ml-engineering
async-profiler
ml-engineering | async-profiler | |
---|---|---|
9 | 10 | |
9,928 | 7,175 | |
- | 1.9% | |
9.7 | 8.7 | |
10 days ago | 10 days ago | |
Python | C++ | |
Creative Commons Attribution Share Alike 4.0 | 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.
ml-engineering
- Accelerators
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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
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ML Engineering Online Book
OK, the pdf is ready now: https://github.com/stas00/ml-engineering#pdf-version
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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
async-profiler
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JVM Profiling in Action
We'll use async-profiler and flame graphs for profiling. To simplify the process, we'll run the code using JBang.
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The Return of the Frame Pointers
JIT'ed code is sadly poorly supported, but LLVM has had great hooks for noting each method that is produced and its address. So you can build a simple mixed-mode unwinder, pretty easily, but mostly in process.
I think Intel's DNN things dump their info out to some common file that perf can read instead, but because the *kernels* themselves reuse rbp throughout oneDNN, it's totally useless.
Finally, can any JVM folks explain this claim about DWARF info from the article:
> Doesn't exist for JIT'd runtimes like the Java JVM
that just sounds surprising to me. Is it off by default or literally not available? (Google searches have mostly pointed to people wanting to include the JNI/C side of a JVM stack, like https://github.com/async-profiler/async-profiler/issues/215).
- FLaNK Stack 29 Jan 2024
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Tracking Java Native Memory with JDK Flight Recorder
debugging native calls in itself is also painful. I have switched to using async-profiler (https://github.com/async-profiler/async-profiler) instead of JFR for most of my usecases.
A. it tracks native calls by default
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Show HN: Javaflame – Simple Flamegraph for your Java application
https://github.com/async-profiler/async-profiler#flame-graph...
Ok, Windows is not supported. But IntelliJ made a fork which works on Windows.
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Lettuce (Redis) + Mybatis (MySQL) take up most of the CPU in production - Is it normal? Did you observe that in your environment? Any ways to optimize it?
Hi, today I used async-profiler to check the CPU usage of my Spring Boot app (just a normal backend) in production. Surprisingly, Lettuce (Redis) + Mybatis (MySQL) take up most of the CPU time. I am not talking about wall time here, but CPU time, since I know database requests need to wait for milliseconds and thus wall time will be very long. Therefore, I wonder:
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A question about Http4s new major version
You can use async-profiler to see what is happening under the hood.
- Reducing code size in (Rust) librsvg by removing an unnecessary generic struct
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what is your favorite programming trick/tool that not many People know about?
I have used visual vm quite a bit. https://github.com/async-profiler/async-profiler is also amazing... Throw the binary on the system and fire it up. It also profiles down into native code as well if you do that kind of thing.
What are some alternatives?
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.
jmh - https://openjdk.org/projects/code-tools/jmh
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
container-jfr - Secure JDK Flight Recorder management for containerized JVMs
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
jfr-libraries - a list of libraries that generate JFR events
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
Arthas - Alibaba Java Diagnostic Tool Arthas/Alibaba Java诊断利器Arthas
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.
opentelemetry-java-instrumentation - OpenTelemetry auto-instrumentation and instrumentation libraries for Java
AtomGPT - 中英文预训练大模型,目标与ChatGPT的水平一致
junit-jfr - a JUnit 5 extension that generates JFR events