TensorFlow on AMD GPU! DirectML Tutorial and Testing
If you have been following the latest developments in the world of machine learning and artificial intelligence, you have probably heard of TensorFlow. It is an open-source machine learning library developed by Google and is widely used for building and training machine learning models. With the recent advancements in hardware technology, it is now possible to run TensorFlow on AMD GPUs using DirectML, an API that provides hardware acceleration for machine learning tasks on AMD GPUs.
Setting up TensorFlow with DirectML on AMD GPU
To get started with using TensorFlow on AMD GPU with DirectML, you will need to install the necessary software and drivers. First, make sure you have the latest drivers for your AMD GPU installed. Then, install the latest version of TensorFlow that includes support for DirectML. Once you have these prerequisites in place, you can start using TensorFlow with DirectML on your AMD GPU.
Tutorial: Running TensorFlow on AMD GPU with DirectML
Now that you have TensorFlow and DirectML set up on your system, you can start running machine learning tasks on your AMD GPU. Here is a simple tutorial to get you started:
- Import TensorFlow and DirectML libraries in your Python script
- Create a simple machine learning model using TensorFlow’s high-level APIs
- Configure TensorFlow to use DirectML for running the model on your AMD GPU
- Run your machine learning model and evaluate its performance on the AMD GPU
Testing TensorFlow on AMD GPU with DirectML
Once you have successfully set up TensorFlow with DirectML on your AMD GPU and have run some machine learning tasks, it is important to test the performance and compare it with running the same tasks on other hardware. You can benchmark the performance of TensorFlow on your AMD GPU using DirectML and compare it with running the tasks on a CPU or other GPUs. This will give you a better understanding of the capabilities of running TensorFlow on AMD GPU with DirectML.
By utilizing the power of AMD GPUs with DirectML support, you can significantly accelerate your machine learning tasks and improve the overall performance of your models. With the growing popularity of AMD GPUs in the machine learning community, TensorFlow’s support for DirectML on AMD GPUs opens up new opportunities for leveraging these GPUs for machine learning and AI applications.
Give it a try and see how TensorFlow on AMD GPU with DirectML can enhance your machine learning workflows!
How is the timing on the cpu ? For thay benchmark that you ran
there's also something to note with the 3060 being faster than the 3070ti, the 3060 has 12gb of VRAM compared to the 3070ti's 8gb. maybe in your test it doesn't fill it all the way and still shows the difference in performance between DML and CUDA, but 8gb for AI is very low and 12gb is really the minimum you should consider. so if you're on a budget and want to primarily do AI, i'd look for a 3060, a used A2000 12GB or a used nvidia tesla p40 with its 24GB granted you might need to mod it to add active cooling
Can you please share pythontest_large.py file ?
thank you for the educational video..
Please continue posting more such content.
Subscribed!
You deserve it.
Best wishes from India 🇮🇳
Hi, thank you for this video. At 8:05, you mentioned that you had not tested integrated graphics. Have you done so since then? I would be interested in using tensorflow with the integrated AMD Radeon™ 760M in the AMD Ryzen™ 5 7640U.
Does anybody know if this is possible?
Thank you so much! The is the most useful video I could find on the topic. Successfully installed DirectML for my use of tensorflow on an AMD RX 6500 XT. The only thing: I could find it as tensorflow-directml-plugin, not tensorflow-directml for installation. Also, tf.test.is_gpu_available() has been depreciated, so you should use command tf.config.list_physical_devices('GPU') instead.
Thanks for this, much appreciated – the 3070 performance figure was useful, in that I have upgraded to an RX 6700 XT 12GB GPU on a Ryzen 5 platform, which approximates the 3070 (more or less) – performance therefore in a loud shout is more than good enough for RNN training on a 2000 point Dataset – approx 7.8s per training is pretty darn good.
I was able to install everything with no errors but when I tried to import tensorflow as tf I got the error
"Illegal Instructions", my GPU is RX 570 8GB. I'm not sure how to fix it
could you provide your test script?Tthat would be pretty helpful for us noobs learning the ropes of machine learning
Pronounced Ubooooontu, lol
will it be faster without using WSL? running directml in windows
hi, Im new to machine learning. I wanted to know if what are the disadvantages of using directml compared to cuda. is it only performance? or anythingelse
8:02 I've tried on AMD 6800h (with 680m GPU) and it's worked!
Another problem is with memory management. when the training is complete it wont clear the GPU mem.
Is that working in Windows or only in Linux?
Wouldn't it be better to use ROCm?
Do you know why I'm getting this?:
(tensorflow-directml) g-ubuntu@DESKTOP-69P003J:~$ pip install tensorflow-directml
ERROR: Could not find a version that satisfies the requirement tensorflow-directml (from versions: none)
ERROR: No matching distribution found for tensorflow-directml
I have a GPU AMD 6800XT
thanks a lot for the video!