A few ready-to use python tools for machine learning
Project description
FlashPythonToolbox
A few ready-to use python tools for machine learning
Install
pip install flashtool
Logger (to file)
For every print function, we print a copy to file without changing the whole file.
Logger(filepath = "./log.txt", mode = "w", stdout = None)
filepath
: output path. Default: "./log.txt"mode
: write mode, e.g., w,w+,a,a+. Default: "w"stdout
: capture which print source. Default: "sys.stdout"
Example:
from flashtool import Logger
import sys
sys.stdout = Logger("path/to/log/file/log.txt")
sys.stdout = Logger("path/to/log/file/log.txt","w")
sys.stdout = Logger("path/to/log/file/log.txt","w", sys.stdout)
Email Module
Default Sender: gtflashauto@gmail.com
. You need a password for this.
# Function api
def send_email(port = 587,password = None,
sender_email = "gtflashauto@gmail.com",
smtp_server="smtp.gmail.com",
receiver_email = None,
subject="Subject",
message="Hello! ",
attachment=None):
...
# Example
from flashtool import send_email
send_email(receiver_email="xxx@gmail.com", subject="Hi", message="first email")
Track Job
Track your job. Send you an email when it is done.
Tracking by PID
- Step 1: find your job pid by
ps
, e.g.,ps aux | grep python
- Step 2:
from flashtool import trackpid
trackpid(12345,5,"xxx@gmail.com")
trackpid([1357,2468],5,"xxx@gmail.com")
API:
def trackpid(pid, checktime=1, receiver_email=None, sender_email="gtflashauto@gmail.com", msg=""):
...
pid
: pid or pidschecktime
: check pid everychecktime
secondsmsg
: additional message
Pytorch Utils
GPU Usage
import flashtool as flash; flash.check_torch_memory(brief=True)
brief
: short message (default: Flase)
Auto Script Running
import flashtool as flash;
basescript='script/run.sh'
config={
"DATA": "subset5"
}
flash.run_script(basescript, config)
Profile Timing Line by Line
from flashtool import line_time_profiler
@line_time_profiler()
def f(n=1000):
a = [1 for _ in range(n)]
b = sum(a)
f()
f(10000)
class C:
@line_time_profiler()
def f(self, n=1000):
a = [1 for _ in range(n)]
b = sum(a)
ins = C()
ins.f()
ins.f(10000)
@line_time_profiler(stop=True)
def f(n=1000):
a = [1 for _ in range(n)]
b = sum(a)
f()
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