Voyager SDK

Hi,I just finished setting up everything and now I’m trying to run an example pipeline using.\inference.py yolov7-640x480-coco cars_clip.mp4Building the model seems to work fine but when the inference starts, it either crashes after some images withDetecting... : 2%|█▋ | 37/1802 [00:01<00:39, 44.29frames/s][ERROR][axeCommandQueueExecuteCommandListsAsync]: Wait kernel failed with return code -1.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)or instantly withINFO : Could not exec vainfo: Command '['vainfo'

For anyone building custom embedded Linux images with Yocto, there's now an official BSP layer available for Metis hardware.It’s pretty niche stuff, but if you're working on embedded systems where you need a stripped-down, customised Linux image (think industrial deployments, kiosks, dedicated edge boxes), this is for you.What's in it:The layer includes recipes for the Metis PCIe driver and the udev rules. Add it to your Yocto build, include the packages in your image, and your custom Linux will recognise and talk to Metis hardware out of the box.Currently supports Yocto 5.0 (Scarthgap), with branches for Kirkstone and Dunfell if you're on older versions.One important thing to note:The VoyagerSDK itself still doesn't run natively on Yocto yet. You'll need to run it inside a Docker container on your target system. The kernel driver runs on the host and handles the hardware communication, but inference and SDK

Hello, I try to use a YOLO model on 8 camera streams in parallel on the Metis PCIe card. 20-30 fps per camera stream is all I need. With the 548 fps end-to-end stated for YOLOv8s in https://axelera.ai/metis-aipu-benchmarks, it should be possible to reach ~68 fps per camera. As a small test I wrote a python script, which creates a inference stream with 8 videos as input:from axelera.app import config, display, inf_tracersfrom axelera.app.stream import create_inference_streamdef run(window, stream): for frame_result in stream: window.show(frame_result.image, frame_result.meta, frame_result.stream_id) fps = stream.get_all_metrics()['end_to_end_fp

Hello everyone,I am new to Axelera and working with a Metis device (4 GiB, PCIe, Voyager SDK). Device: metis-0:1:0 Memory: 4GiB Clock: 800MHz SDK: voyager-sdk  I am trying to deploy the InsightFace model for face recognition and would appreciate guidance on model conversion, supported formats, and deployment steps.Thank you in advance,Akash

Hello,I am facing an issue with a Metis Compute Board where any inference command (or any command using the accelerator) immediately fails with the error:ERROR: No devices found This happens regardless of the model or the command I run.Yesterday, I managed to temporarily recover by removing and reinstalling the Docker container with the Voyager SDK, after which inference started working again. I thought this was because I did not use the metis in a month so maybe it needed to be reinstalled.However, today the same error appears again, even though I did not change anything in my setup.I have already tried rebooting the board, but the problem persists. At the moment, no inference works at all, and reinstalling the container no longer

Hello,I’m currently working with the Metis Compute Board and the Voyager SDK, and I had a question regarding development outside of the SDK.Is it possible to run a script like a custom inference or a similar script directly on the board without going through the Voyager SDK pipeline ?I’m mainly asking to better understand the level of low-level access available on the Metis (runtime, APIs, or tools outside the SDK).Thanks in advance for your help.Best regards,

Hello,Following the question I had on running a GEMM only model, I have settled for a model that has Id Conv → Flatten → Gemm . I was wondering if the Flatten operation is just rearranging some pointer values or if it is doing a memory operation (like copying the tensor or something like that)?Thank you very much,

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

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,   

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,How can I implement the following preprocessing to run the end-to-end C/C++ pipeline?I've already implemented the build_gst function of the custom decoder with C++.def override_preprocess(self, img: PIL.Image.Image | np.ndarray) -> torch.Tensor:#trasformazione immagineimg = np.array(img)if img.ndim == 2:img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)else:img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = np.float32(img/255.)#Stima rumoresigma_est = estimate_sigma(img, channel_axis=-1, average_sigmas=True)sigma_est = np.sqrt(sigma_est)sigma_est = min(sigma_est, 0.08) img = cv2.resize(img, (640,480), interpolation=cv2.INTER_LINEAR)img_tensor = torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()noise_tensor = torch.full_like(img_tensor[:1, :, :], fill_value=sigma_est, dtype=torch.float32)final_tensor = torch.cat([img_tensor, noise_te

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

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/

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.

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