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Top 23 Python GPU Projects
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DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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scalene
Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
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ImageAI
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
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server
The Triton Inference Server provides an optimized cloud and edge inferencing solution. (by triton-inference-server)
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skypilot
SkyPilot: Run LLMs, AI, and Batch jobs on any cloud. Get maximum savings, highest GPU availability, and managed execution—all with a simple interface.
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nvitop
An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.
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jittor
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.
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pygraphistry
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
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jetson_stats
📊 Simple package for monitoring and control your NVIDIA Jetson [Orin, Xavier, Nano, TX] series
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fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
Project mention: Can we discuss MLOps, Deployment, Optimizations, and Speed? | /r/LocalLLaMA | 2023-12-06DeepSpeed can handle parallelism concerns, and even offload data/model to RAM, or even NVMe (!?) . I'm surprised I don't see this project used more.
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
Yes, this is coming! Myself and others at OctoML and in the TVM community are actively working on multi-gpu support in the compiler and runtime. Here are some of the merged and active PRs on the multi-GPU (multi-device) roadmap:
Support in TVM’s graph IR (Relax) - https://github.com/apache/tvm/pull/15447
I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md
Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).
I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.
I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.
There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).
Somehow this experience so far was very disappointing.
(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)
Hi. I want to implement an image server similar to Photoprism using ImageAI to tag images based on objects and context. However I don't want to spend to much time working on the frontend, at first I were thinking about using Danbooru and use Flexbooru or the web interface on my phone. But it doesn't have any encryption or password protection (since the purpose of it is to be used as a public image board).
Project mention: CatBoost: Open-source gradient boosting library | news.ycombinator.com | 2024-03-05
Project mention: ChaiNNer – Node/Graph based image processing and AI upscaling GUI | news.ycombinator.com | 2023-07-19There is already an AI framework named Chainer: https://github.com/chainer/chainer
Project mention: Ask HN: Most efficient way to fine-tune an LLM in 2024? | news.ycombinator.com | 2024-04-04
Project mention: tf-quant-finance: NEW Derivatives and Hedging - star count:3911.0 | /r/algoprojects | 2023-06-10
Project mention: Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs | news.ycombinator.com | 2024-03-12That's why the authors recommend pipx for installing nvitop. I am not a sysadmin, but I prefer pipx over relying on the (often outdated) distro sources.
https://github.com/XuehaiPan/nvitop?tab=readme-ov-file#insta...
Project mention: Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs | news.ycombinator.com | 2024-03-12My favorite would be gpustat [1]. This shows the bare minimum amount of information to let's me know that the training has problems/running well
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
Project mention: Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs | news.ycombinator.com | 2024-03-12There’s also asitop https://github.com/tlkh/asitop
Project mention: Show HN: Running LLMs in one line of Python without Docker | news.ycombinator.com | 2023-10-04Hello 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.
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:
```
Extra fun: We find most enterprise/gov graph analytics work only requires 1-2 attributes to go along with the graph index, and those attributes often are already numeric (time, $, ...) or can be dictionary-encoded as discussed here (categorical, ID, ...)... so even 'tough' billion scale graphs are fine on 1 gpu.
Early, but that's been the basic thinking into our new GFQL system: slice into the columns you want, and then do all the in-GPU traversals you want. In our V1, we keep things dataframe-native include the in-GPU data representation, and are already working on the first extensions to support switching to more graph-native indexing for steps as needed.
Ex: https://github.com/graphistry/pygraphistry/blob/master/demos...
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2024-04-15.
Python GPU related posts
- Graph Data Fits in Memory
- Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
- Building a GPT Model from the Ground Up!
- Nvtop: Linux Task Monitor for Nvidia, AMD and Intel GPUs
- The "missing" graph datatype already exists. It was invented in the '70s
- SGLang: Fast and Expressive LLM Inference with RadixAttention for 5x Throughput
- Serving Your Private Code Llama-70B with API, Chat, and VSCode Access
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A note from our sponsor - InfluxDB
www.influxdata.com | 19 Apr 2024
Index
What are some of the best open-source GPU projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Pytorch | 77,544 |
| 2 | DeepSpeed | 32,447 |
| 3 | ivy | 14,016 |
| 4 | tvm | 11,130 |
| 5 | scalene | 11,125 |
| 6 | ImageAI | 8,383 |
| 7 | cupy | 7,753 |
| 8 | catboost | 7,731 |
| 9 | AlphaPose | 7,701 |
| 10 | server | 7,277 |
| 11 | chainer | 5,861 |
| 12 | skypilot | 5,602 |
| 13 | tf-quant-finance | 4,259 |
| 14 | nvitop | 3,899 |
| 15 | gpustat | 3,830 |
| 16 | pytorch-forecasting | 3,578 |
| 17 | jittor | 2,987 |
| 18 | asitop | 2,797 |
| 19 | leptonai | 2,419 |
| 20 | pygraphistry | 2,044 |
| 21 | jetson_stats | 2,018 |
| 22 | PyCUDA | 1,740 |
| 23 | torchrec | 1,719 |
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