keras-nlp insists I use the (buggy) Tensorflow 2.16.1 which does not work with my GPU
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Describe the bug
The latest Tensorflow 2.16.1 has a bug, it doesn't seem to detect GPUs (see for example: http://127.0.0.1:8888/lab?token=59ba515252bb7306a955696efe83ad0b816e730b847fac69
To get around that, I ran pip install tensorflow[and-cuda]==2.15.1
It worked and my GPU was detected.
The problem is when I pip install keras-nlp, it tries to uninstall tensorboard, tensorflow etc. to install their latest versions. I suspect keras does the same too.
I tried pip install keras-nlp --no-deps and got errors during imports (such as ModuleNotFoundError: No module named 'keras_core' (The preinstalled keras version was 2.15.0)
I tried pip uninstall keras keras-nlp then pip install keras==3.0.0 keras-nlp==0.6.3 --no-deps and I got import errors again, such as ModuleNotFoundError: No module named 'rich'
To Reproduce
Here's a colab link. The aspect that's not reproduced is tensorflow not recognizing the GPU. The GPU issue seems to be restricted to PCs.
Expected behavior
I would like to be able to install functional keras and keras-nlp packages with Tf 2.15.1
Additional context
PC windows 11 with WSL2 Ubuntu
nvidi-smi
Thu Mar 21 16:03:26 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.120 Driver Version: 537.58 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4090 On | 00000000:01:00.0 On | Off |
| 0% 33C P8 16W / 450W | 2895MiB / 24564MiB | 3% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
I was able to detect GPU in the colab GPU vm.
I followed the below list of commands.
Create a fresh environment and try.
!pip install -U keras-nlp
!pip install -U tensorflow
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
If you are still unable to detect GPU, you can close this issue and create a new issue in the TensorFlow repo since it is related to TensorFlow.
You link is localhost runtime, we can't access it.
I can see Num GPUs Available: 1 in your colab, what is the issue again?
Got it, Looks like this is the TensorFlow issue for the specific OS.
You can create a new issue in TensorFlow and link this issue for context.
I think it would be nice if keras-nlp was usable by older tensorflow versions, as 2.16.1 version has several bugs in it, and keras-nlp seems to be compatible with tf 2.15
We always try to match the latest TensorFlow version during the time of release, it's the same practice we follow for Keras-Cv as well.
Moreover, TensorFlow 2.16.1 uses Keras 3 as a backend unlike 2.15 version which uses Keras 2 as a backend.
@arsenstonelab thanks for the issue. This is indeed a bit of a rough edge. The issue is actually with tensorflow-text most likely. keras-nlp is unopinionated about tensorflow versions in our package setup, but if you install keras-nlp it will try to install tensorflow-text (the latest version if none is installed). Which in turn will try to install the latest tensorflow version. Which can lead to a big upgrade of tensorflow.
One option is to pin the tf version you want during install for both tensorflow-text and tensorflow. E.g. this works for installing keras-nlp with tf 2.15.
pip install keras-nlp tensorflow-text~=2.15.0 tensorflow~=2.15.0
Does that work for you?
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