InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards. Learn more →
Graphsignal-python Alternatives
Similar projects and alternatives to graphsignal-python
-
-
Scout Monitoring
Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.
-
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. 📈
-
-
SearchWithOpenAI
Quick start. Index multiple documents in a repository using HuggingFace embeddings. Save them in Chroma and / or FAISS for recall. Choose OpenAI or Azure OpenAI APIs to get answers to your questions - Q&A with OpenAI and Azure OpenAI.
-
-
-
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.
-
InfluxDB
Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.
-
-
-
-
-
graphsignal-python discussion
graphsignal-python reviews and mentions
-
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.
-
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!
-
[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.
-
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.
-
[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!
-
[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.
-
[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
-
A note from our sponsor - InfluxDB
www.influxdata.com | 13 Sep 2024
Stats
graphsignal/graphsignal-python is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of graphsignal-python is Python.
Popular Comparisons
- graphsignal-python VS whylogs
- graphsignal-python VS PhoneTracer
- graphsignal-python VS SearchWithOpenAI
- graphsignal-python VS second-brain-agent
- graphsignal-python VS metaflow
- graphsignal-python VS tensor-sensor
- graphsignal-python VS Imaginer
- graphsignal-python VS langchain-cli
- graphsignal-python VS barectf
- graphsignal-python VS Keras