My references for colab commands.

kaggle2colab.py

# get API key from kaggle

# open github nb with colab
# https://colab.research.google.com/github/


!pip install -U -q kaggle
!mkdir -p ~/.kaggle


#upload API key
from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))

!cp kaggle.json ~/.kaggle/
!kaggle datasets list
!kaggle datasets download -d iarunava/cell-images-for-detecting-malaria

!ls
os.listdir()
!unzip 'cell-images-for-detecting-malaria.zip'
os.listdir("cell_images")

# zip folder
!zip -r files.zip files

# save data
np.save("x_val.npy", x_val)

# see if files actually saved
os.listdir()


# Step 1
#---------------------------------------
from google.colab import files
files.download("x_val.npy")
#---------------------------------------
# Step 2
from google.colab import files

with open('example.txt', 'w') as f:
  f.write('some content')
files.download('example.txt')
#---------------------------------------



# see RAM usage:
def spec():
  # memory footprint support libraries/code
  !ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi
  !pip install gputil
  !pip install psutil
  !pip install humanize
  import psutil
  import humanize
  import os
  import GPUtil as GPU
  GPUs = GPU.getGPUs()
  # XXX: only one GPU on Colab and isn’t guaranteed
  gpu = GPUs[0]
  def printm():
    process = psutil.Process(os.getpid())
    print("Gen RAM Free: " + humanize.naturalsize( psutil.virtual_memory().available ), " | Proc size: " + humanize.naturalsize( process.memory_info().rss))
    print("GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))
  printm()
  
 # move or copy files in folder

!mkdir files
!cp y_val.npy files
!mv y_val.npy files
!os.listdir("files/")


#----------------------------------------------------------
# save files in drive
from google.colab import drive
drive.mount('/content/gdrive')
PATH_MAIN = os.path.abspath("gdrive/My Drive/")
PATH_MAIN

# set path to save file
record_path = os.path.join(stor_path, 'model/record{}.csv'.format(exp_name))
# Load file from folder
img = np.load("{}/1.npy".format(stor_path))



with open('/content/gdrive/My Drive/foo.txt', 'w') as f:
  f.write('Hello Google Drive!')
!cat /content/gdrive/My\ Drive/foo.txt
# or 
with open('/content/gdrive/My Drive/Colab Notebooks/rbc notebooks/array.npy', 'w') as f:
  f.write("array.npy")
  
# another way to get data from drive  
!cp "/content/gdrive/My Drive/models.zip" test_folder
!unzip test/models.zip
os.listdir("models")
#----------------------------------------------------------

# run cmd command in python script
import subprocess
out = subprocess.Popen(["ls"], 
           stdout=subprocess.PIPE, 
           stderr=subprocess.STDOUT)

stdout,stderr = out.communicate()
stdout.split()[0] # parse desired output
print(stdout)
#----------------------------------------------------------


# ref: https://medium.com/tensorflow/colab-an-easy-way-to-learn-and-use-tensorflow-d74d1686e309

library_install.py

# To determine which version you're using:
!pip show tensorflow

# For the current version: 
!pip install --upgrade tensorflow

# For a specific version:
!pip install tensorflow==1.2

# For the latest nightly build:
!pip install tf-nightly

# import library not on colab
!pip install -q matplotlib-venn
!apt-get -qq install -y libfluidsynth1

# https://pypi.python.org/pypi/pydot
!apt-get -qq install -y graphviz && pip install -q pydot
import pydot

!apt-get -qq install python-cartopy python3-cartopy
import cartopy

Go to env

conda env export > environment.yml

Make new env from yml file (first line of yml is the env name, edit it if needed)

conda env create -f environment.yml 

More conda refs: https://gist.github.com/pratos/e167d4b002f5d888d0726a5b5ddcca57

How to run c and c++ and other programming languages on google colab? Please follow this article, and it gives a better idea if you don't have enough machines to run the program.
https://debuggingsolution.blogspot.com/2021/10/run-c-program-in-google-colab.html