Merge pull request #34 from cakirmuha/master

Dockerize

Author: hexiangnan

Dockerfile

@@ -0,0 +1,24 @@
+FROM ubuntu:16.04
+
+ENV KERAS_BACKEND theano
+
+RUN mkdir -p /tmp/setup && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+        build-essential \
+        git \
+        python-dev \
+        python-pip \
+        python-setuptools \
+        software-properties-common \
+        wget \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade --user pip==9.0.3 && \
+    pip install Theano==0.8.0 && \
+    pip install keras==1.0.7 && \
+    pip install h5py && \
+    pip install numpy==1.11.0
+
+WORKDIR /home
+CMD ["/bin/bash"]

README.md

@@ -40,6 +40,41 @@ python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --l
 
 Note on tuning NeuMF: our experience is that for small predictive factors, running NeuMF without pre-training can achieve better performance than GMF and MLP. For large predictive factors, pre-training NeuMF can yield better performance (may need tune regularization for GMF and MLP). 
 
+## Docker Quickstart
+Docker quickstart guide can be used for evaluating models quickly.
+
+Install Docker Engine
+- [Ubuntu Installation](https://docs.docker.com/engine/installation/linux/ubuntu/)
+- [Mac OSX Installation](https://docs.docker.com/docker-for-mac/install/)
+- [Windows Installation](https://docs.docker.com/docker-for-windows/install/)
+
+Build a keras-theano docker image 
+```
+docker build --no-cache=true -t ncf-keras-theano .
+```
+
+### Example to run the codes with Docker.
+Run the docker image with a volume (Run GMF):
+```
+docker run --volume=$(pwd):/home ncf-keras-theano python GMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --regs [0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
+```
+
+Run the docker image with a volume (Run MLP):
+```
+docker run --volume=$(pwd):/home ncf-keras-theano python MLP.py --dataset ml-1m --epochs 20 --batch_size 256 --layers [64,32,16,8] --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
+```
+
+Run the docker image with a volume (Run NeuMF -without pre-training): 
+```
+docker run --volume=$(pwd):/home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --reg_mf 0 --reg_layers [0,0,0,0] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1
+```
+
+Run the docker image with a volume (Run NeuMF -with pre-training):
+```
+docker run --volume=$(pwd):/home ncf-keras-theano python NeuMF.py --dataset ml-1m --epochs 20 --batch_size 256 --num_factors 8 --layers [64,32,16,8] --num_neg 4 --lr 0.001 --learner adam --verbose 1 --out 1 --mf_pretrain Pretrain/ml-1m_GMF_8_1501651698.h5 --mlp_pretrain Pretrain/ml-1m_MLP_[64,32,16,8]_1501652038.h5
+```
+* **Note**: If you are using `zsh` and get an error like `zsh: no matches found: [64,32,16,8]`, should use `single quotation marks` for array parameters like `--layers '[64,32,16,8]'`.
+
 ### Dataset
 We provide two processed datasets: MovieLens 1 Million (ml-1m) and Pinterest (pinterest-20). 
 
@@ -56,4 +91,4 @@ test.negative
 - Each line corresponds to the line of test.rating, containing 99 negative samples.  
 - Each line is in the format: (userID,itemID)\t negativeItemID1\t negativeItemID2 ...
 
-Last Update Date: August 2, 2017
+Last Update Date: December 23, 2018