I am trying to run a Deep Learning based Face Recognition Model. But when I run it on Google Colab, it uses only 1.12 GB of GPU out of 42 GB. I have enabled and checked all the configuration of colab and in the code using Pytorch wrapper.
Please help how can I use full resource in Colab.
I guess your issue was discussed here on Stackoverflow
"
The high memory setting in the screen controls the system RAM
rather than GPU memory. The command !nvidia-smi will show GPU memory.
For example:
.. and also was answered here in Google Colab docs
https://colab.research.google.com/notebooks/pro.ipynb
Related
I am using google colab, and I need to know if it uses any GPU for training if I don't do model.to('cuda') and data.to('cuda')?
If you do not use model.to(torch.device('cuda')) and data.to(torch.device('cuda')) your model and all of your tensors will be remaining on default device which is CPU, so they do not understand the existence of GPU. PyTorch uses CPU for its work.
You can see this Link for more information about torch.device.
I'm trying to build a Resnet200D transfer learning model for a kaggle competition but I'm unable to train the model on Google Collab since it runs out of memory even with a batchSize of 1 on CPU as well as GPU. I'm not sure where the memory is getting used up since other participants claim they've been able to train the model with a batch size of 16 as well. If anyone could look at the notebook and leave suggestions, that would be really helpful.
Google Collab Notebook
I want to train googles object detection with faster_rcnn_with resnet101 using mscoco datasetcode. I used only 10,000 images for training purpose.I used graphics: GeForce 930M/PCIe/SSE2. NVIDIA Driver Version:384.90. here is the picture of my GeForce.
And I have 8Gb RAM but in tensorflow gpu it is showed 1.96 Gb.
. Now How can I extend my PGU's RAM. I want to use full system memory.
You can train on the cpu to take advantage of the RAM on your machine. However, to run something on the gpu it has to be loaded to the gpu first. Now you can swap memory in and out, because not all the results are needed at any step. However, you pay with a very long training time and I would rather advise you to reduce the batch size. Nevertheless, details about this process and implementation can be found here: https://medium.com/#Synced/how-to-train-a-very-large-and-deep-model-on-one-gpu-7b7edfe2d072.
Based on this github link https://github.com/dennybritz/cnn-text-classification-tf , I want to classified my datasets on Ubuntu-16.04 on GPU.
For running on GPU, I've been changed line 23 on text_cnn.py to this : with tf.device('/gpu:0'), tf.name_scope("embedding"):
my first dataset for train phase has 9000 documents and it's size is about 120M and
second one for train has 1300 documents and it's size is about 1M.
After running on my Titan X server with GPU, I have got errors.
Please guide me, How can I solve this issue?
Thanks.
You are getting Out of Memory error, so first thing to try is smaller batch size
(default is 64). I would start with:
./train.py --batch_size 32
Most of the memory is used to hold the embedding parameters and convolution parameters. I would suggest reduce:
EMBEDDING_DIM
NUM_FILTERS
BATCH_SIZE
try embedding_dim=16, batch_size=16 and num_filters=32, if that works, increase them 2x at a time.
Also if you are using docker virtual machine to run tensorflow, you might be limited to use only 1G of memory by default though you have 16G memory in your machine. see here for more details.
I am looking for options to serve parallel predictions using caffe model from GPU. Since GPU comes with limited memory, what are the options available to achieve parallelism by loading the net only once?
I have successfully wrapped my segmentation net with tornado wsgi + flask. But at the end of the day, this is most equivalent serving from a single process. https://github.com/BVLC/caffe/blob/master/examples/web_demo/app.py.
Is having my own copy of net for each process a strict requirement, since the net is read-only after the training is done? Is it possible to rely on fork for parallelism?
I am working on a sample app which serves result from segmentation model. It utilizes copy on write and loads the net in the master once and serve memory references for the forked children. I am having trouble starting this setup in a web server setting. I get a memory error when I try to initialize the model. The web server I am using here is uwsgi.
Have anyone achieved parallelism by loading the net only once (since GPU memory is limited) and achieved parallelism for serving layer? I would be grateful if any one of you can point me in the right direction.