Thanks for the clarification. gRPC is slow. We have in-house experiments showing on RDMA-capable networks an optimized implementation can achieve significant speed up over gRPC. And I bet Google's internal version is even faster.
MxNet has a highly efficient network stack that's open source; Caffe2 uses gloo, which is open source; CNTK primarily uses Open MPI, NCCL and soon NCCL2.0. I think it's fair that Google also open source the internal network stack because it is the key to scaling.
Most convolutional networks are not a stress test for scaling because the model size/computation ratio is too low. Use a speech model that has many fully connected layers, or VGG16/19, the communication cost will dominate, and that's when CNTK's 1-bit SGD and Block Momentum really shine.
Scalability is part of TensorFlow's claimed advantages. If someone adopts TF on their own cluster, would they get the same scalability story as marketed?
MxNet has a highly efficient network stack that's open source; Caffe2 uses gloo, which is open source; CNTK primarily uses Open MPI, NCCL and soon NCCL2.0. I think it's fair that Google also open source the internal network stack because it is the key to scaling.
Most convolutional networks are not a stress test for scaling because the model size/computation ratio is too low. Use a speech model that has many fully connected layers, or VGG16/19, the communication cost will dominate, and that's when CNTK's 1-bit SGD and Block Momentum really shine.
Again, I work at Microsoft.