I'm trying to package my python code to publish in on Anaconda cloud. The folder structure looks like this:
.
├── 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
The meta.yaml file:
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.
Command I am using to build the package (haasad is the name of the channel of the pypardiso package):
conda build conda-recipe -c haasad
The build is successful and I have uploaded it here:
https://anaconda.org/monomonedula/sten
However, after installation using both local build like so:
conda install sten --use-local -c haasad
and the build uploaded to the cloud
conda install -c monomonedula sten -c haasad
I am encountering several problems.
- When using python 3.7 I am unable to import my package even though it is listed in
conda list(I have double checked everything, I am using the correct interpreter). - When using python 3.8 I am able to import and use it, but I am unable to install stellargraph for an unknown reason. The error message:
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
Output of conda search sten -c monomonedula --info:
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
Output of conda search stellargraph -c stellargraph --info:
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
What am I missing here and how do I package it properly?