如何修复自定义conda程序包的conda UnsatifiableError？

. ├── conda-recipe │ ├── build.bat │ ├── build.sh │ └── meta.yaml ├── demos │ ├── datasets │ │ ├── com-amazon.all.dedup.cmty.txt │ │ ├── com-amazon.ungraph.txt │ │ ├── email-Eu-core-department-labels.txt │ │ └── email-Eu-core.txt │ ├── directed_example.ipynb │ ├── email_eu_core_network_evaluation-Copy1.ipynb │ ├── node_classification.ipynb │ └── zacharys_karate_club_evaluation.ipynb ├── LICENSE.txt ├── README.md ├── setup.py ├── sten │ ├── embedding.py │ └── __init__.py ├── test │ ├── __init__.py │ └── test_system.py ├── zachary_computed.png └── zachary_expected.png

package: name: sten version: "0.1.0" source: path: .. build: number: 0 noarch: generic requirements: build: - python >=3.7 - setuptools run: - python >=3.7 - pypardiso >=0.2.2 - numpy >=1.18.1 - networkx >=2.4 - scipy >=1.4.1 - markdown test: imports: - sten.embedding about: home: https://github.com/monomonedula/simple-graph-embedding license: Apache License 2.0 license_file: LICENSE.txt summary: Simple deterministic algorithm for generating graph nodes topological embeddings.

Collecting package metadata (current_repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source. Collecting package metadata (repodata.json): done Solving environment: failed with initial frozen solve. Retrying with flexible solve. Solving environment: \ Found conflicts! Looking for incompatible packages. This can take several minutes. Press CTRL-C to abort. failed UnsatisfiableError: The following specifications were found to be incompatible with each other: Output in format: Requested package -> Available versions

sten 0.1.0 py38_0 ----------------- file name : sten-0.1.0-py38_0.tar.bz2 name : sten version : 0.1.0 build : py38_0 build number: 0 size : 16 KB license : Apache License 2.0 subdir : noarch url : https://conda.anaconda.org/monomonedula/noarch/sten-0.1.0-py38_0.tar.bz2 md5 : 53661562513861f9433b252c8ae7b5f4 timestamp : 2020-05-23 19:24:36 UTC dependencies: - markdown - networkx >=2.4 - numpy >=1.18.1 - pypardiso >=0.2.2 - python >=3.7 - scipy >=1.4.1 sten 0.1.0 py38_0 ----------------- file name : sten-0.1.0-py38_0.tar.bz2 name : sten version : 0.1.0 build : py38_0 build number: 0 size : 16 KB license : Apache License 2.0 subdir : noarch url : https://conda.anaconda.org/monomonedula/noarch/sten-0.1.0-py38_0.tar.bz2 md5 : 53661562513861f9433b252c8ae7b5f4 timestamp : 2020-05-23 19:24:36 UTC dependencies: - markdown - networkx >=2.4 - numpy >=1.18.1 - pypardiso >=0.2.2 - python >=3.7 - scipy >=1.4.1

stellargraph 1.0.0 py_0 ----------------------- file name : stellargraph-1.0.0-py_0.tar.bz2 name : stellargraph version : 1.0.0 build : py_0 build number: 0 size : 7.8 MB license : Apache Software subdir : noarch url : https://conda.anaconda.org/stellargraph/noarch/stellargraph-1.0.0-py_0.tar.bz2 md5 : e62b9c897d0a5481159c1e7cb8024717 timestamp : 2020-05-05 07:54:44 UTC dependencies: - gensim >=3.4.0 - ipykernel - ipython - matplotlib >=2.2 - networkx >=2.2 - numpy >=1.14 - pandas >=0.24 - python >=3.6 - scikit-learn >=0.20 - scipy >=1.1.0 - tensorflow >=2.1.0

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1楼 · 发布于 2024-09-24 20:40:07

问题出在meta.yaml文件中。根据Anaconda blog post的说法：

Noarch generic packages allow users to distribute docs, datasets, and source code in conda packages

及

Noarch Python packages cut down on the overhead of building multiple different pure Python packages on different architectures and Python versions by sorting out platform and Python version-specific differences at install time

因为要分发纯python包，所以应该使用noarch: python。另外，您不需要在build和run中设置python版本。我在下面添加了一个更新的meta.yaml。它还包含构建脚本，因此不再需要build.sh和build.bat文件。它还将许可证部分修复为conda可以接受的内容（仍然是Apache2.0）

package:
  name: sten
  version: "0.1.0"

source:
  path: ..

build:
  number: 0
  noarch: python
  script: {{ PYTHON }} setup.py install

requirements:
  build:
    - python
    - setuptools
  run:
    - python
    - pypardiso >=0.2.2
    - numpy >=1.18.1
    - networkx >=2.4
    - scipy >=1.4.1
    - markdown

test:
  imports:
    - sten.embedding

about:
  home: https://github.com/monomonedula/simple-graph-embedding
  license: Apache-2.0
  license_family: Apache
  license_file: LICENSE.txt
  summary: Simple deterministic algorithm for generating graph nodes topological embeddings.

以下是我使用上述meta.yaml文件构建和安装此软件包的步骤：

git clone https://github.com/monomonedula/simple-graph-embedding
cd simple-graph-embedding/
git checkout 'refactor&cleanup'
# Replace contents of `meta.yaml`
rm conda-recipe/build.sh conda-recipe/build.bat
conda create  yes -n testing python=3.7 conda-build conda-verify
conda activate testing
conda-build  channel haasad .

下面是我在3.7环境中安装软件包的步骤。您将在conda-build日志输出的底部找到sten-0.1.0-py_0.tar.bz2的完整路径

conda install -n testing /home/jakub/miniconda3/envs/testing/conda-bld/noarch/sten-0.1.0-py_0.tar.bz2
conda activate testing
$CONDA_PREFIX/bin/python -c "import sten"

我还在Python3.8环境中测试了安装

conda create  yes -n testing3.8 python=3.8
conda install -n testing3.8 /home/jakub/miniconda3/envs/testing/conda-bld/noarch/sten-0.1.0-py_0.tar.bz2
conda activate testing3.8
$CONDA_PREFIX/bin/python -c "import sten"

conda install use-local sten对我不起作用，有一个GitHub issue关于它

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