DirectML | accelerated DirectX | GPU library

by microsoft Python Version: tensorflow-directml-1.15.3.dev200626 License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | DirectML Summary

DirectML is a Python library typically used in Hardware, GPU, Deep Learning, Tensorflow applications. DirectML has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However DirectML build file is not available. You can download it from GitHub.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm. When used standalone, the DirectML API is a low-level DirectX 12 library and is suitable for high-performance, low-latency applications such as frameworks, games, and other real-time applications. The seamless interoperability of DirectML with Direct3D 12 as well as its low overhead and conformance across hardware makes DirectML ideal for accelerating machine learning when both high performance is desired, and the reliability and predictability of results across hardware is critical. More information about DirectML can be found in Introduction to DirectML. Visit the DirectX Landing Page for more resources for DirectX developers.

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Quality

Security

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Support

DirectML has a medium active ecosystem.

It has 1498 star(s) with 228 fork(s). There are 63 watchers for this library.

It had no major release in the last 12 months.

There are 115 open issues and 167 have been closed. On average issues are closed in 113 days. There are 16 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DirectML is tensorflow-directml-1.15.3.dev200626

Quality

DirectML has no bugs reported.

Security

DirectML has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DirectML is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

DirectML releases are available to install and integrate.

DirectML has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are available. Examples and code snippets are not available.

Top functions reviewed by kandi - BETA

kandi has reviewed DirectML and discovered the below as its top functions. This is intended to give you an instant insight into DirectML implemented functionality, and help decide if they suit your requirements.

Train the model
Generate k - means anchors for k - means clustering
R Check anchors in the dataset
Check anchor order
Run yolo
Build an example from an annotation
Build a dictionary of bounding boxes
Add images to tfrecord
Build lookup for synset_to_human
Processes image files
Detect inference images
Deploy a model
Get a single split
Plots images
Load pyramid images
Runs the VisualWakeWords dataset
Yolo loss function
Cache dataset labels
Create visual wakeword annotations
Build an example
Concatenate images
Parse the model dictionary
Process an XML file
Evaluate the model
Build a map of bounding boxes
Adds images to a tfrecord
Train one epoch
Create a dataset
Build a lookup dictionary for synset to human readable

Get all kandi verified functions for this library.

DirectML Key Features

No Key Features are available at this moment for DirectML.

DirectML Examples and Code Snippets

Install from Source

Scala

Lines of Code : 10

License : Permissive (Apache-2.0)

Copy

cd REPO_ROOT


sbt clean assembly

sbt -Dgpu=true clean assembly

sbt -Dgpu=true 'set test in assembly := {}' clean assembly

$REPO_ROOT/target/scala-2.13/ai-serving-assembly-.jar

java -jar ai-serving-assembly-.jar

java -Donnxruntime.backend=cuda -

Community Discussions

Trending Discussions on DirectML

ERROR: Could not find a version that satisfies the requirement tensorflow-directml

Why is my VSCode trying to use cuda even though I installed directml (I'm on amd)?

WinML inference time on GPU 3 time slower than Tensorflow python

QUESTION

ERROR: Could not find a version that satisfies the requirement tensorflow-directml

Asked 2022-Feb-18 at 06:40

I tried to install:

...

ANSWER

Answered 2022-Feb-18 at 06:40

Seems that the library supports Python 3.5, 3.6 and 3.7. Python3.8 is not supported at the moment (1). Is your pip installation connected to any of those versions? Try using pip --version to confirm to which Python version it is connected. In case that it shows a Python 2 version, try using pip3 install tensorflow-directml.

Source https://stackoverflow.com/questions/71169187

QUESTION

Why is my VSCode trying to use cuda even though I installed directml (I'm on amd)?

Asked 2021-Sep-16 at 20:29

I have a tensor flow object detection project I want to build and read that it would be slow on cpu. Thats when someone told me to use directml because I have an AMD gpu and not a NVIDIA one.

I have created an anaconda environment which I called "directml" and installed tensorflow and directml on it (see the picture). If I now try to run my test application which I found from this tutorial (https://docs.microsoft.com/en-us/windows/ai/directml/gpu-tensorflow-windows):

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ANSWER

Answered 2021-Sep-16 at 20:29

You shouldn't install tensorflow only tensorflow-directml. Because now python is importing tensorflow not tensorflow-directml. Uninstall tensorflow and it should fix imports.

Source https://stackoverflow.com/questions/69213247

QUESTION

WinML inference time on GPU 3 time slower than Tensorflow python

Asked 2020-Apr-15 at 05:22

I try to use a tensorflow model trained on python in WinML. I successfully convert protobuf to onnx. The following performance result are obtained :

WinML 43s
OnnxRuntime 10s
Tensorflow 12s

The inference on CPU take arround 86s.

On performance tools WinML doesn't seem to correctly use the GPU in comparison of other. It's seemed WinML use DirectML as backend (We observe DML prefix on Nvidia GPU profiler). Is it possible to use Cuda inference Engine with WinML ? Did anyone observe similar result, WinML being abnormally slow on GPU ?

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ANSWER

Answered 2020-Apr-15 at 05:22

I got some answer about this WinML performance. My network use LeakyRelu that was supported by DirectML only in Windows 2004. On Windows previous version, this issue disable the use of DirectML Metacommand thus bad performance. With the new windows version I got good performance with WinML.

Source https://stackoverflow.com/questions/60264844

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DirectML

DirectML is distributed as a system component of Windows 10, and is available as part of the Windows 10 operating system (OS) in Windows 10, version 1903 (10.0; Build 18362), and newer. Starting with DirectML version 1.4.0, DirectML is also available as a standalone redistributable package (see Microsoft.AI.DirectML), which is useful for applications that wish to use a fixed version of DirectML, or when running on older versions of Windows 10.