Experiment Configuration Reference#

The behavior of an experiment is configured via a YAML file. A configuration file is typically passed as a command-line argument when an experiment is created with the HPE Machine Learning Development Environment CLI. For example:

det experiment create config-file.yaml model-directory

Training Units#

Some configuration settings, such as searcher training lengths and budgets, min_validation_period, and min_checkpoint_period, can be expressed in terms of a few training units: records, batches, or epochs.

  • records: A record is a single labeled example (sometimes called a sample).

  • batches: A batch is a group of records. The number of records in a batch is configured via the global_batch_size hyperparameter.

  • epoch: An epoch is a single copy of the entire training data set.

For example, to specify the max_length for a searcher in terms of batches, the configuration would read as shown below.

max_length:
  batches: 900

To express it in terms of records or epochs, records or epochs would be specified in place of batches. For DeepSpeedTrial and TFKerasTrial, records_per_epoch must also be specified if using epochs. Below is an example that configures a single searcher to train a model for 64 epochs.

records_per_epoch: 50000
searcher:
  name: single
  metric: validation_error
  max_length:
    epochs: 64
  smaller_is_better: true

The configured records_per_epoch is only used for interpreting configuration fields that are expressed in epochs. Actual epoch boundaries are still determined by the dataset itself (specifically, the end of an epoch occurs when the training data loader runs out of records).

Note

When the amount of training data for a model is specified using records or epochs, and the batch size does not evenly divide the configured number of inputs, the remaining “partial batch” of data will be dropped (ignored). For example, if an experiment is configured to train a single model on 10 records with a batch size of 3, the model will be trained on only 9 records of data. In the special case where a trial is configured to train on less than a single batch of data, a single complete batch will be used instead.

Training Unit Conversion Limitations (Caveats)#

In most cases, values expressed in one type of training unit can be converted to another type while maintaining the same behavior. However, there are some limitations to consider:

  • Since training units must be positive integers, it is not always possible to convert between different types of units. For example, converting 50 records into batches is not possible if the batch size is 64.

  • When performing a hyperparameter search over a range of values for global_batch_size, the specified batches cannot be converted to a fixed number of records or epochs and hence cause different behaviors in different trials of the search.

  • When using adaptive_asha, a single training unit is treated as atomic (unable to be divided into fractional parts) when dividing max_length into the series of rounds (or rungs) by which we early-stop underperforming trials. This rounding may result in unexpected behavior when configuring max_length with a small number of large epochs or batches.

To verify your search is working as intended before committing to a full run, you can use the CLI’s “preview search” feature:

det preview-search <configuration.yaml>

Metadata#

name#

Optional. A short human-readable name for the experiment.

description#

Optional. A human-readable description of the experiment. This does not need to be unique but should be limited to less than 255 characters for the best experience.

labels#

Optional. A list of label names (strings). Assigning labels to experiments allows you to identify experiments that share the same property or should be grouped together. You can add and remove labels using either the CLI (det experiment label) or the WebUI.

data#

Optional. This field can be used to specify information about how the experiment accesses and loads training data. The content and format of this field is user-defined: it should be used to specify whatever configuration is needed for loading data for use by the experiment’s model definition. For example, if your experiment loads data from Amazon S3, the data field might contain the S3 bucket name, object prefix, and AWS authentication credentials.

workspace#

Optional. The name of the pre-existing workspace where you want to create the experiment. The workspace and project fields must either both be present or both be absent. If they are absent, the experiment is placed in the Uncategorized project in the Uncategorized workspace. You can manage workspaces using the CLI det workspace help command or the WebUI.

project#

Optional. The name of the pre-existing project inside workspace where you want to create the experiment. The workspace and project fields must either both be present or both be absent. If they are absent, the experiment is placed in the Uncategorized project in the Uncategorized workspace. You can manage projects using the CLI det project help command or the WebUI.

Entrypoint#

entrypoint#

Required. A model definition trial class specification or Python launcher script, which is the model processing entrypoint. This field can have the following formats.

Formats that specify a trial class have the form <module>:<object_reference>.

The <module> field specifies the module containing the trial class in the model definition, relative to root.

The <object_reference> specifies the trial class name in the module, which can be a nested object delimited by a period (.).

Examples:

  • MnistTrial expects an MnistTrial class exposed in a __init__.py file at the top level of the context directory.

  • model_def:CIFAR10Trial expects a CIFAR10Trial class defined in the model_def.py file at the top level of the context directory.

  • determined_lib.trial:trial_classes.NestedTrial expects a NestedTrial class, which is an attribute of trial_classes defined in the determined_lib/trial.py file.

These formats follow Python Entry points specification except that the context directory name is prefixed by <module> or used as the module if the <module> field is empty.

Arbitrary Script#

Required. An arbitrary entrypoint script name.

Example:

entrypoint: ./hello.sh

Preconfigured Launch Module with Script#

Required. The name of a preconfigured launch module and script name.

Example:

entrypoint: python3 -m (LAUNCH_MODULE) train.py

LAUNCH_MODULE options:

  • Horovod (determined.launch.horovod)

  • PyTorch (determined.launch.torch_distributed)

  • Deepspeed (determined.launch.deepspeed)

Preconfigured Launch Module with Legacy Trial Definition#

Required. The name of a preconfigured launch module and legacy trial class specification.

Example:

entrypoint: python3 -m (LAUNCH_MODULE) --trial model_def:Trial

LAUNCH_MODULE options: [need literals for these]

  • Horovod (determined.launch.horovod)

  • PyTorch (determined.launch.torch_distributed)

  • Deepspeed (determined.launch.deepspeed)

Legacy Trial Definition#

Required. A legacy trial class specification.

Example:

entrypoint: model_def:Trial

Basic Behaviors#

scheduling_unit#

Optional. Instructs how frequent to perform system operations, such as periodic checkpointing and preemption, in the unit of batches. The number of records in a batch is controlled by the global_batch_size hyperparameter. Defaults to 100.

  • Setting this value too small can increase the overhead of system operations and decrease training throughput.

  • Setting this value too large might prevent the system from reallocating resources from this workload to another, potentially more important, workload.

  • As a rule of thumb, it should be set to the number of batches that can be trained in roughly 60–180 seconds.

records_per_epoch#

Optional. The number of records in the training data set. It must be configured if you want to specify min_validation_period, min_checkpoint_period, and searcher.max_length in units of epochs.

Note

For PyTorchTrial, epoch length is automatically determined using the chief worker’s dataset length, and this value will be ignored.

max_restarts#

Optional. The max_restarts parameter parameter sets a limit on the number of times the HPE Machine Learning Development Environment master can try restarting a trial, preventing an infinite loop if the same error repeatedly occurs. After reach the max_restarts limit for an experiment, any subsequent failed trials will not be restarted and will be marked as errored. An experiment is considered successful if at least one of its trials completes without errors. The default value for max_restarts is 5.

log_policies#

Optional. Defines actions in response to trial logs matching specified regex patterns (Go language syntax). For more information about the syntax, you can visit this RE2 reference page. Actions include:

  • exclude_node: Excludes a failed trial’s restart attempts (due to its max_restarts policy) from being scheduled on nodes with matched error logs. This is useful for bypassing nodes with hardware issues, like uncorrectable GPU ECC errors.

    Note: This option is not supported on PBS systems.

    For the agent resource manager, if a trial becomes unschedulable due to enough node exclusions, and launch_error in the master config is true (default), the trial fails.

  • cancel_retries: Prevents a trial from restarting if a trial reports a log that matches the pattern, even if it has remaining max_restarts. This avoids using resources for retrying a trial that encounters certain failures that won’t be fixed by retrying the trial, such as CUDA memory issues.

Example configuration:

log_policies:
   - pattern: ".*uncorrectable ECC error encountered.*"
     action:
       type: exclude_node
   - pattern: ".*CUDA out of memory.*"
     action:
       type: cancel_retries

These settings may also be specified at the cluster or resource pool level through task container defaults.

retention_policy#

Optional. Defines retention policies for logs related to all trials of a given experiment. Parameters include:

  • log_retention_days: Optional. Overrides the number of days to retain logs for a trial. Acceptable values range from -1 to 32767. If set to -1, logs will be retained indefinitely. If set to 0, logs will be deleted during the next cleanup. To modify the retention settings post-completion for a single trial or the entire experiment, you can use the CLI command det t set log-retention <trial-id> or det e set log-retention <exp-id>. Both commands accept either the argument: --days, which sets the number of days to retain logs from the end time of the task, or --forever which retains logs indefinitely.

Example configuration:

retention_policy:
   log_retention_days: 90

This setting can be defined as a default setting for the entire cluster.

debug option in agent configuration file#

The debug option in the agent configuration file enables more verbose logging for diagnostic purposes when set to true.

While debugging, the logger will display lines highlighted in blue for easy identification.

Validation Policy#

min_validation_period#

Optional. Specifies the minimum frequency at which validation should be run for each trial.

  • The frequency should be defined using a nested dictionary indicating the unit as records, batches, or epochs. For example:

min_validation_period:
   epochs: 2

perform_initial_validation#

Optional. Instructs HPE Machine Learning Development Environment to perform an initial validation before any training begins, for each trial. This can be useful to determine a baseline when fine-tuning a model on a new dataset.

Checkpoint Policy#

HPE Machine Learning Development Environment checkpoints in the following situations:

  • Periodically during training, to keep a record of the training progress.

  • During training, to enable recovery of the trial’s execution in case of resumption or errors.

  • Upon completion of the trial.

  • Prior to the searcher making a decision based on the validation of trials, ensuring consistency in case of a failure.

min_checkpoint_period#

Optional. Specifies the minimum frequency for running checkpointing for each trial.

  • This value should be set using a nested dictionary in the form of records, batches, or epochs. For example:

    min_checkpoint_period:
       epochs: 2
    
  • DeepSpeedTrial and TFKerasTrial: If the unit is in epochs, you must also specify records_per_epoch.

checkpoint_policy#

Optional. Controls how HPE Machine Learning Development Environment performs checkpoints after validation operations, if at all. Should be set to one of the following values:

  • best (default): A checkpoint will be taken after every validation operation that performs better than all previous validations for this experiment. Validation metrics are compared according to the metric and smaller_is_better options in the searcher configuration.

  • all: A checkpoint will be taken after every validation, no matter the validation performance.

  • none: A checkpoint will never be taken due to a validation. However, even with this policy selected, checkpoints are still expected to be taken after the trial is finished training, due to cluster scheduling decisions, before search method decisions, or due to min_checkpoint_period.

Checkpoint Storage#

The checkpoint_storage section defines how model checkpoints will be stored. A checkpoint contains the architecture and weights of the model being trained. Each checkpoint has a UUID, which is used as the name of the checkpoint directory on the external storage system.

If this field is not specified, the experiment will default to the checkpoint storage configured in the master configuration.

Checkpoint Garbage Collection#

When an experiment finishes, the system will optionally delete some checkpoints to reclaim space. The save_experiment_best, save_trial_best and save_trial_latest parameters specify which checkpoints to save. If multiple save_* parameters are specified, the union of the specified checkpoints are saved.

save_experiment_best#

The number of the best checkpoints with validations over all trials to save (where best is measured by the validation metric specified in the searcher configuration).

save_trial_best#

The number of the best checkpoints with validations of each trial to save.

save_trial_latest#

The number of the latest checkpoints of each trial to save.

Checkpoint Saving Policy#

The checkpoint garbage collection fields default to the following values:

save_experiment_best: 0
save_trial_best: 1
save_trial_latest: 1

This policy will save the most recent and the best checkpoint per trial. In other words, if the most recent checkpoint is also the best checkpoint for a given trial, only one checkpoint will be saved for that trial. Otherwise, two checkpoints will be saved.

Examples#

Suppose an experiment has the following trials, checkpoints and validation metrics (where smaller_is_better is true):

Trial ID

Checkpoint ID

Validation Metric

1

1

null

1

2

null

1

3

0.6

1

4

0.5

1

5

0.4

2

6

null

2

7

0.2

2

8

0.3

2

9

null

2

10

null

The effect of various policies is enumerated in the following table:

save_experiment_best

save_trial_best

save_trial_latest

Saved Checkpoint IDs

0

0

0

none

2

0

0

8,7

>= 5

0

0

8,7,5,4,3

0

1

0

7,5

0

>= 3

0

8,7,5,4,3

0

0

1

10,5

0

0

3

10,9,8,5,4,3

2

1

0

8,7,5

2

0

1

10,8,7,5

0

1

1

10,7,5

2

1

1

10,8,7,5

If aggressive reclamation is desired, set save_experiment_best to a 1 or 2 and leave the other parameters zero. For more conservative reclamation, set save_trial_best to 1 or 2; optionally set save_trial_latest as well.

Checkpoints of an existing experiment can be garbage collected by changing the GC policy using the det experiment set gc-policy subcommand of the HPE Machine Learning Development Environment CLI.

Storage Type#

HPE Machine Learning Development Environment currently supports several kinds of checkpoint storage, gcs, s3, azure, and shared_fs, identified by the type subfield. Additional fields may also be required, depending on the type of checkpoint storage in use. For example, to store checkpoints on Google Cloud Storage:

checkpoint_storage:
  type: gcs
  bucket: <your-bucket-name>

Google Cloud Storage#

If type: gcs is specified, checkpoints will be stored on Google Cloud Storage (GCS). Authentication is done using GCP’s “Application Default Credentials” approach. When using HPE Machine Learning Development Environment inside Google Compute Engine (GCE), the simplest approach is to ensure that the VMs used by HPE Machine Learning Development Environment are running in a service account that has the “Storage Object Admin” role on the GCS bucket being used for checkpoints. As an alternative (or when running outside of GCE), you can add the appropriate service account credentials to your container (e.g., via a bind-mount), and then set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the container path where the credentials are located. See Environment Variables for more details on how to set environment variables in containers.

bucket#

Required. The GCS bucket name to use.

prefix#

Optional. The optional path prefix to use. Must not contain ... Note: Prefix is normalized, e.g., /pre/.//fix -> /pre/fix

Amazon S3#

If type: s3 is specified, checkpoints will be stored in Amazon S3 or an S3-compatible object store such as MinIO.

bucket#

Required. The S3 bucket name to use.

access_key#

Required. The AWS access key to use.

secret_key#

Required. The AWS secret key to use.

prefix#

Optional. The optional path prefix to use. Must not contain ... Note: Prefix is normalized, e.g., /pre/.//fix -> /pre/fix

endpoint_url#

Optional. The endpoint to use for S3 clones, e.g., http://127.0.0.1:8080/. If not specified, Amazon S3 will be used.

Azure Blob Storage#

If type: azure is specified, checkpoints will be stored in Microsoft’s Azure Blob Storage.

Please only specify one of connection_string or the account_url, credential tuple.

container#

Required. The Azure Blob Storage container name to use.

connection_string#

Required. The connection string for the Azure Blob Storage service account to use.

account_url#

Required. The account URL for the Azure Blob Storage service account to use.

credential#

Optional. The credential to use with the account_url.

Shared File System#

If type: shared_fs is specified, checkpoints will be written to a directory on the agent’s file system. The assumption is that the system administrator has arranged for the same directory to be mounted at every agent machine, and for the content of this directory to be the same on all agent hosts (e.g., by using a distributed or network file system such as GlusterFS or NFS).

Warning

When downloading checkpoints from a shared file system (e.g., using det checkpoint download), we assume the same shared file system is mounted locally at the same host_path.

host_path#

Required. The file system path on each agent to use. This directory will be mounted to /determined_shared_fs inside the trial container.

Optional Fields

storage_path#

Optional. The path where checkpoints will be written to and read from. Must be a subdirectory of the host_path or an absolute path containing the host_path. If not specified, checkpoints are written to and read from the host_path.

propagation#

Optional. Propagation behavior for replicas of the bind-mount. Defaults to rprivate.

Local Directory#

If type: directory is specified, checkpoints will be written to a local directory. For tasks running on HPE Machine Learning Development Environment platform, it’s a path within the container. For detached mode, it’s simply a local path.

The assumption is that a persistent storage will be mounted at the path parametrized by container_path option using bind_mounts, pod_spec, or other mechanisms. Otherwise, this path will usually end up being ephemeral storage within the container, and the data will be lost when the container exits.

Warning

TensorBoards currently do not inherit bind_mounts or pod_specs from their parent experiments. Therefore, if an experiment is using type: directory storage, and mounts the storage separately, a launched TensorBoard will need the same mount configuration provided explicitly using det tensorboard start <experiment_id> --config-file <CONFIG FILE> or similar.

Warning

When downloading checkpoints (e.g., using det checkpoint download), HPE Machine Learning Development Environment assumes the same directory is present locally at the same container_path.

container_path#

Required. The file system path to use.

Hyperparameters#

The hyperparameters section defines the hyperparameter space for the experiment. The appropriate hyperparameters for a specific model depend on the nature of the model being trained. In HPE Machine Learning Development Environment, it is common to specify hyperparameters that influence various aspects of the model’s behavior, such as data augmentation, neural network architecture, and the choice of optimizer, as well as its configuration.

To access the value of a hyperparameter in a particular trial, use the trial context with context.get_hparam(). For example, you can access the current value of a hyperparameter named learning_rate by calling context.get_hparam("learning_rate").

Note

Every experiment must specify a hyperparameter called global_batch_size. This hyperparameter is required for distributed training to calculate the appropriate per-worker batch size. The batch size per slot is computed at runtime, based on the number of slots used to train a single trial of the experiment (see resources.slots_per_trial). To access the updated values, use the trial context with context.get_per_slot_batch_size() and context.get_global_batch_size().

Note

To learn more about distributed training with Determined, visit the conceptual overview or the intro to implementing distributed training.

The hyperparameter space is defined by a dictionary. Each key in the dictionary is the name of a hyperparameter; the associated value defines the range of the hyperparameter. If the value is a scalar, the hyperparameter is a constant; otherwise, the value should be a nested map. Here is an example:

hyperparameters:
  global_batch_size: 64
  optimizer_config:
    optimizer:
      type: categorical
      vals:
        - SGD
        - Adam
        - RMSprop
    learning_rate:
      type: log
      minval: -5.0
      maxval: 1.0
      base: 10.0
  num_layers:
    type: int
    minval: 1
    maxval: 3
  layer1_dropout:
    type: double
    minval: 0.2
    maxval: 0.5

This configuration defines the following hyperparameters:

  • global_batch_size: a constant value

  • optimizer_config: a top level nested hyperparameter with two child hyperparameters:

    • optimizer: a categorical hyperparameter

    • learning_rate: a log scale hyperparameter

  • num_layers: an integer hyperparameter

  • layer1_dropout: a double hyperparameter

The field optimizer_config demonstrates how nesting can be used to organize hyperparameters. Arbitrary levels of nesting are supported with all types of hyperparameters. Aside from hyperparameters with constant values, the four types of hyperparameters – categorical, double, int, and log – can take on a range of possible values. The following sections cover how to configure the hyperparameter range for each type of hyperparameter.

Categorical#

A categorical hyperparameter ranges over a set of specified values. The possible values are defined by the vals key. vals is a list; each element of the list can be of any valid YAML type, such as a boolean, a string, a number, or a collection.

Double#

A double hyperparameter is a floating point variable. The minimum and maximum values of the variable are defined by the minval and maxval keys, respectively (inclusive of endpoints).

When doing a grid search, the count key must also be specified; this defines the number of points in the grid for this hyperparameter. Grid points are evenly spaced between minval and maxval. See Grid Method for details.

Integer#

An int hyperparameter is an integer variable. The minimum and maximum values of the variable are defined by the minval and maxval keys, respectively (inclusive of endpoints).

When doing a grid search, the count key must also be specified; this defines the number of points in the grid for this hyperparameter. Grid points are evenly spaced between minval and maxval. See Grid Method for details.

Log#

A log hyperparameter is a floating point variable that is searched on a logarithmic scale. The base of the logarithm is specified by the base field; the minimum and maximum exponent values of the hyperparameter are given by the minval and maxval fields, respectively (inclusive of endpoints).

When doing a grid search, the count key must also be specified; this defines the number of points in the grid for this hyperparameter. Grid points are evenly spaced between minval and maxval. See Grid Method for details.

Searcher#

The searcher section defines how the experiment’s hyperparameter space will be explored. To run an experiment that trains a single trial with fixed hyperparameters, specify the single searcher and specify constant values for the model’s hyperparameters. Otherwise, HPE Machine Learning Development Environment supports three different hyperparameter search algorithms: adaptive_asha, random, and grid. To define your own hyperparameter search algorithm, specify the custom searcher. For more information about custom search algorithms, see Custom Search Methods.

The name of the hyperparameter search algorithm to use is configured via the name field; the remaining fields configure the behavior of the searcher and depend on the searcher being used. For example, to configure a random hyperparameter search that trains 5 trials for 1000 batches each:

searcher:
  name: random
  metric: accuracy
  max_trials: 5
  max_length:
    batches: 1000

For details on using HPE Machine Learning Development Environment to perform hyperparameter search, refer to Hyperparameter Tuning. For more information on the search methods supported by HPE Machine Learning Development Environment, refer to Hyperparameter Tuning.

Single#

The single search method does not perform a hyperparameter search at all; rather, it trains a single trial for a fixed length. When using this search method, all of the hyperparameters specified in the hyperparameters section must be constants. By default, validation metrics are only computed once, after the specified length of training has been completed; min_validation_period can be used to specify that validation metrics should be computed more frequently.

metric#

Required. The name of the validation metric used to evaluate the performance of a hyperparameter configuration.

max_length#

Required. The length of the trial.

  • This needs to be set in the unit of records, batches, or epochs using a nested dictionary. For example:

    max_length:
       epochs: 2
    
  • DeepSpeedTrial and TFKerasTrial: If this is in the unit of epochs, records_per_epoch must be specified.

Optional Fields

smaller_is_better#

Optional. Whether to minimize or maximize the metric defined above. The default value is true (minimize).

source_trial_id#

Optional. If specified, the weights of this trial will be initialized to the most recent checkpoint of the given trial ID. This will fail if the source trial’s model architecture is inconsistent with the model architecture of this experiment.

source_checkpoint_uuid#

Optional. Like source_trial_id, but specifies an arbitrary checkpoint from which to initialize weights. At most one of source_trial_id or source_checkpoint_uuid should be set.

Random#

The random search method implements a simple random search. The user specifies how many hyperparameter configurations should be trained and how long each configuration should be trained for; the configurations are sampled randomly from the hyperparameter space. Each trial is trained for the specified length and then validation metrics are computed. min_validation_period can be used to specify that validation metrics should be computed more frequently.

metric#

Required. The name of the validation metric used to evaluate the performance of a hyperparameter configuration.

max_trials#

Required. The number of trials, i.e., hyperparameter configurations, to evaluate.

max_length#

Required. The length of each trial.

  • This needs to be set in the unit of records, batches, or epochs using a nested dictionary. For example:

    max_length:
       epochs: 2
    
  • DeepSpeedTrial and TFKerasTrial: If this is in the unit of epochs, records_per_epoch must be specified.

Optional Fields

smaller_is_better#

Optional. Whether to minimize or maximize the metric defined above. The default value is true (minimize).

max_concurrent_trials#

Optional. The maximum number of trials that can be worked on simultaneously. The default value is 16. When the value is 0 we will work on as many trials as possible.

source_trial_id#

Optional. If specified, the weights of every trial in the search will be initialized to the most recent checkpoint of the given trial ID. This will fail if the source trial’s model architecture is incompatible with the model architecture of any of the trials in this experiment.

source_checkpoint_uuid#

Optional. Like source_trial_id but specifies an arbitrary checkpoint from which to initialize weights. At most one of source_trial_id or source_checkpoint_uuid should be set.

Grid#

The grid search method performs a grid search. The coordinates of the hyperparameter grid are specified via the hyperparameters field. For more details see the Grid Method.

metric#

Required. The name of the validation metric used to evaluate the performance of a hyperparameter configuration.

max_length#

Required. The length of each trial.

  • This needs to be set in the unit of records, batches, or epochs using a nested dictionary. For example:

    max_length:
       epochs: 2
    
  • DeepSpeedTrial and TFKerasTrial: If this is in the unit of epochs, records_per_epoch must be specified.

Optional Fields

smaller_is_better#

Optional. Whether to minimize or maximize the metric defined above. The default value is true (minimize).

max_concurrent_trials#

Optional. The maximum number of trials that can be worked on simultaneously. The default value is 16. When the value is 0 we will work on as many trials as possible.

source_trial_id#

Optional. If specified, the weights of this trial will be initialized to the most recent checkpoint of the given trial ID. This will fail if the source trial’s model architecture is inconsistent with the model architecture of this experiment.

source_checkpoint_uuid#

Optional. Like source_trial_id, but specifies an arbitrary checkpoint from which to initialize weights. At most one of source_trial_id or source_checkpoint_uuid should be set.

Adaptive ASHA#

The adaptive_asha search method employs multiple calls to the asynchronous successive halving algorithm (ASHA) which is suitable for large-scale experiments with hundreds or thousands of trials.

metric#

Required. The name of the validation metric used to evaluate the performance of a hyperparameter configuration.

max_length#

Required. The maximum training length of any one trial. The vast majority of trials will be stopped early, and thus only a small fraction of trials will actually be trained for this long. This quantity is domain-specific and should roughly reflect the length of training needed for the model to converge on the data set.

  • This needs to be set in the unit of records, batches, or epochs using a nested dictionary. For example:

    max_length:
       epochs: 2
    
  • DeepSpeedTrial and TFKerasTrial: If this is in the unit of epochs, records_per_epoch must be specified.

max_trials#

Required. The number of trials, i.e., hyperparameter configurations, to evaluate.

smaller_is_better#

Optional. Whether to minimize or maximize the metric defined above. The default value is true (minimize).

mode#

Optional. How aggressively to perform early stopping. There are three modes: aggressive, standard, and conservative; the default is standard.

These modes differ in the degree to which early-stopping is used. In aggressive mode, the searcher quickly stops underperforming trials, which enables the searcher to explore more hyperparameter configurations, but at the risk of discarding a configuration too soon. On the other end of the spectrum, conservative mode performs significantly less downsampling, but as a consequence does not explore as many configurations given the same budget. We recommend using either aggressive or standard mode.

stop_once#

Optional. If stop_once is set to true, we will use a variant of ASHA that will not resume trials once stopped. This variant defaults to continuing training and will only stop trials if there is enough evidence to terminate training. We recommend using this version of ASHA when training a trial for the max length as fast as possible is important or when fault tolerance is too expensive.

divisor#

Optional. The fraction of trials to keep at each rung, and also determines the training length for each rung. The default setting is 4; only advanced users should consider changing this value.

max_rungs#

Optional. The maximum number of times we evaluate intermediate results for a trial and terminate poorly performing trials. The default value is 5; only advanced users should consider changing this value.

max_concurrent_trials#

Optional. The maximum number of trials that can be worked on simultaneously. The default value is 16, and we set reasonable values depending on max_trials and the number of rungs in the brackets. This is akin to controlling the degree of parallelism of the experiment. If this value is less than the number of brackets produced by the adaptive algorithm, it will be rounded up.

source_trial_id#

Optional. If specified, the weights of every trial in the search will be initialized to the most recent checkpoint of the given trial ID. This will fail if the source trial’s model architecture is inconsistent with the model architecture of any of the trials in this experiment.

source_checkpoint_uuid#

Optional. Like source_trial_id, but specifies an arbitrary checkpoint from which to initialize weights. At most one of source_trial_id or source_checkpoint_uuid should be set.

Resources#

The resources section defines the resources that an experiment is allowed to use.

slots_per_trial#

Optional. The number of slots to use for each trial of this experiment. The default value is 1; specifying a value greater than 1 means that multiple GPUs will be used in parallel. Training on multiple GPUs is done using data parallelism. Configuring slots_per_trial to be greater than max_slots is not sensible and will result in an error.

Note

Using slots_per_trial to enable data parallel training for PyTorch can alter the behavior of certain models, as described in the PyTorch documentation.

max_slots#

Optional. The maximum number of scheduler slots that this experiment is allowed to use at any one time. The slot limit of an active experiment can be changed using det experiment set max-slots <id> <slots>. By default, there is no limit on the number of slots an experiment can use.

When the cluster is deployed with an HPC workload manager, this value is ignored and instead managed by the configured workload manager.

Warning

max_slots is only considered when scheduling jobs; it is not currently used when provisioning dynamic agents. This means that we may provision more instances than the experiment can schedule.

weight#

Optional. The weight of this experiment in the scheduler. When multiple experiments are running at the same time, the number of slots assigned to each experiment will be approximately proportional to its weight. The weight of an active experiment can be changed using det experiment set weight <id> <weight>. The default weight is 1.

When the cluster is deployed with an HPC workload manager, this value is ignored and instead managed by the configured workload manager.

shm_size#

Optional. The size of /dev/shm for task containers. The value can be a number in bytes or a number with a suffix (e.g., 128M for 128MiB or 1.5G for 1.5GiB). Defaults to 4294967296 (4GiB). If set, this value overrides the value specified in the master configuration.

priority#

Optional. The priority assigned to this experiment. Only applicable when using the priority scheduler. Experiments with smaller priority values are scheduled before experiments with higher priority values. If using Kubernetes, the opposite is true; experiments with higher priorities are scheduled before those with lower priorities. Refer to Scheduling for more information.

When the cluster is deployed with an HPC workload manager, this value is ignored and instead managed by the configured workload manager.

resource_pool#

Optional. The resource pool where this experiment will be scheduled. If no resource pool is specified, experiments will run in the default GPU pool. Refer to Resource Pools for more information.

is_single_node#

Optional. When true, all the requested slots for the tasks are forced to be scheduled in a single container on a single node, or in a single pod. When false, it may be split across different nodes or pods. Defaults to false for experiments. This field is set to true for notebooks, tensorboards, shells, and commands, and cannot be modified.

Note

This option is currently not supported by Slurm RM.

devices#

Optional. A list of device strings to pass to the Docker daemon. Each entry in the list is equivalent to a --device DEVICE command-line argument to docker run. devices is honored by resource managers of type agent but is ignored by resource managers of type kubernetes. See master configuration for details about resource managers.

agent_label#

Optional. This field has been deprecated and will be ignored. Use resource_pool instead.

Bind Mounts#

The bind_mounts section specifies directories that are bind-mounted into every container launched for this experiment. Bind mounts are often used to enable trial containers to access additional data that is not part of the model definition directory.

This field should consist of an array of entries; each entry has the form described below. Users must ensure that the specified host paths are accessible on all agent hosts (e.g., by configuring a network file system appropriately).

host_path#

Required. The file system path on each agent to use. Must be an absolute filepath.

container_path#

Required. The file system path in the container to use. May be a relative filepath, in which case it will be mounted relative to the working directory inside the container. It is not allowed to mount directly into the working directory (i.e., container_path == ".") to reduce the risk of cluttering the host filesystem.

For each bind mount, the following optional fields may also be specified:

read_only#

Optional. Whether the bind-mount should be a read-only mount. Defaults to false.

propagation#

Optional. Propagation behavior for replicas of the bind-mount. Defaults to rprivate.

For example, to mount /data on the host to the same path in the container, use:

bind_mounts:
  - host_path: /data
    container_path: /data

It is also possible to mount multiple paths:

bind_mounts:
  - host_path: /data
    container_path: /data
  - host_path: /shared/read-only-data
    container_path: /shared/read-only-data
    read_only: true

Environment#

The environment section defines properties of the container environment that is used to execute workloads for this experiment. For more information on customizing the trial environment, refer to Customize Your Environment.

image#

Optional. The Docker image to use when executing the workload. This image must be accessible via docker pull to every HPE Machine Learning Development Environment agent machine in the cluster. Users can configure different container images for NVIDIA GPU tasks using cuda key (gpu prior to 0.17.6), CPU tasks using cpu key, and ROCm (AMD GPU) tasks using rocm key. Default values:

  • determinedai/pytorch-tensorflow-cuda:0.33.0 for NVIDIA GPUs.

  • determinedai/pytorch-tensorflow-cpu:0.33.0 for CPUs.

  • determinedai/environments:rocm-5.0-pytorch-1.10-tf-2.7-rocm-0.26.4 for ROCm.

When the cluster is configured with resource_manager.type: slurm and container_run_type: singularity, images are executed using the Singularity container runtime which provides additional options for specifying the container image. The image can be:

  • A full path to a local Singularity image (beginning with a / character).

  • Any of the other supported Singularity container formats identified by prefix (e.g. instance://, library://, shub://, oras://, docker-archive:// or docker://). See the Singularity run command documentation for a full description of the capabilities.

  • A Singularity image provided via the singularity_image_root configured for the cluster as described in Provide a Container Image Cache.

  • If none of the above applies, HPE Machine Learning Development Environment will apply the docker:// prefix to the image.

When the cluster is configured with resource_manager.type: slurm and container_run_type: podman, images are executed using the Podman container runtime. The image can be any of the supported PodMan container formats identified by transport (e.g. docker: (the default), docker-archive:, docker-daemon:, or oci-archive:). Visit the Podman run command documentation for a full description of the capabilities.

When the cluster is configured with resource_manager.type: slurm and container_run_type: enroot, images are executed using the Enroot container runtime. The image name must resolve to an Enroot container name created by the user before launching the HPE Machine Learning Development Environment task. To enable the default docker image references used by HPE Machine Learning Development Environment to be found in the Enroot container list the following transformations are applied to the image name (this is the same transformation performed by the enroot import command):

  • Any forward slash character in the image name (/) is replaced with a plus sign (+)

  • Any colon (:) is replaced with a plus sign (+)

See Enroot Requirements for more information.

force_pull_image#

Optional. Forcibly pull the image from the Docker registry, bypassing the Docker or Singularity built-in cache. Defaults to false.

registry_auth#

Optional. Defines the default Docker registry credentials to use when pulling a custom base Docker image, if needed. Credentials are specified as the following nested fields:

  • username (required)

  • password (required)

  • serveraddress (required)

  • email (optional)

environment_variables#

Optional. A list of environment variables that will be set in every trial container. Each element of the list should be a string of the form NAME=VALUE. See Environment Variables for more details. You can customize environment variables for CUDA (NVIDIA GPU), CPU, and ROCm (AMD GPU) tasks differently by specifying a dict with cuda (gpu prior to 0.17.6), cpu, and rocm keys.

pod_spec#

Optional. Only applicable when running HPE Machine Learning Development Environment on Kubernetes. Applies a pod spec to the pods that are launched by HPE Machine Learning Development Environment for this task. See Customize a Pod for details.

add_capabilities#

Optional. A list of Linux capabilities to grant to task containers. Each entry in the list is equivalent to a --cap-add CAP command-line argument to docker run. add_capabilities is honored by resource managers of type agent but is ignored by resource managers of type kubernetes. See master configuration for details about resource managers.

drop_capabilities#

Optional. Just like add_capabilities but corresponding to the --cap-drop argument of docker run rather than --cap-add.

proxy_ports#

Optional. Expose configured network ports on the chief task container. See Exposing Custom Ports for details.

Optimizations#

The optimizations section contains configuration options that influence the performance of the experiment.

aggregation_frequency#

Optional. Specifies after how many batches gradients are exchanged during distributed training. Defaults to 1.

average_aggregated_gradients#

Optional. Whether gradients accumulated across batches (when aggregation_frequency > 1) should be divided by the aggregation_frequency. Defaults to true.

average_training_metrics#

Optional. For multi-GPU training, whether to average the training metrics across GPUs instead of only using metrics from the chief GPU. This impacts the metrics shown in the HPE Machine Learning Development Environment UI and TensorBoard, but does not impact the outcome of training or hyperparameter search. This option is currently supported for PyTorchTrial and TFKerasTrial instances. Defaults to true.

gradient_compression#

Optional. Whether to compress gradients when they are exchanged during distributed training. Compression may alter gradient values to achieve better space reduction. Defaults to false.

mixed_precision#

Optional. Whether to use mixed precision training with PyTorch during distributed training. Setting O1 enables mixed precision and loss scaling. Defaults to O0 which disables mixed precision training. This configuration setting is deprecated; users are advised to call context.configure_apex_amp in the constructor of their trial class instead.

tensor_fusion_threshold#

Optional. The threshold in MB for batching together gradients that are exchanged during distributed training. Defaults to 64.

tensor_fusion_cycle_time#

Optional. The delay (in milliseconds) between each tensor fusion during distributed training. Defaults to 1.

auto_tune_tensor_fusion#

Optional. When enabled, configures tensor_fusion_threshold and tensor_fusion_cycle_time automatically. Defaults to false.

Reproducibility#

The reproducibility section specifies configuration options related to reproducible experiments. See Reproducibility for more details.

experiment_seed#

Optional. The random seed to use to initialize random number generators for all trials in this experiment. Must be an integer between 0 and 231–1. If an experiment_seed is not explicitly specified, the master will automatically generate an experiment seed.

Profiling#

The profiling section specifies configuration options for the HPE Machine Learning Development Environment system metrics profiler. See HPE Machine Learning Development Environment Profiler for a more detailed walkthrough.

enabled#

Optional. Enables system metrics profiling on the experiment, which can be viewed in the Web UI. Defaults to false.

Slurm Options#

The slurm section specifies configuration options applicable when the cluster is configured with resource_manager.type: slurm.

gpu_type#

Optional. An optional GPU type name to be included in the generated Slurm --gpus or --gres option if you have configured GPU types within your Slurm gres configuration. Specify this option to select that specific GPU type when there are multiple GPU types within the Slurm partition. The default is to select GPUs without regard to their type. For example, you can request the tesla GPU type with:

slurm:
   gpu_type: tesla

sbatch_args#

Optional. Additional Slurm options to be passed when launching trials with sbatch. These options enable control of Slurm options not otherwise managed by HPE Machine Learning Development Environment. For example, to specify required memory per CPU and exclusive access to an entire node when scheduled, you could specify:

slurm:
   sbatch_args:
      - --mem-per-cpu=10
      - --exclusive

slots_per_node#

Optional. The minimum number of slots required for a node to be scheduled during a trial. If gres_supported is false, specify slots_per_node in order to utilize more than one GPU per node. It is the user’s responsibility to ensure that slots_per_node GPUs will be available on nodes selected for the job using other configurations such as targeting a specific resource pool with only GPU nodes or specifying a Slurm constraint in the experiment configuration.

PBS Options#

The pbs section specifies configuration options applicable when the cluster is configured with resource_manager.type: pbs.

pbsbatch_args#

Optional. Additional PBS options to be passed when launching trials with qsub. These options enable control of PBS options not otherwise managed by HPE Machine Learning Development Environment. For example, to specify that the job should have a priority of 1000 and a project name of MyProjectName, you could specify:

pbs:
   pbsbatch_args:
      - -p1000
      - -PMyProjectName

Requesting of resources and job placement may be influenced through use of -l, however chunk count, chunk arrangement, and GPU or CPU counts per chunk (depending on the value of slot_type) are controlled by HPE Machine Learning Development Environment; any values specified for these quantities will be ignored. Consider if the following were specified for a CUDA experiment:

pbs:
   pbsbatch_args:
      - -l select=2:ngpus=4:mem=4gb
      - -l place=scatter:shared
      - -l walltime=1:00:00

The chunk count (two), the GPU count per chunk (four), and the chunk arrangement (scatter) will all be ignored in favor of values calculated by HPE Machine Learning Development Environment.

slots_per_node#

Optional. Specifies the minimum number of slots required for a node to be scheduled during a trial. If gres_supported is set to false, specify slots_per_node in order to utilize more than one GPU per node. It is the user’s responsibility to ensure that slots_per_node GPUs will be available on the nodes selected for the job using other configurations such as targeting a specific resource pool with only slots_per_node GPU nodes or specifying a PBS constraint in the experiment configuration.