81–90 of 114 posts
Aren't AWS, Azure and GCP all comparatively expensive? They are good for certain workloads and it's good that this project exists, but if you wanted cheap cloud resources, you'd probably need to look at one of the smaller cloud vendors: Hetzner, Scaleway, DigitalOcean, Vultr, Contabo and others. Of course, if you need GPUs, things can get limiting.
(my hobby project)
SkyPilot devs here, happy to answer any questions. GitHub repo (Apache 2 license): https://github.com/skypilot-org/skypilot Getting started is easy: $ pip install "skypilot[aws,gcp,azure]" # Pick your clouds $ sky check $ sky launch
Earlier quoted context omitted.
That's true. Most ML algorithms have the iterate-until-converge pattern. How about some tasks like hyper parameter tuning or trying out different algorithms against the same data set? Those can be run in parallel. ML research should aim to produce more parallel algorithms.
state of the art models don't fit on a single gpu anymore, not for a while
I realize this isn't always entirely decoupled in certain online learning approaches. I don't work in ML, am certainly not an expert, and am genuinely curious where this space is at now in terms of hardware requirements for SOTA methodologies these days, especially inference phase HW requirements for just running stuff that's out there.
I remember when the "cloud" hype was first beginning to make a splash. A pillar selling point was "savings". "Reduce your costs", they said. "Don't worry about those expensive hardware costs", or management. "Only pay for what you need", they said. Until everyone was hooked and the tooling was everywhere. Even just putting the pricing for hardware/services aside, the bandwidth charges alone are quite amazing. Most ha…
Most people don’t bother refactoring their application and leave whatever IIS garbage they have running on windows 20xx in a cloud instance (to say nothing about the windows licensing cost). Also universities don’t have the same cost models as companies. Their labs have less money - except those institutions with large endowments - but they all have access to near limitless “slave” labor in the form of grad students…
We’re using SkyPilot and Skyplane at Berkeley and the Department of Defense to scale our damage assessment from satellite imagery machine learning models [0] to run for all of Ukraine, all the time! We’re able to detect instances of war crimes rapidly and use them to help plan the reconstruction of Ukraine. [0] https://xview2.org/
I remember when the "cloud" hype was first beginning to make a splash. A pillar selling point was "savings". "Reduce your costs", they said. "Don't worry about those expensive hardware costs", or management. "Only pay for what you need", they said. Until everyone was hooked and the tooling was everywhere. Even just putting the pricing for hardware/services aside, the bandwidth charges alone are quite amazing. Most ha…
Most people don’t bother refactoring their application and leave whatever IIS garbage they have running on windows 20xx in a cloud instance (to say nothing about the windows licensing cost). Also universities don’t have the same cost models as companies. Their labs have less money - except those institutions with large endowments - but they all have access to near limitless “slave” labor in the form of grad students…
I remember when the "cloud" hype was first beginning to make a splash. A pillar selling point was "savings". "Reduce your costs", they said. "Don't worry about those expensive hardware costs", or management. "Only pay for what you need", they said. Until everyone was hooked and the tooling was everywhere. Even just putting the pricing for hardware/services aside, the bandwidth charges alone are quite amazing. Most ha…
"Simplify to succeed and Complicate to profit" -- (likely) Tim Ferris. I sat out of the cloud hoopla and it cost me opportunities - Now, I am dragging my feet into it to be employable.. but the Anti-Cloud (River?) is going to be a concept. 39 signals already did a blog post and change in strategy. I look forward to the economically lean years of 2023 and 2024 and HN front page articles about how moving away from Clou…
This. I work on sensors, signal processing and multi-sensor data and could always get a local prototyping rig. But the cloud got so big that I have to be at least somewhat comfortable with working in it just to stay employable.
Before sharing anything I check that it works in a Jupyter notebook running in a docker hosted on AWS getting data from some cloud bucket. But for real work I switch to Vim on a local machine with a few TBs of sensor data to experiment on and enjoy an order of magnitude speed improvement.
> I look forward to the economically lean years of 2023 and 2024 and HN front page articles about how moving away from Cloud and owning your own server is saving 'millions'.
I think there is a strong business case for this. Probably not fully off-cloud, but streamlining cloud portions for cost efficiency and going away from keeping everything in the cloud "because zero ingress costs". My 2c.
I remember when the "cloud" hype was first beginning to make a splash. A pillar selling point was "savings". "Reduce your costs", they said. "Don't worry about those expensive hardware costs", or management. "Only pay for what you need", they said. Until everyone was hooked and the tooling was everywhere. Even just putting the pricing for hardware/services aside, the bandwidth charges alone are quite amazing. Most ha…
I've been using SkyPilot for 3 months within an ML platform project and it is exactly as awesome as it sounds. The overall experience for launching and managing compute is thoughtful and ergonomic in a way that we've been conditioned not to expect from cloud compute (let alone GPU compute). It was a real shock to start using it, and now every time I need to jump back into the weeds of a cloud provider's APIs I'm remi…
I remember when the "cloud" hype was first beginning to make a splash. A pillar selling point was "savings". "Reduce your costs", they said. "Don't worry about those expensive hardware costs", or management. "Only pay for what you need", they said. Until everyone was hooked and the tooling was everywhere. Even just putting the pricing for hardware/services aside, the bandwidth charges alone are quite amazing. Most ha…
Reducing cost was always about 1) getting rid of the hardware infrastructure team and 2) getting rid of the tax of having to think about the hardware as a CIO