firelab 0.0.26

An experimental framework to run pytorch experiments

## firelab (version 0.0.26) ### 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 `

### Future plans [ ] Run in daemon. [ ] Implement firelab ls command [ ] 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? [ ] More meaningful error messages. [ ] Does model release GPU after training is finished (when we do not use HPO)? [ ] Proper handling of errors in HPO: should we fail on the first exception? Should we try/catch result.get() in process pool? [x] Make trainers run without config.firelab, this will make it possible to run trainer from python [ ] Does continue_from_iter work?

### 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 - Why do we pass both exp_dir and exp_name everywhere in manager.py? We should care only about exp_path I suppose? - Looks like we do not need the dublicating logic of directories creation in manager.py anymore since it is in BaseTrainer - Rename BaseTrainer into Trainer?