MLflow
Graal
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MLflow | Graal | |
---|---|---|
54 | 156 | |
17,185 | 19,748 | |
2.1% | 0.8% | |
9.9 | 10.0 | |
5 days ago | 6 days ago | |
Python | Java | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
MLflow
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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EL5: Difference between OpenLLM, LangChain, MLFlow
MLFlow - http://mlflow.org
- Explain me how websites like Dall-E, chatgpt, thispersondoesntexit process the user data so quickly
- [D] What licensed software do you use for machine learning experimentation tracking?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
MLflow:
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Options for configuration of python libraries - Stack Overflow
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
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[Q] Is there a tool to keep track of my ML experiments?
Hi, you should have a look at ML flow https://mlflow.org or weight and biases https://wandb.ai/site
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Looking for recommendations to monitor / detect data drifts over time
Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?
Graal
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Java 23: The New Features Are Officially Announced
Contrary to what vocal Kotlin advocates might believe, Kotlin only matters on Android, and that is thanks to Google pushing it no matter what.
https://spectrum.ieee.org/the-top-programming-languages-2023
https://snyk.io/reports/jvm-ecosystem-report-2021/
And even so, they had to conceed Android and Kotlin on their own, without the Java ecosystem aren't really much useful, thus ART is now updatable via Play Store, and currently supports OpenJDK 17 LTS on Android 12 and later devices.
As for your question regarding numbers, mostly Java 74.6%, C++ 13.7%, on the OpenJDK, other JVM implementations differ, e.g. GraalVM is mostly Java 91.8%, C 3.6%.
https://github.com/openjdk/jdk
https://github.com/oracle/graal
Two examples from many others, https://en.wikipedia.org/wiki/List_of_Java_virtual_machines
- FLaNK Stack 05 Feb 2024
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Apple releases Pkl – onfiguration as code language
Pkl was built using the GraalVM Truffle framework. So it supports runtime compilation using Futurama Projections. We have been working with Apple on this for a while, and I am quite happy that we can finally read the sources!
https://github.com/oracle/graal/tree/master/truffle
Disclaimer: graalvm dev here.
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Live Objects All the Way Down: Removing the Barriers Between Apps and VMs
That's pretty interesting. It's not as aggressive as Bee sounds, but the Espresso JVM is somewhat similar in concept. It's a full blown JVM written in Java with all the mod cons, which can either be compiled ahead of time down to memory-efficient native code giving something similar to a JVM written in C++, or run itself as a Java application on top of another JVM. In the latter mode it obviously doesn't achieve top-tier performance, but the advantage is you can easily hack on it using all the regular Java tools, including hotswapping using the debugger.
When run like this, the bytecode interpreter, runtime system and JIT compiler are all regular Java that can be debugged, edited, explored in the IDE, recompiled quickly and so on. Only the GC is provided by the host system. If you compile it to native code, the GC is also written in Java (with some special conventions to allow for convenient direct memory access).
What's most interesting is that Espresso isn't a direct translation of what a classical C++ VM would look like. It's built on the Truffle framework, so the code is extremely high level compared to traditional VM code. Details like how exactly transitions between the interpreter/compiled code happen, how you communicate pointer maps to the GC and so on are all abstracted away. You don't even have to invoke the JIT compiler manually, that's done for you too. The only code Espresso really needs is that which defines the semantics of the Java bytecode language and associated tools like the JDWP debugger protocol.
https://github.com/oracle/graal/tree/master/espresso
This design makes it easy to experiment with new VM features that would be too difficult or expensive to implement otherwise. For example it implements full hotswap capability that lets you arbitrarily redefine code and data on the fly. Espresso can also fully self-host recursively without limit, meaning you can achieve something like what's described in the paper by running Espresso on top of Espresso.
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Crash report and loading time
I'm also using GraalVM if that's of any help.
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Quarkus 3.4 - Container-first Java Stack: Install with OpenJDK 21 and Create REST API
Quarkus is one of Java frameworks for microservices development and cloud-native deployment. It is developed as container-first stack and working with GraalVM and HotSpot virtual machines (VM).
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Level-up your Java Debugging Skills with on-demand Debugging
Apologies, I didn't mean to imply DCEVM went poof, just that I was sad it didn't make it into OpenJDK so one need not do JDK silliness between the production one and the "debugging one" since my experience is that's an absolutely stellar way to produce Heisenbugs
And I'll be straight: Graal scares me 'cause Oracle but I just checked and it looks to the casual observer that it's straight-up GPLv2 now so maybe my fears need revisiting: https://github.com/oracle/graal/blob/vm-23.1.0/LICENSE
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Rust vs Go: A Hands-On Comparison
> to be compiled to a single executable is a strength that Java does not have
I think this is very outdated claim: https://www.graalvm.org/
- Leveraging Rust in our high-performance Java database
- Java 21 makes me like Java again
What are some alternatives?
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Liberica JDK - Free and 100% open source Progressive Java Runtime for modern Java™ deployments supported by a leading OpenJDK contributor
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Adopt Open JDK - Eclipse Temurin™ build scripts - common across all releases/versions
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
awesome-wasm-runtimes - A list of webassemby runtimes
guildai - Experiment tracking, ML developer tools
maven-jpackage-template - Sample project illustrating building nice, small cross-platform JavaFX or Swing desktop apps with native installers while still using the standard Maven dependency system.
dvc - 🦉 ML Experiments and Data Management with Git
SAP Machine - An OpenJDK release maintained and supported by SAP
tensorflow - An Open Source Machine Learning Framework for Everyone
wasmer - 🚀 The leading Wasm Runtime supporting WASIX, WASI and Emscripten