book
graphsignal-python
book | graphsignal-python | |
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2 | 30 | |
183 | 200 | |
0.0% | 1.0% | |
2.7 | 8.1 | |
12 months ago | 21 days ago | |
Jupyter Notebook | Python | |
- | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
book
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[P] Using Sparsity & Clustering to compress your models: Efficient Deep Learning Book
We now have a new chapter focusing on sparsity and clustering, two advanced compression techniques that you can use to reduce the footprint of your model (size, latency, etc.) while retaining your model accuracy. You can read the chapter here, and go through the accompanying codelabs here.
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[P] Efficient Deep Learning Book
The goal is to introduce these ideas in a single place, without having to parse many papers, try to get a working code sample, and then spend time debugging. With the accompanying codelabs, we hope that our readers can make their models 4-20x smaller, faster, and better in quality.
graphsignal-python
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Show HN: Python Monitoring for LLMs, OpenAI, Inference, GPUs
We've built it for apps that use LLMs and other ML models. The lightweight Python agent autoinstruments OpenAI, LangChain, Banana, and other APIs and frameworks. Basically by adding one line of code you'll be able to monitor and analyze latency, errors, compute and costs. Profiling using CProfile, PyTorch Kineto or Yappi can be enabled if code-level statistics are necessary.
Here is a short demo screencast for a LangChain/OpenAI app: https://www.loom.com/share/17ba8aff32b74d74b7ba7f5357ed9250
In terms of data privacy, we only send metadata and statistics to https://graphsignal.com. So no raw data, such as prompts or images leave your app.
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Show HN: Python Monitoring for AI: LLMs, OpenAI, Inference, GPUs
Hi HN. I'm excited to share our AI-focused application monitoring and analytics for Python!
We've built it for apps that use LLMs and other ML models. The lightweight Python agent autoinstruments OpenAI, LangChain, Banana, and other APIs and frameworks. Basically by adding one line of code you'll be able to monitor and analyze latency, errors, compute and costs. Profiling using CProfile, PyTorch Kineto or Yappi can be enabled if code-level statistics are necessary.
Here is a short demo screencast for a LangChain/OpenAI app: https://www.loom.com/share/17ba8aff32b74d74b7ba7f5357ed9250
In terms of data privacy, we only send metadata and statistics to https://graphsignal.com. So no raw data, such as prompts or images leave your app.
We'd love to hear your feedback or ideas!
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[N] Monitor OpenAI API Latency, Tokens, Rate Limits, and More with Graphsignal
Here is a blog post with more info and screenshots: Monitor OpenAI API Latency, Tokens, Rate Limits, and More. And the GitHub repo.
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Monitor OpenAI API Latency, Tokens, Rate Limits, and More
Relying on hosted inference with LLMs, such as via OpenAI API, in production has some challenges. The use of APIs should be designed around unstable latency, rate limits, token counts, costs, etc. To make it observable we've built tracing and monitoring specifically for AI apps. For example, the OpenAI Python library is monitored automatically, no need to do anything. We'll be adding support for more libraries. If you'd like to give it try, see https://github.com/graphsignal/graphsignal or the docs.
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[N] Easily profile FastAPI model serving
We've added a simple way to profile any model serving endpoint, including FastAPI, to identify bottlenecks and make inference (incl. data processing) faster, especially for big models and data. Wanted to share it here in case someone is struggling with profiling and monitoring of deployed code and models. By default, generic Python profiler will automatically profile some of the inferences (and measure all inferences). You can also specify other profilers for PyTorch, TensorFlow, Jax and ONNX Runtime. All profiles and metrics will be available on the SaaS dashboard, no need to setup anything. A couple of links to get started: Repo: https://github.com/graphsignal/graphsignal FastAPI example: https://graphsignal.com/docs/integrations/fastapi/ Happy for any feedback!
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[P] Using Sparsity & Clustering to compress your models: Efficient Deep Learning Book
Thanks for sharing! That's a very timely topic. I've actually created a profiler to track and analyze inference optimizations, i.e. enable the optimize-verify-evaluate loop.
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[N] Accuracy-Aware Inference Optimization Tracking and Profiling
To address all these problems, we've built a tool to track inference optimizations, see how accuracy is affected, verify that the optimizations were applied and locate any bottlenecks for further improvements. All in one place.
- Show HN: Graphsignal – ML profiler to speed up training and inference
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