davidbuniat

8 ms·

31.
by davidbuniat 4y ago
One more thing regarding the compression -> you can both deeplake.link() to your raw data lake without touching it, or use deeplake.read() which preservers the compression as long as it matches with tensor default compression. https:&#x
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by davidbuniat 4y ago
There's indeed plenty of tools out there, so the next few years are very interesting in terms of seeing which approach gets adopted by the wider audience. Feedback from the community (inc. feedback from you!) is instrumental in designi
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by davidbuniat 4y ago
Ah, thank you so much for noticing this! this is a very important piece of feedback (we will fix it shortly). What we meant was the 10TB is the first tier of the enterprise plans, and the rest are more custom-billed (typically because those
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by davidbuniat 4y ago
As a matter of fact, I do agree - the CV space is less crowded (until now). That's hopefully where we come in! thanks a lot for the input, and thanks for trying us out. You know, it's always a work-in-progress, but we've actu
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by davidbuniat 4y ago
thanks for the context and links! I replied to your comment slightly above in one comment. :)
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by davidbuniat 4y ago
Re: HF - we know them and admire their work (primarily, until very recently, focused on NLP, while we focus mostly on CV). As mentioned in the post, a large part of Deep Lake, including the Python-based dataloader and dataset format, is ope
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by davidbuniat 4y ago
You can store your data either remotely or locally (see here on how https://docs.activeloop.ai/getting-started/creating-datasets... ). You can then visualize your datasets if their stored on our cloud, in AWS/GCP,
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by davidbuniat 4y ago
Thanks - it's very insightful to also hear your perspective as someone coming in from the BI space (if you have any more insights, please post them here, too). We have this internal joke where when one says their analytics is based on
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by davidbuniat 4y ago
"...I would argue that outside big tech, this is 0.1%." Fair point regarding the unlabeled/unstructured data. One could also argue that labeled data isn't going to be a prerequisite forever (see https://ai.fa
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by davidbuniat 4y ago
a very fair point, we do usually say that our main (possible) competition are the "traditional" data lakes! We've spent 4+ years designing this system to be specifically resolving the issue for unstructured AI data like video
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Launch HN: Activeloop (YC S18) – Data lake for deep learning
64 points by davidbuniat 4y ago|24 comments
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by davidbuniat 6y ago
That's pretty great! This is like mixpanel for devtools. How do you address security and privacy concerns on behalf of developers?
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by davidbuniat 6y ago
Oh nice, it's like open-source version of W&B, and seems much better than Tensorboard. How many experiments I can compare at the same time without crashing chrome? :)
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by davidbuniat 6y ago
We have been an early adopter at https://github.com/activeloopai/Hub . It helped me to quickly review large PRs that otherwise took us long time to understands the high-level picture.
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Show HN: Fast and simple access self-driving datasets(github.com)
2 points by davidbuniat 7y ago|1 comments
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Show HN: Simple and fast access to Waymo Open Dataset in Python(medium.com)
1 points by davidbuniat 7y ago|0 comments
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Show HN: Snark Hub is hosting nuScenes dataset for autonomous driving(medium.com)
1 points by davidbuniat 7y ago|0 comments
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Show HN: Hub – Serverless Scalable Numpy Array for Managing Datasets(github.com)
3 points by davidbuniat 7y ago|0 comments
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Meta Arrays can store ImageNet in a single array?(medium.com)
2 points by davidbuniat 7y ago|0 comments
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Training Object Recognition Model YOLO on Snark AI(blog.snark.ai)
2 points by davidbuniat 8y ago|0 comments
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Lightr – Worlds Smallest Smart Charger(indiegogo.com)
1 points by davidbuniat 8y ago|2 comments
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Show HN: Snark Hyper – Serverless Deep Learning and Hyper-Parameter Search(medium.com)
6 points by davidbuniat 8y ago|0 comments
53.
by davidbuniat 8y ago
Following your notation, consider our new price will be $x+y', where y' is the mining price difference. You are right my claim that y'<y is slightly weak (was based on "gap is not negligible" assumption, see belo
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by davidbuniat 8y ago
Lets agree that if the mining price goes up, it will be always cheaper than public cloud. Otherwise, you would have run mining on AWS/GCP and be profitable. In this scenario, we are still able to offer cheaper price to the customers an
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by davidbuniat 8y ago
That's what we count on as well.
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by davidbuniat 8y ago
In case of crypto downturn renting GPUs will be even cheaper. Actually, it will be a bigger win for our users and also for us.
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by davidbuniat 8y ago
Good suggestion! Our cheapest instances are actually perfect for learning ML/DL. We are also working on adding Jupyter support so it will be even easier. What do you think?
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by davidbuniat 8y ago
Yep, totally agree!
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by davidbuniat 8y ago
Great! Once you rent a P106 you have full access to the GPU, and we don't run mining or ML tasks. If you want to deploy large-scale computation and significantly reduce your costs, we can help by running mining at the same time under y
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by davidbuniat 8y ago
Great you asked this. We try to say that for privacy sensitive data processing (e.g. face recognition), we are eager to work closely with clients and hardware providers to ensure security and compliance to regulations such as Privacy Shield

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