[HN Gopher] Show HN: Running LLMs in one line of Python without ...
___________________________________________________________________
Show HN: Running LLMs in one line of Python without Docker
Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We
are building a platform to run any AI models as easy as writing
local code, and to get your favorite models in minutes. It's like
container for AI, but without the hassle of actually building a
docker image. We built and contributed to some of the world's most
popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes,
etc. We also managed hundreds of thousands of computers in our
previous jobs. And we found that the AI software stack is usually
unnecessarily complex - and we want to change that. Imagine if you
are a developer who sees a good model on github, or HuggingFace. To
make it a production ready service, the current solution usually
requires you to build a docker image. But think about it - I have a
few python code and a few python dependencies. That sounds like a
huge overhead, right? lepton.ai is really a pythonic way to free
you from such difficulties. You write a simple python scaffold
around your PyTorch / TensorFlow code, and lepton launches it as a
full-fledged service callable via python, javascript, or any
language that understands OpenAPI. We use containers under the
hood, but you don't need to worry about all the infrastructure nuts
and bolts. One of the biggest challenge in AI is that it's really
"all-stack": in addition to a plethora of models, AI applications
usually involves GPUs, cloud infra, web services, DevOps, and
SysOps. But we want you to focus on your job - and we take care of
the rest "boring but essential" work. We're really excited we get
to show this to you all! Please let us know your thoughts and
questions in the comments.
Author : jiayq84
Score : 36 points
Date : 2023-10-04 16:37 UTC (6 hours ago)
(HTM) web link (www.lepton.ai)
(TXT) w3m dump (www.lepton.ai)
| johnnyo wrote:
| How is data secured and protected?
|
| Is the company collecting all my prompts and responses?
|
| I don't see a privacy policy or anything like that linked on the
| main page.
| jiayq84 wrote:
| Thanks - the policies are listed here:
| https://www.lepton.ai/policies
|
| we'll put a link on our homepage.
|
| In short - we do not collect, record, or log any of your
| prompts and responses. They are computed in memory, returned
| and discarded on the fly.
| brucethemoose2 wrote:
| llama.cpp (and derivative projects) is quickly becoming SOTA for
| many use cases, and it basically has zero dependencies.
|
| Kobold.cpp, for example, provides an entire web UI and API with
| python, and _3_ python packages (numpy, sentencepiece, and gguf
| which is the llama.cpp library). The llm itself is a single file
| you can get with curl or whatever. It takes less than a minute to
| compile against the native CPU /acclerator architecture, with
| nothing but the GPU libs themself, which nets better performance
| than a generic binary distribution.
|
| ...Its not "one line" I guess, but I can hardly imagine a simpler
| setup. It doesn't really need docker or a fancy container.
| bddppq wrote:
| lepton is at a different layer comparing to llama.cpp, in fact
| for LLM model files that are of GGUF format, it's using
| llama.cpp (ctransformers to be precise) as the execution engine
| jiayq84 wrote:
| Thanks - we definitely agree that llama.cpp is great. Big fan
| of their optimizations. We are more or less orthogonal to the
| engines though - in the sense that we serve as the
| infra/platform to run and manage those implementations easily.
| For example, we support running a wider range of models - for
| example sdxl is one single line too:
|
| lep photon run -n sdxl -m hf:stabilityai/stable-diffusion-xl-
| base-1.0 --local
|
| It's really about how to productize a wide range of models as
| easy as possible.
| brucethemoose2 wrote:
| SDXL is indeed a monster to install and setup. The UIs are
| even worse.
|
| IDK if the GPL license is compatible with your business, but
| I wonder if you could package Fooocus or Fooocus-MRE into a
| window? Its a hairy monster to install and run, but I've
| never gotten such consistently amazing results from a single
| prompt box + style dropdown box (including native HF
| diffusers and other diffusers-based frontends). The automatic
| augmentations to the SDXL pipine are amazing:
|
| https://github.com/MoonRide303/Fooocus-MRE
| jiayq84 wrote:
| Oh wow yeah, that is a beast. Let me give it a shot.
| hiatus wrote:
| How does this compare to ollama.ai
| ideal0227 wrote:
| Oollama.ai focuses on making it as easy as possible to run
| models locally. We aim to provide a seamless experience that
| feels the same whether you're developing locally or deploying
| remotely for production.
|
| declaimer: work at Lepton AI.
| hiatus wrote:
| I've been using a remote ollama server with a local jupyter
| notebook. The langchain configuration allows me to specify
| the ollama host. So I can develop locally with remote models.
| I guess I still don't see the difference. Does lepton
| decouple the HTTP server from the model backend?
| timsuchanek wrote:
| Looks interesting! Your llama 2 demo is unfortunately down:
| https://imgur.com/a/MLw6dAk
| jiayq84 wrote:
| Great catch! Our cloud machine encountered a cuda error (the
| GPU fell off PCIe) - had to restart it. It's back to normal
| now.
|
| All the more reason to have a managed version of services :)
| [deleted]
| [deleted]
| swyx wrote:
| congrats yangqing et al! i was really impressed by your llama2
| demo https://llama2.lepton.run/ where you showed that you were
| the "fastest llama runners"
| (https://twitter.com/swyx/status/1695183902770614724). definitely
| needed for model hosting infra.
| jiayq84 wrote:
| Thanks so much for the warm words!
| [deleted]
| jiayq84 wrote:
| To show some actual coding examples, We have made the python
| library open-source at https://github.com/leptonai/leptonai/.
| With it, launching a common HuggingFace model is as simple as a
| one liner. For example, if you have a GPU, Stable Diffusion XL is
| as simple as:
|
| pip install -U leptonai
|
| lep photon run -n sdxl -m hf:stabilityai/stable-diffusion-xl-
| base-1.0 --local
|
| And you have a local OpenAPI server that runs it! Go to
| http://0.0.0.0:8080/docs, or use your favorite OpenAPI client.
|
| We've been building AI API services using such tools ourselves.
| The easiest way to try out Lepton is to head to
| https://lepton.ai/playground and use our API service for popular
| models: Stable Diffusion, LLaMA, WhisperX, and other interesting
| showcases
|
| We are proud of our performance. For example, we have probably
| the fastest LLaMA 7B and 70B model APIs, and it costs $0.8 to run
| 1 million tokens inference - we believe it's the most affordable
| one in the market. In addition, during the open beta phase,
| calling these services is free when you sign up for the Lepton AI
| platform.
|
| Under the hood, we wrote a platform to allow you to run things
| easily on the cloud with ease. For example, if you find Pygmalion
| to be a great conversation model but you don't have a GPU, use
| lepton's Remote() capability to launch a service:
|
| from leptonai import Remote
|
| pygmalion = Remote("hf:PygmalionAI/pygmalion-2-7b",
| resource_shape="gpu.a10")
|
| Wait a few minutes for the model to be downloaded and run, and
| you can now use it as if it were a standard python function:
|
| print(pygmalion.run(inputs="Once upon a time",
| max_new_tokens=128))
|
| If you are interested in the operational details, you can find
| fine-grained controls at https://dashboard.lepton.ai/ as a fully
| managed platform - we also support BYOC (bring your own compute)
| if you are an enterprise needing more autonomy over
| infrastructure.
| rw2 wrote:
| Is not having to build a dockers image worth $100 a month? I do
| find server setup to be a pain but I think if I will use a model
| for a year, I can take the time(3-4 hours) to set it up. Only
| with constant switching of models would I use a service like
| this.
|
| I never set up bigger models like LLAMA on servers. Other hacker
| news people can chime in.
| [deleted]
| jiayq84 wrote:
| It's not only about "building a docker" but also maintaining
| multiple models, multiple environments and a lot of users.
| Imagine there is a group of engineers each needing to deploy
| their own models: one needs tensorflow 1.x, one needs
| tensorflow 2.x, one needs pytorch and one needs a very strange
| combination of dependencies. Trust me, things get complex very
| easily:
|
| https://github.com/leptonai/examples/blob/main/advanced/whis...
|
| I definitely agree that for a fixed use case, building a docker
| once and for all is probably the simplest and best approach.
| However, it quickly gets more complex and out of hand.
|
| Also the basic plan is free for independent developers. You
| don't need to pay more than as if you were using EC2 instances,
| but with the platform convenience - we definitely hope it's
| worth it!
| sbierwagen wrote:
| Llama 70B but no Falcon 180B?
| bddppq wrote:
| Are there some examples(prompts) that Falcon 180B is performing
| better than Llama 70B?
| moffkalast wrote:
| Hardly anyone can even run a 70B model, let alone 180B. Any
| anecdata will be extremely rare.
| jiayq84 wrote:
| In theory one can have 640G = 8 * 80G A100s memory and
| launch it. 180B Falcon with fp16 will be 360G, so there
| would be enough memory. It's definitely going to be very
| expensive indeed.
___________________________________________________________________
(page generated 2023-10-04 23:01 UTC)