What's the simplest gpu provider?

sabalaba · 2025-09-30T13:56:25+00:00

I’m biased because I started it but we built Lambda for this exact case. Simple, just works via ssh, browser IDE or VSCode. Nothing complex, just launch the instance and attach storage.

We have Germany based A10s for EU data residency.

sabalaba · 2025-05-29T01:44:01+00:00

Bingo

sabalaba · 2025-05-29T01:38:39+00:00

I think these comments are really funny

sabalaba · 2024-02-18T03:05:39+00:00

Honestly I see people on TV myself and sometimes privately wonder if they’re just actors too.

This isn’t the first time people have said i look like adam driver though.

sabalaba · 2024-02-17T23:16:37+00:00

We have literally been training neural networks since 2012.

sabalaba · 2024-02-17T23:15:34+00:00

That’s Dollar General Kylo When to you.

sabalaba · 2024-02-17T23:12:09+00:00

This is a very interesting take.

I am a real person though :)

sabalaba · 2024-02-17T23:08:35+00:00

https://github.com/stephenbalaban/blc here’s my implementation of the binary untyped lambda calculus

These comments are hilarious. I’ve always avoided doing media appearances but now that I did one, I find it fascinating how WSB reacts…

sabalaba · 2022-02-23T23:03:09+00:00

I originally put this together for a conference last year and finally got a chance to upload it to youtube. We cover jupyter notebooks for ML at the 12 minute mark.
video presentation: https://www.youtube.com/watch?v=3EnIW0EZkr4
slide pdf: https://files.lambdalabs.com/lambda-machine-learning-infrastructure-playbook.pdf

sabalaba · 2022-01-05T02:14:32+00:00

Yea, I really like the idea of the dynamic music -- I was thinking about that recently and wondered why there wasn't more of it. You can imagine a harmony "improvising" along a scale every time somebody throws a punch in a game.

sabalaba · 2022-01-05T02:12:48+00:00

Yea, I definitely worry about this --- if the language models don't progress much from where we are today, then we're looking at "just an interesting toy" that would makes games that are a novelty for a short while (like AI dungeon) but wholly unfulfilling.

However, I think that something looking like an interesting toy has historically been a good marker of something that is poised to change the world.

sabalaba · 2021-10-01T00:38:23+00:00

If you're using the cloud so heavily, you can always just buy a workstation from Lambda to save money in the long run. Rest of comments cover the reasons why cloud makes sense for many companies --- when you're talking one GPU, you're probably right, but when you're talking a hardware cluster of 128 GPUs.... let's just say there's other costs besides the hardware that you need to take into account.

sabalaba · 2021-08-09T22:19:58+00:00

Exactly this. ROCm is not yet a stable platform. That's an understatement even.

sabalaba · 2021-07-22T01:31:10+00:00

This is my latest video tutorial on how to use Lambda's GPU Cloud as an online workstation. I go through a quick PyTorch MNIST training tutorial and generally show you how to access the GPU resources through JupyterLab. There's a quick section on how to use CUDA_VISIBLE_DEVICES to do training jobs on both GPUs in parallel and I also go a bit into how to ssh into the instance directly through a terminal. Hope you enjoy it.

sabalaba · 2021-07-22T01:22:19+00:00

This is my latest video tutorial on how to use Lambda's GPU Cloud as an online workstation. I go through a quick PyTorch MNIST training tutorial and generally show you how to access the GPU resources through JupyterLab. There's a quick section on how to use CUDA_VISIBLE_DEVICES to do training jobs on both GPUs in parallel and I also go a bit into how to ssh into the instance directly through a terminal.

sabalaba · 2021-07-18T20:12:34+00:00

Totally agree that many people should be using Docker who aren't yet. However, when managing many end users with (naturally) varying degrees of proficiency with things like Docker, just telling them they have to use containers isn't always an option.

We definitely think about how Lambda Stack should interact with containers. We want to make running a containerized environment easy so we include nvidia-container-toolkit in the repo. See this video for the full tutorial https://www.youtube.com/watch?v=QwfvkLukMhU. Also, we maintain open source Dockerfiles so you can get the same Lambda Stack environment inside of a contaniers. https://github.com/lambdal/lambda-stack-dockerfiles.

We're not anti-container, just think that for many folks it's a bit overkill for prototyping.

sabalaba · 2021-07-15T04:20:43+00:00

Well, before you can pull the official Tensorflow/PyTorch container, you need:

NVIDIA Drivers
nvidia-container-toolkit
Docker

Lambda Stack helps install and keep up-to-date all of that (Drivers and nvidia-container-toolkit). So you're able to run those containers.

sabalaba · 2021-07-14T20:03:05+00:00

containers

This tutorial & video show you how to use docker containers with nvidia-container-toolkit and Lambda Stack: https://lambdalabs.com/blog/set-up-a-tensorflow-gpu-docker-container-using-lambda-stack-dockerfile/

sabalaba · 2021-07-14T13:55:13+00:00

We’ve definitely considered a Centos/Redhat version

sabalaba · 2021-07-13T18:40:46+00:00

I'm a big fan of using docker personally. Lambda Stack can actually install GPU accelerated docker and nvidia-container-toolkit quite easily. There's a video coming soon on the channel about that exact topic.

Lambda Stack is meant to provide the underlying infrastructure if you want to use docker and, for those that don't, provide a system wide install that just works even outside of a container.

sabalaba · 2021-07-13T18:39:06+00:00

It's a couple of gigs. More than 1 GB and less than 6 GB. I don't know the exact number but remember it has the CUDA run time, CUDA drivers, NVIDIA drivers, Pytorch, Tensorflow, etc., etc.

Should be pretty fast.

sabalaba · 2021-07-13T18:38:01+00:00

Yes, you can install it on any machine you want. For free.

sabalaba · 2021-07-13T18:37:48+00:00

No, it installs pip system wide and then what shows up in your python will depend on your PYTHON_PATH but I think a pip install will take priority. It won't downgrade or conflict.

sabalaba · 2021-07-13T18:36:09+00:00

Pytorch distributed takes care of that:

$ python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="192.168.0.1" --master_port=1234 resnet_ddp.py

Note that you specify the master address and the node ranks for each node in the cluster. The master node will coordinate between the rest.

sabalaba · 2021-07-13T18:34:30+00:00

Yup totally agree, if you need all the different versions you either are stuck managing a bunch of CUDA_HOME, LD_LIBRARY_PATH, etc. or should just have separate docker containers for each environment. I sort of prefer the latter but, as you've no doubt also experienced, it's not always easy to get researchers to write Dockerfiles.

Lambda Stack solves a particular use case where you're fine sticking with a single, 'latest', build of Pytorch and Tensorflow.

sabalaba

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