11–20 of 47 posts
This is really cool; publishing an AMI seems like such a good win for you guys; configuration is done, you get paid as customers use it. Hopefully you'll see some good uptake.
Do you have a support for spot instances?
Do you have a support for spot instances?
I'd like to see something in the cloud thats bare-metal / full access to GPUs (Maybe a good idea to start one). For scaling higher with a very large number of GPUs, you'd need Infiniband but at some point there is going to be a bandwidth tradeoff.
It would be interesting if someone could run some benchmarks of these instances versus a physical server.
I've found amazon GPU instances to be really expensive (even the spot prices have been high recently), especially if you need it for longer runs for deep learning. The other issue is that the additional layers of virtualization create bandwidth overhead issues. I'd like to see something in the cloud thats bare-metal / full access to GPUs (Maybe a good idea to start one). For scaling higher with a very large number of…
I've found amazon GPU instances to be really expensive (even the spot prices have been high recently), especially if you need it for longer runs for deep learning. The other issue is that the additional layers of virtualization create bandwidth overhead issues. I'd like to see something in the cloud thats bare-metal / full access to GPUs (Maybe a good idea to start one). For scaling higher with a very large number of…
Looks like you can install their software on baremetal GPUs too: boost.bitfusion.io. Doesn't say if they have support for Infiniband though.
We're adding support for other clouds, particularly ones with higher-end GPUs so feedback like this is good to know.
That's some really cool tech. It seems like it's Linux only. Is there windows support planned? That would solve the problem with wanting to run code on the GPU within a Linux VM while the host is windows.
Windows support is coming in mid-April, stay tuned!
Earlier quoted context omitted.
Windows support is coming in mid-April, stay tuned!
That's great! Reading the documentation it seems there is no support for multiple clients and multiple GPUs (Many-To-Many), is there anything planned on that side?
Just go to the custom link at the bottom of the page, the link is: https://console.aws.amazon.com/cloudformation/home?region=us...
There you can select any number of clients and servers. For example: 5 clients and 1 server (many to one), or 5 clients to 5 servers (many to many).
Earlier quoted context omitted.
That's great! Reading the documentation it seems there is no support for multiple clients and multiple GPUs (Many-To-Many), is there anything planned on that side?
You can absolutely do that. That's actually one of the more interesting configurations: the ability to pool GPU systems. Just go to the custom link at the bottom of the page, the link is: https://console.aws.amazon.com/cloudformation/home?region=us... There you can select any number of clients and servers. For example: 5 clients and 1 server (many to one), or 5 clients to 5 servers (many to many).
Are there benchmarks/code examples for the Monster Machines?
Yes! Whenever you spin up one of our AMIs, there is a README that will guide you through a couple of simple examples. We are about to publish performance results on the monster machines in a few days, so watch out for it. Scaling depends on the compute density of the GPU workload, but in general we've seen pretty good results with 1) Deep learning (caffe) scaling to 16 GPUs (near native scaling with local GPUs, espec…
If you haven't, I could probably contribute some strong and weak scaling testcases.