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Interesting. I do recall that prior to upgrading to CPAI ver2.6.2 (using 2.5.6) the YoloV5.NET model displayed that it was utilising the GPU, however, it seems to utilise CPU with this upgrade. I haven’t rolled back to CPAI ver2.5.6 to confirm my recollection though.
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Yes.
I'm going to try rolling back to 2.5.4.
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Me too.
Rolled back to CPAI ver. 2.5.6. YOLOv5 .NET connected to GPU on startup, and shows it is using GPU (Intel 630) without any user intervention.
Server version: 2.5.6
System: Windows
Operating System: Windows (Microsoft Windows 10.0.19045)
CPUs: Intel(R) Core(TM) i5-7500 CPU @ 3.40GHz (Intel)
1 CPU x 4 cores. 4 logical processors (x64)
GPU (Primary): Intel(R) HD Graphics 630 (1,024 MiB) (Intel Corporation)
Driver: 31.0.101.2111
System RAM: 16 GiB
Platform: Windows
BuildConfig: Release
Execution Env: Native
Runtime Env: Production
Runtimes installed:
.NET runtime: 7.0.5
.NET SDK: Not found
Default Python: Not found
Go: Not found
NodeJS: Not found
Video adapter info:
Intel(R) HD Graphics 630:
Driver Version 31.0.101.2111
Video Processor Intel(R) HD Graphics Family
System GPU info:
GPU 3D Usage 10%
GPU RAM Usage 0
Global Environment variables:
CPAI_APPROOTPATH = <root>
CPAI_PORT = 32168
Module 'Object Detection (YOLOv5 .NET)' 1.9.3 (ID: ObjectDetectionYOLOv5Net)
Valid: True
Module Path: <root>\modules\ObjectDetectionYOLOv5Net
AutoStart: True
Queue: objectdetection_queue
Runtime: dotnet
Runtime Loc: Shared
FilePath: bin\ObjectDetectionYOLOv5Net.exe
Pre installed: False
Start pause: 1 sec
Parallelism: 0
LogVerbosity:
Platforms: all
GPU Libraries: installed if available
GPU Enabled: enabled
Accelerator:
Half Precis.: enable
Environment Variables
CUSTOM_MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\custom-models
MODELS_DIR = <root>\modules\ObjectDetectionYOLOv5Net\assets
MODEL_SIZE = MEDIUM
Status Data: {
"inferenceDevice": "GPU",
"inferenceLibrary": "DirectML",
"canUseGPU": true,
"successfulInferences": 179,
"failedInferences": 0,
"numInferences": 179,
"averageInferenceMs": 245,
"histogram": {
"person": 63,
"car": 262,
"bus": 19,
"truck": 2
},
"numItemsFound": 346
}
Started: 16 May 2024 7:52:55 AM Central Standard Time
LastSeen: 16 May 2024 8:07:00 AM Central Standard Time
Status: Started
Requests: 179 (includes status calls)
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So it looks like its a CPAI ver2.6.2 issue then.
YoloV5 .NET GPU works successfully on CPAI ver2.5.6 but doesn't in CPAI ver2.6.2
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i have a fresh install of code project 2.6.4 on ubuntu server 24.04, object detection installed fine on on the install but trying to install LPR and Training and i get a 404 error for both of them
Server version: 2.6.4
System: Linux
Operating System: Linux (Ubuntu 24.04)
CPUs: 12th Gen Intel(R) Core(TM) i7-12700K (Intel)
1 CPU x 12 cores. 20 logical processors (x64)
GPU (Primary): NVIDIA GeForce RTX 2060 (6 GiB) (NVIDIA)
Driver: 535.161.08, CUDA: 12.2 (up to: 12.2), Compute: 7.5, cuDNN:
System RAM: 31 GiB
Platform: Linux
BuildConfig: Release
Execution Env: Native
Runtime Env: Production
Runtimes installed:
.NET runtime: 7.0.18
.NET SDK: 7.0.118
Default Python: 3.12.3
Go: Not found
NodeJS: Not found
Rust: Not found
Video adapter info:
TU104 [GeForce RTX 2060] (rev a1):
Driver Version
Video Processor
System GPU info:
GPU 3D Usage 10%
GPU RAM Usage 1.9 GiB
Global Environment variables:
CPAI_APPROOTPATH = <root>
CPAI_PORT = 32168
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Are you still getting 404s?
cheers
Chris Maunder
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Upgraded to 2.6.2, Blue Iris intermittent AI Error 500 are back.
Can anyone explain the actual root cause of these errors? I'm getting a bit tired of the game between BI and CodeProject where updating one or the other has a 75% chance of re-introducing these errors.
Thanks
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I'm sure you've reported stuff before but it's extremely difficult for us to remember everyone's details on everything.
Can you please let us know
- what version of the server you're using
- on which module you're seeing the error
- The actual error (screen shot would be good)
- Have you tested the image that's throwing the 500 using the CodeProject.AI Explorer. It uses the same API as Blue Iris
cheers
Chris Maunder
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For sure. For reference I was on 2.5.4 w/ Blue Iris 5.8.8.12 and had no AI errors.
Updated to CodeProject 2.6.2. Intermittent errors started every 15-30min on random cameras.
Decided to upgrade BI to latest 5.9.0.5. Still exist.
I'm using Coral TPU, tried various models and sizes. Multi-TPU disabled.
I don't know what images it's failing on unfortunately; BI doesn't give me the optics into that.
 09-05-24-1817 hosted at ImgBB — ImgBB[^]
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Does it work better if multi-TPU is enabled?
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It does not. I've now shifted to 2.6.4 on Ubuntu at the recommendation that it's stable, looked good for a day or so but 500 errors are back sporadically.
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Hm. I wonder if it’s affect d by something like the ‘parallelism’ parameter? I think right now it’s set to 16 for Coral.
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I don't know if it's relevant, but if I sit and watch the web interface I can often see the Coral module on the status tab switching between CPU/TPU and the latency will increase for the CPU processed images then it flips back to TPU.
I see nothing in the logs about why it is flapping between them.
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Huh. Weird. Does anything work better or worse as it flips between them? What is the timing like, does it spend a lot of time in one or the other? Does it work any better if you added a time.sleep(1) before the allocator to give the driver a second to catch up?
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Coral on Windows is not very reliable I'm afraid. It works, but intermittent errors are not uncommon. I use the .NET Object Detection module on Windows since it uses DirectML and so will make the best use of available hardware.
cheers
Chris Maunder
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What do you recommend for Coral? Docker? Linux? I can pivot if it's known to be more reliable.
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I’ve been running Ubuntu 20.04 and it’s been rock solid.
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I'd second Seth's suggestion on Ubuntu if you want to use Coral.
cheers
Chris Maunder
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Spun up an Ubuntu 22.04 VM, passed the PCI-E Coral through to the VM (running on Proxmox), installed CPAI 2.6.4 and so far so good, no 500 errors.
Running in multi-tpu mode even though I only have a single card.
EfficientDetLite small model.
Averaging 10-15ms.
Thanks for the suggestion. Decoupling CPAI and BI has been on my list to do for a while just wasn't sure the Linux version of CPAI was more stable and worth the effort.
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10:25:08:Unable to download module 'ObjectDetectionCoral' from https:
Is the url correct? It is under KB article. I tested downloading license plate module without issue.
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Thanks very much for your report. Could you please share your System Info tab from your CodeProject.AI Server dashboard?
Thanks,
Sean Ewington
CodeProject
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Please below
Server version: 2.5.1
System: Docker
Operating System: Linux (Linux 6.6.20-production+truenas #1 SMP PREEMPT_DYNAMIC Tue Apr 23 01:22:22 UTC 2024)
CPUs: Intel(R) Xeon(R) CPU E5-2430 0 @ 2.20GHz (Intel)
2 CPUs x 6 cores. 12 logical processors (x64)
System RAM: 31 GiB
Platform: Linux
BuildConfig: Release
Execution Env: Docker
Runtime Env: Production
.NET framework: .NET 7.0.15
Default Python: 3.10
Video adapter info:
System GPU info:
GPU 3D Usage 0%
GPU RAM Usage 0
Global Environment variables:
CPAI_APPROOTPATH = <root>
CPAI_PORT = 32168
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Thanks very much for that. This is an older version, and I would recommend upgrading and see if that helps you.
Thanks,
Sean Ewington
CodeProject
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It doesn't matter what I have turned on... facial recognition, license plates, object detection - the resulting video turns all of the background a crunchy grey and only the moving objects are visible
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I'm sorry but I don't even know where to start. Can you please explain what your are doing, what systems you are using, maybe a step-by-step of what you are doing and then we can start dissescting this.
cheers
Chris Maunder
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