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MACH

MACH is a hash-based extreme multi-class classification package. This package supports both sparse datasets and dense datasets. The training process is implemented in Tensorflow and supports GPU acceleration. Inference process consists of two stages: prediction stage and merging stage. In prediction stage, MACH uses Tensorflow to perform prediction for each meta classifier. In merging stage, MACH uses Numpy to merge results from all meta-classifiers. The merging utilizes python's multi processing module to achieve multi-core parallelization. GPU acceleration for merging stage will be supported in the future.

Required installation

  • Python 3
  • Tensorflow
  • Numpy

Quickstart

Currently, MACH provides two demos: ODP and Imagenet. The following steps will show users how to download datasets and successfully run MACH on them.

ODP

  1. Download all files from link
  2. Download datasets by typing make odp_train.vw.gz and make odp_test.vw.gz: in shell. Then unarchive .gz files to obtain .vw files.
  3. Open odp folder and use the following script to convert datasets from vw format to tfrecords format: python3 save_tfrecords.py vwFileName outputFileName. To save the original training set as training.tfrecords, simply typing python3 save_tfrecords.py odp_train.vw training.tfrecords
  4. After converting files to tfrecords format, change TRAIN_FILE and TEST_FILE fields in odp_demo.py to the location of your ODP datasets.
  5. To start training and predicting ODP dataset, simply typing python3 odp_demo.py -b 32 -r 50. This line will start training for 50 meta-classifiers with 32 buckets. You may change the parameters to run different experiments.

Imagenet

  1. Download all files from link
  2. Download datasets by typing training.txt.gz and make testing.txt.gz in shell. Then unarchive .gz files to obtain .txt files.
  3. Open imagenet folder and use the following script to convert datasets from txt format to tfrecords format: python3 save_tfrecords.py txtFileName outputFileName. To save the original training set as training.tfrecords, simply typing python3 save_tfrecords.py training.txt training.tfrecords. Both the source file and target file will be extremely large. Be sure to have enough disk space.
  4. After converting files to tfrecords format, change TRAIN_FILE and TEST_FILE fields in imagenet_demo.py to the location of your imagenet datasets.
  5. To start training and predicting ODP dataset, simply typing python3 imagenet_demo.py -b 512 -r 20. This line will start training for 20 meta-classifiers with 512 buckets. You may change the parameters to run different experiments.

Running MACH on other datasets

  • By modifying source codes in odp or imagenet folders, users can run MACH on other large scale datasets.

Sparse datasets

  • The ODP dataset used in demo is a sparse dataset and therefore all the codes in odp folder is designed for sparse datasets.
  • Because both training process and predicting process rely on Tensorflow and tfrecords format, before running MACH, users need to first convert their datasets to tfrecords format specified in save_to_tfrecords function in odp/util.py. This function essentially reads sparse format data line by line, stores indices and values separately for each data entry, and writes results into tfrecords format. Feature index and label must starts from 0.
  • After the conversion finished, users will need to modify NUM_FEATURES, NUM_CLASSES, TRAIN_FILE, TEST_FILE in odp_demo.py to accommodate their datasets. If the user wishes to only perform training or predicting, the user can modify train_odp.py and predict_odp.py in a similar manner.
  • Running MACH will be similar to the tutorials shown in Quickstart section.

Dense datasets

  • The Imagenet dataset used in demo is a dense dataset and therefore all the codes in imagenet folder is designed for dense datasets.
  • Because both training process and predicting process rely on Tensorflow and tfrecords format, before running MACH, users need to first convert their datasets to tfrecords format specified in save_to_tfrecords function in imagenet/util.py. This function essentially reads sparse format data line by line, creates an empty Numpy array, fill in values to corresponding indexes, and writes results into tfrecords format. The new file may be larger than the original file because the densifing operation. Feature index and label must starts from 0.
  • After the conversion finished, users will need to modify NUM_FEATURES, NUM_CLASSES, TRAIN_FILE, TEST_FILE in imagenet_demo.py to accommodate their datasets. If the user wishes to only perform training or predicting, the user can modify train_imagenet.py and predict_imagenet.py in a similar manner.
  • Running MACH will be similar to the tutorials shown in Quickstart section.