DeepSpeed Autotune: User Guide#

Getting the most out of DeepSpeed (DS) requires aligning the many DS parameters with the specific properties of your hardware and model. Determined AI’s DeepSpeed Autotune (dsat) helps to optimize these settings through an easy-to-use API with very few changes required in user-code, as we describe in the remainder of this user guide. dsat can be used with DeepSpeedTrial, Core API, and Hugging Face Trainer.

How it Works#

You do not need to create a special configuration file to use dsat. Assuming you have DeepSpeed code which already functions, autotuning is as easy as inserting one or two helper functions into your code and modifying the launch command.

For instance, let’s say your directory contains DeepSpeed code and a corresponding single trial experiment configuration file deepspeed.yaml. Then, after inserting a line or two of dsat-specific code per the instructions in the following sections, launching the dsat experiments is as easy as replacing the usual experiment-launching command:

det experiment create deepspeed.yaml .


python3 -m determined.pytorch.dsat asha deepspeed.yaml .

The above uses Determined AI’s DeepSpeed Autotune with the asha algorithm, one of three available search methods:

  • asha: Adaptively searches over randomly selected DeepSpeed configurations, allocating more compute resources to well-performing configurations. See this introduction to ASHA for more details.

  • binary: Performs a simple binary search over the batch size for randomly-generated DS configurations.

  • random: Conducts a search over random DeepSpeed configurations with an aggressive early-stopping criteria based on domain-knowledge of DeepSpeed and the search history.

DeepSpeed Autotune is built on top of Custom Searcher (see Custom Search Methods) which starts up two separate experiments:

  • single Search Runner Experiment: This experiment coordinates and schedules the trials that run the model code.

  • custom Experiment: This experiment contains the trials referenced above whose results are reported back to the search runner.

Initially, a profiling trial is created to gather information regarding the model and computational resources. The search runner experiment takes this initial profiling information and creates a series of trials to search for the DS settings which optimize FLOPS_per_gpu, throughput (samples/second), or latency timing information. The results of all such trials can be viewed in the custom experiment above. The search is informed both by the initial profiling trial and the results of each subsequent trial, all of whose results are fed back to the search runner.


Determined’s DeepSpeed Autotune is not compatible with pipeline or model parallelism. The to-be-trained model must be a DeepSpeedEngine instance (not a PipelineEngine instance).

User Code Changes#

To use dsat with DeepSpeedTrial, Core API, and Hugging Face Trainer, specific changes must be made to your user code. In the following sections, we will describe specific use cases and the changes needed for each.


To use Determined’s DeepSpeed Autotune with DeepSpeedTrial, you must meet the following requirements.

First, it is assumed that a base DeepSpeed configuration exists in a file (written following the DeepSpeed documentation here). We then require that your Determined yaml configuration points to the location of that file through a deepspeed_config key in its hyperparameters section. For example, if your default DeepSpeed configuration is stored in ds_config.json at the top-level of your model directory, your hyperparameters section should include:

  deepspeed_config: ds_config.json

Second, your DeepSpeedTrial code must use our get_ds_config_from_hparams() helper function to get the DeepSpeed configuration dictionary which is generated by DeepSpeed Autotune for each trial. These dictionaries are generated by overwriting certain fields in the base DeepSpeed configuration referenced in the step above. The returned dictionary can then be passed to deepspeed.initialize as usual:

from determined.pytorch.deepspeed import DeepSpeedTrial, DeepSpeedTrialContext
from determined.pytorch import dsat

class MyDeepSpeedTrial(DeepSpeedTrial):
  def __init__(self, context: DeepSpeedTrialContext) -> None:
      self.hparams = self.context.get_hparams()
      config = dsat.get_ds_config_from_hparams(self.hparams)
      model = ...
      model_parameters= ...

      model_engine, optimizer, train_loader, lr_scheduler = deepspeed.initialize(
          model=model, model_parameters=model_parameters, config=config

Using Determined’s DeepSpeed Autotune with a DeepSpeedTrial instance requires no further changes to your code.

For a complete example of how to use DeepSpeed Autotune with DeepSpeedTrial, visit the Determined GitHub Repo and navigate to examples/deepspeed_autotune/torchvision/deepspeed_trial .


To find out more about DeepSpeedTrial, visit Usage Guide.

Core API#

When using DeepSpeed Autotune with a Core API experiment, there is one additional change to be made following the steps in the DeepSpeedTrial section above.

The forward, backward, and step methods of the DeepSpeedEngine class need to be wrapped in the dsat_reporting_context() context manager. This addition ensures that the autotuning metrics from each trial are captured and reported back to the Determined master.

Here is an example sketch of dsat code with Core API:

for op in core_context.searcher.operations():
   for (inputs, labels) in trainloader:
       with dsat.dsat_reporting_context(core_context, op): # <-- The new code
           outputs = model_engine(inputs)
           loss = criterion(outputs, labels)

In this code snippet, core_context is the Context instance which was initialized with determined.core.init(). The context manager requires access to both core_context and the current SearcherOperation instance (op) to appropriately report results. Outside of a dsat context, dsat_reporting_context is a no-op, so there is no need to remove the context manager after the dsat trials have completed.

For a complete example of how to use DeepSpeed Autotune with Core API, visit the Determined GitHub Repo and navigate to examples/deepspeed_autotune/torchvision/core_api .

Hugging Face Trainer#

You can also use Determined’s DeepSpeed Autotune with the Hugging Face (HF) Trainer and Determined’s DetCallback callback object to optimize your DeepSpeed parameters.

Similar to the previous case (Core API), you need to add a deepspeed_config field to the hyperparameters section of your experiment configuration file, specifying the relative path to the DS json config file.

Reporting results back to the Determined master requires both the dsat.dsat_reporting_context context manager and DetCallback.

Furthermore, since dsat performs a search over different batch sizes and Hugging Face expects parameters to be specified as command-line arguments, an additional helper function, get_hf_args_with_overwrites(), is needed to create consistent Hugging Face arguments.

Here is an example code snippet from a Hugging Face Trainer script that contains key pieces of relevant code:

from determined.transformers import DetCallback
from determined.pytorch import dsat
from transformers import HfArgumentParser,Trainer, TrainingArguments,

hparams = self.context.get_hparams()
parser = HfArgumentParser(TrainingArguments)
args = sys.argv[1:]
args = dsat.get_hf_args_with_overwrites(args, hparams)
training_args = parser.parse_args_into_dataclasses(args, look_for_args_file=False)

det_callback = DetCallback(core_context, ...)
trainer = Trainer(args=training_args, ...)
with dsat.dsat_reporting_context(core_context, op=det_callback.current_op):
    train_result = trainer.train(resume_from_checkpoint=checkpoint)


  • The dsat_reporting_context context manager shares the same initial SearcherOperation as the DetCallback instance through its op=det_callback.current_op argument.

  • The entire train method of the Hugging Face trainer is wrapped in the dsat_reporting_context context manager.

To find examples that use DeepSpeed Autotune with Hugging Face Trainer, visit the Determined GitHub Repo and navigate to examples/hf_trainer_api.

Advanced Options#

The command-line entrypoint to dsat has various available options, some of them search-algorithm-specific. All available options for any given search method can be found through the command:

python3 -m determined.pytorch.dsat asha --help

and similar for the binary and random search methods.

Flags that are particularly important are detailed below.

General Options#

The following options are available for every search method.

  • --max-trials: The maximum number of trials to run. Default: 64.

  • --max-concurrent-trials: The maximum number of trials that can run concurrently. Default: 16.

  • --max-slots: The maximum number of slots that can be used concurrently. Defaults to None, i.e., there is no limit by default.

  • --metric: The metric to be optimized. Defaults to FLOPS-per-gpu. Other available options are throughput, forward, backward, and latency.

  • --run-full-experiment: If specified, after the dsat experiment has completed, a single experiment will be launched using the specifications in the deepspeed.yaml overwritten with the best-found DS configuration parameters.

  • --zero-stages: This flag allows the user to limit the search to a subset of the stages by providing a space-separated list, as in --zero-stages 2 3. Default: 1 2 3.

asha Options#

The asha search algorithm randomly generates various DeepSpeed configurations and attempts to tune the batch size for each configuration through a binary search. asha adaptively allocates resources to explore each configuration (providing more resources to promising lineages) where the resource is the number of steps taken in each binary search (i.e., the number of trials).

asha can be configured with the following flags:

  • --max-rungs: The maximum total number of rungs to use in the ASHA algorithm. Larger values allow for longer binary searches. Default: 5.

  • --min-binary-search-trials: The minimum number of trials to use for each binary search. The r parameter in the ASHA paper. Default: 3.

  • --divisor: Factor controlling the increased computational allotment across rungs, and the decrease in their population size. The eta parameter in the ASHA paper. Default: 2.

  • --search-range-factor: The inclusive, initial hi bound on the binary search is set by an approximate computation (the lo bound is always initialized to 1). This parameter adjusts the hi bound by a factor of search-range-factor. Default: 1.0.

binary Options#

The binary search algorithm performs a straightforward search over the batch size for a collection of randomly-drawn DS configurations. A single option is available for this search: --search-range-factor, which plays precisely the same role as in the asha Options section above.

random Options#

The random search algorithm performs a search over randomly drawn DS configurations and uses a semi-random search over the batch size.

random can be configured with the following flags:

  • --trials-per-random-config: The maximum batch size configuration which will tested for a given DS configuration. Default: 5.

  • --early-stopping: If provided, the experiment will terminate if a new best-configuration has not been found in the last early-stopping trials. Default: None, corresponding to no such early stopping.