graphsignal-python
metaflow
graphsignal-python | metaflow | |
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30 | 24 | |
200 | 7,644 | |
1.0% | 2.0% | |
8.1 | 9.2 | |
22 days ago | 2 days ago | |
Python | Python | |
Apache License 2.0 | 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.
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
metaflow
- FLaNK Stack 05 Feb 2024
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metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
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Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
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[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
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Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
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Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
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Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
What are some alternatives?
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
second-brain-agent - 🧠 Second Brain AI agent
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
Imaginer - Imagine with AI
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
Keras - Deep Learning for humans
kedro-great - The easiest way to integrate Kedro and Great Expectations
tensor-sensor - The goal of this library is to generate more helpful exception messages for matrix algebra expressions for numpy, pytorch, jax, tensorflow, keras, fastai.
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
PhoneTracer - Gets GPS location of phone numbers
dvc - 🦉 ML Experiments and Data Management with Git