djl | An Engine-Agnostic Deep Learning Framework in Java | Machine Learning library
kandi X-RAY | djl Summary
kandi X-RAY | djl Summary
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and functions like any other regular Java library. You don't have to be machine learning/deep learning expert to get started. You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration.
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Top functions reviewed by kandi - BETA
- Run the benchmark .
- Creates a residual unit for this convolution .
- Downloads the xnet from the platform .
- Converts an input string to a command line .
- Creates a parallel sequential block .
- Adds an index item to the index .
- Creates an nIndex from an index
- Prepares the usage file .
- Creates a full lexical block .
- Get the session options .
djl Key Features
djl Examples and Code Snippets
License : Strong Copyleft (CC BY-SA 4.0)
Copypublic DateTime Timestamp { get; }
public class LongToDateConverter : IValueConverter
{
public object Convert(object value, Type targetType, object parameter, CultureInfo culture)
{
if (!(value is long SET @after := '2022-04-04',
@before := '2022-04-06';
SELECT *
FROM `presences` AS p
JOIN (SELECT * FROM `days` WHERE `day` BETWEEN @after AND @before)
AS d ON d.`day` BETWEEN Date(p.`start`) AND Date(p.`end`) -- The core n = 4 # number of cores
m = 1 # number of threads per core
TOTAL_MEMORY = 16282416 kB
TOTAL_MEMORY * min(1, 1 / 4)
> 4070604
lscpu --all --extended
➜ lscpu --all --extended
CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE MAXMHZ MINMHZ
0 0 0 0 0:0:0:0 yes 6700.0000 800.0000
1 0 0 0 0:0:0:0 yes 67onnx == 1.9.0
onnxruntime == 1.8.1
pytorch == 1.9.0+cu111
torchvision == 0.10.0+cu111
# Netcore 6
export PATH=$PATH:$HOME/.dotnet/tools
export DOTNET_ROOT=$HOME/.dotnet
export PATH=$PATH:$DOTNET_ROOT
❯ dotnet ef
_/\__
---==/ \\
___ ___ - task: UseDotNet@2
displayName: 'Install .NET Core sdk 6.x'
inputs:
version: 6.x
- task: UseDotNet@2
displayName: 'Use .NET Core sdk'
inputs:
packageType: 'sdk'
version: '6.0.x'
includePreviewVersions: true
# .NET Core Function App to Windows on Azure
# Build a .NET Core functio- task: DotNetCoreCLI@2
displayName: Test
inputs:
command: test
version: '6.0.x' //better to put this in a variable
projects: '$(Parameters.TestProjects)'
arguments: '--configuration $(BuildConfiguration)'
aws codeartifact list-package-versions --domain kickingtires --repository kickingtires --format maven --namespace edu.excelsior.api --package core
Community Discussions
Trending Discussions on djl
QUESTION
I have a PyTorch model trained on the 17flowers dataset, and converted via PyTorch's tracing to a JIT model. I have tested the inference output for the PyTorch model and the JIT converted model, and the results are equivalent there. This leads me to believe there is an issue with my implementation of the DJL framework.
There is an issue when I attempt to utilized DJL for inference utilizing the converted JIT model, which is necessary for DJL. I am not getting 100% match, which I expected.
The Kotlin implementation for djl.ai is straightforward and essentially follows the instructions here.
I have a sanitized version of the Kotlin code below:
...ANSWER
Answered 2021-Apr-18 at 18:58The discrepancy most likely comes from image pre-processing:
QUESTION
How do I call a custom mxnet operator from DJL? E.g. the my_gemm operator from the examples.
ANSWER
Answered 2021-Apr-11 at 15:09It is possible by manually calling the JnaUtils in the same way as the built-in mxnet engine does, just with your custom lib. For the my_gemm example, this looks like this:
QUESTION
Is there a way to query the maximum total memory on a GPU in DJL? E.g. an NVIDIA 1080ti should return a value of 11GB.
I am working with the mxnet engine, but an engine-agnostic solution would of course be preferable.
...ANSWER
Answered 2021-Apr-03 at 23:28Yes, DJL has integrated with CUDA APIs directly by JNA in https://github.com/awslabs/djl/blob/d2c47d0f2d663b8a5794c21d971420a99f2d47cd/api/src/main/java/ai/djl/util/cuda/CudaUtils.java. So it's engine-agnostic solution.
QUESTION
I'm trying to deploy a project to Maven to bintray which is successfully uploads the pom file but returns an Error code 401 when uploading jar files.
I have gone through the bintray tutorial, set my settings.xml up properly, added the correct distributionManagement etc fields to my pom file, made sure the project is not a snapshot etc. but cannot seem to get the uploading of jar files working? I've been trying to figure out what might be going on but with no success.
Details on the stack trace pom.xml, settings.xml and eclipse configuration are below. The entire project can be found on github
Am I missing something obvious here? Any help much appreciated.
...ANSWER
Answered 2020-Sep-23 at 03:30The error indicates an access issue based upon the HTTP 401 code message:
Error code 401, Unauthorized
Double check your username and password is correct.
Better yet do not put your password into plaintext and instead use a password hash.
QUESTION
I am trying out the Aws Djl platform. I want to load a custom trained tensorflow model and perform inference. I couldnot find a direct example to in the official github that does this. Can anyone guide me on the same.
...ANSWER
Answered 2020-Jul-01 at 19:23Here is demo project: https://github.com/aws-samples/djl-demo/tree/master/pneumonia-detection
And here is the document about loading tensorflow model: https://github.com/awslabs/djl/blob/master/docs/tensorflow/how_to_import_keras_models_in_DJL.md and: https://github.com/awslabs/djl/blob/master/docs/load_model.md
QUESTION
I have now spent way too much time trying to resolve this on my own, but I seem unable to do this on my own. I wanted to try out DJL, looked at some examples and into the tutorial, and was very excited about it because it seems like a very modern, simple and versatile library. However, I ran into a problem I am unable to solve.
DJL proposes using its MXNet Engine as default engine in the tutorial. It is included via the maven dependency ai.djl.mxnet:mxnet-engine:0.5.0. When running the sample code from the tutorial, I encounter the following error message:
ANSWER
Answered 2020-May-31 at 05:42You need to include MXNet native dependency: See example project's pom.xml file: https://github.com/awslabs/djl/blob/master/examples/pom.xml#L64-L72
For more detail about MXNet engine dependency, see: https://github.com/awslabs/djl/tree/master/mxnet/mxnet-engine#installation
For Windows, you can use: gradlew build in if you are using CMD, ./ is linux/mac convention, and should work if you are using gitbash on Windows.
QUESTION
I have a processor which depends on some jar as a dependency. However changes are made to the dependency jar quite frequenctly. So I want to be able to specify the jar path as a property of processor and let the nifi load the jar each time I modify the path in the property and restart the processor. This is supposed to be doable using dynamicallyModifiesClasspath as explained in this article. However I am unable to do this. Below is my code of hello world program using dynamicallyModifiesClasspath property:
(Below, Djl stands for "dynamic jar loading", a random prefix I guessed to name this hello world program.)
DjlDependencyClass.java
This is the class on which my nifi processor depends and I want to dynamically change its jar path in my nifi processor.
...ANSWER
Answered 2019-May-29 at 08:19The API document for PropertyDescriptor mentions the following note:
If a component contains a PropertyDescriptor where dynamicallyModifiesClasspath is set to true, the component must also be annotated with @RequiresInstanceClassloading, otherwise the component will be considered invalid.
I'm not sure whether that 'invalid' part still holds true but yeah, after updating your processor code to have it annotated with @RequiresInstanceClassloading, I was able to get it working.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install djl
You can use djl like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the djl component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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