server.pycontains the API code for the Flask server (back-end)main.pycontains the streamlit code (front-end)
pip install plotly streamlit pandas requests flaskTo run access a remote jupyter notebook, you will need to do the following:
~/.ssh/config):Host FLIP # access point server
HostName access.engr.oregonstate.edu
User ONIDusername
Host DGX # target server
HostName submit-b.hpc.engr.oregonstate.edu
User ONIDusername
| name: Automated Experiments | |
| on: | |
| push: | |
| branches: [ main ] | |
| jobs: | |
| build_on_cn-gpu5: | |
| runs-on: [ cn-gpu5 ] | |
| timeout-minutes: 4320 | |
| defaults: | |
| run: |
| # Create 4 tunnels, each for different ports, with only https enabled | |
| # This way the ngrok process stays bellow the Free plan limit (4 tunnels) | |
| # command: ngrok start --all # to start all of them | |
| # command: ngrok start note tb # to run jupyter notebook server and tensorboard server only | |
| # refer to this page for more info: https://ngrok.com/docs#multiple-tunnels | |
| authtoken: ... | |
| log: ngrok.log | |
| tunnels: | |
| # to run jupyter notebook server |
It wasn't obvious on PyTorch's documentation of how to use PyTorch Profiler (as of today, 8/12/2021), so I have spent some time to understand how to use it and this gist contains a simple example to use.
python>=1.9.0
torchvision>=0.10.0
numpy
matplotlib
| # source: https://twitter.com/PrasoonPratham/status/1461267623266635778/photo/1 (@PrasoonPratham on Twitter) | |
| import datetime | |
| import hashlib | |
| class Block: | |
| def __init__(self, data): | |
| self.data = data | |
| self.blockNo = 0 | |
| self.next = None | |
| self.nonce = 0 |