PGM projects
This is an improved implementation of the paper Stochastic Gradient VB and the Variational Auto-Encoder by Kingma and Welling. It uses ReLUs and the adam optimizer, instead of sigmoids and adagrad. These changes make the network converge much faster.
pip install -r requirements.txt
python main.py<<In the following instructions, replace with “sai” >>
git clone https://github.com/nithin127/nest-vae
cd nest-vae
To get the tensorboard working:
conda create --name sai python=3.6 #just do it. Don’t question
source activate sai
conda install pytorch torchvision cuda80 -c soumith
pip install tensorflow-gpu
pip install tensorboardX
pip uninstall torchvision #coz existing version is crooked
pip install git+https://github.com/pytorch/vision.git #This is the correct one
cd nest-vae
git checkout devel-tristan #to go into tristan’s directory. Yes, “devel-tristan” NOT “devel-yourname”
git branch #idk why, but pls do this
git pull #to pull all his recent commits
cd tristan/
cd pytorch_tutorial_vae/ #this is where you will get enlightened
<<In a different terminal>>
<<replace gottipav with your elisaID in the following instructions>>
<<replace 1996 with your year of birth>>
ssh -X gottipav@elisa1.iro.umontreal.ca -L 6006:localhost:1996
ssh -X gottipav@bart15.iro.umontreal.ca -L 1996:localhost:6006 #bart15 is the GPU.
replace it with whatever GPU you are using
Activate your environment
Go to your directory
tensorboard --logdir .logs --port 6006
Now, python main.py in the /nest-vae/tristan/pytorch_tutorial_vae in your earlier terminal and you can see the tensorboard opening in a browser and doing some stuff
python main.py
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