wfalcon

6 ms·

1.
LitServe: Build custom AI inference engines(github.com)
1 points by wfalcon 11mo ago|0 comments
2.
Build your own AI model inference engines(github.com)
1 points by wfalcon 1y ago|0 comments
3.
Agent sandbox: safe, persistent cloud environments for agents(lightning.ai)
2 points by wfalcon 1y ago|1 comments
4.
by wfalcon 2y ago
Studios are a great google colab alternative with: - persistent storage (setup env and data persists across restarts) - free ssh and connect your local IDE - CPU setup, GPU run (do setup work on CPUs and switch to run on a GPU when ready) -
5.
Lightning AI hub: Production AI in your cloud in minutes(lightning.ai)
16 points by wfalcon 2y ago|0 comments
6.
Deploy dedicated DeepSeek 32B on L40 GPUs ($8/hour)(lightning.ai)
19 points by wfalcon 2y ago|6 comments
7.
I'm crazy: I deployed DeepSeek in my company VPC in <24 hours without Kubernetes(lightning.ai)
5 points by wfalcon 2y ago|2 comments
8.
Deploy a custom Llama 3 API in 15 lines of code(lightning.ai)
1 points by wfalcon 2y ago|0 comments
9.
Thunder – A new compiler for PyTorch that can use many device executors at once(github.com)
11 points by wfalcon 3y ago|1 comments
10.
Remote GPU development with VSCode [video](youtube.com)
1 points by wfalcon 3y ago|0 comments
11.
by wfalcon 3y ago
this is also what our (Lightning AI) lit-gpt library does. https://github.com/Lightning-AI/lit-gpt
12.
by wfalcon 4y ago
welcome to Lightning AI! Mike, super excited to drive the future of ML together.
13.
Interview with Facebook AI SWAV first author, (SOTA in Self-supervised learning)(youtube.com)
1 points by wfalcon 6y ago|0 comments
14.
5-Minute deep dive into multi-GPU training(youtube.com)
1 points by wfalcon 6y ago|0 comments
15.
by wfalcon 6y ago
Just to be 100% clear. We want people to build on Lightning and we want companies to deliver value and products for their users. We place no limitations on how Lightning is use or what products people will build. Our in-process patent portf
16.
by wfalcon 6y ago
Just to be 100% clear. We want people to build on Lightning and we want companies to deliver value and products for their users. We place no limitations on how Lightning is use or what products people will build. Our in-process patent portf
17.
by wfalcon 6y ago
No! we want people to build on Lightning and we want companies to deliver value and products for their users.
18.
by wfalcon 6y ago
No! we want people to build on Lightning and we want companies to deliver value and products for their users.
19.
by wfalcon 6y ago
No, we support other frameworks too! Just that if you use lightning you'll have zero friction. Well as with the others... you might run into issues inherent in the other framework's hard to work with designs.
20.
by wfalcon 6y ago
maybe determined should come up with its own API instead of copying Lightning's :) Not a nice move for the opensource spirit. Also, pretty sure it's a violation of our patent and 100% copyright infringement.
21.
by wfalcon 6y ago
we highlight these issues in our docs explicitly. https://pytorch-lightning.readthedocs.io/en/latest/tpu.html#...
22.
by wfalcon 6y ago
I 100% agree. We don't want to misrepresent TPU support. In fact, we explicitly warn users in our docs. Open to suggestions about how we can communicate this much better to our users. We just need to be a part of the effort to help bri
23.
by wfalcon 6y ago
super cool! One of the professors at my lab at NYU CILVR (Kyle Cranmer) i believed was super involved with this. Will definitely sync up with him! Thanks for the heads up!
24.
by wfalcon 6y ago
Like I said... pytorch and tensorflow team are working very hard to make this work. And yes, it's not a 1:1 with tensorflow, but we're making progress very aggressively.
25.
by wfalcon 6y ago
The grid is how you harness and distribute power and electricity.... like that coming from lightning :) Second, electricity was a great new technology (ie: AI), but you needed the power grid to make it usable - that's grid AI.
26.
by wfalcon 6y ago
it's honestly just a different approach. ML ops is adjusting your code to work with the cloud and managing all that. For us is basically integrating clouds directly into your code so the barrier disappears and the cloud providers becom
27.
by wfalcon 6y ago
We currently have google engineers training on TPU pods with PyTorch Lightning. TPU support is VERY real... but yes, sometimes it breaks but PyTorch and Google are working very hard to bridge that gap. But we have dedicated partners at Goog
28.
by wfalcon 6y ago
Lightning is built for researchers by researchers... we've already taken a much different approach. 100% agree with you that going the other way is likely not the best approach. Lightning + Grid elevates and turns non experts closer to
29.
by wfalcon 6y ago
Well... we're not really an ML ops startup haha. I am ALSO pessimistic about ML Ops startups. But calling Grid an ML Ops startup is like calling Lightning Keras... maybe at a quick blink it looks like that, but that's where the si
30.
by wfalcon 6y ago
yes! our goal is to completely remove any engineering from the AI research -> production lifecycle. Not just a marginal improvement on that experience but a 10x completely different approach.

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