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69 Topics
Axelera CMX
Axelera Team
Axelera CMXAxelera Team
published in Support Central
New to the Community? Here's everything you need to knowBlog

🌟 Welcome Axplorers! Here’s a crash course in getting the most out of your brand new Axelera® AI community. Your hub for innovation, collaboration and Axploration!You’ve just stepped into Axelera AI’s new community, where ideas thrive, connections are forged, and the future of AI is shaped. This platform is designed to help you engage, share, and discover all things Axelera and AI. What Can You Do Here?Explore: Dive into discussions, post your thoughts, and share your expertise. Support: Get help, find support, and help others to do amazing things. Learn: Discover resources, tutorials, and insider tips on Axelera’s technology. Connect: Network with AI enthusiasts, innovators, and the Axelera team. Come say hello and introduce yourself! Compete: Earn points, badges, and ranks for your activity—top the leaderboard for rewards! Creating a New PostYou’re welcome to create as many new posts in as many different places as possible. In fact, you’re encouraged to do so!Just click the “New Pos

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Axelera CMX
Axelera Team
W
Cadet
WGPravinCadet
posted in Metis PCIe
PCIE Metis card voyage sdk Failing and Basic Inference code is not working

I'm currently setting up the Axelera Voyager SDK on Ubuntu 22.04.5 LTS with a verified PCIe Metis AIPU card (rev 02 properly detected via lspci, and metis module loaded). However, during installation with install.sh --all --media, several symbolic link warnings occurred (e.g., media: invalid symlink), and although the runtime reports as installed, the file libaxruntime.so is missing entirely from runtime/lib/. This results in the inference script failing with: Failed to create device manager: libaxruntime.so: cannot open shared object file. Additionally, the AxInference.cpp build fails due to missing axruntime/axruntime.hpp. I’ve already tried clearing ~/.cache/axelera/ and reinstalling the runtime, but the issue persists. Would appreciate any guidance to resolve the missing runtime files and complete the setup.Thanks Pravin Kumar R

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mcunha
Ensign
mcunhaEnsign
asked in Metis M.2
Firmware update

I am having a problem while updating the firmware version to 1.3.0, which is present in the release of voyager-sdk 1.3.1 on GitHub (branch - release/v1.3). I am using the Aetina RK3588 Evaluation Kit. Here is the filename containing the firmware:firmware_release_public_v1.3.0.tar.gzThe output of axdevice command is : Device 0: metis-0:1:0 1GiB m2 flver=1.3.0-rc3 bcver=1.4 clock=800MHz(0-3:800MHz) mvm=0-3:100%Here is the error I am facing:(venv) aetina@aetina:~/devel/axelera/firmware/firmware_release_public_v1.3.0$ ./flash_update.sh --fw-update>>> Metis Firmware update>>> the following operations updates the flash content Make sure to only run this operation if really needed Also make sure to not power cycle the devices or it might be rendered non-functional.Do you want to continue? [y/n] y>>> loading new AIPU runtime stage0[00:15:56.989,227] <inf> app: Start pcie check thread 0x833a00>>> set flash force flag>>> flash firmware[

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dbrenes
Cadet
S
Ensign
saraEnsign
asked in Metis M.2
Inferece.py - RTSP Stream

Hi again ​@Spanner  😊I'm doing other tests and I want to use the inference.py script to detect persons in a stream via rtsp.I’m using the command “ ./inference.py yolov8s-coco-onnx ‘rtsp….’ --pipe gst --frame-rate 0 --rtsp-latency 0 --show-stream-timing”.With the command ‘--show-stream-timing’ I get a latency of almost 2000 ms.I'd like to know if this latency is just in the stream or if it involves the inference time and what I can do to have the stream in real time. Thank you!

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Axelera Team
S
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SiegfriedCadet
asked in Metis M.2
Not enough memory using AxRuntime and M.2 Card on host PC

I am using the Metis M.2 Card on my host PC. It is detected using `lspci` and `axdevice`. I am able to run inference using `./inference.py yolov5m-v7-coco-tracker usb:0`. However when I try to use the AxRuntime API (Python) I get the following: from axelera.runtime import Context,context = Context()model_path = "/home/user/axelera/testing/data/yolov5/compile/compiled_model/model.json"model = context.load_model(model_path)batch_size = 1connection = context.device_connect(None, batch_size)instance = connection.load_model_instance(model, num_sub_devices=batch_size, aipu_cores=batch_size)[ERROR][axeDeviceMemoryAllocate]: Not enough memory: free memory 1531904, request memory 16520704.[ERROR][axeDeviceMemAlloc]: Device memory allocate failed: size 16520704.[ERROR][axeMemAllocDevice]: Device memory allocate failed: 0x70010001.Error at zeMemAllocDevice(context, &desc, size, alignment, device, &addr): mem_alloc_device: 249: Exit with error code: 0x70010001 : ZE_RESULT_ERROR_NOT_AVAILAB

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Spanner
Axelera Team
G
Cadet
GiodstCadet
asked in Voyager SDK
Yolo11 with custom weights conversion and deploy

Hi,When trying to deploy yolo11 with custom weights, I noticed a significant performance drop due to the model conversion to onnx. Can I ask what method and parameters were used to convert the axelera yolo11s-coco-onnx model?Thank you!

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Spanner
Axelera Team
M
Cadet
mgrunerCadet
asked in Metis M.2
Low performance of the Metis M2 on the Raspberry Pi5

Hi! I'm testing the Metis M2 on a Rapsberry Pi 5 using the Ubuntu 22.04 container. I can successfully get results using the inference test suggested by the article:./inference.py yolov8s-coco-onnx ./media/traffic1_480p.mp4 Unfortunately, I get around 4FPS. Is this expected? If I profile the device and the host separately (using --show-host-fps and --show-device-fps) I see that the device is running at ~800FPS while the host is the one bottlenecking at ~4FPS.I tried enabling GLES processing using export AXELERA_OPENGL_BACKEND=gles,3,1but, unfortunately, it makes not much of a difference. I also tried setting the PCI to gen3 by setting the following in the /boot/firmware/config.txt, without any luck:dtparam=pciex1_gen=3As a reference: these are the instructions I'm following:- https://support.axelera.ai/hc/en-us/articles/26362016484114-Bring-up-Voyager-SDK-in-Raspberry-Pi-5- https://support.axelera.ai/hc/en-us/articles/25953148201362-Install-Voyager-SDK-in-a-Docker-ContainerIs this perfo

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Axelera Team
G
Cadet
GiodstCadet
asked in Metis M.2
Failed to get valid board type

Solved

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G
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F
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Falcon9Cadet
asked in Metis M.2
M.2 AI Inference Acceleration card

Non c’è modo di aggiungere RAM a questa bellezza 😁?E’ perfetta per il mio Laptop o su Orange PI ( Embedded ).PCIe 5.0 ? LLM non gira su M.2 ? 😣Leggendo l’SDK… Interessante… 🤔

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F
Cadet
mcunha
Ensign
mcunhaEnsign
asked in Voyager SDK
Support to zsh

I am using zsh as my shell application. While activating the environment, I encountered this error:   voyager-sdk git:(release/v1.3) ✗ source venv/bin/activate _AX_set_env:4: bad substitutionDo you think providing support for shells other than bash would help? 

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Spanner
Axelera Team
F
Cadet
Falcon9Cadet
asked in Voyager SDK
Metis M.2

Esistono e quali sono le architetture di riferimento ?Oppure dei benchmark ( in caso di tuning 🚗)...A scanso di equivoci e per un Proof-Of-Concept 😎

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Spanner
Axelera Team
F
Cadet
Falcon9Cadet
asked in Metis M.2
Metis M.2 Parallelo

Un’altra domanda 😅:Forse leggendo… comunque:Funzionano in parallelo ? 😁 Grazie

71
Spanner
Axelera Team
K
Cadet
ku15921Cadet
asked in Metis M.2
Error installing metis-dkms

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 metis-0.07.16 DKMS files...Building for 6.1.99Building for architecture aarch64Building initial module for 6.1.99ERROR (dkms apport): kernel package linux-headers-6.1.99 is not supportedError! Bad return status for module build on kernel: 6.1.99 (aarch64)Consult /var/lib/dkms/metis/0.07.16/build/make.log for more information.dpkg: error pro

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Axelera Team
dbrenes
Cadet
dbrenesCadet
asked in Metis M.2
Metis M.2 Not Recognized by lspci on Intel system

Hello! I’ve been trying to connect and use the Axelera Metis M.2 card with no success. The same M.2 on a different PC was recognized correctly by using:lspci | grep AxeleraBut now the card cannot be recognized at all on the new system. Here are some specs of the current host:Dell 5820 CPU: Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz RAM: 32 GB GPU: 2 Quadro RTX 5000 OS: Ubuntu 24.04 I’ve also tried lspci | grep 1f9d with no success. The Metis driver is already installed and loaded with modprobe.I'd appreciate any help or insight on additional steps I could try.Additionally, this is the adapter I’m using for the Axelera card: 

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Axelera Team
G
Cadet
GillesCadet
asked in Voyager SDK
Compiling yolov5 doesn't give correct input/output shapes

I’ve used the yolov5s.pt file provided from the official yolov5 repository and converted it to onnx with this command:  python3 export.py --weights yolov5s.pt --imgsz 640 --batch-size 1 --include onnx --opset 17  . I’ve then tried to compile this with this command:  compile -i /home/axelera/Vision.AxeleraTesting/data/yolov5s.onnx -o /home/axelera/Vision.AxeleraTesting/data/compile --overwriteI got this output:09:49:45 [INFO] Dump used CLI arguments to: /home/vintecc/axelera/Vision.AxeleraTesting/data/compile/cli_args.json09:49:45 [INFO] Dump used compiler configuration to: /home/vintecc/axelera/Vision.AxeleraTesting/data/compile/conf.json09:49:45 [INFO] Input model has static input shape(s): ((1, 3, 640, 640),). Use it for quantization.09:49:45 [INFO] Data layout of the input model: NCHW09:49:45 [INFO] Using dataset of size 100 for calibration.09:49:45 [INFO] In case of compilation failures, turn on 'save_error_artifact' and share the archive with Axelera AI.09:49:45 [INFO] Quantizing

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Axelera Team
S
Ensign
saraEnsign
asked in Voyager SDK
inference on a frame obtained from the memory space

Hi again ​@Spanner ! 😊I need to infer an image obtained from byte data stored in a memory space (https://docs.python.org/3/library/mmap.html) and after converting the bytes I get an image.I've seen the sources available for create_inference_stream and I know that I can make inferences to images (https://github.com/axelera-ai-hub/voyager-sdk/blob/release/v1.2.5/docs/tutorials/application.md), but I have to specify the/path/to/file. But in this case I won't have that information.In the code I'll have this line where I read the bytes in the memory space:        frame_size=image_width*image_height*3 //RGB        frame_bytes = mm.read(frame_size)And then I get the new image:      frame = np.frombuffer(frame_bytes,dtype=np.uint8).reshape((image_height,image_width, 3))Is there any way to infer “frame”?Thank you!

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Spanner
Axelera Team
P
Cadet
PepijnCadet
asked in Metis PCIe
deploy.py takes really long yolov8 model

Hi, I’m trying to deploy and inference with a yolov8 segmentation model. I tried to follow the tutorial, but I just get:INFO: deploying model yolov8sseg-coco for 4 cores. This may take a while… How long should this run? I left it on for 3.5 hours and nothing happened (except the loading bar). This seems to be a model included in your library: yolov8sseg-coco.Any idea if I can turn on more extensive logging to see what is happening? Thank you in advance!  

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Steven Hunsche
Axelera Team
G
Cadet
GiodstCadet
asked in Voyager SDK
Yolo11l custom weigts deploy

Hi, I’m getting very pool performance on Yolo11l custom weigts after the conversion in onnx.I’ve used opset 17 and 16, do you have any suggestions?Thanks.

151
G
Cadet
A
Cadet
AfikCadet
asked in Metis M.2
Yocto Driver\Layer for Metis

Hi, We are considering adding an AI Accelerator for our board (i.MX8, yocto).Do you support yocto OS?Is there any reference design or example that runs Metis on yocto platform? Thanks,Afik

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Steven Hunsche
Axelera Team
N
Cadet
negredoCadet
asked in Metis M.2
QNAP NAS compatibility

Will Metis M.2 ( AXE-BME20M1AR01B02) work with QNAP NAS?

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Spanner
Axelera Team
G
Cadet
GiodstCadet
asked in Metis M.2
WASB model for tennis ball detection

How can I deploy WASB model on metis m2?

242
G
Cadet
F
Cadet
fatima-zahra.chenaniCadet
asked in Voyager SDK
axelera-runtime.tar.gz

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,   

422
F
Cadet
lorenzo.lamberti
Cadet
lorenzo.lambertiCadet
asked in Voyager SDK
Voyager-sdk on singularity

Dear all, I am trying to set up the metis PCIe accelerator on a intel workstation. Later we will move to a full risc-v embedded setup.In out lab workstation we are forced to use singularity instead of docker, so i am trying to translate the docker container into a singularity one.I think i am very close to make everything work. However, i am now stuck with a problem i can’t figure how to solve. in particular, i get a weird error when i try to run a simple ./inference.py with one of the pre-trained models. Below the full description on how i create, run the container, and how i run the example from the voyager sdk my `voyager-sdk-1.2.5.def` fileBootstrap: dockerFrom: ubuntu:22.04%labels Author You Version voyager-sdk-1.2.5%environment export DISPLAY=$DISPLAY%post apt-get update && apt-get install -y \ sudo \ git \ pciutils \ lsb-release \ x11-utils curl \ wget \ gnupg2 \ lsb-release \ software-prop

705
P
Cadet
M
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maximiliankirCadet
asked in Voyager SDK
Yolo Performance Issues

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_fps'] print(fps.value)def main(): tracers = inf_tracers.create_tracers('core_temp', 'end_to_end_fps', 'cpu_usage') stream = create_inference_stream( network="yolov5s-v7-coco", sources=[ str(config.env.framework / "media/traffic1_1080p.mp4"), str(config.env.framework / "media/traffic1_

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M
Cadet
J
Cadet
jjprCadet
asked in Metis PCIe
Enabling Metis PCIe on RISC-V host, some low level memory problems

Hi all,I am trying to enable the the usage of the Metis PCIE rev02 on RISC-V Hosts, in particular the Sifive P550. Currently I could successfully (at least I think so), install the drivers and the device gets correctly identified and mapped in the system. I attach the loggings just to be sure: dmesg (after rescan):[  858.500785] pci 0000:01:00.0: [1f9d:1100] type 00 class 0x120000[  858.500843] pci 0000:01:00.0: reg 0x10: [mem 0x04380000-0x04380fff 64bit][  858.500865] pci 0000:01:00.0: reg 0x18: [mem 0x08000000-0x09ffffff][  858.500922] pci 0000:01:00.0: reg 0x30: [mem 0x00000000-0x0000ffff pref][  858.500943] pci 0000:01:00.0: Max Payload Size set to 512 (was 128, max 512)[  858.501115] pci 0000:01:00.0: supports D1[  858.501121] pci 0000:01:00.0: PME# supported from D0 D1 D3hot[  858.516166] pci_bus 0000:01: busn_res: [bus 01] end is updated to 01[  858.516197] pcieport 0000:00:00.0: BAR 14: assigned [mem 0x42000000-0x44ffffff][  858.516209] pci 0000:01:00.0: BAR 2: assigned [mem 0x

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