I know nothing about photography, but I'll just comment on this point:
> (I'm guessing this is a CM4/CM5) is a disaster for a camera board. Nobody wants a 20s boot every time you want to take a picture, cameras need to be near instantaneous.
You can boot an RPI in a couple hundred milliseconds.
Maybe when google actually did searches. A coworker today was unable to find a very straightforward quoted text on google, on duckduckgo the first few hits were exactly what we were looking for.
Ha! I have spent the last 2 years on this idea as a pet research project and have recently found a way of learning the wiring in a scalable fashion (arbitrary number of input bits, arbitray number of output bits). Would love to chat with someone also obsessed with this idea.
I also I'm very interested. I had played around a lot with Differentiable Logic Networks a couple of months ago and how to make the learned wiring scale to bigger number of gates. I had a couple of ideas that seemed to worked in a smaller scale, but that had trouble converging with deeper networks.
Isn't competition in free markets something Republicans believe in anymore?
Because forcing Americans to buy inferior locally-made products at a premium through artificial restrictions surely isn't that.
Free trade and globalization are also a pacifying force, by creating mutual dependencies between countries.
No but the point is valid. Say country A decides to protect its environment and hence imposes costly pollution control measures on its manufacturers. Country B meanwhile pollutes to the max. Country B's products are going to be cheaper than country A's. Therefore country A imposing a balancing tarrif on Country B (until they stop polluting) seems at least potentially reasonable.
11. notice that there's a unicode rendering error ("'" for apostrophe) on kernel_initializer and bias_initializer default arguments in the documentation, and wonder why on earth for such a high-level API one would want to expose lora_rank as a first class construct. Also, 3 out of the 5 links in the "Used in the guide" links point to TF1 to TF2 migration articles - TF2 was released 5 years ago.
Yep in Netflix case they pack bare-metal instances with a very large amount of containers and oversubscribe them (similar to what Borg reports: hundreds of containers per VM is common), so there are always more runnable threads than CPUs and your runqueues fill up.
I'm curious as to the capacity of the bare metal hosts you operate such that you can oversubscribe CPU without exhausting memory first or forcing processes to swap (which leads to significantly worse latency than typical scheduling delays). My experience is that most machines end up being memory bound because modern software—especially Java workloads, which I know Netflix runs a lot of—can be profligate memory consumers.
Workloads tend to average out if you pack dozens or hundreds into one host. Some need more CPU and some need more memory, but some average ratio emerges ... I like 4GB/core.
Yep. In Netflix case each Titus host can run hundreds of containers per bare-metal instance at any given time. One advantage of running a multi-tenant platform like this is that you get better observability on multi-tenancy issues since you're doing the scheduling yourself and know who is collocated with who. It's much harder to debug noisy-neighbor issues when it's happening on the cloud provider side and your caches get thrashed by random other AWS customers.
One thing I was pitching internally when advocating for this platform is that when you have the scale to run it for the economics to make sense, you can reclaim some of AWS margins instead of having your cold tiny VMs subsidize other AWS customers higher perf. If you run the multi-tenant platform yourself, you can oversubscribe every app in a way that makes sense for your business and trade latency or throughput of software for $ on a per-container basis, so you can make much more granular and optimal decisions globally. VS having each team individually right-size their own app deployed on VMs and sharing CPU caches with randos.
I remember once at Netflix we investigated a weird latency issue on a random load balancer instance and got AWS involved: it turned out to be a noisy-neighbor on the underlying VM that gets chopped up into multiple customer-facing LB instances.
> (I'm guessing this is a CM4/CM5) is a disaster for a camera board. Nobody wants a 20s boot every time you want to take a picture, cameras need to be near instantaneous.
You can boot an RPI in a couple hundred milliseconds.