jetson-inference
gitlab-foss
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7,349 | - | |
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15 days ago | - | |
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MIT License | - |
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jetson-inference
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Can this NVIDIA Jetson Nano handle advanced machine learning tasks?
Jetson Nano’s are obsolete and no longer supported; but to answer your question, this might be a good place to start.
- help with project involving object detection and tracking with camera
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Jetson Nano 2GB Issues During Training (Out Of Memory / Process Killed) & Other Questions!
I’m trying to do the tutorial, where they retrain the neural network to detect fruits (jetson-inference/pytorch-ssd.md at master · dusty-nv/jetson-inference · GitHub 1)
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Jetson Nano
Jetson-Inference is another amazing resource to get started on. This will allow you to try out a number of neural networks (classification, detection, and segmentation) all with your own data or with sample images included in the repo.
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Pretrained image classification model for nuts and bolts (or similar)
Hello! I'm looking for some pre trained image classification models to use on a Jetson Nano. I already know about the model zoo and the pre trained models included in the https://github.com/dusty-nv/jetson-inference repo. For demonstration purposes, however, I need a model trained on small objects from the context of production, ideally nuts, bolts, and similar small objects. Does anyone happen to know a source for this? Thanks a lot!
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PyTorch 1.8 release with AMD ROCm support
> They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference
I was aware of that repository but from taking a cursory look at it I had thought dusty was just converting models from PyTorch to TensorRT, like here[0, 1]. Am I missing something?
> I get 140 fps on a Xavier NX
That really is impressive. Holy shit.
[0]: https://github.com/dusty-nv/jetson-inference/blob/master/doc...
[1]: https://github.com/dusty-nv/jetson-inference/issues/896#issu...
- NVIDIA DLSS released as a plugin for Unreal Engine 4
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Help getting started
If you have a screen and keyboard and mouse plugged into the Nano, I would recommend starting with Hello AI World on https://github.com/dusty-nv/jetson-inference#hello-ai-world
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I'm tired of this anti-Wayland horseshit
Well, don't get me wrong. I do like my Jetson Nano. For a hobbyist who likes to tinker with machine learning in their spare time it's definitely a product cool and there are quite a few repositories on Github[0, 1] with sample code.
Unfortunately… that's about it. There is little documentation about
- how to build a custom OS image (necessary if you're thinking about using Jetson as part of your own product, i.e. a large-scale deployment). What proprietary drivers and libraries do I need to install? Nvidia basically says, here's a Ubuntu image with the usual GUI, complete driver stack and everything – take it or leave it. Unfortunately, the GUI alone is eating up a lot of the precious CPU and GPU resources, so using that OS image is no option.
- how deployment works on production modules (as opposed to the non-production module in the Developer Kit)
- what production modules are available in the first place ("Please refer to our partners")
- what wifi dongles are compatible (the most recent Jetson Nano comes w/o wifi)
- how to convert your custom models to TensorRT, what you need to pay attention to etc. (The official docs basically say: Have a look at the following nondescript sample code. Good luck.)
- … (I'm sure I'm forgetting many other things that I've struggled with over the past months)
Anyway. It's not that this information isn't out there somewhere in some blog post, some Github repo or some thread on the Nvidia forums[2]. (Though I have yet to find a reliably working wifi dongle…) But it usually takes you days orweeks to find it. From a product which is supposed to be industry-grade I would have expected more.
[0]: https://github.com/dusty-nv/jetson-inference
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Basic Teaching
https://github.com/dusty-nv/jetson-inference#system-setup
gitlab-foss
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GitHub Actions Are a Problem
* Gitlab EE (enterprise edition) is closed, but Gitlab CE (community edition) is open source (https://gitlab.com/gitlab-org/gitlab-foss/)
* I didn't follow the Gitea drama too closely, but my understanding is that Forgejo was a fork born out of that situation
* I've heard the SourceHut guy is a controversial figure, so avoiding it because of that isn't unreasonable. I will just say that "spite forks" tend not to last very long
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Server-Side Request Forgery in Rails
Gitlab uses an UrlBlocker class to prevent malicious users from exploiting SSRF via the webhook URL. This class validates the URL and blocks everything which is a local network, but before the 11.5.1 version, they didn't think about an IPv6 format, which maps to IPv4: [0:0:0:0:0:ffff:127.0.0.1]. Replacing the part of 127.0.0.1 to any IP address also worked, and this vulnerability made it possible to send requests to the internal network of a GitLab instance. You can read the issue report here: (https://gitlab.com/gitlab-org/gitlab-foss/-/issues/53242 )[https://gitlab.com/gitlab-org/gitlab-foss/-/issues/53242]
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Automating deployment to kubernetes
I recommend Auto DevOps and hooking your project up to the Kubernetes cluster. Auto DevOps is a standard CI/CD template that GitLab uses by default when .gitlab-ci.yml is not present. It can automatically package up certain types of applications, including those with a Dockerfile in the root of the repo. If the project is hooked up to a Kubernetes cluster and all the right variables are present, it builds that docker image and then fills in a Helm chart template containing that image and deploys it to the cluster.
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Beautifying our UI: Giving Gitlab build features a fresh look
Thanks. This was also requested for the UI 7 years ago
https://gitlab.com/gitlab-org/gitlab-foss/-/issues/12776
and then closed with the claim that this was implemented, when in fact, it was not.
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How we cut down our CI build times by 50%
Similar to fsync, these are designed to ensure data integrity, but in a test setup, they don't matter. You can read more about these in the Postgres doc on non-durability. and explore some benchmarks from Gitlab here. Interestingly, CircleCI's old Postgres images had these features disabled by default, but the newer ones don't seem to.
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Is Jenkins still the king?
Most all of those things are possible with Argo Workflows or Tekton with very great effort. But a sustainable system with all the features built-in.
- So weird, stage named test is not displayed in pipeline
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Gitlab for FOSS reporting
If you wish to clone a copy of GitLab without proprietary code, you can use the read-only mirror of GitLab located at https://gitlab.com/gitlab-org/gitlab-foss/. However, please do not submit any issues and/or merge requests to that project.
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Gitlab runners unable to clone over http(s) when git access set to SSH only.
GitLab versions 10.7 and later, allow the HTTP(S) protocol for Git clone or fetch requests done by GitLab Runner from CI/CD jobs, even if you select Only SSH.
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No words v2💀
it sure does
What are some alternatives?
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
gitlab
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tensorflow - An Open Source Machine Learning Framework for Everyone
CryptPad - Collaborative office suite, end-to-end encrypted and open-source.
yolov5-deepsort-tensorrt - A c++ implementation of yolov5 and deepsort
taiga-docker - [Moved to: https://github.com/taigaio/taiga-docker]
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
cmark-gfm - GitHub's fork of cmark, a CommonMark parsing and rendering library and program in C
obs-studio - OBS Studio - Free and open source software for live streaming and screen recording
markup - Determines which markup library to use to render a content file (e.g. README) on GitHub