robot VS horovod

(February 28) - Unifying all Machine Learning Frameworks. This is a hands-on interactive coding session and live demo. We will explain how Ivy is solving an ML unification problem.

In this talk, we will show how unifying all Machine Learning (ML) frameworks could save everybody a HUGE amount of time and energy. Through interactive coding sessions and live demos, we will explain how Ivy (checkout lets-unify.ai) is solving this unification problem. We will focus on demos using Ivy’s 3D vision and robotics libraries, solving 3D robotic navigation and perception tasks in a 3D simulator, all in real-time. Checkout https://github.com/ivy-dl/robot for examples! Finally, we will explore how you can join and contribute to the growing Ivy community, and help us in our mission to truly unify all ML frameworks once and for all.

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.

[[email protected]] match_arg (utils/args/args.c:163): unrecognized argument quiet [[email protected]] HYDU_parse_array (utils/args/args.c:178): argument matching returned error [[email protected]] parse_args (ui/mpich/utils.c:1639): error parsing input array [[email protected]] HYD_uii_mpx_get_parameters (ui/mpich/utils.c:1691): unable to parse user arguments [[email protected]] 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.