Determined aims to support reproducible machine learning experiments: that is, the result of running a Determined experiment should be deterministic, so that rerunning an experiment produces an identical model. This ensures that in the event of model loss, recovery is possible by rerunning the experiment responsible for its creation.


While the current version of Determined offers limited support for reproducibility, challenges arise due to the inherent complexity of the hardware and software stack typically utilized in deep learning environments.

Determined effectively manages and reproduces several sources of randomness, including:

  • Hyperparameter sampling decisions.

  • The initial weights for a given hyperparameter configuration.

  • Data shuffling during trial training.

  • Utilization of dropout or other random layers.

However, it’s important to note that Determined does not currently provide mechanisms for controlling non-deterministic floating-point operations. Most modern deep learning frameworks employ floating-point operations that may result in non-deterministic outcomes, particularly on GPUs. Achieving reproducible results is feasible when training exclusively on CPUs, as elaborated in the following sections.

Random Seeds#

Each Determined experiment is associated with an experiment seed: an integer ranging from 0 to 231–1. The experiment seed can be set using the reproducibility.experiment_seed field of the experiment configuration. If an experiment seed is not explicitly specified, the master will assign one automatically.

The experiment seed is used as a source of randomness for any hyperparameter sampling procedures. The experiment seed is also used to generate a trial seed for every trial associated with the experiment.

In the Trial interface, the trial seed is accessible within the trial class using self.ctx.get_trial_seed().

Coding Guidelines#

To achieve reproducible initial conditions in an experiment, please follow these guidelines:

  • Use the np.random or random APIs for random procedures, such as shuffling of data. Both PRNGs will be initialized with the trial seed by Determined automatically.

  • Use the trial seed to seed any randomized operations (e.g., initializers, dropout) in your framework of choice. For example, Keras initializers accept an optional seed parameter. Again, it is not necessary to set any graph-level PRNGs (e.g., TensorFlow’s tf.set_random_seed), as Determined manages this for you.

Deterministic Floating Point on CPUs#

When doing CPU-only training with TensorFlow, it is possible to achieve floating-point reproducibility throughout optimization. If using the TFKerasTrial API, implement the optional session_config() method to override the default session configuration:

def session_config(self) -> tf.ConfigProto:
    return tf.ConfigProto(
        intra_op_parallelism_threads=1, inter_op_parallelism_threads=1


Disabling thread parallelism may negatively affect performance. Only enable this feature if you understand and accept this trade-off.

Pausing Experiments#

TensorFlow does not fully support the extraction or restoration of a single, global RNG state. Consequently, pausing experiments that use a TensorFlow-based framework may introduce an additional source of entropy.