| #!/bin/bash | |
| # conda-auto-env automatically activates a conda environment when | |
| # entering a folder with an environment.yml file. | |
| # | |
| # If the environment doesn't exist, conda-auto-env creates it and | |
| # activates it for you. | |
| # | |
| # To install add this line to your .bashrc or .bash-profile: | |
| # |
| #!/bin/bash | |
| #SBATCH --partition general | |
| #SBATCH --nodes 1 | |
| #SBATCH --ntasks-per-node 1 | |
| #SBATCH --mem 16G | |
| #SBATCH --gres gpu:1 | |
| #SBATCH --time 1-0:00:00 | |
| #SBATCH --job-name jupyter-notebook | |
| #SBATCH --output jupyter-notebook-%J.log |
| name: null # env was made with --prefix | |
| channels: | |
| - conda-forge # added a third party channel | |
| - defaults | |
| dependencies: | |
| - numpy=1.16.3=py37h926163e_0 | |
| - opencv=3.4.2=py37h6fd60c2_1 | |
| - pandas=0.24.2=py37h0a44026_0 | |
| - pip=19.1.1=py37_0 | |
| - pip: # Packages installed from PyPI |
Simple steps:
Download .tar.bz from official site and extract it. Version 2.79
wget https://download.blender.org/release/Blender2.79/blender-2.79b-linux-glibc219-x86_64.tar.bz2
Extract:
tar -xvf <blender_version>
sudo mv /{path_to}/{Extracted folder} /opt/blender
Create a symbolic link for use from the command line:
| #!/bin/bash | |
| #SBATCH -p p100 | |
| #SBATCH --gres=gpu:1 | |
| #SBATCH -c 4 | |
| #SBATCH --mem=16G | |
| #SBATCH --nodes=1 | |
| #SBATCH --ntasks=1 | |
| ##SBATCH --ntasks-per-node=1 | |
| #SBATCH --time=04:00:00 | |
| #SBATCH --job-name=jupyter-notebook |
alias sq='squeue -o "%.18i %.9P %.8j %.8u %.6c %.8b %.8m %.8T %.10M %.9l %.6D %R" -A $(PARTITION_NAME)'
alias sinfo='sinfo -p all,$(PARTITION1), $(PARTITION2) -el -o "%15N %.6D %.9P %.7T %.4c %.13G %.8z %.6m %.6w %20E"'
alias sqk='squeue -u $(USERNAME)'
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| import time, logging, platform | |
| import wandb | |
| def wandb_init(opt, *args, **kwargs): | |
| """A quick naive example """ | |
| wandb_identifier = get_run_identifier(opt, *args, **kwargs) | |
| wandb.init(project="aProj", name=wandb_identifier, notes="<Intriguing notes>") |
| from functools import TopologicalSorter | |
| graph = {"A": {"D"}, "B": {"D"}, "C": {"E", "H"}, "D": {"F", "G", "H"}, "E": {"G"}} | |
| ts = TopologicalSorter(graph) | |
| list(ts.static_order()) | |
| # ['H', 'F', 'G', 'D', 'E', 'A', 'B', 'C'] |
| #!/bin/bash | |
| # Create virtual env in Conda | |
| conda create -n aenv python=3.8 | |
| conda activate aenv | |
| # To [list|remove] | |
| #conda env [list|remove] [-n aenv] | |
| # Add Conda env to Jupyter Notebook | |
| pip install --user ipykernel |
| #!/bin/bash | |
| # Author: Raeid Saqur | |
| # Email: raeidsaqur@cs.toronto.edu | |
| LOCAL_KEY=id_rsa | |
| REMOTE_USER=raeidsaqur | |
| REMOTE_SERVER="vdm1.vectorinstitute.ai" # use dedicated data mover server networked to Vaughan | |
| PROJ_PATH="<SOME/PROJ/PATH>" | |
| LOCAL_PROJ_DIR="$HOME/Research/projects/${PROJ_PATH}" |