djl
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An Engine-Agnostic Deep Learning Framework in Java
我需要libdjl_paddle.so这个动态库的arm架构的版本 但是在目前的github项目里没有找到 官网也没有可以供下载的包 请问可以通过源码编译么 源码就是这个项目里对应的.java文件吗
Has been reasoning non-stop, and then the computer black screen, memory continued to grow, yolov8 demo [  ](url)
## Description I tried to deploy Yolov5 to a Ubuntu PC using DJL. However, a memory leak occurred during inference. After experimenting, I found that the memory leak was only...
在加载yolov5模型,压测接口,OnnxRuntime引擎会内存一直递增不会回收,PyTorch引擎会把内存稳定在一个区间里,同一份代码。 djl的版本是0.19.0,希望你们可以测试一下,用你们给的代码仍会出现,我在mac,linux上测试会出现同样的问题
## Description 本来是把djl推理yolov5模型整合到springboot项目中,压测发现内存无法回收,随着压测的增加内存也增加。 注意:和这个issues并不是一个问题https://github.com/deepjavalibrary/djl/issues/2800 ,它这个在main线程中执行确实不会有问题,但是异步执行就会有内存泄漏问题。 在spring项目中内存并不会回收 项目中没有用到opencv ### Expected Behavior 100个线程预测就会出现内存变大并且并不会回收  ## How to Reproduce? ``` import ai.djl.Device; import ai.djl.MalformedModelException; import ai.djl.ModelException; import ai.djl.inference.Predictor; import ai.djl.modality.cv.Image; import ai.djl.modality.cv.ImageFactory; import...
## Description Greetings, I'm using a custom-made model with DJL for object detection but it's giving me an error: Incompatible shapes: [1,14,14,128] vs. [1,20,20,128] (For MOBILENET_V2_I320) & Incompatible shapes: [1,14,14,128]...
我描述一下我的问题,有两个基础docker镜像,一个是x86另一个是arm架构的,里面环境是一样的就是Java和tomcat,同一份代码,在x86里可以正常使用,arm里就是一直报加载错误,onnx引擎都下下来了,这个问题我改如何解决呢 可以看下图片所需要的引擎都下载下来了 主要错误: 1.Caused by: java.lang.UnsatisfiedLinkError: Unable to load library '/root/.djl.ai/mxnet/1.9.1-mkl-linux-aarch64/libmxnet.so': 2.Caused by: ai.djl.engine.EngineException: Failed to load MXNet native library 想请问下,我aarch64架构是缺少啥东西么?
https://github.com/deepjavalibrary/djl/issues/2573 I have similar problem,my environment is as follows: _linux ,gpu t4 CUDA: 113 ARCH: 75 DJL version: 0.23.0 ai.djl.util.Platform - Found placeholder platform from: cu113-linux-x86_64:2.10.1 Default Engine: TensorFlow:2.10.1, capabilities:...
In djl.jupyter.load_pytorch_model.ipynb,when I am running " Criteria criteria = Criteria.builder() .setTypes(Image.class, Classifications.class) .optModelPath(Paths.get("build/pytorch_models/resnet18")) .optOption("mapLocation", "false") // this model requires mapLocation for GPU .optTranslator(translator) .optProgress(new ProgressBar()).build(); ZooModel model = criteria.loadModel(); "...