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Release Notes

Version 0.12

Version 0.12.4

Release Date: May 14, 2020

  • Breaking Change: Users are no longer automatically logged in as the “determined” user. Refer to Users for more details.

  • Support multi-slot notebooks. The number of slots per notebook cannot exceed the size of the largest available agent.

  • Authentication sessions now expire after 7 days.

  • Improve log messages for tf.keras trial callbacks.

  • Add nvidia-container-toolkit support.

  • Fix an error in the experimental bert_glue_pytorch example.

  • The tf.keras examples for the Native and Trial APIs now refer to the same model.

  • Add a topic guide explaining Determined’s approach to Elastic Infrastructure.

  • Add a topic guide explaining the Native API (experimental).

  • UI: The Determined favicon acquires a small dot when any slots are in use.

  • UI: Fix an issue with command sorting in the WebUI.

  • UI: Fix an issue with badges appearing as the wrong color.

Version 0.12.3

Release Date: April 27, 2020

  • Add a tutorial for the new (experimental) Native API.

  • Add support for locally testing experiments via det e create --local.

  • Add determined.experimental.Determined class for accessing ExperimentReference, TrialReference, and Checkpoint objects.

  • TensorBoard logs now appear under the storage_path for shared_fs checkpoint configurations.

  • Allow commands, notebooks, shells, and TensorBoards to be killed before they are scheduled.

  • Print container exit reason in trial logs.

  • Choose a better default for the --tail option of command logs.

  • Add REST API endpoints for trials.

  • Support the execution of a startup script inside the agent docker container

  • Master and agent Docker containers will have the ‘unless-stopped’ restart policy by default when using det-deploy local.

  • Prevent the det trial logs -f command from waiting for too long after the trial being watched reaches a terminal state.

  • Fix bug where logs disappear when an image is pulled.

  • Fix bug that affected the use of LRScheduler in PyTorchTrial for multi-GPU training.

  • Fix bug after master restart where some errored experiments would show progress indicators.

  • Fix ordering of steps from det t describe --json.

  • Docs: Added topic guide for effective distributed training.

  • Docs: Reorganize install documentation.

  • UI: Move the authenticated user to the top of the users list filter on the dashboard, right after “All”.

Version 0.12.2

Release Date: April 21, 2020

Breaking Changes

  • Rename PEDL to Determined. The canonical way to import it is via import determined as det.

  • Reorganize source code. The frameworks module was removed, and each framework’s submodules were collapsed into the main framework module. For example:

    • det.frameworks.pytorch.pytorch_trial.PyTorchTrial is now det.pytorch.PyTorchTrial

    • det.frameworks.pytorch.data.DataLoader is now det.pytorch.DataLoader

    • det.frameworks.pytorch.checkpoint.load is now det.pytorch.load

    • det.frameworks.pytorch.util.reset_parameters is now det.pytorch.reset_parameters

    • det.frameworks.keras.tf_keras_trial.TFKerasTrial is now det.keras.TFKerasTrial

    • det.frameworks.tensorflow.estimator_trial.EstimatorTrial is now det.estimator.EstimatorTrial

    • det.frameworks.tensorpack.tensorpack_trial is now det.tensorpack.TensorpackTrial

    • det.frameworks.util and det.frameworks.pytorch.util have been removed entirely

  • Unify all plugin functions under the Trial class. make_data_loaders has been moved to two functions that should be implemented as part of the Trial class. For example, PyTorchTrial data loaders should now be implemented in build_training_data_loader() and build_validation_data_loader() in the trial definition. Please see updated examples and documentation for changes in each framework.

  • Trial classes are now required to define a constructor function. The signature of the constructor function is:

    def __init__(self, context) -> None:

    where context is an instance of the new det.TrialContext class. This new object is the primary mechanism for querying information about the system. Some of its methods include:

    • get_hparam(name): get a hyperparameter by name

    • get_trial_id(): get the trial ID being trained

    • get_experiment_config(): get the experiment config for this experiment

    • get_per_slot_batch_size(): get the batch size appropriate for training (which will be adjusted from the global_batch_size hyperparameter in distributed training experiments)

    • get_global_batch_size(): get the effective batch size (which differs from per-slot batch size in distributed training experiments)

    • distributed.get_rank(): get the unique process rank (one process per slot)

    • distributed.get_local_rank(): get a unique process rank within the agent

    • distributed.get_size(): get the number of slots

    • distributed.get_num_agents: get the number of agents (machines) being used

  • The global_batch_size hyperparameter is required (that is, a hyperparameter with this name must be specified in the configuration of every experiment). Previously, the hyperparameter batch_size was required and was manipulated automatically for distributed training. Now global_batch_size will not be manipulated; users should train based on context.get_per_slot_batch_size(). See Distributed and Parallel Training for more context.

  • Remove download_data(). If users wish to download data at runtime, they should make sure that each process (one process per slot) downloads to a unique location. This can be accomplished by appending context.get_rank() to the download path.

  • Remove det.trial_controller.util.get_rank() and det.trial_controller.util.get_container_gpus(). Use context.distributed.get_rank() and context.distributed.get_num_agents() instead.

General Improvements

  • tf.data.Dataset is now supported as input for all versions of TensorFlow (1.14, 1.15, 2.0, 2.1) for TFKerasTrial and EstimatorTrial. Please note that Determined currently does not support checkpointing tf.data.Dataset inputs. Therefore, when resuming training, it resumes from the start of the dataset. Model weights are loaded correctly as always.

  • TFKerasTrial now supports five different types of inputs:

    1. A tuple (x_train, y_train) of NumPy arrays. x_train must be a NumPy array (or array-like), a list of arrays (in case the model has multiple inputs), or a dict mapping input names to the corresponding array, if the model has named inputs. y_train should be a NumPy array.

    2. A tuple (x_train, y_train, sample_weights) of NumPy arrays.

    3. A tf.data.Dataset returning a tuple of either (inputs, targets) or (inputs, targets, sample_weights).

    4. A keras.utils.Sequence returning a tuple of either (inputs, targets) or (inputs, targets, sample weights).

    5. A det.keras.SequenceAdapter returning a tuple of either (inputs, targets) or (inputs, targets, sample weights).

  • PyTorch trial checkpoints no longer save in MLflow’s MLmodel format.

  • The det trial download command now accepts -o to save a checkpoint to a specific path. PyTorch checkpoints can then be loaded from a specified local filesystem path.

  • Allow the agent to read configuration values from a YAML file.

  • Include experiment ID in the downloaded trial logs.

  • Display checkpoint storage location in the checkpoint info modal for trials and experiments.

  • Preserve recent tasks’ filter preferences in the WebUI.

  • Add task name to det slot list command output.

  • Model definitions are now downloaded as compressed tarfiles (.tar.gz) instead of zipfiles (.zip).

  • startup-hook.sh is now executed in the same directory as the model definition.

  • Rename projects to examples in the Determined repository.

  • Improve documentation:

    • Add documentation page on the lifecycle of an experiment.

    • Add how-to and topic guides for multi-GPU (both for single-machine parallel and multi-machine) training.

    • Add a topic guide on best practices for writing model definitions.

  • Fix bug that occasionally caused multi-machine training to hang on initialization.

  • Fix bug that prevented TensorpackTrial from successfully loading checkpoints.

  • Fix a bug in TFKerasTrial where runtime errors could cause the trial to hang or would silently drop the stack trace produced by Keras.

  • Fix trial lifecycle bugs for containers that exit during the pulling phase.

  • Fix bug that led to some distributed trials timing out.

  • Fix bug that caused tf.keras trials to fail in the multi-GPU setting when using an optimizer specified by its name.

  • Fix bug in the CLI for downloading model definitions.

  • Fix performance issues for experiments with very large numbers of trials.

  • Optimize performance for scheduling large hyperparameter searches.

  • Add configuration for telemetry in master.yaml.

  • Add a utility function for initializing a trial class for development (det.create_trial_instance)

  • Add security.txt.

  • Add det.estimator.load() to load TensorFlow Estimator saved_model checkpoints into memory.

  • Ensure AWS EC2 keypair exists in account before creating the CloudFormation stack.

  • Add support for gradient aggregation in Keras trials for TensorFlow 2.1.

  • Add TrialReference and Checkpoint experimental APIs for exporting and loading checkpoints.

  • Improve performance when starting many tasks simultaneously.

Web Improvements

  • Improve discoverability of dashboard actions.

  • Add dropdown action menu for killing and archiving recent tasks on the dashboard.

  • Add telemetry for web interactions.

  • Fix an issue around cluster utilization status showing as “No Agent” for a brief moment during initial load.

  • Add Ace editor to attributions list.

  • Set UI preferences based on the logged-in user.

  • Fix an issue where the indicated user filter was not applied to the displayed tasks.

  • Improve error messaging for failed actions.