There are a few reasons why CUDA may or may not work on Colab. Here are some of the most common reasons:
- Reason 1: You have not selected the GPU runtime. To use GPU on Colab, you need to select the GPU runtime from the “Runtime” menu. You can do this by following these steps:
1. Open a new or existing Colab notebook.
2. Click the "Runtime" menu at the top.
3. Select "Change runtime type."
4. Select "GPU" from the "Hardware accelerator" dropdown menu in the pop-up window.
5. Click "SAVE."- Reason 2: You are using an older version of Colab. Google regularly updates Colab with new features and improvements. If you are using an older version of Colab, it is possible that version does not support GPU. To check your Colab version, click the “Help” menu and select “About Google Colab.”
- Reason 3: The Colab server you are using does not have a GPU available. Google offers a variety of Colab servers, some of which have GPUs. If the Colab server you are using does not have a GPU available, you will not be able to use the GPU.
- Reason 4: You are using code that is not compatible with GPU. If your code is not designed to run on GPU, it will not be able to use the GPU. To check if your code is compatible with GPU, you can use a GPU compatibility checker.
If you have checked all of the above reasons and are still having trouble running CUDA on Colab, you can contact Google support.
Here are some tips to help you ensure that you can use GPU on Colab:
- Always select the GPU runtime when you create a new Colab notebook.
- Update Colab to the latest version.
- Check if the Colab server you are using has a GPU available.
- Use code that is designed to run on GPU.
Not using CUDA:
#from this
pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipe.scheduler.config)
pipe = pipe.to("cuda")--------------------------------------------------------------
#to this
pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_id,
torch_dtype=torch.float32,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipe.scheduler.config)
#pipe = pipe.to("cuda")
