I developed a CNN using tensorflow and python2.7. I am now switching my code to python3.5. I have both python versions on my machine and I have two tensorflow versions installed (one through pip and one through pip3). I am using Linux 16.04.
When I try to run my code using the python3 command, it takes a very long time to load and doesn't start training (where it used to take 3 seconds). It slows down my entire machine, so it's probably a memory issue. My coworker gets a memory error when running the same code on Windows (his machine has 128GB of memory).
My CNN has only two convolutional layers and one fully connected layer, and I'm loading less than 100MB of data.
Why is tensorflow acting differently when I change the python version?
Related
I try to install dice library with the command conda but impossible it returns me as error:
CondaMemoryError: The conda process ran out of memory. Increase system memory and/or try again
My PC starts to heat up and I see python using a lot of RAM insanely.
Or I have 16GB of RAM and I'm on the anaconda enterprise version and on Windows.
If anyone has a solution, I've looked everywhere and found nothing.
It does the same thing to me every time I want to install a library or remove.
I am trying to create a GPU environment in Jupyter notebook to run CNN models but have had trouble. I am on MacOS (Big Sur) and was following the instructions from: https://www.techentice.com/how-to-make-jupyter-notebook-to-run-on-gpu/
First, to create a separate GPU environment in Jupyter understand that I need CUDA toolkit. However, found out that CUDA toolkit no longer supports Mac.
Second, understand that I have to download tensor flow GPU which apparently doesn't support MAC/python 3.7.
would be grateful for any help or advice please. essentially I just want to be able to run my code on GPU as CPU is way too slow for machine learning models. is there any way around this?
similarly to the topic below, keras stopped working.
tf.keras - Training on first epoch not progressing despite using GPU memory
I've a python 3.7 anaconda installation on windows
cuda 10.2 and cudnn installed
3080 GPU
keras 2.3.1
TF 1.4
A few days ago everything was running perfectly. Then after installing pytorch keras stopped working. The same script I was training before now get stuck on the first epoch. No errors are displayed when running model.fit (verbose 2). Simply the whole memory is full (even using a very small dataset) and the training is not advancing.
As additional information pytorch displayed an error about the impossibility to use cuda.
I've tried to format the whole PC (factory reset) and the issue is still happening.
I'm out of ideas. Any suggestion would be more then welcome.
Thanks!
I really think that factory reset of the whole PC was really not necessary. I would suggest creating two conda virtual environments, one with Tensorflow and the other with PyTorch. Conda virtual environments are a really useful, they keep things separated and this might be really useful for your application. Here there is the Anaconda official reference explaining how to manage the environments.
I am not experienced user in Python. I have been working with R for the years, but keras implemented there doesn't provide any reproducible examples of working with object detection architectures like Faster R-CNN. I found a lot of examples that harness Python, but I faced troubles just even run some examples from the first lines: it is all built on the downloading through pip operator (in terminal in Ubuntu or orther Linux OS), while analogues for Windows conda users are not provided.
That is, I even don't know how to install module mrcnn from the one example on my Windows machine. Should I suffer further? I have had a very bad experience trying launch compatible versions of CUDA, cudNN and other things for my keras on Ubuntu. And now I am returning to the Windows, but... keras in R still doesn't provide any suggestions for object detection techniques.
Does somebody have links for Faster or Mask R-CNN implementation with conda examples for downloading prerequisites? My googling is failed here. Or in R-keras.
I have referred to a number of tutorials and built an object detection model using Faster-RCNN on an Anaconda Virtual Environment. Now I want to show case this model, and find problem when I run it on a different system without Anaconda, I try running it on CMD. In fact, it doesn't run at all.
I have done my research on exporting the model but hit a deadend each time.
I use Anaconda Prompt + Windows 10 + NVidia GPU + Tensorflow-gpu=1.5 to run model on my dedicated system.
I would want to know how can I export this to a different PC which doesn't have the GPU or the Anaconda installed. Or am I completely wrong in the approach and need all the dependencies used when I run it on my system?