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Benchmark popular Neural Networks frameworks for word2vec on different hardware platforms
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README.md

benchmark-word2vec-frameworks

A repository for benchmarking popular Neural Networks frameworks used to learn unsupervised word embeddings, on different hardware platforms. This repository is a result of this issue raised on the Gensim repo which achieves the following objectives:

  • Compare word2vec implementations of popular neural network frameworks by reporting metrics like time to train, peak memory, word vectors quality etc.
  • Benchmark these implementaions across various cloud platforms
  • Carry out the benchmarking in self-contained fully reproducible scripts for repeatable deployment

Frameworks

Below is a short description of the frameworks supported and the code used to train the word vectors.

Framework Description Code used References
Tensorflow TensorFlow is an open source software library for numerical computation using data flow graphs. Widely used for conducting machine learning and deep neural networks research. Can run on both CPU or GPU. The code used in benchmarking has been directly picked up and modified from the tutorials on the official github page. link to github code
OriginalC This tool has been made available by the original author(Tomas Mikolov)and provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. Use unmodified code and simply run the executable by setting relevant parameters link to code link to toolkit
Gensim Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. Refer to the code given on the word2vec tutorial on the official github repo link to reference code
DL4J Deeplearning4j is a commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Modified word2vec example given on the official github repo link to github example
Spark Apache Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis Tweaked official word2vec example to exploit multiple computing nodes link to example code

Running benchmark

Docker

For the purpose of reproducibilty, the benchmarks need to be run inside a docker.

  • Build your own docker using the Docker file provided in the repo. This step assumes you have installed docker. Run the following command from the directory containing the this repository.

docker build -f Dockerfile-cpu-tfsource -t manneshiva/playground:benchmarkword2vec-cpu-tfsource .

  • Download the pre-built docker image from Docker's public registry.

docker pull manneshiva/playground:benchmarkword2vec-cpu-tfsource

Images on Docker Hub

GPU benchmarks

Exploiting GPU to train word vectors inside docker requires nvidia-docker. Requirements, installation guide and why it is necessary can be found here. Follow the same steps mentioned above replacing docker with nvidia-docker and benchmarkword2vec-cpu-tfsource with benchmarkword2vec-gpu-tfsource.

docker build -f Dockerfile-gpu-tfsource -t manneshiva/playground:benchmarkword2vec-gpu-tfsource .

P.S.: In order to benefit from sse4.2/avx/fma optimizations (which results in a faster training time for tensorflow), tensorflow has been built from source. In case your machine doesn't support this, use benchmarkword2vec-{c/g}pu.

Run the docker image:

docker run -v absPathTo/persistent/:/benchmark-word2vec-frameworks/persistent/ -w /benchmark-word2vec-frameworks/ --rm -it -p 8888:8888 manneshiva/playground:benchmarkword2vec-cpu-tfsource /bin/bash

nvidia-docker run -v absPathTo/persistent/:/benchmark-word2vec-frameworks/persistent/ -w /benchmark-word2vec-frameworks/ --rm -it -p 8888:8888 manneshiva/playground:benchmarkword2vec-gpu /bin/bash

Run

To run all the benchmarks, eg:

python benchmark.py --fname /benchmark-word2vec-frameworks/persistent/enwiki-20170501-2M.cor \
--frameworks tensorflow originalc dl4j gensim --epochs 4 --size 100 --window 5 --min_count 30 \
--negative 5 --workers 4 --sample 0.001 --sg 1 --batch_size 64 --alpha 0.025 --platform aws \
|& tee persistent/benchmark.log

usage help : python benchmark.py -h

Available options:

Parameter Description
--frameworks Specify frameworks to run the benchmarks on(demilited by space). If None provided, benchmarks will be run on all supported frameworks.
--file Path to text corpus
--epochs Number of iterations (epochs) over the corpus.
--size Dimensionality of the embeddings/feature vectors.
--window Maximum distance between the current and predicted word within a sentence.
--min_count This will discard words that appear less than MIN_COUNT times.
--workers Use these many worker threads to train the model.
--sample Set threshold for occurrence of words. Those that appear with higher frequency in the training data will be randomly down-sampled; useful range is (0, 1e-5)
--sg Use the skip-gram model; default is 1 (use 0 for continuous bag of words model)
--negative Number of negative examples; common values are 3 - 10 (0 = not used)
--alpha The initial learning rate. eg. 0.025

On completion, the report, trained vectors and logs file can be found in persistent/.

Jupyter Notebook

To generate graphics from the report,fire up the notebook, go to localhost:8888 and open visualize_report.ipynb

jupyter notebook --allow-root

Running benchmark on cloud

Modify the hosts file in the ansible/ folder and run the following command:

ansible-playbook -i hosts benchmark.yml -v

This will install Docker, pull the Docker image and download the preprocessed wiki corpus(can be in this repository's release) without manual intervention. Once this command is complete, start the docker and run benchmark.

You can’t perform that action at this time.