THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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| work num | server num | Per Node FPS(pic/s) | Scaling Efficiency |
|---|---|---|---|
| 8 | 8(worker and server share node) | 19.87 | 67.81% |
| 8 | 8 | 27.3 | 93.17% |
| 8 | 4 | 22.7 | 77.47% |
| 8 | 2 | 11.11 | 37.90% |
Command line: python tools/launch.py -n 8 -s <server_num> --launcher ssh -H hosts python example/image-classification/train_vgg16.py --kv-store dist_sync
Following is the result of MXNet multinode with mpi allreduce supported from our proof of concept (ready):
| Node Num | Per Node FPS(pic/s) | Scaling Efficiency |
|---|---|---|
| 8 | 27.76 | 94.74% |
Command line: mpirun -n 8 -ppn 1 -machinefile hosts python example/image-classification/train_vgg16.py --kv-store dist_sync_mpi
MPI Allreduce's good scalability comes from the tremendous communication time decrease compared with parameter server if not allocated enough server num.
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