I am trying to train the YOLOv7 network, but I should limit the GPU usage to 8Gb.
What I understood doing experiments also on Google Colab is that before starting the training, the network tries to occupy most of the available memory regardless of how much it is or how much it is needed for training. In fact, on Google Colab it requires 11 GB out of 14 to start the training, while on my GPU it requires 45 out of 50, although then the actual training phase requires only 6GB.
I tried to minimize the parameters (batch size, workers) but nothing changes as, as mentioned, the problem is the pre-training allocation which is fixed.
I tried using the function of pytorch
torch.cuda.set_per_process_memory_fraction(0.16, CUDA_VISIBLE_DEVICES)
but this function does not cause the network to use only 8GB but causes, if exceeded 8GB, an error.
on YOLOX there is the "-o" parameter which, if omitted, avoids the allocation of pre-training memory and therefore uses only the memory it needs during training but I have not found the equivalent of this parameter on YOLOv7.
Is it possible to make YOLOv7 see only 8GB available and therefore allocate a smaller amount of GB?
Or is it possible that pre-training allocation is avoided like in YOLOX?
Related
I am researching the machine specs needed for DETR training.
However, I only have a geforce 1660 super and I got an "out of memory" error. So, please let me know how much machine specs you have to use to complete the DETR training.
Please help me with my research.
DETR(https://github.com/facebookresearch/detr)
Your are getting out of memory error because your GPU memory isn't sufficient to hold up the batch-size you input. Try running the code with minimum batch-size possible, see how much memory it consumes, increase the batch-size slightly, again check increase in memory consumption. This was you will be able to estimate how much GPU memory you require to run it with the actual batch-size.
I had the same issue, I switched to a machine with larger GPU memory (around 24 GB) and then everything worked fine!
I want to train a large object detection model using TF2, preferrably the EfficientDet D7 network. With my Tesla P100 card that has 16 GB of memory I am running into an "out of memory" exception, i.e. not enough memory on the graphics card can be allocated.
So I am wondering what my options are in this case. Is it correct that if I would have multiple GPUs, then the TF model would be split so that it fills memory of both cards? So in my case, with a second Tesla card again with 16 GB I would have 32 GB in total during training? If that is the case would that also be true for a cloud provider, where I could utilize multiple GPUs?
Moreover, if I am wrong and it would not work to split a model for multiple GPUs during training, what other approach would work in order to train a large network that does not fit into my GPU memory?
PS: I know that I could reduce the batch_size to 1, but unfortunately that does still not solve my issue for the really large models ...
You can use multiple GPU's in GCP (Google Cloud Platform) atleast, not too sure about other cloud providers. And yes, once you do that, you can train with a larger batch size (exact number would depend on the GPU, it's memory and how may you GPU's you have running in your VM)
You can check this link for the list of all GPU's available in GCP
If you're using the object detection API, you can check this post regarding training using multiple GPU's.
Alternatively, if you want to go with a single GPU, one clever trick would be to use the concept of gradient accumulation where you could virtually increase your batch size without using too much extra GPU memory, which is discussed in this post
I am running the GPT-2 code of the large model(774M). It is used for the generation of text samples through interactive_conditional_samples.py , link: here
So I've given an input file containing prompts which are automatically selected to generate output. This output is also automatically copied into a file. In short, I'm not training it, I'm using the model to generate text.
Also, I'm using a single GPU.
The problem I'm facing in this is, The code is not utilizing the GPU fully.
By using nvidia-smi command, I was able to see the below image
https://imgur.com/CqANNdB
It depends on your application. It is not unusual to have low GPU utilization when the batch_size is small. Try increasing the batch_size for more GPU utilization.
In your case, you have set batch_size=1 in your program. Increase the batch_size to a larger number and verify the GPU utilization.
Let me explain using MNIST size networks. They are tiny and it's hard to achieve high GPU (or CPU) efficiency for them. You will get higher computational efficiency with larger batch size, meaning you can process more examples per second, but you will also get lower statistical efficiency, meaning you need to process more examples total to get to target accuracy. So it's a trade-off. For tiny character models, the statistical efficiency drops off very quickly after a batch_size=100, so it's probably not worth trying to grow the batch size for training. For inference, you should use the largest batch size you can.
Hope this answers your question. Happy Learning.
I use multigpu to train a model with pytorch. One gpu uses more memory than others, causing "out-of-memory". Why would one gpu use more memory? Is it possible to make the usage more balanced? Is there other ways to reduce memory usage? (Deleting variables that will not be used anymore...?) The batch size is already 1. Thanks.
DataParallel splits the batch and sends each split to a different GPU, each GPU has a copy of the model, then the forward pass is computed independently and then the outputs of each GPU are collected back to one GPU instead of computing loss independently in each GPU.
If you want to mitigate this issue you can include the loss computation in the DataParallel module.
If doing this is still an issue, then you might want model parallelism instead of data parallelism: move different parts of your model to different GPUs using .cuda(gpu_id). This is useful when the weights of your model are pretty large.
I'm trying to train a model (implementation of a research paper) on K80 GPU with 12GB memory available for training. The dataset is about 23 GB and after data extraction, it shrinks to 12GB for the training script.
At about 4640th step (max_steps being 500,000), I receive the following error saying Resource Exhausted and the script stops soon after that. -
The memory usage at the beginning of the script is:
I went through a lot of similar questions and found that reducing the batch-size might help but I have reduced the batch-size to 50 and the error persists. Is there any other solution except switching to a more powerful GPU?
This does not look like a GPU Out Of Memory (OOM) error but more like you ran out of space on your local drive to save the checkpoint of your model.
Are you sure that you have enough space on your disk or that the folder you save to doesn't have a quotta?