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lumo is a streamlined and efficient library that simplifies the management of all components required for experiments and focuses on enhancing the experience of deep learning practitioners.

• Experimental Management: Assign unique id and path for each run, distinguish and store various file types; manage code snapshots through git; record all information generated during the experiment to ensure traceability and reproducibility.
• Parameter Management: Provides more convenient parameter management than argparser based on fire.
• Runtime Configuration: Provides configuration management under multi-level scopes.
• Visualization: Provides an Jupyter-compatible interactive dashboard for experiment management based on Panel.
• Additional optimization for deep learning:
  • Training: Provides easily extendable training logic based on Trainer and provides comprehensive callback logic.
  • Optimizer: Integrated parameter and optimizer construction.
  • Data: Abstraction of dataset construction process, combination of multiple DataLoaders, etc.
  • Distributed Training: Also supports multiple training acceleration frameworks, unified abstraction, and easy switching at any time.
• More utilities...
• Type Hint: Support as much as possible for modern IDE's auto-completion.

• ☁️ Installation
• 📖 Quick Start
  • 🔸 Embedding into Existing Projects
  • 🔸 Building from Scratch
  • 🔸 Visual Interface
  • 🔸 re-run
  • 🔸 backup
• 📜 License
• Full Document

Install the published and tested version:

pip install -U lumo

Or install the latest version from the dev1 branch:

pip install git+https://github.com/lumo-tech/lumo@dev1

The experiment panel depends on Panel, which needs to be installed separately:

pip install panel 

Here are two classic scenarios:

For existing projects, you can quickly embed lumo by following these steps:

• Import lumo and initialize Logger and Experiment:

import random from lumo import SimpleExperiment, Params, Logger, Meter, Record logger = Logger() exp = SimpleExperiment(exp_name='my_exp_a') exp.start() logger.add_log_dir(exp.mk_ipath())

• Initialize parameters:

params = Params() params.dataset = params.choice('cifar10', 'cifar100') params.alpha = params.arange(default=1, left=0, right=10) params.from_args() # python3 train.py --dataset=cifar100 --alpha=0.2 print(params.to_dict()) # {"dataset": "cifar100", "alpha": 0.2}

• Record parameters and store information during training:

exp.dump_info('params', params.to_dict()) print(exp.test_name) params.to_yaml(exp.mk_ipath('params.yaml')) for i in range(10): max_acc = exp.dump_metric('Acc', random.random(), cmp='max') logger.info(f'Max acc {max_acc}') ckpt_fn = exp.mk_bpath('checkpoints', f'model_{i}.ckpt') ... # save code given ckpt_fn record = Record() for batch in range(10): m = Meter() m.mean.Lall = random.random() m.last.lr = batch record.record(m) logger.info(record) exp.end()

If you want to start a new deep learning experiment from scratch, you can use lumo to accelerate your code development. Below are examples of lumo training at different scales:

one-fine training:

Experimental project:

In jupyter:

from lumo import Watcher w = Watcher() df = w.load() widget = w.panel(df) widget.servable()

Manually filtered experiments for visualization:

You can directly use the command line:

lumo board [--port, --address, --open] 

Experiment that failed due to certain reasons can be re-run by using the unique experiment ID (test_name) , extra parameters can be reassigned and replaced.

lumo rerun 230313.030.57t --device=0 

Backing up experiment information to a Github issue (based on PyGitHub):

from lumo import Experiment, Watcher from lumo import glob glob[ 'github_access_token'] = 'ghp_*' # Default value for `access_token`. It is recommended to store the access_token in the global configuration `~/.lumorc.json`. w = Watcher() df = w.load() # Selecting a single experiment for backup exp = Experiment.from_cache(df.iloc[0].to_dict()) issue = exp.backup('github', repo='sailist/image-classification-private', access_token='ghp_*', update=True, # If already backed up, overwrite the previous issue labels=None, # Optional labels ) print(issue.number) # Batch backup and add labels based on each experiment's parameters issues = df.apply( lambda x: Experiment.from_cache(x.to_dict()).backup(..., labels=[x['params'].get('dataset', '')]), axis=1 )

{'agent': nan, 'backup': {'23-03-17-003438': {'backend': 'github', 'number': 9, 'repo': '...'}, }, 'exception': nan, 'execute': {'cwd': '~/Documents/Python/lumo', 'exec_argv': ['~/Documents/Python/lumo/a.py'], 'exec_bin': '~/.pyenv/versions/3.9.16/bin/python3.9', 'exec_file': '~/Documents/Python/lumo/a.py', 'repo': '~/Documents/Python/lumo'}, 'exp_name': 'my_exp_a', 'git': {'commit': '1014b6b5', 'dep_hash': 'c93b8c4e340882f55cf0c8e125fa0203', 'repo': '~/Documents/Python/lumo'}, 'hooks': {'Diary': {'loaded': True, 'msg': ''}, 'FinalReport': {'loaded': True, 'msg': ''}, 'GitCommit': {'loaded': True, 'msg': ''}, 'LastCmd': {'loaded': True, 'msg': ''}, 'LockFile': {'loaded': True, 'msg': ''}, 'RecordAbort': {'loaded': True, 'msg': ''}}, 'lock': {'accelerate': '0.16.0', 'decorator': '5.1.1', 'fire': '0.5.0', 'hydra': '1.3.2', 'joblib': '1.2.0', 'lumo': '0.15.0', 'numpy': '1.24.2', 'omegaconf': '2.3.0', 'psutil': '5.9.4', 'torch': '1.13.1'}, 'note': 'This is a Note', 'params': {'alpha': 1, 'dataset': 'cifar10'}, 'paths': {'blob_root': '~/.lumo/blob', 'cache_root': '~/.lumo/cache', 'info_root': '~/.lumo/experiments'}, 'pinfo': {'hash': '0af4b77497c85bc5b65ccbdd9ff4ca0f', 'obj': {'argv': ['~/.pyenv/versions/3.9.16/bin/python3.9', '~/Documents/Python/lumo/a.py'], 'pid': 63975, 'pname': 'python3.9', 'pstart': 1678898740.099484}, 'pid': 63975}, 'progress': {'end': '23-03-16-004542', 'end_code': 0, 'last_edit_time': '23-03-16-004542', 'ratio': 1, 'start': '23-03-16-004542', 'update_from': None}, 'tags': [], 'test_name': '230316.000.8ct', 'trainer': nan} 

Distributed under the Apache License Version 2.0. See LICENSE for more information.