vulkan_best_practice_for_mobile_developers
Vulkan best practice for mobile developers (by ARM-software)
MNN
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba (by alibaba)
| vulkan_best_practice_for_mobile_developers | MNN | |
|---|---|---|
| 2 | 3 | |
| 634 | 8,313 | |
| 1.7% | 0.9% | |
| 1.8 | 8.0 | |
| about 3 years ago | 12 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
vulkan_best_practice_for_mobile_developers
Posts with mentions or reviews of vulkan_best_practice_for_mobile_developers.
We have used some of these posts to build our list of alternatives
and similar projects.
-
How expensive is descriptor set creation/update?
Vulkan best practices(Arm): https://github.com/ARM-software/vulkan_best_practice_for_mobile_developers/blob/master/samples/performance/descriptor_management/descriptor_management_tutorial.md
- Cannot understand RenderPass, and how Subpass relate to them
MNN
Posts with mentions or reviews of MNN.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-03.
-
[D][R] Deploying deep models on memory constrained devices
However, I am looking on this subject through the problem of training/finetuning deep models on the edge devices, being increasingly available thing to do. Looking at tflite, alibaba's MNN, mit-han-lab's tinyengine etc..
-
What’s New in TensorFlow 2.10?
There are a ton of mobile deployment options that support PyTorch+TF models. It's hard to argue TFLite is the best.
https://github.com/alibaba/MNN
-
Newbie having error code of cannot build selected target abi x86 no suitable splits configured
I found a solution on GitHub check your app's build.gradle, defaultConfig section - you need to add x86 to your ndk abiFilters ndk.abiFilters 'armeabi-v7a','arm64-v8a', 'x86' GitHub Hope it will help. You have to find that file and edit it as given here
What are some alternatives?
When comparing vulkan_best_practice_for_mobile_developers and MNN you can also consider the following projects:
The-Forge - The Forge Cross-Platform Rendering Framework PC Windows, Steamdeck (native), Ray Tracing, macOS / iOS, Android, XBOX, PS4, PS5, Switch, Quest 2
tensorflow - An Open Source Machine Learning Framework for Everyone