Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.
| import numpy as np | |
| def get_tf_mat(i, dh): | |
| a = dh[i][0] | |
| d = dh[i][1] | |
| alpha = dh[i][2] | |
| theta = dh[i][3] | |
| q = theta |
| import numpy as np | |
| def get_tf_mat(i, dh): | |
| a = dh[i][0] | |
| d = dh[i][1] | |
| alpha = dh[i][2] | |
| theta = dh[i][3] | |
| q = theta |
| #!/usr/bin/env python3 | |
| import pickle | |
| """ | |
| Convert PyTorch models trained in Python 2 to Python 3-compatible models. | |
| """ | |
| checkpoint = torch.load("model.pt") | |
| with open("model.pkl", 'wb') as outfile: |
| #!/home/anuj/projects/personal/personal_env/bin/python | |
| import pyautogui | |
| import time | |
| if __name__ == '__main__': | |
| searchbar_position = (901, 80) | |
| mirror_option_position = (1089, 326) | |
| apply_button_position = (1397, 222) |
Updated 4/11/2018
Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.