mpi4jax VS horovod

I've seen a couple of posts of folks using JAX for scientific computing (e.g. physics) workloads without much issue. The parallel primitives work just as well across multiple CPUs as they do on accelerators. If you're on a cluster, also worth looking into https://github.com/PhilipVinc/mpi4jax.

This series of books was actually purchased during the "Double Eleven: Readmoo Three Books 25% Off" promotion, following "Refreshing the Future". However, the content of this book is also quite interesting and kept me reading non-stop. As a tech geek, besides using Uber's services, I'm also interested in its many open-source projects. Whether it's the zap project or the machine learning platform Horovod, they are open-source projects that I really like.

Broke: using Horovod

And in case scaling is really important to you. May I suggest you look into Horovod?

Training is not the same as inference (doing the segmentation), so that scale is probably off by a lot. One or two orders of magnitude just depending on the specifics of what hardware you're running on, and your training and eval dataset would be several orders of magnitude smaller. FAANGs would parallelize that training as well (don't remember if UNet is inherently parallelizable for training) via their internal equivalent of Horovod, so they'll do a GPU-month worth of training in less than a day.

[mpiexec@pc-132-75.customer.ask4.lan] match_arg (utils/args/args.c:163): unrecognized argument quiet [mpiexec@pc-132-75.customer.ask4.lan] HYDU_parse_array (utils/args/args.c:178): argument matching returned error [mpiexec@pc-132-75.customer.ask4.lan] parse_args (ui/mpich/utils.c:1639): error parsing input array [mpiexec@pc-132-75.customer.ask4.lan] HYD_uii_mpx_get_parameters (ui/mpich/utils.c:1691): unable to parse user arguments [mpiexec@pc-132-75.customer.ask4.lan] main (ui/mpich/mpiexec.c:127): error parsing parameters I believe this is due to mpich being installed: https://github.com/horovod/horovod/issues/1637

Check out Horovod - https://github.com/horovod/horovod

If you just want to run tensorflow or pytorch for a Jupyter notebook, setting the environment shouldn't be difficult. I know that AWS has a marketplace of preconfigured images. However, you can go as advanced as setting up a cluster of gpu-equipped nodes to setup Horovod (https://github.com/horovod/horovod) to do distributed machine learning. Yes, there's a learning curve, but you cannot acquire this skillet any other way.

I'm the maintainer for an open source project called Horovod that allows you to distribute deep learning training (e.g., TensorFlow) on platforms like Spark.

You'll want to use horovod to run keras in a distributed system. Then use Slurm to manage the cluster and run the job.