There are a few reasons why the DQN model in Stable Baselines 3 might not be using CUDA, even though it recognizes your GPU:
TensorFlow is not using GPU by default: TensorFlow, the deep learning framework used by Stable Baselines 3, does not use GPU by default. You need to explicitly set the GPU as the device in TensorFlow using the tf.device context.
CUDA not installed: Make sure you have CUDA installed on your system and that the path to CUDA is set in the environment variables.
GPU not supported by TensorFlow: Not all GPUs are supported by TensorFlow. Make sure your GPU is supported by checking the TensorFlow compatibility matrix.
Incorrect version of TensorFlow installed: Stable Baselines 3 requires TensorFlow 2.x, so make sure you have the correct version of TensorFlow installed.
Here's an example of how to use TensorFlow's GPU:
python
Copy code
import tensorflow as tf
with tf.device("/GPU:0"):
# Your DQN code
If you still encounter issues, try reinstalling TensorFlow or Stable Baselines 3 with GPU support. Additionally, you can try using a different deep learning framework that supports CUDA, such as PyTorch, to see if that resolves the issue.
Another possible solution is to check your CUDA and CuDNN version compatibility with TensorFlow. Make sure the CUDA version is compatible with the TensorFlow version you have installed. You can also try updating or downgrading either TensorFlow or CUDA to a version that is known to work well together.
It's also a good idea to check the system requirements for the GPU you are using. Some GPUs might require additional drivers or libraries to be installed in order to work properly with TensorFlow.
If you are still encountering problems, try reaching out to the Stable Baselines community for additional support. They might be able to provide you with more specific guidance based on your setup and the issue you are encountering.
Another important aspect to check is the memory allocation for your GPU. When running the DQN model, make sure that your GPU has enough memory to handle the computations. If the GPU runs out of memory, TensorFlow will automatically fall back to using the CPU instead. You can use tools such as nvidia-smi to monitor the GPU memory usage and adjust the memory allocation accordingly.
It is also important to make sure that the GPU is not being shared by other applications or processes. This can cause performance degradation and may lead to TensorFlow falling back to using the CPU. Make sure to close any other applications that are running on the GPU before running your DQN model.
In conclusion, there are several potential reasons why the DQN model in Stable Baselines 3 might not be using CUDA, even though it recognizes your GPU. By checking and verifying the requirements and compatibility of your GPU, CUDA and TensorFlow, as well as the memory allocation and usage of the GPU, you should be able to resolve the issue and successfully run the DQN model using CUDA.