11–20 of 35 posts
Also, much more minor grievance but I really dislike websites that don't work on my 15" laptop, what's going on here? http://i.imgur.com/q13lCLK.png
I'm not a big fan of taking the openness in machine learning and making it a web based product, for me the "whoa" moment from the new approachable machine learning frameworks is that I can train a Tensorflow network on my computer then embedded it in an Android/iOS app that'll work offline. Also, much more minor grievance but I really dislike websites that don't work on my 15" laptop, what's going on here? http://i.i…
> "Using GPUs inside of containers is a challenge. There are driver issues, system dependencies, and configuration challenges. It’s a new space that’s not well-explored, yet. There’s not a lot of people out there trying to run multiple GPU jobs inside a Docker container.” Er, Nvidia itself has an official Docker application which allows containers to interface with the host GPU, optimized for the deep learning use ca…
Algorithmia founder here. nvidia-docker is helpful but does not address all the issues with running GPU computing inside of docker. There are driver issues on the host OS, and the real challenge is running multiple GPU jobs inside of separate docker containers and sharing the GPU. I agree that building models is still definitely a big challenge, but the tooling and knowledge is getting better every day. Either way, o…
It is not the final solution for containerized GPU applications.
How does this compare in price to AWS GPU instances?
The service operates at a higher level than EC2, and pricing is calculated on a per-second of compute basis. Comparing prices is going to depend a lot on the specifics of your workload and your affinity for managing infrastructure.
Does anybody know if these "free APIs" are actually used to get "free training" for the API owner's models? I mean, is it free as in free beer or as in facebook?
Unless the users of this service then feed whether the answer given by the service was correct back into the service, I don't see how it would help to train their model.
Happy to be corrected by someone with a better understanding of the space.
The history of machine learning startups is littered with companies that thought a hosted web service was a good idea. The problem with this model is that big data, by definition, is costly to move. So if a managed service is not generating and storing the data you need to process with machine learning or deep learning (as you might conceivably with AWS), then you probably don't want to move your data to those algori…
There's a clear trend in the industry to increasingly rely on cloud services, so it seems reasonable that machine learning would follow the same trend. As long as the compute is in the same data center, data transfer is rarely the bottleneck for these kinds of deep learning algorithms, which is why we designed algorithmia to be able to operate anywhere -- on all the major cloud providers, as well as on premise.
> "Using GPUs inside of containers is a challenge. There are driver issues, system dependencies, and configuration challenges. It’s a new space that’s not well-explored, yet. There’s not a lot of people out there trying to run multiple GPU jobs inside a Docker container.” Er, Nvidia itself has an official Docker application which allows containers to interface with the host GPU, optimized for the deep learning use ca…
Earlier quoted context omitted.
The service operates at a higher level than EC2, and pricing is calculated on a per-second of compute basis. Comparing prices is going to depend a lot on the specifics of your workload and your affinity for managing infrastructure.
So, "more expensive than AWS".
