Experimental framework to run pytorch experiments
## firelab (version 0.0.6) ### About Framework for running DL experiments with pytorch. Provides the following useful stuff: - parallel hyperparameters optimization - allows to start/continue your experiment with easy commands from yml config file - easier to save checkpoints, write logs and visualize training - useful utils for HP tuning and working with pytorch (look them up in utils.py)
### Installation ` pip install firelab `
### TODO - daemon - firelab ls - easier profiling (via contexts?) - there are some interseting features in https://github.com/vpj/lab - add commit hash to summary - create new branch/commit for each experiment? - move HPO logic out from manager - more meaningful error messages
### Useful commands: - firelab ls — lists all running experiments - firelab start / firelab stop / firelab pause / firelab continue — starts/stops/pauses/continues experiments
### Useful classes - BaseTrainer — controls the experiment: loads data, runs/stops training, performs logging, etc
Cool staff firelab can do: - Reduces amount of boilerplate code you write for training/running experiments - Keep all experiment arguments and hyperparameters in a expressive config files - Visualize your metrics with tensorboard through [tensorboardX](https://github.com/lanpa/tensorboard-pytorch) - Save checkpoints and logs with ease. - Fixes random seeds for you by default (in numpy, pytorch and random). Attention: if you use other libs with other random generators, you should fix random seeds by yourself (we recommend taking it from hyperparams)
### Usage: #### Configs Besides your own configs, firelab adds its inner staff, which you can use or change as hyperparameter: - name of the experiment - random_seed
Experiment name determines where config is. Experiment name can’t duplicate each other.
### TODO - Interactive config builder - Clone experiment/config - Add examples with several trainers in them
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