Merge pull request #265 from graph4ai/distribution

Distribution

Author: Lingfei Wu

distribution/README.md

@@ -0,0 +1,81 @@
+# Graph4NLP
+Graph4NLP is the library for the easy use of Graph Neural Networks for NLP
+
+## Installation
+Users can install Graph4NLP using pip:
+```shell
+pip install graph4nlp         # CPU Version
+```
+
+For GPU supported versions, use the following commands:
+```shell
+pip install graph4nlp-cu92    # CUDA 9.2 Version
+pip install graph4nlp-cu101   # CUDA 10.1 Version
+pip install graph4nlp-cu102   # CUDA 10.2 Version
+pip install graph4nlp-cu110   # CUDA 11.0 Version
+```
+
+## Distributing this package
+Currently releasing via PyPI is supported. An automated build and distributing shell script is `./build_and_upload_to_pypi.sh`.
+You can directly run this script after proper configuration, which will be elaborated below.
+
+### Build the package
+To build the package, run `setup.py`. You need to specify whether the binary distribution file(wheel) or source code distribution(tgz)
+is wanted. If you want to build the binary version, run
+```shell
+python setup.py bdist_wheel --universal
+```
+The `bdist_wheel` option is to say that the binary distribution of wheel file will be built. And the `--universal` option
+means the wheel file will fit all platforms.
+
+If you also want to package the source code for distribution, run
+```shell
+python setup.py sdist
+```
+Here `sdist` means source distribution. In `build_and_upload_to_pypi.sh` both options will be built. 
+
+### Inside `setup.py`
+#### Version 
+Everytime the package is updated, a newer version number needs to be specified, otherwise the updated package
+cannot be uploaded to PyPI. To update the version number, you need to change the `version` parameter in the `setup()`
+  function call in `setup.py`
+  
+#### CUDA Compatibility
+The only reason why we keep different packages for different CUDA versions is that DGL does so.
+In fact our current implementation has nothing to do with CUDA since it is basically a Python frontend of DGL. 
+However, CUDA version still needs to be specified in order to make the DGL dependency explicit. 
+
+When running `setup.py`, you will be prompted to input the CUDA version. 
+You may just input 'none' for a CPU distribution, otherwise you need to specify the version number explicitly, e.g. `10.2`. 
+Then a prefix will be generated and appended to the package name, distinguishing among versions with different CUDA dependency.
+For example `graph4nlp-cu101`.
+
+Note that this is just a temporal solution for the distribution of the library.
+This part needs to be optimized in the following releases.
+
+### Uploading to PyPI
+The `twine` python package is used for uploading the package to PyPI. To use it, first install it using pip:
+```shell
+pip install twine
+```
+
+Then you need to specify your credential, namely to authenticate your account that hosts Graph4NLP on PyPI.
+
+Currently I am using my personal account (swangeh) to host this package and a dedicated account should be used in the future.
+You can manually input your username and password everytime you upload the package to PyPI by running
+```shell
+twine upload dist/*
+```
+In this way there will be prompts to guide you. 
+To save your time you can also generate a credential on PyPI and save it locally in `~/.pypirc`. 
+For example inside your `~/.pypirc` it will be
+```text
+[pypi]
+username = <your username>
+password = <your password>
+```
+For details please refer to PyPI.
+
+### Putting them all together
+Above is the decomposition of what's inside `build_and_upload_to_pypi.sh`.
+You can just run this script to save your time for building and releasing the package.

build_and_upload_to_pypi.sh renamed to distribution/build_and_upload_to_pypi.sh

@@ -1,9 +1,12 @@
 from ....modules.utils.config_utils import get_yaml_config
-import os
 
 str2yaml = {"dependency": "dependency.yaml", "constituency": "constituency.yaml", "ie": "ie_graph_construction.yaml",
             "node_emb": "node_emb.yaml", "node_emb_refined": "node_emb_refine.yaml"}
 
+import os
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+
 
 def get_graph_construction_args(graph_construction_name):
     """
@@ -34,7 +37,7 @@ def get_graph_construction_args(graph_construction_name):
     """
     if graph_construction_name in str2yaml.keys():
         yaml_name = str2yaml[graph_construction_name]
-        path = os.path.join("graph4nlp/pytorch/modules/config/graph_construction", yaml_name)
+        path = os.path.join(dir_path, yaml_name)
         config = get_yaml_config(path)
         return config
     else: