Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
sudo pip3 install PyMySQL
Connection in PyMySQL is not autocommit by default. You must commit to save your changes.
# suppose conn is the connection object
conn.commit()
parameterized sql: %s …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
pyodbc only supports qmark
https://github.com/mkleehammer/pyodbc/wiki
This issues arises in Python3 but not Python2, so the simplest way is to use Python2 if …
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
app.run(host= '0.0.0.0')
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
sudo service mysql restart
mysqladmin -u root -p variables | grep port
mysqladmin --help list the locations of my.cnf.
mysqladmin --help
peewee
PyMySQL
MySQLdb
If your data can be fit into the CPU memory,
it is a good practice to save your data into one pickle file
(or other format that you know how to deserialize).
This comes with several advantages.
First, it is easier and faster to read from a single big file rather than many small files.
Second, it avoids the possible system error of openning too many files
(even though avoiding lazying data loading is another way to fix the issue).
Some example datasets (e.g., MNIST)
have separate training and testing files (i.e., 2 pickle files),
so that research work based on it can be easily reproduced.
I personally suggest that you keep only 1 file containing all data
when implementing your own Dataset class.
You can always use the function torch.utils.data.random_split