This is a template repository, which can be used to initialize new repositories for deep learning fast prototyping. The template allows training for single-node single-GPU models, single-node multi-GPU models, or multi-node multi-GPU models with SLURM. Checkpointing and auto-requeuing are supported on slurm as well.

• Create a new repository on GitHub with name: [new-repo-name]
• Follow these steps:

$ git clone https://github.com/ramyamounir/Template.git
$ mv Template [new-repo-name]
$ cd [new-repo-name]
$ git remote set-url origin https://github.com/ramyamounir/[new-repo-name].git
$ git add .
$ git commit -m "Template copied"
$ git push

This template follows a modular approach where the main components of the code (architecture, loss, scheduler, trainer, etc.) are organized into subdirectories.

• The train.py script contains all the arguments (parsed by argparse) and nodes/GPUS initializer (slurm or local). It also contains code for importing the dataset, model, loss function and passing them to the trainer function.
• The lib/trainer/trainer.py script defines the details of the training procedure.
• The lib/dataset/[args.dataset].py imports data and defines the dataset function. Creating a data directory with a soft link to the dataset is recommended, especially for testing on multiple datasets.
• The lib/core/ directory contains definitions for loss, optimizer, scheduler functions.
• The lib/util/ directory contains helper functions organized by file name. (i.e., helper functions for distributed training are placed in the lib/util/distributed.py file).

Try the following example

python train.py -gpus 0

Try the following example

python train.py -gpus 0,1,2

Try the following example

python train.py -slurm -slurm_nnodes 1 -slurm_ngpus 4 -slurm_partition general

Try the following example

python train.py -slurm -slurm_nnodes 2 -slurm_ngpus 8  -slurm_partition general

• To get more information about available arguments, run: python train.py -h
• To automatically start Tensorboard server as a different thread, add the argument: -tb
• To overwrite model log files and start from scratch, add the argument: -reset; otherwise, it will use the last weights as a checkpoint and continue writing to the same Tensorboard log files - if the same model name is used.
• To choose specific node names on SLURM, use the argument: -slurm_nodelist GPU17,GPU18 as an example.
• If running on a GPU with Tensor cores, using mixed precision models can speed up your training. Add the argument -fp16 to try it out. If it makes training unstable due to the loss of precision, don't use it :)
• The template allows you to switch architectures, datasets and trainers easily by passing different arguments. For example, different architectures can be added to the lib/arch/[arch-name].py directory and passing the arguments as -arch [arch-name] or -trainer [trainer-name] or -dataset [dataset-name]
• The stdout and stderr will be printed in the shared directory. We only print the first GPU output. Make sure to change the shared directory in lib/utils/distributed.py depending on the cluster you are using.
• if you find a bug in this template, open a new issue or a pull request. Any collaboration is more than welcome!

This repository is released under the Apache 2.0 license as found in the LICENSE file.