Advanced Base Configuration

Here, we show how some advanced base properties in the options.yaml can be adjusted. They should be written without indentation in the options.yaml file.

param device:

The device in which the training should be run. Takes two possible values: cpu and gpu. Default: cpu

param base_precision:

Override the base precision of all floats during training. By default an optimal precision is obtained from the architecture. Changing this will have an effect on the memory consumption during training and maybe also on the accuracy of the model. Possible values: 64, 32 or 16.

param seed:

Seed used to start the training. Set all the seeds of numpy.random, random, torch and torch.cuda (if available) to the same value seed. If seed is not the initial seed will be set to a random number. This initial seed will be reported in the output folder

param wandb:

If you want to use Weights and Biases (wandb) for logging, create a new section with this name. The parameters of section are the same as of the wandb.init method and a minimal example of the section is:

wandb:
  project: my_project
  name: my_run_name
  tags:
    - tag1
    - tag2
  notes: This is a test run

All parameters of your options file will be automatically added to the wandb run so you don’t have to set the config parameter.

Important

You need to install wandb with pip install wandb if you want to use this logger. Before running also set up your credentials with wandb login from the command line. See wandb login documentation for details on the setup.

In the next tutorials we show how to override the default parameters of an architecture.