Nvidia container memory leak. With just 1 stream you won’t notice the memory leak.
- Nvidia container memory leak Yes, it’s argus/apps/camera you can run it to change sensor mode and check if any memory leak and check the code follow to compare. Modified 14 years, 2 months ago. 0-dp-20. from omni. This Subreddit is community run and does not represent NVIDIA in any capacity unless specified. py Install “lsof” tool $ sudo apt-get install lsof Run your In order to run this example using nvidia-docker you can do the following: docker pull camerai/nvcr. Is it due to memory leak in any elements in the app? yuweiw October 16 But everytime “Inference” work done, the memory increase 0. When testing on rtspsrc, after few hours of operating, the memory leaks can accumulate up to 60GB. , Best, Chay So I thought it might be a good idea to just disable the nvidia telemetry if possible. But doesn’t happen while using h265/h264 decoders. 7812 MiB RssFile: 687. Open 1 of 2 tasks. When running Python DeepStream in a Docker container (based on the official NVIDIA Docker image running Ubuntu 22. Chen It occurs both natively and when run from within singularity container (converted from the docker image) where the host’s memory remains unavailable after the container is closed. DRIVE AGX Orin General. I didn’t think much of it, but my comp has been crashing some time after I start up WoW since then- Task manager says it’s always chewing up power, loading screens seem a little longer/laggy. I'm running on a GTX 580, for which nvidia-smi --gpu-reset is not supported. This issue is happening with RTSP stream. I traced the problem to the cuMemImportFromShareableHandle function. x86_64 #1 SMP At the same time, if we divide the monolithic app into 2 smaller apps (Model A in container A, model B in container B) and run it via Nvidia-Docker, we see that GPU memory consumption is higher than if we run it as a monolithic single app. The app pipeline is simple : rtspsrc(2 video stream) → streammux → nvinfer Please provide complete information as applicable to your setup. The issue seems to be related to the rpcrdma module. It looks like the issue comes from the tracker and I kinda have a feeling it’s because of stationary objects (parking cars) being endlessly tracked. I also have the Beta Nvidia app installed. I wonder if there is any bug or compatibility issue with dlib and Jetpack. txt (4. Additionally, when closing the cuda context associated to the decode, there is a very, very minor memory leak. log you shared, the “definitely lost” memory leak is about 93kB. Dear Nvidia Support Team, I hope this message finds you well. as can bee seen here there is signicficantly less memory leak compared to how it performed from the clients RTSP source. I have attached a minimal example below. I’m guessing the Supervisor then restarts HA after which everything is fine until the memory creeps up again, then rinse and repeat. TensorRT Version: 7. 82 CUDA Version: 10. The problem is we don't free srcData in event_msg_meta_release_func. Please stay tuned for the next release. ¥ÿÿWdо–Ö nfÀ® g>,% (h Hï7 €ªEBæ «?~ýùç¿?%0€c €ÿ F“ÙbµÙ N—Ûãåíãëçï³Ìªú÷çED"€Ð ÝÚÌü¦Ð‰¼æÚó* ü”8I‘L’ E — ù ý·k¸Î™H6I×Gò>ˆ „¯{ Õ ª¢ŒËŒsy¢ T¬ÿÛ¯>íÿ¼@j_ ! œø[ *éî×}†–fþ,Ó ø ¾ úÛ 2€Š]NŒ ѨbUÕ«× ^wHFÅÅlÌï ïzä•ÆF7¡¨ð‹ÉPsì¦ ØBHB s¦ñm̾áJÓß)‰˜Gh wÏl Fix NVIDIA Container high Disk, GPU, Memory usage. Game launches and plays fine for variable amounts of time, then freezes and has a 50/50 chance of crashing. Right • Hardware Platform (Jetson / GPU) Jetson Orin Nano • DeepStream Version Deepstream 7 • JetPack Version (valid for Jetson only) Jetpack 6 • TensorRT Version libnvinfer-dev 8. 5 KB) Possibly related issues: Memory leak in DeepStream - #15 by Fiona. 11 GPU Type: 1080Ti Nvidia Driver Version: 440. 13: 97: target-docker-container running cuda-samples require unintended extra permission. 3 Operating System + Version: Debian9 Python Version (if applicable): 3. Ask Question Asked 14 years, 2 months ago. 15 / 436. Uses 10% of CPU constantly, does not stop. 0 Baremetal or Container (if container which image + tag): Relevant Files. 11 kernel with nvidia GPU Nov 6, 2024 dhiltgen added linux nvidia Issues relating to Nvidia GPUs and CUDA labels Nov 6, 2024 The DCGM has a memory leak？ #340. 26 CUDA Version: V10. 13 and Here is CUDA Toolkit version information. Which Im betting is the culprit. But both of them are closed now saying merged at a commit. cudlaImportExternalSemaphore During the engineering testing phase, it was discovered that the cudlaImportExternalSemaphore API leaked 1B of memory every time. exe was the process using the most memory after closing out the game. 1, GCID: 33958178, BOARD: t186ref, EABI: aarch64, DATE: Tue Aug 1 19:57:35 UTC 2023 ESC to enter Setup. So I have tried the following: 1. xvd/virtual dr” I assume you're referring to the 'Nvidia Container' process. PresentMon_x64. And no, returning objects by value doesn't leak memory if the object itself is correct. There isn't really a specific kernel I'm debugging—it's a 70k-line project that I've started to look at (closed source unfortunately) and the memory leak could be hiding almost anywhere. 0 • JetPack Version 6. Did some research as I noticed a program using an unreasonable but not an absurd amount of memory after running the game. Accelerated Computing. NVIDIA container starts with PC when sunshine service is enabled on startup. core. increasing idle temps by 20 degrees C If sunshine serivce is not started automatically and instead done manually, NVIDIA container does not turn on until moonlight connects to the server, however this process continues after disconnection using Hi @g-ogawa do you mean you have an DS python application which are based on and the nvinferserver config? From your dockerfile, seems it’s not enough to get these setup, could you share a complete setup? Thanks! I found a forum discussing that Anno might have a memory leak issue, and that even if you had 64GB RAM, you'd still eventually run out of RAM due to the leak, it would just take longer to get there. I’ve run Compute sanitizer does only check for device memory leaks. If you have an Nvidia graphics card, the Nvidia Container This issue reports a potential memory leak observed when running NVIDIA Triton Server (v24. The odd thing about the memory leak is that the memory is not released when our application is terminated. 7 KB) Download attachment on to Jetson device and rename to nvmemstat. happy2048 opened this issue Apr 21, 2022 0 42h gpu-operator-node-feature-discovery-worker-slmd8 1/1 Running 0 42h nvidia-container-toolkit-daemonset-5kj7z 1/1 Running 0 42h nvidia-container-toolkit-daemonset-t84ns 1/1 Running 0 42h nvidia-container-toolkit-daemonset-tk6hg 1/1 Running 0 42h nvidia Hi Team, I realize that when send event through msg broker, the memory is increase by the time. I am currently working with the Nvidia CUDA interface and have encountered an issue while running the cudaNvSci sample from CUDA 11. 2. • NVIDIA GPU Driver Version (valid for GPU only) 470. For previous NCCL release notes, refer to the NCCL Archives. I have 2 rigs with 8 1080 ti per rig and both have issues with random GPU showing IDLE after 24 hrs or so even though I have 0. There are many 1 object leaks that contribute constant and insignificant leak. 0 CUDNN Version: 7. txt on dgpu with DS6. AMD 5800X. Booting to the menu it climbed to 12. A sample pipeline like so: gst-launch-1. We have fixed some memory leak issues in the latest versions, but we cannot provide corresponding patches separately for a particular The valgrind logs show memory leaks in the new nvstreammux. 5430 MiB lsof: 5 PID: 1645075 16:20:15 Total used hardware memory: 0. 04. 0000 KiB Free: 0. 33. 5 Operating System: ubuntu 18. 7. 6 Tensorflow Version (if applicable): 2. deepstream-test3: RTSP from client side : The memory usage is not coming from rtspsrc as the memory leak is in the GPU memory. ; about rtsp test issue, seems it is related to rtsp source. 5. This can be mitigated by using a different malloc implementation. Still testing on JetPack 4. so with the number of object being leaked increase over time. The server seems to hold onto physical I am also having the exact same issue, huge memory leaks since 6. Related topics October 8, 2024 Memory Leak of deepstream-test3 (using grpc, triton-server) DeepStream SDK. viewport") Though I found another way to create multiple cameras, I’m still curious about how this would lead to the memory leak problem. How to set up Deepstream 6. I am saying this since most people are reporting Nvidia container starting to use the CPU after playing the game. 1, an inference request was sent to tritonserver, so memory was consumed by about 8176. g. 32-1 Operating System + Version: Ubuntu 18. 4 github samples or Devzone release. 6 | 4 Chapter 3. 02 · triton-inference-server/server · GitHub, but always get GPU memory leaks from 2mb each run to 1GB for complex models. I was able to reproduce this behaviour on two different test systems with nvc++ 23. 01 CUDA Version: 10. In short, a user can create a very simple loop in a shell script and harvest whatever random data in memory. 9. Am running the latest major version of docker (Docker version 1. 2 CUDNN Version: 8. 3 / Deepstream 7. cc:290] Collecting metrics for GPU 0: NVIDIA GeForce RTX 3080 Laptop GPU I0520 01:16:41. the only new modification is replacing nveglglessink with fakesink(I did not test nveglglessink ). (I think) I was running Mangohud with monitoring turned on for memory, vram, and swap. For example, lines 416-428 of the valgrind log show a memory leak at line 1058 of gstnvstreammux. I hope we get a reply soon. Actually return-by-value doesn't necessarily leads to a copy. CUDA. If you uncomment line 38-39 you will solve the memory leak, but this will lead to corrupted video frames when connecting this video decoding bin to an actual Deepstream pipeline (with nvinfer, etc) main. 82 card : Quadro RTX 8000 os: centos 7, 64bit i try following code on Mac, memory was not leak. 8GB before the game crashed. The T4 probably can’t decode more than 30-40 1080 streams. • Hardware Platform (Jetson / GPU) rtx3050 • DeepStream Version 6. The memory leak troubleshooting tool we use is Valgrind. isaac. free_gbuffer(sessionid) pyds. 0% rejections, hash rate NEVER drops, and the shares accepted per card Description. 0 • JetPack Version (valid for Jetson only) 4. It shows up for GFN but on my system it uses virtually no resources. I hope I have clarify my Anyone else having an issue with what seems like a memory leak with Nvidia drivers. I have also noticed that it’s not completely because of Triton, but switching to nvinfer seemed to lower the leak amount for my custom application. 04 Python Version (if applicable): python 3. Before looking at the potential solution, what we need to do is suspend NVIDIA Container, restart your computer and see if the issue persists. Hi @yuweiw The memory growth is happened by deepstream docker container. Servers running with xprtrdma Tell us about the hardware configuration of the GPU, including the output of 'nvidia-smi' What is the environment? Is DCGM-Exporter running on bare metal or in a virtual environment, container, pod, etc? Same here, Dedicated server (docker container on unRAID) and started to see memory from my 32GB avail; the container had over 24 Utilized and a few in swap this morning. ; could you share your use scenario? why do you need to restart {{Framework. 1 PyTorch Version (if After installing 436. 2 CUDNN Version: 7. 243 CUDNN Version: 7. With previous driver relaeses, no such leak occurs. As mentioned above, my problem occurs after I recreate a new pipeline after I unref the pipeline, the memory will increase/leak, while if the pipeline works well, I can get the video and the memory is stable. 12 This is the NCCL 2. 03 CUDA Version: 11. boxerab June 3, 2023, 12:59pm 1. Please attach a self-contained repro app to the bug report. 0000 KiB Client: 0. Where the problem was about system(EDK2) not switching slots. the user confirmed the fix works. I had memory leaks before (worse yet, gpu memory leaks are happening constantly for the last two months), but today I updated to 107. Please review the changes and Hey @marcoslucianops Yes, I am in a similar situation. The same code does not leak on either Pascal or Turing based workstations. alloc_nvds_event_msg_meta(user_event_meta). As a result, device memory remained occupied. Please find attached the This leak shouldn’t happen though, even in this situation, so I have opened a Qt issue to report it. mcgarry and downloaded libgstnvvideo4linux2 . Nvidia Driver Version: 440. Model is fixed size To reproduce memory leaks I used batch 1. cuda-memcheck doesn’t seem to be compatible with deepstream. gstreamer, docker, deepstream. [DS 5. 04), I observe that the container’s memory usage increases after completing one pipeline and creating a new one. This issue reports a potential memory leak observed when running NVIDIA Triton Server (v24. 1 triton mirror in nvinferserver with tritonserver Grpc data interactive way, there is a memory leak My CUDA program crashed during execution, before memory was flushed. The worst part is I don't remember when all the leaks started happening When I’m using some IP cameras with MJPEG h264 streams, my python deepstream-nvdsanalytics app appears memory leak, with VMRss continous increase. 407857 44 libtorch. WTF is Nividia container and why it consumes 20% of GPU !! Help Archived post. After further investigation, it seems the partition where UEFI variables are stored, is getting full Hello, Some time ago (a couple of weeks maybe?) I noticed that all my programs using graphics libraries (Vulkan, SFML, Allegro) suddenly started leaking memory on exit (as reported by Asan). 2 • NVIDIA GPU Driver Version (valid for GPU only) nvidia-smi: NVIDIA-SMI 540. While my final application is different, I have observed the leak using the linked application (deepstream-rtsp-in-rtsp-out) as is, without modifications aside from setting the nvinferserver config to one that uses grpc and YOLOv4, and yes, that is also the repository I used to compile the post processing plugin for YOLOv4. 4 grows linearly, which makes sense for the replay buffer with 1M transitions. This morning after I rebuilt the container (8AM) to try 5. 35. import tensorrt as trt import Unity Container Memory Leaks. 04 Describe the problem you are having I have only just started experiencing, or at least, noticed (8PM )that my docker frigate can experience runaway memory usage. 0 test5 app to implement the runtime source addition/deletion The problem is docker won’t calculate cuda and pytorch used memory, if you use docker stats of a pytorch container, it will be ~100MB memory usage, but actually it took over 3GB memory to run the container, most of them are used in GPU. 1 Like. 2. But with other cameras like hikvision, there is no memory leak . 1 with dGPU as our Deepstream 6. extensions import enable_extension enable_extension("omni. 1. 1-0 and Cuda 11. I can share the video via DM with an nvidia engineer who wishes to reproduce. Share Sort by: Best. I turned off zswap and turned on regular swap on disk, but it doesn’t help. However, when the request is completely terminated and nvidia-smi is checked, the memory becomes 5680, and as a result of repeating the request and checking nvidia-smi, A problem arises in which memory waste gradually accumulates. When I check nvdia-smi, I can see the memory usage climb while the decoder utilization is only ~30%. 7 release notes. Was after coming back to my PC after idle, but not come back from sleep. 0000 KiB VmSize: 23601. With just 1 stream you won’t notice the memory leak. 1 Release . io-nvidia-caffe-18. 6GB with 1. Memory Leak of deepstream-test3 (using grpc, triton-server) Restarting the pipeline all the time will cause Hello! Since the v471. It literally becomes 4. CUDA Programming and Performance. How to free memory after Inference invoked? detectnet-camera. 1 and it went straight to the gutter. py (7. description : 'Join the GeForce community. The Nvidia Display Container is another process that you could disable in order to try and rectify the problem at hand. 4 TensorFlow Version (if applicable): PyTorch Version (if I have included here the container memory usage when running the app for 30 minutes with a 1080p 10fps 4000 kb/s h264 input RTSP stream. txt (6. Experienced this exact same issue today. We have been tracing what appears to be a memory leak in the kernel on the Jetson Orin Nano Developer Kit, running both the original JetPack 6. But i'm having this issue that looks like a memory leak, that's the only way i can explain it. Disable Nvidia Display Container. NVIDIA Developer Forums Detecting memory leaks. I have tried to narrow down the problem by completely removing decoders and just using a test video source with streammux, nvinfer, tracker and Diagnosing memory leaks in containers is more complex than in traditional environments due to container isolation, but it’s essential for maintaining robust and resilient production systems. So I’ve run the following tests using the container nvcr. I believe this is unrelated to the ARM-specific issue in #1421, so that's why I'm creating a new issue to track it. F11 to enter Boot Manager Menu. WHat happes is - i play a game, and FPS slowly starts to "drain". Failed to find memory test protocol Hi there, back in Dec, I heard there was an Nvidia memory leak is with WoW. cc:1029] TRITONBACKEND_Initialize: pytorch I0520 01:16:41. then I did not observe obvious CPU memory leak using top command-line. New leak reveals Nvidia's CES 2025 lineup: GeForce RTX 5090, RTX 5080, RTX 5070 Ti and RTX 5070 to be shown I think I found the issue. 04 Host installed with DRIVE OS Docker Containers [*] other. py. 82. Hit continue and I watched memory hit 15. 0GA_Jetson_App] Capture HW & SW Memory Leak log nvmemstat. in my log memory-0305. Thanks. Ok, we were able isolate the camera that is causing this issue. Hasa October 17, 2024, Model: NVIDIA Jetson Xavier NX Developer Kit - Jetpack 5. io/nvidia/deepstream: version 6. 2 • JetPack Version (valid Hi, We are using the Video Codec SDK to play and loop video and we have noticed that while looping, memory usage escalates and appears to be uncapped. Refer to the Support Matrix for the Description I am upgrading tensorrt4 to tensorrt7, when using dynamic input in tensorrt7, gpu usage was keep growing, but use fixed batchsize, its performance is slower than dynamic input. wei, I want to confirm if you are using DRIVE AGX Orin Devkit platform as you marked other in Hardware Platform. 5 LTS; Isaac Sim 2022. @mfoglio I noticed this happens to me especially when using nvjpeg decoders. 0000 KiB hardware memory: 0. It keeps working in any range between [1, 35] available cpus and gets hanging when cpus count is 36. In this case something like unique_ptr would be fine, or even just storing structA by value. I was hoping the problem would go away after some system upgrade, but so far it hasn’t. New comments cannot be posted and votes cannot be cast. 4. Hope this will help anyone meet the problem about memory leak when send event by nvmsgbroker plugin. free_gbuffer(sr_user_context_buf) You seem to have provided some legacy code, please refer to this FAQ. cpp. image (2) 1369×2054 106 KB While this level of memory increase for one stream is tolerable, we’d like to run quite a few streams constantly on a system that won’t be restarted too often, so a constant memory usage Docker daemon memory leak has been talked about in this issue and this issue. My (basic) analysis shows the culprit to be Nvidia drivers. Installing old version of the drivers While searching why files <= 700 bytes would be corrupted in our HPC environment, I discovered that they are not only “corrupted”, but contain parts of the memory. NVIDIA Developer Forums Cudla api cudlaImportExternalSemaphore memory-leak. Both memory and CPU usage is @SivaRamaKrishnaNV What your machine is like in a Drive OS and CUDA environment. A place for everything NVIDIA, come talk about news, drivers, rumors, GPUs, the industry, show-off your build and more. However, we are encountering the same memory leak problem in Description. It seems that PyTorch reserves some GPU for itself. 5 GPU Type: GTX1080 Nvidia Driver Version: 430. Why Memory Leak EDIT: Just confirmed it. 1? show post in topic. [Overview] • Hardware Platform (Jetson / GPU) = Jetson Orin NX 16G • DeepStream Version = DS 7. csv) are found in the This was driving me crazy yesterday, btw Nvidia container started using 20-30% of my 12900k in the same moment I started playing Warzone. py <gpu_num> heaptrack: no major memory leaks (detects ~6MB of memory leak in gstreamer which should be OK) Restarting containers We have also tried restarting certain containers to boil down the issue as restarting the containers frees a certain amount of swap memory. To make sure that the leaks are not caused by us and that it is possible to easily reproduce them we created a simple command line GStreamer pipeline with a single RTSP camera as input. Apprently, creating and immediately destroying a Vulkan instance is enough to NVIDIA Developer Forums Cudla api cudlaImportExternalSemaphore memory-leak. 0-samples These were the observation. If you are on a supported platfotm and you believe (after careful checking of your code) that there is a problem with the CUFFT library (such as a memory leak), please consider filing a bug report through the registered developer website. 30 Operating System + Version: Windows 10 Python Version (if applicable): TensorFlow Version (if applicable): PyTorch Version (if applicable): Baremetal or Container (if container which image + tag • Hardware Platform (Jetson / GPU) Jetson Xavier (AGX and NX) • DeepStream Version 6. Tcmalloc and jemalloc are installed in the Triton container and can be used by specifying the library in LD_PRELOAD. Also, I see cuDLAStandaloneMode is not part of CUDA 11. 6. 1 Applications May Be Deployed in a Docker Container NVIDIA DeepStream SDK Developer Guide 5. In the FAQ, there is the following code to release memory # release native memory pyds. It run well finally. 12: Possible UEFI memory leak and partition full. inf_amd64_45030e1b94489c65\Display. Reply reply Nvidia driver - possible memory leak? graphics/kernel/drivers Hello! I recently got a 1060 Nvidia card, an upgrade from the Ryzen 5 5600g iGPU. c. Redshift can't fix it as it is not on their side of things it is more of Windows Memory Allocation issue due to NVIDIA driver. 04), I observe that the container’s memory usage When running the example in #1421 on an x86 machine (Gorby) using the cuda-quantum Docker containers, a memory leak is appearing (as can be seen by watching memory usage in nvidia Using Triton v23. 12. 04 Python Version (if applicable): TensorFlow Version (if applicable): PyTorch Version (if applicable): Baremetal or Container (if container which image + Due to the size of those execution plans, this seemed like a memory leak (it technically isn't, but you'd most likely run out of memory before having cached every combination of conv params). Placing cudaDeviceReset() in the beginning of the program is only affecting the current context created by the process and doesn't flush the memory allocated before it. 5 GB + 2. This is problematic for me as my NVIDIA Developer Forums Kernel level memory leak LT 36. 1 caused by Display Container memory leak . # Nvidia Triton 21. 12 | 3 Chapter 2. It can possibly be avoided due to RVO or, if c++11 is used and RVO is impossible the return value is moved instead of beeing copied (moving a vector costs about three pointer assignments, so that doesn't really cost much). Their memory limit is set to 600 Mb but in fact they need about 400 Mb to run. 3. I have only recently installed and configured Frigate and since the outset, I seem to be having a memory leak. 3 in a docker container. I’ve noticed that there is a jetson L4T patch that fix the mjpeg nvv4l2decoder memory leak issue, but not with dGpu. NCCL Release 2. Can you give us a reference? I tested deepstream_test1_app. window. i used script from DeepStream SDK FAQ - Intelligent Video Analytics / DeepStream SDK - NVIDIA Developer Forums to check HW & SW Memory log. Best When I install nvidia drivers, I only install There is Riva deployed in EKS, and we observe high memory usage during and after our load test the count of threads before the test run: root@riva-api-en-primary-5d94766b8f-tczqs:/opt/riva# ps auxwwH | grep riva_server | wc -l 25 root@riva-api-en-primary-5d94766b8f-tczqs:/opt/riva# ps auxwwH | grep tritonserver | wc -l 70 NVIDIA Collective Communication Library (NCCL) RN-08645-000_v2. Hi，thank you for your replies very much I’ve already tried nvv4l2decoder,but there are still memory leaks. 41 driver of Nvidia, the Nvidia container LS process from the System 32 route (C:\Windows\System32\DriverStore\FileRepository\nvmii. With more, NVDEC usage goes above 100%, and this leads to the memory leak. 3 Operating System + Version (if applicable): 1. PID: 1645075 16:20:14 Total used hardware memory: 0. /home/nvidia# cat /etc/nv_tegra_release # R35 (release), REVISION: 4. Much like the 3 aforementioned applications, the display container can There seems to be GDDR7 memory across the board, at least for the top three SKUs. Enter to continue boot. Hello everyone, I have observed a strange behaviour and potential memory leak when using cufft together with nvc++. I see people having same issues in forums but nobody ever posts a link to As it loops, the container memory will run out and python got killed. in googletest) the cuda memory of my Nvidia Jetson Orin Nano runs out after several minutes and instantiations. (This is my understanding) When I define a large (>1024 byte) fixed-size array within a kernel, the compiler appears to create a global memory allocation which persists past the completion of the kernel and, as far as I have been able to tell, there is no way to free this allocation for the remainder of the program. dll, killed the nvcontainer. 1 [L4T 35. 2 • Issue Type( questions I confirmed that the Python sample app provided by NVIDIA has deepstream-test3 that uses triton-server, so I would like to check its operation and check if there is a memory leak. 0; Driver version 525. docker. 03 with Python/TensorRT Backend (image from Nvidia Container Registry) and performing the inference with dynamic input size, there seems to be a memory Posted by rcioffe: “Nvidia Container memory leak due to Xbox play anywhere . There is no memory leak here. There is a huge difference between memory used by pods and node memory usage , when we check it on worker node it seems that containerd itself using the most memory, the problem happens for one of our product teams as we use reserved kubernetes clusters for product teams and all of our kubernetes clusters have the same configuration with • Hardware Platform (Jetson / GPU) Jetson Xavier NX • DeepStream Version 5. Dear @haihua. Generally speaking, memory leaks do not lead to the size growth of disk. exe and for now it works :) It might come up again, when updating to a new Nvidia driver/Geforce experience or even after restarting the computer, but for now it works. description ? Framework. Wow. NVIDIA Container NVIDIA TelemetryApi helper for NvContainer NVIDIA LocalSystem PSA: 418. 09-py3) with model-control-mode=explicit. Browse categories, post your questions, or just chat with other members. 0, build 4595d4f), but still face a monotonically increasing memory usage issue. 4-201. it is minimal, how long did you test? About the fix above, this is the original topic. but i can not get memory leak. 04-devel docker • NVIDIA GPU Driver Version (valid for GPU only) 10. 04-py2:mem_leak nvidia-docker run -it --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 camerai/nvcr. Some users report success using a memory optimizer software that can trim unutilized RAM, such as Process Lasso. 3-1+cuda12. NVIDIA Developer Forums Using the same docker container from NVIDIA NGC. In later versions, this was fixed by introducing an LRU cache with a default size of 10,000 entries (this is controlled by the env var Here is the result of the only -1 results (from my initial run without --ipc=host or --privileged): only_negative_1. 12 has been tested with the following: ‣ Deep learning framework containers. On Intel cards everything is correct, but on Nvidia there is huge memory leak when I create and close window. My environment: Ubuntu 20. – There is only one OpenGL context on render thread. In particular one stays around 1GB while the other continuously goes up to above the 16GB. Because app is getting crashed after couple of days, and the memory is not released even after the docker container is stopped, but the memory is released when the docker container is deleted from the machine. utils. 0 machine does not reproduce the issue. 407833 44 libtorch. 01 • Issue Type( questions, new requirements, bugs) docker stats <Deepstream-container-name> I observed that the memory utilzation kept on increasing. https:// Valgrind is reporting me: ==10549== 120 bytes in 5 blocks are still reachable in loss record 9 of 18 ==10549== at 0x4A05FBB: malloc (vg_replace_malloc. 3. The leak amounts approximately to the size of one surface, and leaks from hi DaneLLL, Thanks for you quick reply! I have reviewed your recommended post, however, I think it is not helpful to my problem. 7 This is the NCCL 2. I have discovered that when I comment out the line // assign_image(img, dlib::cv_image<bgr_pixel>(temp)); there is no memory leak, but it also stops face recognition. Compatibility NCCL 2. Intelligent Video Analytics. 1] Hardware: - Module: NVIDIA Jetson Xavier NX (16GB ram) Platform: - Distribution: Ubuntu 20. 2 Problem: Basic Logic I have modified deepstream5. But it showed up once I put a But we can’t use the parallel compiling because the memory leak As we see it, it’s the memory’s driver that has a leak, because we can reproduce it if we use the glMaxShaderCompilerThreadsARB function. This same system did not have issues a month ago running the same thing. davidr-PA opened this issue Aug 15, 2022 · 182 comments Previously there was 3 About the valgrind. for (auto window : mWindows). 7 KB), the RES returned 2. cc at r20. WSL2 + Docker causes severe memory leaks in vmmem process, consuming all my machine's physical memory #8725. 4-6 players, and i thought it was just a glitch, but ill be forced to reboot daily at this rate. 5 socket image blows up exponentially, suggesting a memory leak, while that with pytorch 1. 068271 44 metrics. so by vincent provided, It works. 2g after every loop. GStreamer sink elements sending upstream QOS events lead to memory leaks in the DeepStream pipeline. Can anyone suggest how to fix this problem? In deepstream6. 48 (both tested) Windows drivers on Maxwell workstations (GTX 970, GTX 950), “cuvidDestroyDecoder” started leaking. could you check if testing the following cmd still has the same issue? GPU Type: NVidia RTX 5000 and NVidia P5200 Nvidia Driver Version: Checked with Various 419, and 431 CUDA Version: 10. 0 • JetPack Version (valid for Jetson only) None • TensorRT Version Same as deepstream 5. In this scenario2, there is very little memory leak happening. py setup network once and do inference everytime when camera captures image, but Setup: • Hardware Platform (Jetson / GPU) GPU Titan V • DeepStream Version 5. NVIDIA Collective Communication Library (NCCL) RN-08645-000_v2. 0000 KiB|VmSize: These two lines of code I believe are responsible for increasing memory. I now saw that when I instantiate my application several times (e. DeepStream SDK. 1] • TensorRT Version 7. The results suggest that there is a leak. 0 container work correctly with Jetpack 6. 407853 44 libtorch. When testing them on the Nvidia Orin (both Jetson and Drive), the memory utilized keeps increasing steadily. I had recently updated my NVIDIA {{Framework. Autonomous Machines. '}} After performing image inference repeatedly on triton-inference-server, a memory overflow error occurred and image inference could not be performed. 20GB total memory use, tabs crashing, firefox going completely white before redrawing everything again. memory leak with webgl , driver version 440. Applications should make sure to set the property "qos" to Hi All, some devices with AGX Xavier were not able to run OTA updated after some time, the first investigation was done at Redundant A/B rootfs not switching with set-active-boot-slot but working with set-SR-BR. dhiltgen changed the title Memory leaks after each prompt Memory leaks after each prompt on 6. 7 GB (2. 3 NVIDIA GPU: Tesla T4 NVIDIA Driver Version: 460. The server seems to hold onto physical RAM after inference requests are completed, leading to memory exhaustion over time. Files The files mentioned below (AppDec. GPU memory keeps increasing when running tensorrt inference in a for loop. '}} and can you use this method to get a valgrind memory leak analysis log? about the crash log, to narrow down this isue, can you capture a resource monitoring log? can you run with gdb to get a crash stack? (Jetson / GPU) gpu • DeepStream Version 6. i see in this link Jetson Nano shows 100% CPU Usage after 30 minutes with Deepstream-app demo - #3 by vincent. 8GB/16GB and the swap hit 8. 04-py2:mem_leak /bin/bash cd mem_leak_test python job. Can you specicy how you check memory status? |PID: 24300 18:43:48|Hardware memory: Total: 0. 12 release notes. 7 has been tested with the following: ‣ Deep learning framework containers. The exact memory freed in most cases is arbitrary and has the following range: We have fixed some memory leak issues in the latest versions, but we cannot provide corresponding patches separately for a particular previous version. io/nvidia/deepstream:6. 4 with Deepstream 5. 04 host (Nvidia T4 GPU), everything works fine and the memory consumption is steady. RTX 4090. • Jetson Orin • DeepStream 7. 2070 MiB VmRSS: 3164. Things go wrong only with memory-consuming applications (I have two of those), it requires 3 Gb to build in-memory structures and runs with a 6 Gb constraint. 85. 6 • Issue Type( questions, new requirements, bugs) Bugs Hi, I have noticed that when running DeepStream apps in Python on Jetson Xavier devices for a long duration, the app seems to run out of memory at some point • NVIDIA GPU Driver Version (valid for GPU only) N/A • Issue Type( questions, new requirements, bugs) bugs there will be a memory leak on our device. 0 release and the “rev 2” release that showed up in the SDK Manager a week or so back. so is on the Nano platform, i don’t know if there’s Hello, now use the latest nvcr. I use memory profile of visual studio performance profiler for take snapshot of heap. My graphic card and driver version is NVIDIA RTX 3080 Ti and 537. Environment. For host memory, you need to use a different tool, for example valgrind. cpp, watchmem and windows. 1 • TensorRT Version latest for ngc container • NVIDIA GPU Driver Version (valid for Thank you for verifying, at least this means it’s not an issue with my environment. Our application, Description GPU memory keeps increasing when running tensorrt inference in a for loop Environment TensorRT Version: 7. 4 I0520 01:16:41. Game launches and plays fine for variable amounts of Hello @mchi, thanks for the response. 3) • Issue Type( Our team is using CUDLA Standalone mode. 2070 Here is my test code. With nvinferserver + grpc it leaked about 200mb/5 minutes for 8 parallel processes, but with nvinfer it got down to about 200mb/2 hours. 12; GPUs: GeForce RTX 2080 Ti & GeForce RTX 3090; Code to reproduce the memory leak: • Hardware Platform (Jetson / GPU) dGPU • DeepStream Version 6. 6: 1088: January 2, 2024 HW decoder/encoder failure Always use a memory-managing class- these are completely immune to memory leaks and such related problems unless you work very hard to do something very stupid. 3 • NVIDIA GPU Driver Version (valid for GPU only) • Issue Type( questions, new requirements, bugs) questions • How to reproduce the issue ? But when the same code runs on both Jetson Xavier or Nano, the memory increases gradually. 12: Can anybody help me to find a memory leak in the kernel, please? 10GiB on 16GiB system is used by kernel dynamic memory after repeated call to Ollama (local AI runner) accelerated with NVidia/Cuda 12. txt. We can provide a game key and a developer version access with the parallel compiling that causes the leak to anyone at NVIDIA. Does the DeepStream SDK 7. Same code on centos, memory leak , please help me gpu memory usage keep rising , <----- code below -----> const fs = require(‘fs’) const path = require(‘path’) const puppeteer = require(‘puppeteer In particular, if you look at the first image in this post, notice the memory usage with pytorch 1. So for months now I've been debugging why HA had a memory leak, would crash and restart, andupdated an issue I had been tracking in github with this info, but figured worth a post here for those that may google it later. 11. Memory Leak Issue Ever since the recent maintenance and update, I've been having severe memory and processing issues when attempting to play XIV, with CPU usage being maxed out and memory usage skyrocketing even when I'm only sitting on the title screen. After terminating the process. 130 CUDNN Version: 7. It seems like the creation of a cufftHandle allocates some memory which is occasionally not deallocated when the handle is destroyed. 6[L4T 32. [url][QTBUG-69429] NULL parenting a parent of a window container containing a QOpenGLWindow make Xorg leaks VRAM - Qt Bug Tracker. 14: 468: July 29, 2024 Hello All! I am experiencing high levels of memory leak on my system. I am running HomeAssistantOS in VirtualBox We’re plagued by memory leaks during work on a 4x RTSP camera based person detection setup running on a Xavier NX. You can try to run that on your platform directly wihout docker. 2G, so some time later the server crash. To do that I naively renamed the NvTelemetryAPI64. driveos-cuda. cc:1039] Triton TRITONBACKEND API version: 1. fc40. 2383 MiB RssAnon: 2467. c:207) Some systems which implement malloc() may not release memory back to the operating system right away causing a false memory leak. NvContainer) is using the disk at the point of freezing the PC for 2 or 3 secs even when idle and almost When running the example in #1421 on an x86 machine (Gorby) using the cuda-quantum Docker containers, a memory leak is appearing (as can be seen by watching memory usage in nvidia-smi grow without bounds). 13: 95: target-docker-container running cuda-samples require unintended extra permission. 0 Driver Version: N/A CUDA Version: 12. 0. Viewed 4k times 2 Hi I´m working on a project that uses Enterprice Libraries´s Unity container to resolve dependencies for exception handling, cache, logging and db access but we keep getting a lot of leaked objects into memory. Is mWindows a container of shared_ptr? To check This is the result showed by nvmemstat. Refer to the Support Matrix for the Description I am dealing with some issues where the same executable, config files, model, and dlls work have a constant memory usage on one system but not another. I don’t know, but I’m using my GTX 1050 to test with 20 rtsp streams (H264 1080p 30fps) dropped to NVIDIA Developer Forums Deepstream GPU Memory Leak in nvv4l2decoder. cc:1045] 'pytorch' 9. 4 and native Ubuntu Linux 18. Open comment sort options. 0 • FPS drops • Run my pipeline • custom pipeline with yolo Hello, I have a fps drop issue correlated with a memory leak on Deepstream C++ on my yolo pipeline I have constant fps for 3/4 hours at ~ 30 fps then a drops to 20 unit the memory is too high and a reboot of the app append. I found the problem at pyds. Re-installing the drivers 2. However, the most amount of memory leak is found in liblsan. TensorRT Version: 6. When testing them on an X86 Ubuntu 20. 0 GA (L4T 36. I0520 01:16:41. 81 TDR's on 7 and 8. 2 GB). 0 filesrc no gpu memory leak: 791MB; gpu memory leak: 2123MB Environment. Memory Leak when using Virtual Memory API (cuMemImportFromShareableHandle) DRIVE AGX Orin General. This thread is archived New NVIDIA Game Ready & Studio Drivers, New GeForce ‘Alan Wake 2’ RTX bundle, New NVIDIA DLSS Games, including the ‘Call of Duty Modern Warfare III’ beta. We also called the function cudlaMemUnregister to release the resource that we use. After the add-on is started it will creep up until HA becomes unresponsive. This memory leak causes DrvPresentBuffers crash after 20-30 creations and destroys of window. 1 I’m running into memory and CPU issues using DeepStream libnvds_nvmultiobjecttracker with nvDCF tracker config. Strangely this only occurs on Deepstream 6. kit. nvbugs. I of course now suspect a memory leak, however when running my application with compute-sanitizer it does Hi, I trying to use cuda shared memory to communicate with TRITON My code is based on server/simple_cuda_shm_client. Also, When running Python DeepStream in a Docker container (based on the official NVIDIA Docker image running Ubuntu 22. No NVIDIA Stock Discussion. but this . NVIDIA Container NVIDIA TelemetryApi helper for NvContainer NVIDIA LocalSystem Container NVIDIA Message Bus for NvContainer huge memory leaks since 6. 0 CUDNN Version: Hi Guys, I develop an application which does image manipulations using cuda. ~10-15% CPU being taken by PresentMon then Nvidia Container but can't remember the order I killed them in. 8GB of swap. M337ING I have now replicated the problem with a much simpler code example without any appsources or appsinks (please see attachment). 32. 0 • JetPack Version (valid for Jetson only) = JetPack 6. . At the same time, if we divide the monolithic app into 2 smaller apps (Model A in container A, model B in container B) and run it via Nvidia-Docker, we see that GPU memory consumption is higher than if we run it as a monolithic single app. 08 TensorRt Memory Leak ###### tags: `NVIDIA Triton Inference Server` **Descript I think this is Cinema 4D / Redshift issue that is happening due to bad NVIDIA driver. root@fedora:~# uname -a Linux fedora 6. iyauaw sani quumn imqa ybauch toc eys fnpol kbest qzpldi