rest.li
ml-engineering
| rest.li | ml-engineering | |
|---|---|---|
| 2 | 9 | |
| 2,441 | 10,032 | |
| 0.2% | - | |
| 8.3 | 9.7 | |
| 6 days ago | 25 days ago | |
| Java | 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.
rest.li
- FLaNK Stack 29 Jan 2024
-
LinkedIn Adopts Protocol Buffers and Reduces Latency Up to 60%
From rest.li's github page[0] -
At LinkedIn, we are focusing our efforts on advanced automation to enable a seamless, LinkedIn-wide migration from Rest.li to gRPC. gRPC will offer better performance, support for more programming languages, streaming, and a robust open source community. There is no active development at LinkedIn on new features for Rest.li. The repository will also be deprecated soon once we have migrated services to use gRPC. Refer to this blog[1] for more details on why we are moving to gRPC.
[0] - https://github.com/linkedin/rest.li
[1] - https://engineering.linkedin.com/blog/2023/linkedin-integrat...
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?
Swagger - The content of swagger.io
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.
Feign - Feign makes writing java http clients easier
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Jersey - Eclipse Jersey Project - Read our Wiki:
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
Dropwizard - A damn simple library for building production-ready RESTful web services.
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
Retrofit - A type-safe HTTP client for Android and the JVM
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.
Microserver - Microserver is a Java 8 native, zero configuration, standards based, battle hardened library to run Java Rest Microservices via a standard Java main class. Supporting pure Microservice or Micro-monolith styles.
AtomGPT - 中英文预训练大模型，目标与ChatGPT的水平一致