Python logging package for easy reproducible experimenting in research.
Why you may need this package
This project is meant to provide an easy-to-use (as easy as possible) package to enable reproducible experimenting in research. Here is a struggling situation you may also encountered:
I am doing some project. I got a fatanstic idea some time (one week, one month, or even one year) ago. Now I am looking at the results of that experiment, but I just cannot reproduce them anymore. I cannot remember which script and what hyper-prarameters I used. Even worse, since then I’ve modified the code (a lot). I don’t know where I messed it up…
If you do not use this package, usually, what you can do may be:
- First, use Github to manage your code. Always run experiments after
- Second, before each experiment, set up a unique experiment folder (with a unique ID to label that experiment — we call it
- Third, when running an experiment, print your git commit ID (we call it
argumentsin the log.
Every result is uniquely binded with an
ExpID, corresponding to a unique experiment folder. In that folder,
arguments are saved. So ideally, as long as we know the
ExpID, we should be able to rerun the experiment under the same condition.
These steps are pretty simple, but if you implement them over and over again in each project, it can still be quite annoying. This package is meant to save you with basically 3~4 lines of code change.
Step 0: Install the package (>= python3.4)
# --upgrade to make sure you install the latest version pip install smilelogging --upgrade
Step 1: Modify your code
Here we use the official PyTorch ImageNet example to give an example.
# 1. add this at the head of code from smilelogging import Logger # 2. replace argument parser parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') ==> from smilelogging import argparser as parser # 3. add logger and change print if necessary args = parser.parse_args() ==> args = parser.parse_args() logger = Logger(args) global print; print = logger.log_printer.logprint # change print function so that logs can be printed to a txt file
TIPS: overwriting the default python print func may not be a good practice, a better way may be
logprint = logger.log_printer.logprint, and use it like
logprint('Test accuracy: %.4f' % test_acc). This will print the log to a txt file at path
Step 2: Run experiments
The original ImageNet training snippet is:
CUDA_VISIBLE_DEVICES=0 python main.py -a resnet18 [imagenet-folder with train and val folders]
Now, try this:
CUDA_VISIBLE_DEVICES=0 python main.py -a resnet18 [imagenet-folder with train and val folders] --project_name Scratch__resnet18__imagenet --screen_print
This snippet will set up an experiment folder under path
XXXthing is an
ExpIDautomatically assigned by the time running this snippet. Below is an example on my PC:
Experiments/ └── Scratch__resnet18__imagenet_SERVER138-20211021-145936 ├── gen_img ├── log │ ├── git_status.txt │ ├── gpu_info.txt │ ├── log.txt │ ├── params.yaml │ └── plot └── weights
✨Congrats❗You’re all set❗
As seen, there will be 3 folders automatically created:
log. Log text will be saved in
log/log.txt, arguments saved in
log/params.yaml and in the head of
log/log.txt. Below is an example of the first few lines of
cd /home/wanghuan/Projects/TestProject CUDA_VISIBLE_DEVICES=1 python main.py -a resnet18 /home/wanghuan/Dataset/ILSVRC/Data/CLS-LOC/ --project Scracth_resnet18_imagenet --screen_print ('arch': resnet18) ('batch_size': 256) ('cache_ignore': ) ('CodeID': f30e6078) ('data': /home/wanghuan/Dataset/ILSVRC/Data/CLS-LOC/) ('debug': False) ('dist_backend': nccl) ('dist_url': tcp://188.8.131.52:23456) ('epochs': 90) ('evaluate': False) ('gpu': None) ('lr': 0.1) ('momentum': 0.9) ('multiprocessing_distributed': False) ('note': ) ('pretrained': False) ('print_freq': 10) ('project_name': Scracth_resnet18_imagenet) ('rank': -1) ('resume': ) ('screen_print': True) ('seed': None) ('start_epoch': 0) ('weight_decay': 0.0001) ('workers': 4) ('world_size': -1) [180853 22509 2021/10/21-18:08:54] ==> Caching various config files to 'Experiments/Scracth_resnet18_imagenet_SERVER138-20211021-180853/.caches'
Note, it tells us
- (1) where is the code
- (2) what snippet is used when running this experiment
- (3) what arguments are used
- (4) what is the CodeID — useful when rolling back to prior code versions (
git reset --hard <CodeID>)
- (5) where the code files (*.py, *.json, *.yaml etc) are backuped — note the log line “==> Caching various config files to …”. Ideally, CodeID is already enough to get previous code. Caching code files is a double insurance
- (6) At the begining of each log line, the prefix “[180853 22509 2021/10/21-18:08:54]” is automatically added if the
logprintfunc is used for print, where
180853is short for the full ExpID
22509is the program pid (useful if you want to kill the job, e.g.,
kill -9 22509)
More explanantions about the folder setting
weights folder is supposed to store the checkpoints during training; and
gen_img is supposed to store the generated images during training (like in a generative model project). To use them in the code:
weights_path = logger.weights_path gen_img_path = logger.gen_img_path
More explanantions about the arguments and more tips
--screen_printmeans the logs will also be print to the console (namely, your screen). If it is not used, the log will only be saved to
log/log.txt, not printed to screen.
- If you are debugging code, you may not want to create an experiment folder under
Experiments. Then use
--debug, for example:
CUDA_VISIBLE_DEVICES=0 python main.py -a resnet18 [imagenet-folder with train and val folders] --debug
This will save all the logs in
Debug_Dir, instead of
Experiments is expected to store the formal experiment results).
- Add training and testing metric (like accuracy, PSNR) plots.
Collaboration / Suggestions
Currently, this is still a baby project. Any collaboration or suggestions are welcome to Huan Wang (Email: