Basic framework for training models with PyTorch
Basic framework for training stuff in PyTorch. It's quite tailored to projects
I've been working on lately, so it's meant for personal use. Its sole purpose is
to do away with
boilrplate code, and having it here makes it easier to
share it across projects.
pip install boilr
There's a usage example that can be useful as template. It's a basic VAE for MNIST quickly hacked together. The example files are:
Install requirements and run the example:
pip install -r requirements.txt CUDA_VISIBLE_DEVICES=0 python example.py
CUDA_VISIBLE_DEVICES=0 python example_evaluate.py --ll --ll-samples 100 --load $RUN_NAME
using the name of the folder in
output/ generated from running the example.
The following functionalities are available out-of-the-box:
- Easy logging of metrics to tensorboard and to a pickle file. Metrics are collected at every training step, smoothed, and logged/saved at a specified frequency. The amount of smoothing is also customizable.
- Summaries of the metrics are automatically printed after each training and testing phase. This can be easily customized.
- Training speed, gradient norm (global and per-parameter), and L2 norm of the model parameters are all automatically logged.
- It's easy to save images from testing, in a dedicated folder.
- Gradient clipping (by global norm), controllable through a command-line argument.
- Automatic model checkpointing, with command-line argument to control the maximum number of recent checkpoints to be kept.
- Command-line argument to resume training from checkpoint, and everything is taken care of.
- Progress bar for training and testing, using
tqdm. Can be switched off.
- Data-dependent initialization (command-line argument).
- Reproducibility: set random seed across all devices and Python libraries.
- A suite of utility classes and methods in the packages
boilr.utils(most of them for internal use). In particular
boilr.utils.vizmight be more generally useful.
- A long list of command-line arguments to control some of the behaviour above.
Some arguments are not directly used, but it's convenient to have them already defined: e.g. if a custom
DataLoaderis necessary, the batch size is easily accessible with
args.batch_size; and when creating the optimizer, the learning rate is
boilr.optionsfor package-wide options. Usually it's not necessary to change them, but they give some more flexibility.
There are built-in command-line arguments with default values. These defaults can be easily
overridden programmatically when making the experiment class that subclasses
The built-in arguments are the following:
batch-size: training batch size (default: None)
test-batch-size: test batch size (default: None)
lr: learning rate (default: None)
max-grad-norm: maximum global norm of the gradient. It is clipped if larger. If None, no clipping is performed. (default: None)
seed: random seed (default: 54321)
tr-log-every: log training metrics every this number of training steps (default: 1000)
ts-log-every: log test metrics every this number of training steps. It must be a multiple of
ts-img-every: save test images every this number of training steps. It must be a multiple of
--ts-log-every(default: same as
checkpoint-every: save model checkpoint every this number of training steps (default: 1000)
keep-checkpoint-max: keep at most this number of most recent model checkpoints (default: 3)
max-steps: max number of training steps (default: 1e10)
max-epochs: max number of training epochs (default: 1e7)
nocuda: do not use cuda (default: False)
descr: additional description for experiment name
dry-run: do not save anything to disk (default: False)
resume: load the run with this name and resume training
VAEExperimentManager, the following arguments are available:
ll-every: evaluate log likelihood (with the importance-weighted bound) every this number of training steps (default: 50000)
ll-samples: number of importance-weighted samples to evaluate log likelihood (default: 100)
- subclass a base dataset manager class;
- subclass a base model class;
- subclass a base experiment manager class (the model class is used in here);
- make a short script that creates the experiment object, uses it to create a
boilr.Trainer, and runs the trainer;
- optionally, subclass the base evaluator to set up an "offline" evaluation pipeline.
See below for more details.
Dataset manager class (1)
boilr.data.BaseDatasetManager must be subclassed. The subclass must implement
_make_datasets which should return a tuple
(train, test) with the training
and test sets as PyTorch
A basic implementation of
_make_dataloaders is already provided, but can be overridden to make
custom data loaders.
Model class (2)
One of the model classes must be subclassed to inherit core methods in the base implementation
These models also automatically subclass
torch.nn.Module (so it must implement
BaseModel) defines a method
sample_prior that must be implemented by subclasses.
Experiment manager class (3)
One of the base experiment classes in
boilr.experiments must be subclassed. The subclass must implement:
_make_datamanagerto create the dataset manager, which should subclass
_make_modelto create the model, which should subclass
_make_optimizerto create the optimizer, which should subclass
forward_passto perform a simple single-pass model evaluation and returns losses and metrics;
test_procedureto evaluate the model on the test set (usually heavily based on the
Typically should be overridden:
_check_args(or a subset of these) to manage parsing of command-line arguments;
_make_run_descriptionwhich returns a string description of the run, used for output folders;
save_imagesto save output images (e.g. reconstructions and samples in VAEs).
May be overridden for additional control:
post_backward_callbackis called by the
Trainerafter the backward pass but before the optimization step;
get_metrics_dicttranslates a dictionary of results to a dictionary of metrics to be logged (by default this simply copies over the keys);
test_log_strreturn log strings for test and training metrics.
Note: The class
VAEExperimentManager implements default
methods for variational inference with VAEs.
Example training script (4)
from boilr import Trainer from my_experiment import MyExperimentClass if __name__ == "__main__": experiment = MyExperimentClass() trainer = Trainer(experiment) trainer.run()
Offline evaluator class (5)
If offline evaluation is necessary,
boilr.eval.BaseOfflineEvaluator can be subclassed by implementing:
runto run the evaluation;
- as above,
_check_args(or a subset of these) to manage parsing of command-line arguments.
run can be executed by simply calling the evaluator object.
- It also works without
tensorboard, but it won't save tensorboard logs.
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