f13b69ca60269eec370/pillow-11.2.1-cp310-cp310-win_amd64.whl (2.7 MB) Collecting MarkupSafe>=2.0 (from jinja2->torch==2.1.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/44/06/e7175d06dd6e9172d4a69a72592cb3f7a996a9c396eee29082826449bbc3/MarkupSafe-3.0.2-cp310-cp310-win_amd64.whl (15 kB) Collecting charset-normalizer<4,>=2 (from requests->torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/7d/d7/96970afb4fb66497a40761cdf7bd4f6fca0fc7bafde3a84f836c1f57a926/charset_normalizer-3.4.2-cp310-cp310-win_amd64.whl (105 kB) Collecting idna<4,>=2.5 (from requests->torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl (70 kB) Collecting urllib3<3,>=1.21.1 (from requests->torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl (128 kB) Collecting certifi>=2017.4.17 (from requests->torchvision==0.16.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl (159 kB) Collecting mpmath<1.4,>=1.1.0 (from sympy->torch==2.1.0) Using cached https://mirrors.ustc.edu.cn/pypi/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB) Installing collected packages: mpmath, urllib3, typing-extensions, sympy, pillow, numpy, networkx, MarkupSafe, idna, fsspec, filelock, charset-normalizer, certifi, requests, jinja2, torch, torchvision, torchaudio Successfully installed MarkupSafe-3.0.2 certifi-2025.4.26 charset-normalizer-3.4.2 filelock-3.18.0 fsspec-2025.3.2 idna-3.10 jinja2-3.1.6 mpmath-1.3.0 networkx-3.4.2 numpy-2.2.5 pillow-11.2.1 requests-2.32.3 sympy-1.14.0 torch-2.1.0 torchaudio-2.1.0 torchvision-0.16.0 typing-extensions-4.13.2 urllib3-2.4.0

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting tensorflow-gpu==2.5.0 Downloading https://pypi.tuna.tsinghua.edu.cn/packages/56/23/76efd2d809706ade990c4481b608dc3ebd47df83f5069af86da97a10f93e/tensorflow_gpu-2.5.0-cp38-cp38-manylinux2010_x86_64.whl (454.4 MB)  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 454.4/454.4 MB 1.2 MB/s eta 0:00:0000:0100:09 [?25hCollecting imageio Using cached https://pypi.tuna.tsinghua.edu.cn/packages/1e/b7/02adac4e42a691008b5cfb31db98c190e1fc348d1521b9be4429f9454ed1/imageio-2.35.1-py3-none-any.whl (315 kB) Collecting numpy~=1.19.2 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/66/d7/3b133b17e185f14137bc8afe7a41daf1f31556900f10238312a5ae9c7345/numpy-1.19.5-cp38-cp38-manylinux2010_x86_64.whl (14.9 MB) Collecting absl-py~=0.10 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/97/75/f5e61fb67ecbe45c31035b17562464e11b91a2b8a351bae5ca0db2969e3b/absl_py-0.15.0-py3-none-any.whl (132 kB) Collecting astunparse~=1.6.3 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl (12 kB) Collecting flatbuffers~=1.12.0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/eb/26/712e578c5f14e26ae3314c39a1bdc4eb2ec2f4ddc89b708cf8e0a0d20423/flatbuffers-1.12-py2.py3-none-any.whl (15 kB) Collecting google-pasta~=0.2 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl (57 kB) Collecting h5py~=3.1.0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/67/b2/c20d2f11ca86132f70799da2fc213772676025f556e1f4404754d000600a/h5py-3.1.0-cp38-cp38-manylinux1_x86_64.whl (4.4 MB) Collecting keras-preprocessing~=1.1.2 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/79/4c/7c3275a01e12ef9368a892926ab932b33bb13d55794881e3573482b378a7/Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB) Collecting opt-einsum~=3.3.0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bc/19/404708a7e54ad2798907210462fd950c3442ea51acc8790f3da48d2bee8b/opt_einsum-3.3.0-py3-none-any.whl (65 kB) Collecting protobuf>=3.9.2 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/85/e4/07c80521879c2d15f321465ac24c70efe2381378c00bf5e56a0f4fbac8cd/protobuf-5.29.5-cp38-abi3-manylinux2014_x86_64.whl (319 kB) Collecting six~=1.15.0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ee/ff/48bde5c0f013094d729fe4b0316ba2a24774b3ff1c52d924a8a4cb04078a/six-1.15.0-py2.py3-none-any.whl (10 kB) Collecting termcolor~=1.1.0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/8a/48/a76be51647d0eb9f10e2a4511bf3ffb8cc1e6b14e9e4fab46173aa79f981/termcolor-1.1.0.tar.gz (3.9 kB) Preparing metadata (setup.py) ... [?25ldone [?25hCollecting typing-extensions~=3.7.4 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/60/7a/e881b5abb54db0e6e671ab088d079c57ce54e8a01a3ca443f561ccadb37e/typing_extensions-3.7.4.3-py3-none-any.whl (22 kB) Requirement already satisfied: wheel~=0.35 in /data2/shizhongxi/.conda/envs/mmlab/lib/python3.8/site-packages (from tensorflow-gpu==2.5.0) (0.44.0) Collecting wrapt~=1.12.1 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/82/f7/e43cefbe88c5fd371f4cf0cf5eb3feccd07515af9fd6cf7dbf1d1793a797/wrapt-1.12.1.tar.gz (27 kB) Preparing metadata (setup.py) ... [?25ldone [?25hCollecting gast==0.4.0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b6/48/583c032b79ae5b3daa02225a675aeb673e58d2cb698e78510feceb11958c/gast-0.4.0-py3-none-any.whl (9.8 kB) Collecting tensorboard~=2.5 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/bc/a2/ff5f4c299eb37c95299a76015da3f30211468e29d8d6f1d011683279baee/tensorboard-2.14.0-py3-none-any.whl (5.5 MB) Collecting tensorflow-estimator<2.6.0,>=2.5.0rc0 (from tensorflow-gpu==2.5.0) Using cached https://pypi.tuna.tsinghua.edu.cn/packages/ec/78/b27f73e923becc6e79e18fe112cf75e3200d1ee35b0dba8fa46181bce56c/tensorflow_estimator-2.5.0-py2.py3-none-any.whl (462 kB) INFO: pip is looking at multiple versions of tensorflow-gpu to determine which version is compatible with other requirements. This could take a while. 31mERROR: Could not find a version that satisfies the requirement keras-nightly~=2.5.0.dev (from tensorflow-gpu) (from versions: none) 31mERROR: No matching distribution found for keras-nightly~=2.5.0.dev

(pytorch) C:\Users\29862>pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126 Looking in indexes: https://download.pytorch.org/whl/cu126 Collecting torch Downloading https://download.pytorch.org/whl/cu126/torch-2.8.0%2Bcu126-cp313-cp313-win_amd64.whl.metadata (29 kB) Collecting torchvision Downloading https://download.pytorch.org/whl/cu126/torchvision-0.23.0%2Bcu126-cp313-cp313-win_amd64.whl.metadata (6.3 kB) Collecting filelock (from torch) Using cached https://download.pytorch.org/whl/filelock-3.13.1-py3-none-any.whl.metadata (2.8 kB) Collecting typing-extensions>=4.10.0 (from torch) Using cached https://download.pytorch.org/whl/typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB) Collecting sympy>=1.13.3 (from torch) Using cached https://download.pytorch.org/whl/sympy-1.13.3-py3-none-any.whl.metadata (12 kB) Collecting networkx (from torch) Using cached https://download.pytorch.org/whl/networkx-3.3-py3-none-any.whl.metadata (5.1 kB) Collecting jinja2 (from torch) Using cached https://download.pytorch.org/whl/Jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) Collecting fsspec (from torch) Using cached https://download.pytorch.org/whl/fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB) Requirement already satisfied: setuptools in c:\users\29862\miniconda3\envs\pytorch\lib\site-packages (from torch) (78.1.1) Collecting numpy (from torchvision) Downloading https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-win_amd64.whl.metadata (59 kB) Collecting pillow!=8.3.*,>=5.3.0 (from torchvision) Downloading https://download.pytorch.org/whl/pillow-11.0.0-cp313-cp313-win_amd64.whl.metadata (9.3 kB) Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch) Using cached https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB) Collecting MarkupSafe>=2.0 (from jinja2->torch) Downloading https://download.pytorch.org/whl/MarkupSafe-2.1.5.tar.gz (19 kB) Preparing metadata (setup.py) ... done Downloading https://download.pytorch.org/whl/cu126/torch-2.8.0%2Bcu126-cp313-cp313-win_amd64.whl (2915.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━ 2.6/2.9 GB 17.5 MB/s eta 0:00:17 WARNING: Connection timed out while downloading. WARNING: Attempting to resume incomplete download (2634.8 MB/2915.4 MB, attempt 1) Resuming download https://download.pytorch.org/whl/cu126/torch-2.8.0%2Bcu126-cp313-cp313-win_amd64.whl (2634.8 MB/2915.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.9/2.9 GB 21.2 MB/s 0:00:14 Downloading https://download.pytorch.org/whl/cu126/torchvision-0.23.0%2Bcu126-cp313-cp313-win_amd64.whl (6.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 19.1 MB/s 0:00:00 Downloading https://download.pytorch.org/whl/pillow-11.0.0-cp313-cp313-win_amd64.whl (2.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.6/2.6 MB 17.6 MB/s 0:00:00 Using cached https://download.pytorch.org/whl/sympy-1.13.3-py3-none-any.whl (6.2 MB) Using cached https://download.pytorch.org/whl/typing_extensions-4.12.2-py3-none-any.whl (37 kB) Using cached https://download.pytorch.org/whl/filelock-3.13.1-py3-none-any.whl (11 kB) Using cached https://download.pytorch.org/whl/fsspec-2024.6.1-py3-none-any.whl (177 kB) Using cached https://download.pytorch.org/whl/Jinja2-3.1.4-py3-none-any.whl (133 kB) Using cached https://download.pytorch.org/whl/networkx-3.3-py3-none-any.whl (1.7 MB) Downloading https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-win_amd64.whl (12.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 12.6/12.6 MB 20.1 MB/s 0:00:00 Building wheels for collected packages: MarkupSafe DEPRECATION: Building 'MarkupSafe' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the --use-pep517 option, (possibly combined with --no-build-isolation), or adding a pyproject.toml file to the source tree of 'MarkupSafe'. Discussion can be found at https://github.com/pypa/pip/issues/6334 Building wheel for MarkupSafe (setup.py) ... done Created wheel for MarkupSafe: filename=markupsafe-2.1.5-cp313-cp313-win_amd64.whl size=17268 sha256=2a080256bcc19243ade52c3e1e7f2b2182071e3d4fb93679a1314e479d25991c Stored in directory: c:\users\29862\appdata\local\pip\cache\wheels\84\b6\28\95b8e298901ee19b488797095efb5fbed28637ed83215b13b1 Successfully built MarkupSafe Installing collected packages: mpmath, typing-extensions, sympy, pillow, numpy, networkx, MarkupSafe, fsspec, filelock, jinja2, torch, torchvision Successfully installed MarkupSafe-2.1.5 filelock-3.13.1 fsspec-2024.6.1 jinja2-3.1.4 mpmath-1.3.0 networkx-3.3 numpy-2.1.2 pillow-11.0.0 sympy-1.13.3 torch-2.8.0+cu126 torchvision-0.23.0+cu126 typing-extensions-4.12.2 (pytorch) C:\Users\29862>torch --version 'torch' 不是内部或外部命令,也不是可运行的程序 或批处理文件。

Installing build dependencies ... error error: subprocess-exited-with-error × pip subprocess to install build dependencies did not run successfully. │ exit code: 1 ╰─> [78 lines of output] Looking in indexes: https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple Ignoring numpy: markers 'python_version == "3.5"' don't match your environment Ignoring numpy: markers 'python_version == "3.6"' don't match your environment Ignoring numpy: markers 'python_version == "3.7"' don't match your environment Collecting setuptools Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/a9/38/7d7362e031bd6dc121e5081d8cb6aa6f6fedf2b67bf889962134c6da4705/setuptools-75.8.2-py3-none-any.whl (1.2 MB) Collecting wheel Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0b/2c/87f3254fd8ffd29e4c02732eee68a83a1d3c346ae39bc6822dcbcb697f2b/wheel-0.45.1-py3-none-any.whl (72 kB) Collecting scikit-build Using cached https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c3/a3/21b519f58de90d684056c52ec4e45f744cfda7483f082dcc4dd18cc74a93/scikit_build-0.18.1-py3-none-any.whl (85 kB) Collecting cmake Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/18/58/909d6d99acb4e0886d0f660cf4e0fb26f586590e370b2e4ce7a10d06b145/cmake-3.31.6-py3-none-win_amd64.whl (36.4 MB) -------------------------------------- 36.4/36.4 MB 177.3 kB/s eta 0:00:00 Collecting pip Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/c9/bc/b7db44f5f39f9d0494071bddae6880eb645970366d0a200022a1a93d57f5/pip-25.0.1-py3-none-any.whl (1.8 MB) ---------------------------------------- 1.8/1.8 MB 308.2 kB/s eta 0:00:00 Collecting numpy==1.17.3 Downloading https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/b6/d6/be8f975f5322336f62371c9abeb936d592c98c047ad63035f1b38ae08efe/numpy-1.17.3.zip (6.4 MB) --------------------------------------

最新推荐

recommend-type

nuke id通道提取测试文件

nuke id通道提取测试文件
recommend-type

Docker化部署TS3AudioBot教程与实践

### 标题知识点 #### TS3AudioBot_docker - **Dockerfile的用途与组成**:Dockerfile是一个文本文件,包含了所有构建Docker镜像的命令。开发者可以通过编辑Dockerfile来指定Docker镜像创建时所需的所有指令,包括基础镜像、运行时指令、环境变量、软件安装、文件复制等。TS3AudioBot_docker表明这个Dockerfile与TS3AudioBot项目相关,TS3AudioBot可能是一个用于TeamSpeak 3服务器的音频机器人,用于播放音频或与服务器上的用户进行交互。 - **Docker构建过程**:在描述中,有两种方式来获取TS3AudioBot的Docker镜像。一种是从Dockerhub上直接运行预构建的镜像,另一种是自行构建Docker镜像。自建过程会使用到docker build命令,而从Dockerhub运行则会用到docker run命令。 ### 描述知识点 #### Docker命令的使用 - **docker run**:这个命令用于运行一个Docker容器。其参数说明如下: - `--name tsbot`:为运行的容器指定一个名称,这里命名为tsbot。 - `--restart=always`:设置容器重启策略,这里是总是重启,确保容器在失败后自动重启。 - `-it`:这是一对参数,-i 表示交互式操作,-t 分配一个伪终端。 - `-d`:表示后台运行容器。 - `-v /home/tsBot/data:/data`:将宿主机的/home/tsBot/data目录挂载到容器内的/data目录上,以便持久化存储数据。 - `rofl256/tsaudiobot` 或 `tsaudiobot`:指定Docker镜像名称。前者可能是从DockerHub上获取的带有用户名命名空间的镜像,后者是本地构建或已重命名的镜像。 #### Docker构建流程 - **构建镜像**:使用docker build命令可以将Dockerfile中的指令转化为一个Docker镜像。`docker build . -t tsaudiobot`表示从当前目录中读取Dockerfile,并创建一个名为tsaudiobot的镜像。构建过程中,Docker会按顺序执行Dockerfile中的指令,比如FROM、RUN、COPY等,最终形成一个包含所有依赖和配置的应用镜像。 ### 标签知识点 #### Dockerfile - **Dockerfile的概念**:Dockerfile是一个包含创建Docker镜像所有命令的文本文件。它被Docker程序读取,用于自动构建Docker镜像。Dockerfile中的指令通常包括安装软件、设置环境变量、复制文件等。 - **Dockerfile中的命令**:一些常用的Dockerfile命令包括: - FROM:指定基础镜像。 - RUN:执行命令。 - COPY:将文件或目录复制到镜像中。 - ADD:类似于COPY,但是 ADD 支持从URL下载文件以及解压 tar 文件。 - ENV:设置环境变量。 - EXPOSE:声明端口。 - VOLUME:创建挂载点。 - CMD:容器启动时要运行的命令。 - ENTRYPOINT:配置容器启动时的执行命令。 ### 压缩包子文件的文件名称列表知识点 #### 文件命名 - **TS3AudioBot_docker-main**:此文件名表明了这是一个主要的代码库或Dockerfile的存放位置。在开发中,通常main分支代表当前的主版本或正在积极开发的分支。因此TS3AudioBot_docker-main可能表示这是在Dev分支上开发的Dockerfile的主要代码版本。主分支一般比较稳定,并作为新的特性开发的基础。 ### 综合知识点 - **Docker在DevOps中的角色**:Docker作为一种轻量级的容器化技术,在DevOps领域扮演重要角色。它可以快速部署、一致的运行环境、便于测试和迁移应用。通过Dockerfile的编写和docker build命令,开发者可以构建可移植的容器镜像,通过docker run命令则可以快速启动容器实例。 - **TS3AudioBot与TeamSpeak**:TS3AudioBot很可能是一个针对TeamSpeak 3服务器的自动化解决方案。TeamSpeak是一个语音通讯软件,广泛用于线上游戏团队进行沟通。一个音频机器人可以提供自动化的消息通知、音频流控制等功能,提高游戏社区的交流效率。 - **Docker镜像的版本管理**:镜像的命名通常包括用户名、项目名和标签。如rofl256/tsaudiobot中rofl256可能是一个用户名,tsaudiobot可能是项目名称,这样的命名规则有利于镜像的共享和管理。 在介绍以上知识点后,我们理解了标题、描述、标签及文件名称列表中所含的知识点。这些知识点涵盖了Dockerfile的构建、Docker镜像的使用、TS3AudioBot功能以及版本控制等多个方面,为IT专业人员在使用和开发Docker相关项目时提供了必要的信息。
recommend-type

零售销售数据的探索性分析与DeepAR模型预测

### 零售销售数据的探索性分析与DeepAR模型预测 #### 1. 探索性数据分析 在拥有45家商店的情况下,我们选择了第20号商店,来分析其不同部门在三年间的销售表现。借助DeepAR算法,我们可以了解不同部门商品的销售情况。 在SageMaker中,通过生命周期配置(Lifecycle Configurations),我们可以在笔记本实例启动前自定义安装Python包,避免在执行笔记本前手动跟踪所需的包。为了探索零售销售数据,我们需要安装最新版本(0.9.0)的seaborn库。具体操作步骤如下: 1. 在SageMaker的Notebook下,点击Lifecycle Config
recommend-type

llm agent平台

<think>好的,我现在需要帮用户寻找与LLM代理平台解决方案相关的信息。首先,我得明确用户的需求。用户提到了“LLM agent platform solutions”,也就是大型语言模型代理平台的解决方案。这可能涉及到如何构建、部署或优化基于LLM的代理系统。接下来,我应该回忆之前用户提供的引用内容,看看有没有相关的信息可以利用。 根据引用[1],提到构建LLM应用程序的步骤分解,可能涉及到代理平台的设计。引用[2]讨论了评估LLM的挑战,包括可重复性和开源模型的解决方案,这可能影响代理平台的稳定性和选择。引用[3]则提到大模型相关的岗位和面试题,可能涉及实际应用中的技术问题。 接下
recommend-type

Docker实现OAuth2代理:安全的HTTPS解决方案

### 知识点详细说明: #### Dockerfile基础 Dockerfile是一种文本文件,它包含了用户创建Docker镜像所需的命令和参数。Docker通过读取Dockerfile中的指令自动构建镜像。Dockerfile通常包含了如下载基础镜像、安装软件包、执行脚本等指令。 #### Dockerfile中的常用指令 1. **FROM**: 指定基础镜像,所有的Dockerfile都必须以FROM开始。 2. **RUN**: 在构建过程中执行命令,如安装软件。 3. **CMD**: 设置容器启动时运行的命令,可以被docker run命令后面的参数覆盖。 4. **EXPOSE**: 告诉Docker容器在运行时监听指定的网络端口。 5. **ENV**: 设置环境变量。 6. **ADD**: 将本地文件复制到容器中,如果是tar归档文件会自动解压。 7. **ENTRYPOINT**: 设置容器启动时的默认命令,不会被docker run命令覆盖。 8. **VOLUME**: 创建一个挂载点以挂载外部存储,如磁盘或网络文件系统。 #### OAuth 2.0 Proxy OAuth 2.0 Proxy 是一个轻量级的认证代理,用于在应用程序前提供OAuth认证功能。它主要通过HTTP重定向和回调机制,实现对下游服务的安全访问控制,支持多种身份提供商(IdP),如Google, GitHub等。 #### HTTPS和SSL/TLS HTTPS(HTTP Secure)是HTTP的安全版本,它通过SSL/TLS协议加密客户端和服务器之间的通信。使用HTTPS可以保护数据的机密性和完整性,防止数据在传输过程中被窃取或篡改。SSL(Secure Sockets Layer)和TLS(Transport Layer Security)是用来在互联网上进行通信时加密数据的安全协议。 #### Docker容器与HTTPS 为了在使用Docker容器时启用HTTPS,需要在容器内配置SSL/TLS证书,并确保使用443端口。这通常涉及到配置Nginx或Apache等Web服务器,并将其作为反向代理运行在Docker容器内。 #### 临时分叉(Fork) 在开源领域,“分叉”指的是一种特殊的复制项目的行为,通常是为了对原项目进行修改或增强功能。分叉的项目可以独立于原项目发展,并可选择是否合并回原项目。在本文的语境下,“临时分叉”可能指的是为了实现特定功能(如HTTPS支持)而在现有Docker-oauth2-proxy项目基础上创建的分支版本。 #### 实现步骤 要实现HTTPS支持的docker-oauth2-proxy,可能需要进行以下步骤: 1. **准备SSL/TLS证书**:可以使用Let's Encrypt免费获取证书或自行生成。 2. **配置Nginx/Apache服务器**:在Dockerfile中添加配置,以使用SSL证书和代理设置。 3. **修改OAuth2 Proxy设置**:调整OAuth2 Proxy配置以使用HTTPS连接。 4. **分叉Docker-oauth2-proxy项目**:创建项目的分支副本,以便进行修改。 5. **编辑Dockerfile**:在分叉的项目中编写或修改Dockerfile,包括下载基础镜像、设置环境变量、添加SSL证书、配置Nginx/Apache和OAuth2 Proxy等步骤。 6. **构建和测试新镜像**:使用Docker构建镜像,并在安全环境中进行测试,确保HTTPS配置正确,并且OAuth2 Proxy功能正常工作。 7. **部署到生产环境**:在确认无误后,将配置好的镜像部署到生产环境中。 #### 压缩包子文件的文件名称列表 - **docker-oauth2-proxy-master**: 这可能是指在GitHub等代码托管平台上,docker-oauth2-proxy项目的主分支或主仓库。名称列表中的“master”暗示了该文件夹包含的是主分支的代码。 总结来说,要实现一个支持HTTPS的docker-oauth2-proxy,开发者需要进行一系列的配置和编码工作,包括使用Dockerfile来构建自定义的Docker镜像,配置SSL/TLS证书,分叉并修改现有的开源项目代码。通过这些步骤,可以确保OAuth2 Proxy能够安全地处理HTTPS请求,并为下游服务提供安全认证功能。
recommend-type

利用AmazonSageMaker进行图像分类:从理论到实践

# 利用 Amazon SageMaker 进行图像分类:从理论到实践 ## 1. 主题建模与图像分类概述 在数据科学领域,从大量非结构化数据中提取信息和主题至关重要。像 SageMaker 的神经主题模型(NTM)这类主题建模技术,提供了线性和非线性学习方法,能帮助我们深入挖掘数据中的潜在主题。它通过特定的架构和内部机制工作,还支持分布式训练,将数据集分割成多个块进行并行处理。训练完成后,我们可以将模型部署为端点并进行推理,例如解读安然邮件中的主题。 图像分类在过去五年中一直是热门研究领域,因为它能解决众多行业的实际业务问题,如自动驾驶汽车行业就高度依赖图像分类和目标检测模型的准确性。A
recommend-type

wed前端开发网页设计

<think>好的,用户需要寻找关于Web前端开发和网页设计的相关资源或教程。首先,我需要理解用户的具体需求,他们可能刚入门,想要系统学习,或者有一定基础,需要进阶教程。根据用户的问题,他们提到了“教程”和“资源”,可能希望推荐书籍、在线课程、框架文档以及社区论坛等。 接下来,我需要参考用户提供的引用内容。引用[1]提到了周文洁的《HTML5网页前端设计实战》,这是一本配套的实战项目教程,适合有基础的读者,可能可以作为书籍推荐之一。引用[2]概述了Web前端开发的技术分类,包括客户端和服务器端技术,以及常用框架如Bootstrap、React等。引用[3]是关于Delphi的TMS WEB
recommend-type

eosforce下的scatter API应用实例教程

### eosforce使用分散API #### 知识点一:什么是EOSForce EOSForce是以EOSIO为技术基础,旨在为区块链应用提供高性能的公链解决方案。它类似于EOS,也使用了EOSIO软件套件,开发者可以基于EOSIO构建DAPP应用,同时它可能拥有与EOS不同的社区治理结构和经济模型。对于开发者来说,了解EOSForce的API和功能是非常关键的,因为它直接影响到应用的开发与部署。 #### 知识点二:scatter API的介绍 scatter API 是一个开源的JavaScript库,它的目的是为了简化EOSIO区块链上各类操作,包括账户管理和交易签名等。scatter旨在提供一个更为便捷、安全的用户界面,通过API接口与EOSIO区块链进行交互。用户无需保存私钥即可与区块链进行交互,使得整个过程更加安全,同时开发者也能够利用scatter实现功能更加强大的应用。 #### 知识点三:scatter API在EOSForce上的应用 在EOSForce上使用scatter API可以简化开发者对于区块链交互的工作,无需直接处理复杂的私钥和签名问题。scatter API提供了一整套用于与区块链交互的方法,包括但不限于账户创建、身份验证、签名交易、数据读取等。通过scatter API,开发者可以更加专注于应用逻辑的实现,而不必担心底层的区块链交互细节。 #### 知识点四:安装和运行scatter_demo项目 scatter_demo是基于scatter API的一个示例项目,通过它可以学习如何将scatter集成到应用程序中。根据提供的描述,安装该项目需要使用npm,即Node.js的包管理器。首先需要执行`npm install`来安装依赖,这个过程中npm会下载scatter_demo项目所需的所有JavaScript包。安装完成后,可以通过运行`npm run dev`命令启动项目,该命令通常与项目中的开发环境配置文件(如webpack.config.js)相对应,用于启动本地开发服务器和热重载功能,以便开发者实时观察代码修改带来的效果。 #### 知识点五:配置eosforce到scatter 在scatter_demo项目中,将eosforce配置到scatter需要进入scatter的设置界面。scatter提供了一个可视化的界面,允许用户管理自己的区块链网络配置。在scatter设置中选择“网络”一栏,然后选择“新建”,在此步骤中需要选择“eos”作为区块链类型。之后,将eosforce的节点配置信息填入对应区域,完成网络的设置。这样,scatter就能够连接到eosforce区块链,用户可以通过scatter API与eosforce区块链进行交互。 #### 知识点六:npm包管理器及安装命令 npm是Node.js的包管理器,它在Node.js项目中扮演着至关重要的角色,用来安装、管理和分享项目所需的代码包。`npm install`是npm的常用命令,用于安装项目依赖。在执行此命令时,npm会根据项目根目录中的`package.json`文件自动下载并安装所需的所有依赖包。这个文件描述了项目的依赖关系,包括每个依赖的版本范围。通过这种方式,开发者可以确保项目在不同的环境中都能够有相同的行为表现。 #### 结论 了解和掌握如何在EOSForce中使用scatter API对于区块链应用开发者来说是极其重要的。scatter简化了与EOSIO区块链的交互过程,提高开发效率,降低了实现复杂功能的技术门槛。在实际操作中,开发者需要关注如何正确安装scatter_demo项目,如何配置eosforce节点到scatter中,以及如何利用scatter API开发出功能完善的区块链应用。通过这些知识点的了解,开发者可以更好地把握EOSForce生态中的开发实践,并在项目中实现安全、便捷的区块链交互。
recommend-type

AI模型评估与应用拓展

# AI模型评估与应用拓展 ## 1. 模型性能评估 ### 1.1 实验假设 我们有三个模型,假设第一个基于周一和周二点击数据训练的模型,在预测周四后期应用下载量方面,不如第二和第三个模型。第二个基于周一到周三点击数据训练的模型,性能也不如第三个基于周一到周四大部分时间点击数据训练的模型。 ### 1.2 特征重要性分析 以下代码用于分析每个模型中重要的特征: ```python exp_lst = ['exp1', 'exp2', 'exp3'] for exp in exp_lst: model_file = os.path.join(sm_output_loc, exp,
recommend-type

elasticsearch serverless

<think> 首先,用户要求了解 Elasticsearch Serverless 的相关信息,包括介绍、使用和优势等。参考的引用内容提供了背景信息,我需要基于这些引用进行回答。引用 [1] 到 [5] 提供了关键点。 关键引用内容: - [^1]: Elasticsearch Serverless 基于 Elasticsearch 的优势,满足现代工作负载的需求,如大数据集、AI 搜索和不可预测的流量。 - [^2]: Elasticsearch Serverless 是云端全托管的服务,基于云原生 Serverless 技术,提供自动弹性和免运维能力,解决资源成本问题,兼容 ELK 生