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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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eriknw committed Dec 14, 2020
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85 changes: 85 additions & 0 deletions recipes/cogdl/meta.yaml
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{% set name = "cogdl" %}
{% set version = "0.1.2" %}

package:
name: {{ name|lower }}
version: {{ version }}

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

build:
noarch: python
number: 0
script: "{{ PYTHON }} -m pip install . -vv"

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

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

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

extra:
recipe-maintainers:
- eriknw
- paul-tqh-nguyen

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