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Configuration Templates

At a typical organization, many Determined configuration files will contain similar settings. For example, all of the training workloads run at a given organization might use the same checkpoint storage configuration. One way to reduce this redundancy is to use configuration templates. With this feature, users can move settings that are shared by many experiments into a single YAML file that can then be referenced by configurations that require those settings.

Each configuration template has a unique name and is stored by the Determined master. If a configuration specifies a template, the effective configuration of the task will be the result of merging the two YAML files (configuration file and template). The semantics of this merge operation is described below. Determined stores this expanded configuration so that future changes to a template will not affect the reproducibility of experiments that used a previous version of the configuration template.

A single configuration file can use at most one configuration template. A configuration template cannot itself use another configuration template.

Working with Templates in the CLI

The Determined command-line interface can be used to list, create, update, and delete configuration templates. This functionality can be accessed through the det template sub-command. This command can be abbreviated as det tpl.

To list all the templates stored in Determined, use det template list. You can also use the -d or --detail option to show additional details.

$ det tpl list
Name
-------------------------
template-s3-tf-gpu
template-s3-pytorch-gpu
template-s3-keras-gpu

To create or update a template, use det tpl set template_name template_file.

$ cat > template-s3-keras-gpu.yaml << EOL
description: template-s3-keras-gpu
checkpoint_storage:
  type: s3
  access_key: my-access-key
  secret_key: my-secret-key
  bucket: my-bucket-name
EOL
$ det tpl set template-s3-keras-gpu template-s3-keras-gpu.yaml
Set template template-s3-keras-gpu

Using Templates to Simplify Experiment Configurations

An experiment can use a configuration template by using the --template command-line option to specify the name of the desired template.

Here is an example demonstrating how an experiment configuration can be split into a reusable template and a simplified configuration.

Consider the experiment configuration below:

name: mnist_tf_const
checkpoint_storage:
  type: s3
  access_key: my-access-key
  secret_key: my-secret-key
  bucket: my-bucket-name
data:
  base_url: https://s3-us-west-2.amazonaws.com/determined-ai-datasets/mnist/
  training_data: train-images-idx3-ubyte.gz
  training_labels: train-labels-idx1-ubyte.gz
  validation_set_size: 10000
hyperparameters:
  base_learning_rate: 0.001
  weight_cost: 0.0001
  global_batch_size: 64
  n_filters1: 40
  n_filters2: 40
searcher:
  name: single
  metric: error
  max_length:
    batches: 500
  smaller_is_better: true

You may find that the values for the checkpoint_storage field are the same for many experiments and you want to use a configuration template to reduce the redundancy. You might write a template like the following:

description: template-tf-gpu
checkpoint_storage:
  type: s3
  access_key: my-access-key
  secret_key: my-secret-key
  bucket: my-bucket-name

Then the experiment configuration for this experiment can be written as below:

description: mnist_tf_const
data:
  base_url: https://s3-us-west-2.amazonaws.com/determined-ai-datasets/mnist/
  training_data: train-images-idx3-ubyte.gz
  training_labels: train-labels-idx1-ubyte.gz
  validation_set_size: 10000
hyperparameters:
  base_learning_rate: 0.001
  weight_cost: 0.0001
  global_batch_size: 64
  n_filters1: 40
  n_filters2: 40
searcher:
  name: single
  metric: error
  max_length:
    batches: 500
  smaller_is_better: true

To launch the experiment with the template:

$ det experiment create --template template-tf-gpu mnist_tf_const.yaml <model_code>

Merge Behavior

Suppose we have a template that specifies top-level fields a and b and a configuration that specifies fields b and c. The merged configuration will have fields a, b, and c. The value for field a will simply be the value set in the template. Likewise, the value for field c will be whatever was specified in the configuration. The final value for field b, however, depends on the value’s type:

  • If the field specifies a scalar value, the merged value will be the one specified by the configuration (the configuration overrides the template).

  • If the field specifies a list value, the merged value will be the concatenation of the list specified in the template and that specified in the configuration.

    Note that there are exceptions to this rule for bind_mounts and resources.devices. It may be the case that the both the original config and the template will attempt to mount to the same container_path, which would result in an unsable config. In those situations, the original config is preferred, and the conflicting bind mount or device from the template is omittied in the merged result.

  • If the field specifies an object value, the resulting value will be the object generated by recursively applying this merging algorithm to both objects.