# packages

pkgId: 4663b40a88879d9697aa54ff0b82c91b9fa601eeafa33c705edbc3edd8cc7fa8
name: app-rootstrap-product
arch: src
version: 10.0.0
epoch: 0
release: 1
summary: Package for product rootstrap configuration
rpm_license: Apache-2.0
rpm_vendor: tizen
rpm_group: Unspecified
location_href: app-rootstrap-product-10.0.0-1.src.rpm
checksum_type: sha256
description:
  Package that updates /etc/tizen-platform.conf with product version from tizen-platform-config-product


pkgId: 50e9a5c4510ae69e92b272b3adfbcea40d161c7be7a9a75e1f50d3ab860aa1ee
name: capi-machine-learning-inference
arch: src
version: 1.8.8
epoch: 0
release: 0
summary: Tizen native API for NNStreamer
rpm_license: Apache-2.0
rpm_vendor: tizen
rpm_group: Machine Learning/ML Framework
rpm_packager: MyungJoo Ham <myungjoo.ham@samsung.com>
location_href: capi-machine-learning-inference-1.8.8-0.src.rpm
checksum_type: sha256
description:
  Tizen ML(Machine Learning) native API for NNStreamer.
  You can construct a data stream pipeline with neural networks easily.


pkgId: 2f3cafcc84b5e224054b584a0136151214c480818aeef12aa26e3ef66c5638e4
name: nnstreamer
arch: src
version: 2.7.0
epoch: 0
release: 0
summary: gstreamer plugins for neural networks
rpm_license: LGPL-2.1
rpm_vendor: tizen
rpm_group: Machine Learning/ML Framework
rpm_packager: MyungJoo Ham <myungjoo.ham@samsung.com>
location_href: nnstreamer-2.7.0-0.src.rpm
checksum_type: sha256
description:
  NNStreamer is a set of gstreamer plugins to support general neural networks
  and their plugins in a gstreamer stream. NNStreamer is a meta package of
  nnstreamer-core and nnstreamer-configuration


pkgId: e64e56b849a0f1b7ceb4630c8d1a4e8b8677a982eb3cca7cb4206ddc81a59ec6
name: tensorflow2
arch: src
version: 2.18.0
epoch: 0
release: 1
summary: TensorFlow Lite 2.x
rpm_license: Apache-2.0
rpm_vendor: tizen
rpm_group: Unspecified
location_href: tensorflow2-2.18.0-1.src.rpm
checksum_type: sha256
description:
  This is a standalone package of TensorFlow Lite.
  TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It enables low-latency inference of on-device machine learning models with a small binary size and fast performance supporting hardware acceleration.

  See the documentation: https://www.tensorflow.org/lite/


