co-tracker
ml-engineering
co-tracker | ml-engineering | |
---|---|---|
6 | 9 | |
2,435 | 9,853 | |
4.5% | - | |
6.9 | 9.7 | |
13 days ago | 5 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | Creative Commons Attribution Share Alike 4.0 |
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.
co-tracker
- What things are happening in ML that we can't hear oer the din of LLMs?
-
ML Engineering Online Book
Yes, I think this is very tractable for a first project. I've played around with using AI to do optical-only with pose detection models -- if I had to do it again, I would probably start with this model and try to get it running locally:
https://github.com/facebookresearch/co-tracker
This sounds like a perfect place for you to get started!
- FLaNK Stack Weekly 5 September 2023
-
CoTracker: A Revolutionary 2D Point Video Tracker
(arXiv) (GitHub)
-
Meta AI releases CoTracker, a model for tracking any points (pixels) on a video
LICENSE
Attribution-NonCommercial 4.0 International
https://github.com/facebookresearch/co-tracker/blob/main/LIC...
ml-engineering
- Accelerators
-
Gemma: New Open Models
There is a lot of work to make the actual infrastructure and lower level management of lots and lots of GPUs/TPUs open as well - my team focuses on making the infrastructure bit at least a bit more approachable on GKE and Kubernetes.
https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main
and
https://github.com/google/xpk (a bit more focused on HPC, but includes AI)
and
https://github.com/stas00/ml-engineering (not associated with GKE, but describes training with SLURM)
The actual training is still a bit of a small pool of very experienced people, but it's getting better. And every day serving models gets that much faster - you can often simply draft on Triton and TensorRT-LLM or vLLM and see significant wins month to month.
- FLaNK Stack 29 Jan 2024
-
ML Engineering Online Book
OK, the pdf is ready now: https://github.com/stas00/ml-engineering#pdf-version
-
Self train a super tiny model recommendations
this might be interesting: https://github.com/stas00/ml-engineering/blob/master/transformers/make-tiny-models.md
- The AI Battlefield Engineering – What You Need to Know
- Machine Learning Engineering Guides and Tools
What are some alternatives?
tapnet - Tracking Any Point (TAP)
slurm-mail - Slurm-Mail is a drop in replacement for Slurm's e-mails to give users much more information about their jobs compared to the standard Slurm e-mails.
paxml - Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
FLaNK-HuggingFace-BLOOM-LLM - https://huggingface.co/bigscience/bloom into NiFi
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
puck - The visual editor for React
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
openaidemo - Demo of how access the OpenAI API using Java 17
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
morphir - A universal language for business and technology
AtomGPT - 中英文预训练大模型,目标与ChatGPT的水平一致