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
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I am facing difficulties in training a custom image classification model (EfficientNetB0) on a dataset of 36,000 images of size (100, 100, 3). The images consist of alphanumeric characters (0-9 and A-Z) and my goal is to classify them. My attempts at training the model have been unsuccessful due to either high memory usage on Google Colab or overheating on my MacBook Air M1. I am seeking suggestions for free alternative training methods and models that would be suitable for this classification task.
Below I have also attached images of Google Colab issue and a sample image from my dataset.
colab_crash_image
sample_dataset_image
Here, is what I have tried till now:
Using Google Colab for training, didn't work due to high memory usage.
Using inbuilt processor of MacBook Air M1, didn't work due to overheating issue.
Tried implementing transfer learning on EfficientNetB0 with imagenet weights, got training accuracy of 87% but only 46% test accuracy.
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
I am trying to train my Deep Learning model on Google colab where they offer a free K80 GPU. I learned that it can be used for 12 hours at a time and then you have to reconnect to it. But my connection is lost after 10-15 minutes and I cannot reconnect (it stays stuck on Initializing) . What's the issue here ?
This proved to be a network issue in my university. My university has a login portal to access the internet. Bypassing it solved the problem.
I have been able to running a vision training model and it disconnects and stops sometime overnight. It runs hours and may be 12 hours. I also trained the model using the CPU and got the same results although without as many epochs completed. I have searched to see what the time limit is for the CPU without success. The training program uses tensorflow.saver to use checkpoints during training that allow for restarting training from a checkpoint when it is disrupted.
We're trying to develop a Natural Language Processing application that has a user facing component. The user can call models through an API, and get the results back.
The models are pretrained using Keras with Theano. We use GPUs to speed up the training. However, prediction is still sped up significantly by using the GPU. Currently, we have a machine with two GPUs. However, at runtime (e.g. when running the user facing bits) there is a problem: multiple Python processes sharing the GPUs via CUDA does not seem to offer a parallelism speed up.
We're using nvidia-docker with libgpuarray (pygpu), Theano and Keras.
The GPUs are still mostly idle, but adding more Python workers does not speed up the process.
What is the preferred way of solving the problem of running GPU models behind an API? Ideally we'd utilize the existing GPUs more efficiently before buying new ones.
I can imagine that we want some sort of buffer before sending it off to the GPU, rather than requesting a lock for each HTTP call?
This is not an answer to your more general question, but rather an answer based on how I understand the scenario you described.
If someone has coded a system which uses a GPU for some computational task, they have (hopefully) taken the time to parallelize its execution so as to benefit from the full resources the GPU can offer, or something close to that.
That means that if you add a second similar task - even in parallel - the total amount of time to complete them should be similar to the amount of time to complete them serially, i.e. one after the other - since there are very little underutilized GPU resources for the second task to benefit from. In fact, it could even be the case that both tasks will be slower (if, say, they both somehow utilize the L2 cache a lot, and when running together they thrash it).
At any rate, when you want to improve performance, a good thing to do is profile your application - in this case, using the nvprof profiler or its nvvp frontend (the first link is the official documentation, the second link is a presentation).
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.