linux/meta-intel.git, branch zeus-22.0.0

layer: Mark zeus as compatible release

2019-10-09T03:32:32+00:00

Signed-off-by: Khem Raj Signed-off-by: Anuj Mittal

clang: clean up

2019-10-09T03:32:25+00:00

No need to set LLVM_TARGETS_TO_BUILD here as it is set by meta-clang layer. Signed-off-by: Naveen Saini Signed-off-by: Anuj Mittal

linux-intel-rt/4.14: update to v4.14.139

2019-10-01T14:20:38+00:00

Updates rt patchset to -rt66 Signed-off-by: Naveen Saini Signed-off-by: Anuj Mittal

linux-intel/4.14: update to v4.14.146

2019-10-01T14:20:38+00:00

tag 'v4.14.146': (60 commits) Linux 4.14.146 media: technisat-usb2: break out of loop at end of buffer tcp: Don't dequeue SYN/FIN-segments from write-queue tcp: Reset send_head when removing skb from write-queue binfmt_elf: move brk out of mmap when doing direct loader exec floppy: fix usercopy direction PCI: kirin: Fix section mismatch warning iommu/amd: Fix race in increase_address_space() iommu/amd: Flush old domains in kdump kernel keys: Fix missing null pointer check in request_key_auth_describe() x86/hyper-v: Fix overflow bug in fill_gva_list() x86/uaccess: Don't leak the AC flags into __get_user() argument evaluation dmaengine: ti: omap-dma: Add cleanup in omap_dma_probe() dmaengine: ti: dma-crossbar: Fix a memory leak bug net: seeq: Fix the function used to release some memory in an error handling path tools/power turbostat: fix buffer overrun tools/power x86_energy_perf_policy: Fix argument parsing tools/power x86_energy_perf_policy: Fix "uninitialized variable" warnings at -O2 amd-xgbe: Fix error path in xgbe_mod_init() perf/x86/amd/ibs: Fix sample bias for dispatched micro-ops ... Signed-off-by: Naveen Saini Signed-off-by: Anuj Mittal

linux-intel/4.19: update to v4.19.73

2019-10-01T14:20:38+00:00

Also updated kmeta tag 'v4.19.73': (247 commits) Linux 4.19.73 Merge "USB: Fix configuration selection issues introduced in v4.20.0" vhost: make sure log_num < in_num powerpc/tm: Fix restoring FP/VMX facility incorrectly on interrupts powerpc/tm: Remove msr_tm_active() PCI: Reset both NVIDIA GPU and HDA in ThinkPad P50 workaround ext4: unsigned int compared against zero ext4: fix block validity checks for journal inodes using indirect blocks ext4: don't perform block validity checks on the journal inode drm/atomic_helper: Allow DPMS On<->Off changes for unregistered connectors virtio/s390: fix race on airq_areas[] drm/i915: Make sure cdclk is high enough for DP audio on VLV/CHV bcache: fix race in btree_flush_write() bcache: add comments for mutex_lock(&b->write_lock) bcache: only clear BTREE_NODE_dirty bit when it is set NFSv4: Fix delegation state recovery iio: adc: gyroadc: fix uninitialized return code mm/migrate.c: initialize pud_entry in migrate_vma() i2c: at91: fix clk_offset for sama5d2 i2c: at91: disable TXRDY interrupt after sending data gpio: don't WARN() on NULL descs if gpiolib is disabled ... Signed-off-by: Naveen Saini Signed-off-by: Anuj Mittal

linux-intel: fix perf build failure with 4.14 kernel

2019-10-01T01:43:09+00:00

OE-Core started using Python3 for perf module. But older kernel such as 4.14 does not have support for python3. This cause build failure: | Makefile.config:588: Missing perl devel files. Disabling perl scripting support, please install perl-ExtUtils-Embed/libperl-dev | Makefile.config:626: Python 3 is not yet supported; please set | Makefile.config:627: PYTHON and/or PYTHON_CONFIG appropriately. | Makefile.config:628: If you also have Python 2 installed, then | Makefile.config:629: try something like: Backported python3 support, as suggessted here: https://git.yoctoproject.org/cgit/cgit.cgi/poky/commit/meta/recipes-kernel/perf?id=92469aad50b623afa423c19d82ed2e3c667c5e6a Signed-off-by: Naveen Saini Signed-off-by: Anuj Mittal

dldt-inference-engine: fix clDNN install directory

2019-09-30T14:28:09+00:00

Install clDNN to /usr/lib to resolve the following inference engine error when running with GPU plugin: [ ERROR ] Failed to create plugin libclDNNPlugin.so for device GPU Please, check your environment Cannot load library 'libclDNNPlugin.so': libclDNNPlugin.so: cannot open shared object file: No such file or directory /usr/src/debug/dldt-inference-engine/2019r2-r0/git/inference-engine/include/details/os/lin_shared_object_loader.h:36 /usr/src/debug/dldt-inference-engine/2019r2-r0/git/inference-engine/src/inference_engine/ie_core.cpp:277 Signed-off-by: Chin Huat Ang Signed-off-by: Anuj Mittal

dldt-inference-engine: upgrade 2019r1.1 -> 2019r2

2019-09-30T07:34:44+00:00

* Release notes: https://software.intel.com/en-us/articles/OpenVINO-RelNotes * Enable unit tests to be built and tested using ptest mechanism. * Include patches from Clear Linux for build fixes. * Switch to using python3 and threading to using TBB. Switch ENABLE_OPENCV to off so opencv from system is used. * Remove do_install and patch Makefiles instead to install libraries correctly. Signed-off-by: Anuj Mittal

dldt-inference-engine: add recipe

2019-09-28T09:18:30+00:00

This recipe builds the inference engine from opencv/dldt 2019 R1.1 release. OpenVINO™ toolkit, short for Open Visual Inference and Neural network Optimization toolkit, provides developers with improved neural network performance on a variety of Intel® processors and helps further unlock cost-effective, real-time vision applications. The toolkit enables deep learning inference and easy heterogeneous execution across multiple Intel® platforms (CPU, Intel® Processor Graphics)—providing implementations across cloud architectures to edge device. For more details, see: https://01.org/openvinotoolkit The recipe needs components from meta-oe so move it to dynamic-layers/openembedded-layer. GPU plugin support needs intel-compute-runtime which can be built by including clang layer in the mix as well. CPU and GPU plugins have been sanity tested to work using classification_sample. Further fine-tuning is still needed to improve the performance. Original patch by Anuj Mittal. Signed-off-by: Chin Huat Ang Signed-off-by: Anuj Mittal

dldt-model-optimizer: add recipe

2019-09-24T02:24:42+00:00

Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. For more details, see: https://software.intel.com/en-us/openvino-toolkit/deep-learning-cv Since the recipe requires bits from meta-python, move this to the dynamic layers section and add meta-python to BBFILES_DYNAMIC. Signed-off-by: Anuj Mittal