Interrupting and Resuming Training

[burnedsap]

Is it possible to interrupt training, and then resume it later?

Best case scenario, I train it on one machine, interrupt, and then continue training on another. No GPU involved, CPU only.

Or is it possible to interrupt training, and save to a model immediately, before it has trained completely? When I interrupt now, no Models folder is created, to save the models.

Thanks

[cvalenzuela]

Yes, it is possible. Here are the arguments you need to pass to the script:

    parser.add_argument('--init_from', type=str, default=None,
                        help="""continue training from saved model at this path. Path must contain files saved by previous training process:
                            'config.pkl'        : configuration;
                            'chars_vocab.pkl'   : vocabulary definitions;
                            'checkpoint'        : paths to model file(s) (created by tf).
                                                  Note: this file contains absolute paths, be careful when moving files around;
                            'model.ckpt-*'      : file(s) with model definition (created by tf)
                        """)

See https://github.com/ml5js/training-lstm/blob/master/train.py#L67

[burnedsap]

Oh cool, thanks! The file model.ckpt isn't created, so I'm a little worried about whether it actually picks up from where it left off. It does say that it's loading the preprocessed files, but then the time taken to train the model is no different from doing it all over again.

I assume this is because this process is usually meant for re-training models rather than shifting from computer to computer like what I'm wanting to do?

[cvalenzuela]

No, you are right.
The actual code just saves the checkpoint when the model finishes the training: https://github.com/ml5js/training-lstm/blob/master/train.py#L168

We should add support to save the model by default every N amount of iterations.
Here's the original code doing that: https://github.com/sherjilozair/char-rnn-tensorflow/blob/master/train.py#L133

We removed it at some point and now it makes sense to put it back

[burnedsap]

Good spot!

There's no easy way to put it back in right? My knowledge of python is limited, but we would have to rewrite that entire last bit to make it work, right?

[zachwhalen]

Has this ability been implemented (or re-implemented)? It would be very useful, in the spirit of making machine learning accessible, to have the ability to start, stop, and resume training, given the enormous amounts of time training can take. For example, I was trying this just now on an admittedly pretty large input corpus (around 300MB), and if I understand how it's working, it's going to take several weeks of computer time to finish on the fasted computer I could find today. Obviously, finding a good GPU or maybe renting some cloud computing time would speed that up, but ¯_(ツ)_/¯

[burnedsap]

Hello so I tried to figure something out with my rudimentary python, but I can't seem to get anywhere. Some error with the args somewhere.

Any luck with this?