hhh920406 / fast_tffm

fast_tffm: Tensorflow-based Distributed Factorization Machine

Tensorflow-based Distributed Factorization Machine

An efficient distributed factoriazation machine implementation based on tensorflow (cpu only).

1. Support both multi-thread local machine training and distributed training.
2. Can easily benefit from numerous implementations of operators in tensorflow, e.g., different optimizors, loss functions.
3. Customized c++ operators, significantly faster than pure python implementations. Comparable performance (actually faster according to my benchmark) with pure c++ implementation.

Quick Start

Compile

mkdir build
cd build
cmake ../
make
make test
cd ..

Local Training

python fast_tffm.py train sample.cfg

Distributed Training

Open 4 command line windows. Run the following commands on each window to start 2 parameter servers and 2 workers.

python fast_tffm.py dist_train sample.cfg ps 0
python fast_tffm.py dist_train sample.cfg ps 1
python fast_tffm.py dist_train sample.cfg worker 0
python fast_tffm.py dist_train sample.cfg worker 1

Local Prediction

python fast_tffm.py predict sample.cfg

Distributed Prediction

Open 4 command line windows. Run the following commands on each window to start 2 parameter servers and 2 workers.

python fast_tffm.py dist_predict sample.cfg ps 0
python fast_tffm.py dist_predict sample.cfg ps 1
python fast_tffm.py dist_predict sample.cfg worker 0
python fast_tffm.py dist_predict sample.cfg worker 1

Benchmark

1. Local Mode. Training speed compared with difacto using the same configuration

• Configuration: 36672494 training examples, 10 threads, factor_num = 8, batch_size = 10000, epoch_num = 1, vocabulary_size = 40000000
• Difacto: 337 seconds. 108820 examples / second.
• FastTffm: 157 seconds. 233582 examples / second.

2. Distriubuted Mode. (I did not find other open source projects which support distributed training. Difacto claims so, but their distributed mode is not implemeted yet)

• Configuration: 36672494 training examples, 10 threads, factor_num = 8, batch_size = 10000, epoch_num = 1, vocabulary_size = 40000000
• Cluster: 1 ps, 4 workers.
• FastTffm: 49 seconds. 748418 examples / second.

Input Data Format

1. Data File

<label> <fid_0>[:<fval_0>] [<fid_1>[:<fval_1>] ...]

<label>: 0 or 1 if loss_type = logistic; any real number if loss_type = mse.

<fid_k>: An integer if hash_feature_id = False; Arbitrary string if hash_feature_id = True

<fval_k>: Any real number. Default value 1.0 if omitted.

2. Weight File Should have the same line number with the corresponding data file. Each line contains one real number.

Check the data/weight files in the data folder for details. The data files are sampled from criteo lab dataset.