45 points by distrib_parall 1 year ago flag hide 15 comments
distributed_guru 1 year ago next
Fascinating article! The concept of distributed data parallelism has been a game changer for ML model training. Kudos to the team for making this a reality.
parallel_dave 1 year ago next
Agreed! I've been exploring DDP for a while now, and the results are impressive. It's amazing how we can now utilize multiple machines simultaneously to train large ML models within a reasonable time.
mpi_magician 1 year ago next
Indeed, scaling ML model training has never been easier with the help of libraries like Horovod and DDP. Exciting times for AI research!
gradient_girl 1 year ago next
I noticed a speed improvement when using horizontal scaling with DDP across multiple nodes. Vertical scaling worked well initially, but soon reached its limits.
network_ninja 1 year ago next
I've seen similar scaling benefits with various compute clusters and different ML workloads. It's impressive to see this paradigm shift in the industry.
efficient_eric 1 year ago next
@network_ninja, are there any specific compute clusters you recommend using for distributed ML tasks? I've heard great things about the Kubernetes based platforms for this purpose.
cloud_carl 1 year ago next
@efficient_eric, I've had a great experience with cloud-based services like AWS SageMaker, Google AI Platform, and Azure ML. All of these provide customizable ML-oriented computation and network settings, including GPU support.
deep_learner24 1 year ago prev next
Just got started learning about DDP. Great to see the real-life success stories! I'm looking forward to implementing it in my workflow.
data_dude 1 year ago next
Setting up the environment for distributed training can be tricky, but there are many community tutorials and resources that can help make this process smoother.
profiler_pete 1 year ago next
Thanks for the resource, @github_god! It looks like a very informative introduction. Bookmarking it for future use.
ml_mentor 1 year ago next
@profiler_pete, it's essential to understand teh nuances involved with DDP's replicated training and individual variable updates. That's where the real magic happens!
dist_dennis 1 year ago next
This is a fantastic discussion! It's crucial to take a step back and admire the progress made in the last couple of years with distributed ML. Thank you, everyone, for the sharing of knowledge and resources! #progress
github_god 1 year ago prev next
Here's a widely-used MPI tutorial that may help you set up DDP: [link] (www.example.com/mpi_tutorial)
parallel_paul 1 year ago next
Nice find, @github_god. I've been using this same tutorial with great success in my recent projects.
cpu_crusher 1 year ago next
Excellent feedback, @parallel_paul. I'm using DDP with the help of the Pytorch distributed library to build highly parallel model training processes.