I am using colab pro to train a model, and it is endless despite of a simple regression model.
I am set on CPU and elevated ram but still super slow.
My file comes from my google drive, could be the problem here ?
Thanks,
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I have trouble for long waiting when I run my training model with Machine Learning using CNNs. Maybe this because my pc has such a bad specs for machine learning.
I have 50000 images for my X_training and must wait up to 1 hours more until it's done.
I think maybe that someone can solve my problem. Thanks a lot
I would recommend you to use Google Collab. It’s free to use. You can access it withing Google Drive and make sure to change the runtime to GPU. In cases such as CNN, using GPUs can make your training process a lot faster.
Also, I don’t know how you are handling images, but if using TensorFlow/Keras I would also recommend you to use the ImageDataGenerator for not loading all images into memory at once, but loading the images needed within each batch. It can save some resources for the computer
I've got a DL model to train and since the data is quite large I store it on my Google Disk which I mount to my Google Colab instance at the beginning of each session. However, I have noticed that the training of the exact same model with exact same script is 1.5-2 times slower on Google Colab than on my personal laptop. The thing is that I checked the Google Colab GPU and it has 12GB RAM (I'm not sure how can I check the exact model), while my laptop GPU is RTX 2060 which has only 6GB RAM. Therefore, as I'm new user of Google Colab, I've been wondering what might be the reason. Is this because data loading from mounted Disk Google with torch DataLoader slows down the process? Or maybe this is because my personal harddrive is SSD and Google Colab might not have SSD attached to my instance? How can I validate further if I'm not doing anything with my Google Colab setup that slows down the training?
The resources for Google Colaboratory are dynamically assigned to user instances. Short, interactive processes are preferred over long running data loading and processes further info can be found in the documentation:
https://research.google.com/colaboratory/faq.html#resource-limits
Specifically quoted from the above link
"GPUs and TPUs are sometimes prioritized for users who use Colab interactively rather than for long-running computations, or for users who have recently used less resources in Colab...As a result, users who use Colab for long-running computations, or users who have recently used more resources in Colab, are more likely to run into usage limits"
Im currently working on a project creating simulations from defined models. Most of the testing on down-sized models has taken place in google colab to make use of the GPU accelerator option. However, when up-scaling to the full sized models I now exceed the maximum RAM for google colab. Is there an equivalent service which allows for 25GB RAM rather than 12GB. Access to GPU acceleration is still essential
Note: I am outside the US so Google Colab Pro is not an option
I am training a TF ML job on cloud-ml, and it seems the job is stuck after a few iterations (900 iterations). Surprisingly, when I run the code locally it works fine, and also hyper tuning on GCP continues training but runs slower than my local laptop which has a 1060GTX GPU.
I am also using the runtime version 1.6.
I changed the scale-tier and it doesn't help. What can be the issue?
I am apologizing in advance if this issue seems to basic, but I am new to Tensorflow and appreciate any help.
I find that I have to frequently keep rebooting my computer to be able to load models such as VGG16 from keras.applications. I have a fairly high-end machine with 4 GeForce GTX 1080 Ti GPUs and Intel® Core™ i7-6850K CPU # 3.60GHz × 12 for my CPU and use it only for Tensorflow (through Keras).
As soon as I reboot I will be able to successfully load models (such as VGG16) and train on large training datasets. But, if I let my computer sit idle for a while and rerun the same program, I will get a resource exhausted message (OOM) which can be fixed by rebooting my computer again. It is extremely frustrating to keep rebooting my computer every couple of hours. Does anyone know what's going on and how to solve this issue?
If you have batch size > 1, try to use lower batch size, which could lower the memory requirements gor GPU.
Also, if you end with working with the network, check the GPU memory by nvidia-smi, if it was released or not. If not, kill the process which loaded the network (usually some python interpreter).