djl

Deep Java Library

[Kumar Shubham @krshubham1708]

Here's a link to the full video: https://t.co/OzEPAh1PHv

[djl @deepjavalibrary]

Learn how to build your own AI powered video surveillance system in just 150 lines of code https://t.co/MJB5jhzRFr

[djl @deepjavalibrary]

DJL v0.23.0 was released. It adds support for XGBoost and FastText on aarch64, streaming, and LLM text generation. Serving features rolling batches, lmi-dist, PEFT and LoRA handler support, and an s3 cache engine. See the full release notes at https://t.co/pyIBRVUOHs

[Pete Bankhead @petebankhead]

v0.4.0 brings QuPath a lot closer to supporting deep learning routinely, with the help of @deepjavalibrary

It's still early - the focus has been on the unglamorous make-it-possible stuff - but I think it can already be pretty useful.

https://t.co/yemfFmVbK7

[Paul King @paulk_asert]

Natural Language Processing with Groovy, OpenNLP, CoreNLP, Nlp4j, Datumbox, Smile, Spark NLP, DJL and TensorFlow
https://t.co/mMEmQoOEbh
#groovylang @ApacheSpark @ApacheGroovy @deepjavalibrary @TensorFlow @ApacheOpennlp @stanfordnlp @haifengl @Datumbox @oyahiroki

[Paul King @paulk_asert]

Deep learning object detection with @ApacheGroovy, @deepjavalibrary and @ApacheMXNet:
https://t.co/hSyOiTosPf
#groovylang #deeplearning #neuralnets #ai #machinelearning

[djl @deepjavalibrary]

Reminder to join us this Wednesday at 9:00AM PT for our bi-weekly community meeting. See https://t.co/o9bp6QyYTV for details

[djl @deepjavalibrary]

DJL v0.17.0 is now out and features support for @PyTorch linux AArch64 and version v1.11.0, @onnxruntime v1.11.0, two new datasets, and more. Find it now on #MavenCentral and the GitHub release at https://t.co/AaUzVlXE9I

[djl @deepjavalibrary]

To learn how to deploy your @huggingface #nlp #deeplearning model in #Java, check out the latest #djl blog post https://t.co/QJ5X0He08b

[djl @deepjavalibrary]

Read the post on @OpenAtMicrosoft about how @hypefactors was able to scale up their @PyTorch named entity recognition (NER) transformer model to billions of daily inferences using @onnxruntime and #djl: https://t.co/N159KWxQ3u

[djl @deepjavalibrary]

The March monthly news update of new features, content, and fixes for the Deep #Java Library #djl is now available at https://t.co/MDTOUmH43S

[djl @deepjavalibrary]

Learn how @hypefactors was able to simplify their #ml #MLOps using @onnxai and #djl: https://t.co/WgQTHoVEiW

[djl @deepjavalibrary]

DJL 0.16.0 is now out and features support for @ApacheMXNet 1.9.0 with #CUDA 11.2, improved @onnxruntime memory configuration, and a cleaner integration with #FastText. Find it now on #MavenCentral and the GitHub release at https://t.co/LAXwhkDs4J.

[djl @deepjavalibrary]

The January monthly news update of new features, content, and fixes for the Deep #Java Library #djl is now available at https://t.co/lF8KhYfH0n

[djl @deepjavalibrary]

The November monthly news update of new features, content, and fixes for the Deep #Java Library #djl is now available at https://t.co/glQSwVMCYG

[djl @deepjavalibrary]

The October monthly news update of new features, content, and fixes for the Deep #Java Library #djl is now available at https://t.co/gqfxN3ls2M

[djl @deepjavalibrary]

DJL v0.13.0 is now out with new TensorRT and Python engines along with upgrades to djl-bench and djl-serving. Find the full list at https://t.co/U8BCfW8ns3

[djl @deepjavalibrary]

Check out the latest release of #djl 0.12.0 featuring GPU support for #PaddlePaddle and @onnxruntime, support for #AWS Inferentia, Float16, a new benchmarking utility, and more at https://t.co/1glMAFj5je

[djl @deepjavalibrary]

You can speed up your #Java #Scala #DeepLearning by combining #djl, GPUs on @ApacheSpark 3.0, @NVIDIAAI RAPIDS, and run it on #aws #elasticmapreduce. Read https://t.co/JOA2SRfjIq to learn more

[djl @deepjavalibrary]

Aprendizaje profundo con Spark en la biblioteca Deep Java en 10 minutos: https://t.co/Aueo97aBaH