How to run Tensorboard in parallel
https://github.com/NVIDIA/DeepRecommender

According to the above page, I tried to run the NVIDIA's DeepRecommender program.After I activated the pytorch, I run the program as below but it failed.

[I run this Command]

$ python run.py --gpu_ids 0 \
 --path_to_train_data Netflix/NF_TRAIN \
 --path_to_eval_data Netflix/NF_VALID \
 --hidden_layers 512,512,1024 \
 --non_linearity_type selu \
 --batch_size 128 \
 --logdir model_save \
 --drop_prob 0.8 \
 --optimizer momentum \
 --lr 0.005 \
 --weight_decay 0 \
 --aug_step 1 \
 --noise_prob 0 \
 --num_epochs 12 \
 --summary_frequency 1000

[The comments of the Guide.]


  Note that you can run Tensorboard in parallel
  
  $ tensorboard --logdir=model_save


[My Question]

The guide says as above.I don't know how to run in parallel.Please tell me the way. Shoud I open 2 terminal  windows?

[Enviroment]

The detail of the enviroment is as follow.
---> Ubuntu 18.04 LTS, python 3.6, Pytorch 1.2.0, CUDA V10.1.168

[The 1st trial]

After I activated the pytorch,

 $source activate pytorch

 $python run.py --gpu_ids 0 \  (The long parameters are abbreviated here.)


[The Error messages of the 1st trial]

Traceback (most recent call last):
  File "run.py", line 13, in 
    from logger import Logger
  File "/home/user/NVIDIA_DeepRecommender/DeepRecommender-mp_branch/logge                                                                                                 r.py", line 4, in 
    import tensorflow as tf
ModuleNotFoundError: No module named 'tensorflow'

[The 2nd trial]

After I activated the tensorflow-gpu,

 $ source activate tensorflow-gpu

 $python run.py --gpu_ids 0 \  (The long parameters are abbreviated here.)


[The Error messages of the 2nd trial.]

Traceback (most recent call last):
  File "run.py", line 2, in 
    import torch
ModuleNotFoundError: No module named 'torch'

[Expected result]


  $ python run.py --gpu_ids 0 \
   The program can run with no error and finish training the model.


Try either installing tensorflow-gpu in your pytorch environment or pytorch in your tensorflow-gpu environemnt and use that environment to run your program.
