39 lines
1.3 KiB
Plaintext
39 lines
1.3 KiB
Plaintext
|
setuptools==39.1.0 #tensorflow 1.10.0 has requirement setuptools<=39.1.0, but you'll have setuptools 39.2.0 which is incompatible
|
||
|
absl-py==0.2.2
|
||
|
astor==0.6.2
|
||
|
backports.weakref==1.0.post1
|
||
|
bleach==1.5.0
|
||
|
decorator==4.3.0
|
||
|
#enum34==1.1.6 # enum34 is not necessary for python > 3.4
|
||
|
funcsigs==1.0.2
|
||
|
gast==0.2.0
|
||
|
grpcio==1.12.1
|
||
|
html5lib==0.9999999
|
||
|
Markdown==2.6.11
|
||
|
mock==2.0.0
|
||
|
numpy==1.14.5
|
||
|
pbr==4.0.4
|
||
|
protobuf==3.6.0
|
||
|
scipy==1.1.0
|
||
|
six==1.11.0
|
||
|
#sklearn==0.0
|
||
|
termcolor==1.1.0
|
||
|
Werkzeug==0.14.1
|
||
|
# we update to the latest version of the following packages @18 Oct 2018
|
||
|
# the orignal one: https://github.com/williamleif/GraphSAGE
|
||
|
#futures==3.2.0
|
||
|
networkx==2.2
|
||
|
tensorflow==1.10.0
|
||
|
tensorboard==1.10.0
|
||
|
gensim==3.0.1
|
||
|
scikit-learn==0.19.0 #0.20.0 is OK but may get some warnings
|
||
|
# if your want utilize your gpu for speeding up, try simply use the following conda command
|
||
|
# tested in python==3.6.6
|
||
|
|
||
|
# either -> conda install tensorflow-gpu==1.10.0 #this version will help you to install cuda and cudnn
|
||
|
# for cuda driver compatibility: https://docs.nvidia.com/deploy/cuda-compatibility/index.html
|
||
|
# e.g. if driver 384.xx -> conda install tensorflow-gpu=1.10.0 cudatoolkit=9.0
|
||
|
|
||
|
# or -> simply build from docker image: docker pull tensorflow/tensorflow:1.10.0-gpu-py3
|
||
|
# ref: https://www.tensorflow.org/install/docker#gpu_support
|