Axelera Assist

Hi,I'm using NanoPC-T6. I followed the Https://support.axelera.ai/hc/en-us/articles/31859388491794-Bring-up-Voyager-SDK-in-NanoPC-T6 you sent me. However, when I proceed with the test, the test will not proceed with the following error.https://community.axelera.ai/voyager-sdk-2/axelera-runtime-tar-gz-256?postid=2749#post2749 Someone has had the same issue in this community and I need help if there was a fix. (venv) pi@pi-desktop:~/voyager-sdk$ AXELERA_CONFIGURE_BOARD=,20 ./inference.py yolov8l-coco-onnx ./media/traffic1_480p.mp4 --no-display2026-01-02 14:31:47.360506524 [W:onnxruntime:Default, device_discovery.cc:164 DiscoverDevicesForPlatform] GPU device discovery failed: device_discovery.cc:89 ReadFileContents Failed to open file: "/sys/class/drm/card1/device/vendor"Failed to create device manager: libaxruntime.so: axr_create_context: libaxruntime.so: cannot open shared object file: No such file or di

HI, We ran axdevice --report on M.2-aipu to check the number of active cores. The output shows “Device metis-0:2:0”.Does this string confirm that only two cores are available? We had expected four cores (e.g., a notation like “metis-0:4:0”). We want to know from this log whether the hardware is limited to two cores. Best, regardsHiroyuki Kojima

Bonjour,Je n’arrive pas à récupérer le fichier `axelera-runtime.tar.gz`. Le script `install.sh` affiche “Runtime Installed OK” mais ne télécharge ni n’extrait quoi que ce soit.Aucune URL n’apparaît dans les logs et `libaxruntime.so` est absent de mon système.Merci de me fournir un lien de téléchargement direct pour installer le runtime manuellement.Cordialement,   

See:  

Similar to https://community.axelera.ai/voyager-sdk-2/run-simple-gemm-model-1127, I got IndexError when converting a Gemm-only model via “compile -i model.onnx -o model --overwrite”. However, I don’t have Flatten layer. Could you help on checking what’s going wrong? The model I use and logs are attached below, and here are my system information:System: Ubuntu 22.04 x86_64 SDK: version 1.5.2 cloned from https://github.com/axelera-ai-hub/voyager-sdk

​@Spanner  what i get on this code? by launching “axllm.py my.yaml”try: network_yaml_info = yaml_parser.get_network_yaml_info( include_collections=['llm_local', 'llm_cards', 'llm_zoo'] ) parser = config.create_llm_argparser( network_yaml_info, description='Perform LLM inference on an Axelera platform' )How to configure the yaml file what’s inside? can i specify like  llm_local = ip:portllm_cards = /dev/metis0llm_zoo = https://huggingface.com/my/llm.zip…………Orllm_zoo: - url: ${HUG_RELEASE_URL}/llm/myhillarius.zip - url: ${HUG_RELEASE_URL}/llm/codellama:8B.zip - url: ${HUG_RELEASE_URL}/llm/r1-1776:70B.zipBy duck tapeing models to m

r1-1776:70Bcodellamacodegemmapost follow ups by other users

Me and a few other folks would be very interested in having Ollama support the AIPUs. There is a wide range of different inference models on https://ollama.com/. From what I understand these would need to get recompiled to run on the AIPU. I could only find a handful of models in the model zoo and no information about how to recompile any given model. Can you provide instructions on how to recompile other models available online?

Hi,I'm having trouble installing the metis-dkms driver. Can you help me?The device is a NanoPC-T6 from Friendly Elec, and the operating system is Ubuntu 22.04, also provided by Friendly Elec.I'm leaving the error message and OS information below. If there is any more information I should provide, please let me know. Thank you. The Error:$ sudo dpkg -i metis-dkms_0.07.16_all.deb(Reading database ... 155441 files and directories currently installed.)Preparing to unpack metis-dkms_0.07.16_all.deb ...Deleting module metis-0.07.16 completely from the DKMS tree.Unpacking metis-dkms (0.07.16) over (0.07.16) ...Setting up metis-dkms (0.07.16) ...Loading new m

Quick update on Voyager SDK 1.5.2, which was just rolled out and sorts out a few issues from 1.5.1 while adding some quality of life improvements.What's fixed:The main focus here was stability. This version sorts the build failures that were hitting ImageNet networks when using HINT instructions, fixed NormaliseCL so it properly handles inputs that aren't 4-channel, and stopped those segfaults in YOLO decoders when they got an unexpected number of input tensors.New bits:Runtime can now execute AXM files directly, which should make deployment a bit more straightforward. It adds selectable network protocol for RTSP sources in inference.py (gives you more flexibility with inputs), and there are new create_inference_net overloads so existing code compiles without needing a context parameter.The docs have been updated to reflect the AXM execution changes, plus general tidy-ups across tutorials and references.That

Hello everyone on the Axelera team!I'm having fun with the new Metis PCIe card that arrived!Thanks!I'm trying out voyager-sdk version 1.5.1 that supports this card, but it seems to throw an OpenCL runtime error and the sample won’t running.It doesn’t seem to be working with GST.For now, using the ‘--disable-opencl’ argument made it work.Alternatively,‘hardware_caps=config.HardwareCaps(…    opencl=config.HardwareEnable.disabel ...)source code.This isn't a solution, right? 

Hi!First of all, thank you very much for your work — the Axelera tools are really impressive and perform exceptionally well!I’m currently working on deploying LLMs on the METIS PCIe accelerator using the Voyager SDK v1.5.1.  From my understanding, the LLMs currently available are already precompiled, and we simply download the compiled model files.Would it be possible to access the deployment or compilation process so that we can deploy our own fine-tuned LLMs — even if they share the same architecture as the models already available in the model zoo? 

I get this error when I try to build examples:venv) aetina@aetina:/axelera/voyager-sdk-release-v1.5$ make examplesmake -C operatorsmake[1]: Entering directory '/mnt/microsd/axelera/voyager-sdk-release-v1.5/operators'make[1]: Nothing to be done for 'gst_ops_install'.make[1]: Leaving directory '/mnt/microsd/axelera/voyager-sdk-release-v1.5/operators'make -C examplesmake[1]: Entering directory '/mnt/microsd/axelera/voyager-sdk-release-v1.5/examples'ninja -C Release installninja: Entering directory `Release'[1/7] Building CXX object axinferencenet/CMakeFiles/axinferencenet_DPIR.dir/axinferencenet_DPIR.cpp.oFAILED: axinferencenet/CMakeFiles/axinferencenet_DPIR.dir/axinferencenet_DPIR.cpp.o /usr/bin/c++   -O3 -DNDEBUG -std=gnu++20 -fPIE -MD -MT axinferencenet/CMakeFiles/axinferencenet_DPIR.dir/axinferencenet_DPIR.cpp.o -MF axinferencenet/CMakeFiles/axinferencenet_DPIR.dir/axinferencenet_DPIR.cpp.o.d -o axinferencenet/

Environment SoC/Board: Rockchip RK3588, Radxa ROCK 5B Plus    . It has 2 M.2 slots! OS/Kernel: Debian bookworm, 6.1.43-15-rk2312 (aarch64) RAM: 16GB (also tested with mem=4G kernel parameter; total reported ~3.8Gi) Device: Axelera Metis PCIe accelerator, PCI ID 1f9d:1100 (shows as “Axelera AI Metis AIPU”) PCIe topology: Endpoint at 0001:11:00.0 behind upstream bridge 0001:10:00.0 (Gen3 x2) Drivers: axl/metis-dkms 1.2.3; “Kernel driver in use: axl” shown; /dev/metis-1:11:0 device node present when driver bound Container: Voyager SDK tested in Ubuntu 22.04 container (privileged); device access tests moved back to host after hangs observed inside container Device-tree overlay (added to expand ranges window to 48MB)Reason: Default 32-bit non-prefetchable MEM window under fe160000.pcie was ~14MB; Metis BAR2 asked for 32MB (plus headroom), so

After months of joint development, Ultralytics YOLO models now run seamlessly on Axelera AI hardware. So that’s native support through the Voyager SDK, including their new models. What does this mean for us?🔹 Energy-efficient, high-performance inference at the edge🔹 One-step export & deployment of Ultralytics models🔹 Proven performance across vision tasks, from detection to pose estimation And the cool part:🎯 8K Video Processing with Intelligent Tiling: Tiny objects, no detail lost🧠 Multiple Models on One Chip: 4 YOLO models, 1 Metis🏌️ Golf Swing Analysis: Real-time feedback using Ultralytics pose models There’s more info just gone live over in the Ultralytics docs, and you can check out some demos of it in action by ​@Steven Hunsche below!

Hi, Spanner-san. I was able to get the camera demo working by applying the fix suggested by FUKU55-san in another thread (--disable-opencl). (x) # ./inference.py resnet50-imagenet-onnx usb:0 --show-stats (o) # ./inference.py resnet50-imagenet-onnx usb:0 --show-stats --disable-opencl It seems that the integration between GStreamer and OpenCL is unstable in SDK 1.5.1.

Hello,I am testing some models and I am regularly facing this error message :WARNING : No OpenCL GPU devices foundWARNING : Please check the documentation for installation instructionsDetecting... : 6%|██████▍ | 37/670 [00:01<00:14, 44.36frames/s][ERROR][axeCommandQueueExecuteCommandListsAsync]: Wait kernel failed with return code -1400.terminate called after throwing an instance of 'std::runtime_error' what(): axr_run_model failed with Error at zeCommandQueueExecuteCommandLists(cmdqueue, n_cmdlists, cmdlists, nullptr): cmdqueue_run_cmdlists: 319: Exit with error code: 0x7FFFFFFE : ZE_RESULT_ERROR_UNKNOWNAborted (core dumped)I have used this command :./inference.py yolov8m-coco media/alone.mp4 --no-display --show-s

I’ve already redeployed the model:./inference.py yolo11m-custom dataset --show-device-fps --no-display --pipe gstarm_release_ver: g13p0-01eac0, rk_so_ver: 9INFO    : Using dataset valWARNING : Using deprecated heuristic transpose detection. Redeploy the model to include explicit output infoSegmentation fault

Hi,I installed Voyager SDK 1.5.1 on Ubuntu 22.04 and the Metis PCIe card is detected correctly, but running inference results in a segmentation fault.System Setup OS: Ubuntu 22.04 (AMD64) Hardware: Axelera Metis PCIe card SDK Version: Voyager SDK 1.5.1 GPU/CPU: AMD Ryzen + integrated GPU Installation completed successfully: No pending installation tasks. To activate the environment, type: source venv/bin/activate PCIe Card Detectionlspci -tv correctly shows the Metis device:

./install.sh --all --media --verbose[sudo] password for user: +--------------------------------------------------------------------------------------------------+| Axelera AI - Voyager SDK 1.5.1 |+--------------------------------------------------------------------------------------------------+| O/S | Ubuntu 2204 [amd64] OK ||==================================================================================================|| Development | /home/user/voyager-sdk OK || Pyenv | /home/user/.pyenv OK || Python | 3.10.6 OK || Pip | 24.0

 I am considering installing a local AI system in my company.I kindly ask you to provide me with useful documentation to do this.Please consider a user who only has experience with C++ and Linux servers.Below is a brief project description.Project objectivesCreate an autonomous AI platform installed at your premises.Manage and monitor systems installed by customers (lights, sensors, cycles, errors, maintenance).Integrate Antholux3 and Topiaria2 into a single management architecture.Eliminate cloud dependencies → everything local: secure, scalable, proprietary.Create a technology hub for support, diagnostics, updates and remote assistance.General system architecture2.1 Main componentsLocal AI server (mini-PC, NUC or workstation)Internal databaseCommunication brokerRules engine + local AIUnified dashboardOTA module for updating remote systemsLogging and diagnostics system

Hi guys,After a small break, I was able to get the Metis M2 running on my Jetson Orin NX.However, is it possible that when using ‘axdownload’, the models are not precompiled? I tried to download ‘mobilenetv2-imagenet’, and got this error when trying to load it in. So, my guess is, I incorrectly assumed that the downloaded folder included a precompiled one? Or is there maybe a list of models which do include a precompiled model?Cheers (more info below),SebbeError-----------------------ERROR][zeModuleCreate]: Failed with error code: 0x70000004[ERROR][zeModuleCreate]: Build log:sh: 1: riscv64-unknown-elf-gcc: not foundCommand failed (32512)Failed to build module: sh: 1: riscv64-unknown-elf-gcc: not foundCommand failed (32512)[ERROR][zeKernelCreate]: Failed with error code: 2013265925Error at zeKernelCreate(m

Hello everyone,I’ve been facing problems with Ubuntu 22 where the BARs for my Metis M.2 board are not being assigned correctly. The kernel modules are installed and the device is recognized, but the BAR mapping seems inconsistent, which causes issues when running the Voyager SDK natively on Ubuntu 22.To work around this, I’m considering running the Voyager SDK inside a Docker container based on Ubuntu 22 while my host system remains on Ubuntu 24 (where the kernel modules work fine and the board is detected properly).Unfortunately, I cannot access the Axelera AI Help Center (https://support.axelera.ai/hc/en-us/articles/25953148201362-Install-Voyager-SDK-in-a-Docker-Container). Is there any alternative resource or guide that explains how to install and configure the Voyager SDK inside a Docker co

Big update from the Metis world: the epic new 4-core PCIe card is now available to order over on the store.Huge performance shift that opens up seriously scalable edge AI.📊 Key specs: 856 TOPS (INT8) Choice of 16GB and 64GB DRAM ~12,800 FPS on ResNet-50 Runs dozens of streams or parallel models per board 30–58W typical power draw It’s not just the raw speed, it’s the ability to compress multi-camera inference, object detection, tracking, segmentation, and more into a single card that I find especially exciting. You can prototype in days, using the Voyager SDK’s YAML-based pipeline builder and model zoo (everything from YOLOv5 to sm