| Linear Classification Using Sparse Matrix Multiplication |
| =========== |
| This examples trains a linear model using the sparse feature in MXNet. This is for demonstration purpose only. |
| |
| The example utilizes the sparse data loader ([mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)), |
| the sparse dot operator and [sparse gradient updaters](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html#updater) |
| to train a linear model on the |
| [Avazu](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#avazu) click-through-prediction dataset. |
| |
| The example also shows how to perform distributed training with the sparse feature. |
| |
| - `python train.py` |
| |
| Notes on Distributed Training: |
| |
| - For distributed training, please use the `../../tools/launch.py` script to launch a cluster. |
| - For example, to run two workers and two servers with one machine, run `../../../tools/launch.py -n 2 --launcher=local python train.py --kvstore=dist_async` |
