81–90 of 99 posts
Slightly off-topic but I'm curious: Nvidia Volta is advertised as having "tensor cores" - what does it take for a programmer to use them? Will typical Tensorflow or Cafe code take advantage of it? Or should we wait for some new optimized version of ML frameworks?
Yes, the support should already be there for both frameworks.
Hi guys, Dillon here from Paperspace ( https://www.paperspace.com ). We are a cloud that specializes in GPU infrastructure and software. We launched V100 instances a few days ago in our NY and CA regions and its much less expensive than AWS. Think of us as the DigitalOcean for GPUs with a simple, transparent pricing and effortless setup & configuration: AWS: $3.06/hr V100* Paperspace: $2.30 /hr or $980/month for dedi…
Your pricing page notably omits transfer pricing. Do you have free bandwidth between yourself and AWS/GCP/Azure or do you peer at any major exchanges? Getting the data into and out of compute services is the most difficult part financially, at least in my experience.
You can never forget that this is entirely because of compute services ripping you off, not because they're providing a valuable service in return for the transfer pricing.
Earlier quoted context omitted.
Your pricing page notably omits transfer pricing. Do you have free bandwidth between yourself and AWS/GCP/Azure or do you peer at any major exchanges? Getting the data into and out of compute services is the most difficult part financially, at least in my experience.
>Getting the data into and out of compute services is the most difficult part financially, at least in my experience. You can never forget that this is entirely because of compute services ripping you off, not because they're providing a valuable service in return for the transfer pricing.
Even their "direct connect" services cost more than my transit does.
Earlier quoted context omitted.
Your pricing page notably omits transfer pricing. Do you have free bandwidth between yourself and AWS/GCP/Azure or do you peer at any major exchanges? Getting the data into and out of compute services is the most difficult part financially, at least in my experience.
Dan here (also Paperspace team). Totally agree that transfer costs are a significant pain point which is why we do not charge for it. We can peer with other providers (eg with AWS we can leverage Direct Connect directly from our datacenters) but most of our customers don't implement this unless they're moving major traffic.
Here's my results: Testing new Tesla V100 on AWS. Fine-tuning VGG on DeepSent dataset for 10 epochs. GRID 520K (4GB) (baseline): * 780s/epoch @ minibatch 8 (GPU saturated) V100(16Gb): * 30s/epoch @ minibatch 8 (GPU not saturated) * 6s/epoch @ minibatch 32 (GPU more saturated) * 6s/epoch @ minibatch 256 (GPU saturated)
Thanks! Curious how this would scale on the 8x or 16x instances
Earlier quoted context omitted.
Thanks! Curious how this would scale on the 8x or 16x instances
what do you mean? 8 or 16 GPUs? That's require changing the code to use distributed tensorflow...
More details in my blog post at https://aws.amazon.com/blogs/aws/new-amazon-ec2-instances-wi...
This post states, "In order to take full advantage of the NVIDIA Tesla V100 GPUs and the Tensor cores, you will need to use CUDA 9 and cuDNN7." What version of TensorFlow does it use? From what I can tell, TensorFlow doesn't fully support the latest versions yet.
Hmm just tried to spool up a p3.2xlarge in Ireland but hit an instance limit check (it's set at 0), went to request a service limit increase but P3 instances are not listed in the drop down box :(
Hmm just tried to spool up a p3.2xlarge in Ireland but hit an instance limit check (it's set at 0), went to request a service limit increase but P3 instances are not listed in the drop down box :(
Same problem here and it does not seem to be zone specific. I wonder how others worked around this ...
Random question: Why are we still using mostly GPUs for computation rather than CPUs custom-designed for ML tasks?
ML might be a bit of a moving target though.