Dagger.jl
Dagger2
Dagger.jl | Dagger2 | |
---|---|---|
4 | 50 | |
581 | 17,317 | |
1.7% | 0.2% | |
8.9 | 9.1 | |
4 days ago | 5 days ago | |
Julia | Java | |
GNU General Public License v3.0 or later | 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.
Dagger.jl
- Dagger: a new way to build CI/CD pipelines
-
DTable a new distributed table implementation in Julia using Dagger.jl
Firstly, I'll say that we already have work started to implement out-of-core directly in Dagger: https://github.com/JuliaParallel/Dagger.jl/pull/289.
With that PR in place, it should be possible to define a "storage device" which is backed by a database. I haven't had a chance to actually try this, since the PR still needs quite some work and testing, but it's definitely something on my radar!
- From Julia to Rust
-
Cerebras’ New Monster AI Chip Adds 1.4T Transistors
I'm not sure that's necessarily the domain of a low-level package like CUDA.jl though (which I assume you're referring to). That kind of interface is more the domain of higher-level packages like https://github.com/JuliaParallel/Dagger.jl/ and to a lesser extent https://juliagpu.github.io/KernelAbstractions.jl/stable/. Moreover, the jury is still out on whether the built-in Distributed module is an ideal abstraction for every use-case (clusters, heterogeneous compute, etc.)
WRT Nx, my biggest question is how they'll crack the problem of still needing big balls of C++ and the shims everywhere to get acceleration. Creating a compiler that generates efficient GPU or other accelerator code is a massive research project with no clear winners, never mind the challenge of reconciling the very mutation-heavy needs of GPU compute with a mostly immutable language model.
Dagger2
- Dagger 2.49 (KSP, @AssistedInject with @HiltViewModel, more)
- Dagger 2.48 adds alpha KSP support
- Dagger KSP update & Breaking changes required to use Dagger KSP
-
Performance and memory impact of the @Singleton annotation in Dagger
There used to be a thing called "releasable references" which was that. It was removed, though: https://github.com/google/dagger/issues/1117
-
Dependency injection with AWS Lambdas in java
As said in the title, we will focus on the dependency inversion principle and one of its application : dependency injection. For production-ready applications, it would be better to rely on a framework and not implement its own container. For it, the java ecosystem have 3 frameworks available : Spring, Guice and Dagger.
-
Refactoring our Dependency Injection using Anvil
At Reddit, we use Dagger 2 for handling dependency injection (DI) in our Android application. As we’ve scaled the application over the years, we’ve accrued a bit of technical debt in how we have approached this problem.
-
Dagger Python SDK: Develop Your CI/CD Pipelines as Code
Confusing. I initially thought someone ported the Dagger DI framework to Python: https://dagger.dev/
-
Multiplatform dependency injection libraries equivalent to Dagger/Anvil
I'm currently using Dagger and Anvil for my DI needs. It's been working really well, especially around what Anvil permits in terms of multibindings defined on the type declaration rather than in a module. For example:
-
Dagger 2.43 released with support for multiple instances of the same ViewModel using keys 🎉
Great job, I have been waiting for this feature/fix for a long time https://github.com/google/dagger/issues/2328
-
Best libraries for Android Developers
Dagger
What are some alternatives?
earthly - Super simple build framework with fast, repeatable builds and an instantly familiar syntax – like Dockerfile and Makefile had a baby.
Guice - Guice (pronounced 'juice') is a lightweight dependency injection framework for Java 11 and above, brought to you by Google.
julia - The Julia Programming Language
Toothpick - A scope tree based Dependency Injection (DI) library for Java / Kotlin / Android.
DuckDB.jl
Weld - Weld, including integrations for Servlet containers and Java SE, examples and documentation
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
butterknife - Bind Android views and callbacks to fields and methods.
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
HK2
Symbolics.jl - Symbolic programming for the next generation of numerical software
Dynamic CDI - Dynamic Context Dependency Injection