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I was a bit verbose/explicit with dependencies. I base them on what is imported by modules within the package. `dgl` could be added as a dependency if it's added to conda-forge. See dmlc/dgl#1855 and #12522 Even though this is a pure Python package, I run a few of the fastest tests that exercise different files, because this package has some heavy dependencies (pytorch, pytorch_geometric, etc) and I want that warm fuzzy feeling :) I get the tarball from github instead of PyPI, because the PyPI tarball does not (yet) contain the license file or the tests. Currently, the github tarball is not that much larger than from PyPI.
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{% set name = "cogdl" %} | ||
{% set version = "0.1.2" %} | ||
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package: | ||
name: {{ name|lower }} | ||
version: {{ version }} | ||
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source: | ||
#url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz | ||
#sha256: 5e9c3418d8e18095c7b359d1c251087f7255641ceab59d737887e60fc77a0a8d | ||
url: https://github.com/THUDM/cogdl/archive/{{ version }}.tar.gz | ||
sha256: 4c35bcc20e9bd7e9928c6e5a02d2ca974d42ad47255ad7dafd70cbec5da079d6 | ||
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build: | ||
noarch: python | ||
number: 0 | ||
script: "{{ PYTHON }} -m pip install . -vv" | ||
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requirements: | ||
host: | ||
- python | ||
- pip | ||
- setuptools | ||
run: | ||
- python | ||
- gensim | ||
- grave | ||
- joblib | ||
- matplotlib | ||
- networkx | ||
- numpy | ||
- ogb | ||
- optuna | ||
- pandas | ||
- prettytable ==0.7.2 | ||
- pytorch_cluster | ||
- pytorch_geometric | ||
- pytorch_scatter | ||
- pytorch_sparse | ||
- requests | ||
- scikit-learn | ||
- scipy | ||
- six | ||
- tabulate | ||
- texttable | ||
- tqdm | ||
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test: | ||
imports: | ||
- cogdl | ||
- cogdl.data | ||
- cogdl.datasets | ||
- cogdl.layers | ||
- cogdl.models | ||
- cogdl.models.emb | ||
- cogdl.models.nn | ||
- cogdl.tasks | ||
- cogdl.trainers | ||
requires: | ||
- pytest | ||
source_files: | ||
- tests | ||
commands: | ||
# For sanity, run quick tests from a few different files | ||
- pytest -k "test_gcn_cora or test_transe_fb13s or test_sortpool_mutag or test_stpgnn_infomax" tests | ||
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about: | ||
home: https://github.com/THUDM/cogdl | ||
license: MIT | ||
license_family: MIT | ||
license_file: LICENSE | ||
summary: 'An Extensive Research Toolkit for Graphs' | ||
description: | | ||
CogDL is a graph representation learning toolkit that allows researchers | ||
and developers to easily train and compare baseline or custom models for | ||
node classification, link prediction and other tasks on graphs. It provides | ||
implementations of many popular models, including non-GNN Baselines like | ||
Deepwalk, LINE, NetMF, GNN Baselines like GCN, GAT, GraphSAGE. | ||
doc_url: https://keg.cs.tsinghua.edu.cn/cogdl/ | ||
dev_url: https://github.com/THUDM/cogdl | ||
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extra: | ||
recipe-maintainers: | ||
- eriknw | ||
- paul-tqh-nguyen |