ND4S alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view ND4S alternatives based on common mentions on social networks and blogs.
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Zeppelin
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more. -
BigDL
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, ModelScope, etc. -
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FACTORIE
FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference. -
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Clustering4Ever
C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering. -
rscala
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported. Conversely, the R interpreter is embedded in Scala. -
WorkOS - The modern identity platform for B2B SaaS
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
ND4S: Scala bindings for ND4J
ND4S is open-source Scala bindings for ND4J. Released under an Apache 2.0 license.
Main Features
- NDArray manipulation syntax sugar with safer type.
- NDArray slicing syntax, similar with NumPy.
Installation
Install via Maven
ND4S is already included in official Maven repositories.
With IntelliJ, incorporation of ND4S is easy: just create a new Scala project, go to "Project Settings"/Libraries, add "From Maven...", and search for nd4s.
As an alternative, one may simply add the line below to build.sbt and re-build project.
val nd4jVersion = "0.7.2"
libraryDependencies += "org.nd4j" % "nd4j-native-platform" % nd4jVersion
libraryDependencies += "org.nd4j" %% "nd4s" % nd4jVersion
One may want to check our maven repository page and replace 0.7.2 with the latest version.
No need for git-cloning & compiling!
Clone from the GitHub Repo
ND4S is actively developed. You can clone the repository, compile it, and reference it in your project.
Clone the repository:
$ git clone https://github.com/deeplearning4j/nd4s.git
Compile the project:
$ cd nd4s
$ sbt +publish-local
Try ND4S in REPL
The easiest way to play ND4S around is cloning this repository and run the following command.
$ cd nd4s
$ sbt test:console
It starts REPL with importing org.nd4s.Implicits._ and org.nd4j.linalg.factory.Nd4j automatically. It uses jblas backend at default.
scala> val arr = (1 to 9).asNDArray(3,3)
arr: org.nd4j.linalg.api.ndarray.INDArray =
[[1.00,2.00,3.00]
[4.00,5.00,6.00]
[7.00,8.00,9.00]]
scala> val sub = arr(0->2,1->3)
sub: org.nd4j.linalg.api.ndarray.INDArray =
[[2.00,3.00]
[5.00,6.00]]
CheatSheet(WIP)
| ND4S syntax | Equivalent NumPy syntax | Result |
|---|---|---|
| Array(Array(1,2,3),Array(4,5,6)).toNDArray | np.array([[1, 2 , 3], [4, 5, 6]]) | [[1.0, 2.0, 3.0] [4.0, 5.0, 6.0]] |
| val arr = (1 to 9).asNDArray(3,3) | arr = np.arange(1,10).reshape(3,3) | [[1.0, 2.0, 3.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]] |
| arr(0,0) | arr[0,0] | 1.0 |
| arr(0,->) | arr[0,:] | [1.0, 2.0, 3.0] |
| arr(--->) | arr[...] | [[1.0, 2.0, 3.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]] |
| arr(0 -> 3 by 2, ->) | arr[0:3:2,:] | [[1.0, 2.0, 3.0] [7.0, 8.0, 9.0]] |
| arr(0 to 2 by 2, ->) | arr[0:3:2,:] | [[1.0, 2.0, 3.0] [7.0, 8.0, 9.0]] |
| arr.filter(_ > 3) | np.where(arr > 3, arr, 0) | [[0.0, 0.0, 0.0] [4.0, 5.0, 6.0] ,[7.0, 8.0, 9.0]] |
| arr.map(_ % 3) | [[1.0, 2.0, 0.0] [1.0, 2.0, 0.0] ,[1.0, 2.0, 0.0]] | |
| arr.filterBit(_ < 4) | [[1.0, 1.0, 1.0] [0.0, 0.0, 0.0] ,[0.0, 0.0, 0.0]] | |
| arr + arr | arr + arr | [[2.0, 4.0, 6.0] [8.0, 10.0, 12.0] ,[14.0, 16.0, 18.0]] |
| arr * arr | arr * arr | [[1.0, 4.0, 9.0] [16.0, 25.0, 36.0] ,[49.0, 64.0, 81.0]] |
| arr dot arr | np.dot(arr, arr) | [[30.0, 36.0, 42.0] [66.0, 81.0, 96.0] ,[102.0, 126.0, 150.0]] |
| arr.sumT | np.sum(arr) | 45.0 //returns Double value |
| val comp = Array(1 + i, 1 + 2 * i).toNDArray | comp = np.array([1 + 1j, 1 + 2j]) | [1.0 + 1.0i ,1.0 + 2.0i] |
| comp.sumT | np.sum(comp) | 2.0 + 3.0i //returns IComplexNumber value |
| for(row <- arr.rowP if row.get(0) > 1) yield row*2 | [[8.00,10.00,12.00] [14.00,16.00,18.00]] | |
| val tensor = (1 to 8).asNDArray(2,2,2) | tensor = np.arange(1,9).reshape(2,2,2) | [[[1.00,2.00] [3.00,4.00]] [[5.00,6.00] [7.00,8.00]]] |
| for(slice <- tensor.sliceP if slice.get(0) > 1) yield slice*2 | [[[10.00,12.00][14.00,16.00]]] | |
| arr(0 -> 3 by 2, ->) = 0 | [[0.00,0.00,0.00] [4.00,5.00,6.00] [0.00,0.00,0.00]] |
*Note that all licence references and agreements mentioned in the ND4S README section above
are relevant to that project's source code only.