import logging
from collections import defaultdict, Counter
from dataclasses import _MISSING_TYPE, dataclass, fields
from pathlib import Path
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Union,
Tuple,
)
import pyarrow.fs
from ray._private.storage import _get_storage_uri
from ray._private.thirdparty.tabulate.tabulate import tabulate
from ray.util.annotations import PublicAPI, Deprecated
from ray.widgets import Template, make_table_html_repr
from ray.data.preprocessor import Preprocessor
from ray._private.ray_constants import RESOURCE_CONSTRAINT_PREFIX
if TYPE_CHECKING:
from ray.tune.callback import Callback
from ray.tune.progress_reporter import ProgressReporter
from ray.tune.search.sample import Domain
from ray.tune.stopper import Stopper
from ray.train import SyncConfig
from ray.tune.experimental.output import AirVerbosity
from ray.tune.utils.log import Verbosity
from ray.tune.execution.placement_groups import PlacementGroupFactory
# Dict[str, List] is to support `tune.grid_search`:
# TODO(sumanthratna/matt): Upstream this to Tune.
SampleRange = Union["Domain", Dict[str, List]]
MAX = "max"
MIN = "min"
_DEPRECATED_VALUE = "DEPRECATED"
DATASET_CONFIG_DEPRECATION_MSG = """
Use `ray.train.DataConfig` instead of DatasetConfig to configure data ingest for training. See https://docs.ray.io/en/releases-2.6.3/ray-air/check-ingest.html#migrating-from-the-legacy-datasetconfig-api for more details.
""" # noqa: E501
logger = logging.getLogger(__name__)
def _repr_dataclass(obj, *, default_values: Optional[Dict[str, Any]] = None) -> str:
"""A utility function to elegantly represent dataclasses.
In contrast to the default dataclass `__repr__`, which shows all parameters, this
function only shows parameters with non-default values.
Args:
obj: The dataclass to represent.
default_values: An optional dictionary that maps field names to default values.
Use this parameter to specify default values that are generated dynamically
(e.g., in `__post_init__` or by a `default_factory`). If a default value
isn't specified in `default_values`, then the default value is inferred from
the `dataclass`.
Returns:
A representation of the dataclass.
"""
if default_values is None:
default_values = {}
non_default_values = {} # Maps field name to value.
def equals(value, default_value):
# We need to special case None because of a bug in pyarrow:
# https://github.com/apache/arrow/issues/38535
if value is None and default_value is None:
return True
if value is None or default_value is None:
return False
return value == default_value
for field in fields(obj):
value = getattr(obj, field.name)
default_value = default_values.get(field.name, field.default)
is_required = isinstance(field.default, _MISSING_TYPE)
if is_required or not equals(value, default_value):
non_default_values[field.name] = value
string = f"{obj.__class__.__name__}("
string += ", ".join(
f"{name}={value!r}" for name, value in non_default_values.items()
)
string += ")"
return string
@dataclass
@PublicAPI(stability="stable")
class ScalingConfig:
"""Configuration for scaling training.
Args:
trainer_resources: Resources to allocate for the training coordinator.
The training coordinator launches the worker group and executes
the training function per worker, and this process does NOT require
GPUs. The coordinator is always scheduled on the same node as the
rank 0 worker, so one example use case is to set a minimum amount
of resources (e.g. CPU memory) required by the rank 0 node.
By default, this assigns 1 CPU to the training coordinator.
num_workers: The number of workers (Ray actors) to launch.
Each worker will reserve 1 CPU by default. The number of CPUs
reserved by each worker can be overridden with the
``resources_per_worker`` argument.
use_gpu: If True, training will be done on GPUs (1 per worker).
Defaults to False. The number of GPUs reserved by each
worker can be overridden with the ``resources_per_worker``
argument.
resources_per_worker: If specified, the resources
defined in this Dict is reserved for each worker.
Define the ``"CPU"`` and ``"GPU"`` keys (case-sensitive) to
override the number of CPU or GPUs used by each worker.
placement_strategy: The placement strategy to use for the
placement group of the Ray actors. See :ref:`Placement Group
Strategies <pgroup-strategy>` for the possible options.
accelerator_type: [Experimental] If specified, Ray Train will launch the
training coordinator and workers on the nodes with the specified type
of accelerators.
See :ref:`the available accelerator types <accelerator_types>`.
Ensure that your cluster has instances with the specified accelerator type
or is able to autoscale to fulfill the request.
Example:
.. code-block:: python
from ray.train import ScalingConfig
scaling_config = ScalingConfig(
# Number of distributed workers.
num_workers=2,
# Turn on/off GPU.
use_gpu=True,
# Specify resources used for trainer.
trainer_resources={"CPU": 1},
# Try to schedule workers on different nodes.
placement_strategy="SPREAD",
)
"""
trainer_resources: Optional[Union[Dict, SampleRange]] = None
num_workers: Union[int, SampleRange] = 1
use_gpu: Union[bool, SampleRange] = False
resources_per_worker: Optional[Union[Dict, SampleRange]] = None
placement_strategy: Union[str, SampleRange] = "PACK"
accelerator_type: Optional[str] = None
def __post_init__(self):
if self.resources_per_worker:
if not self.use_gpu and self.num_gpus_per_worker > 0:
raise ValueError(
"`use_gpu` is False but `GPU` was found in "
"`resources_per_worker`. Either set `use_gpu` to True or "
"remove `GPU` from `resources_per_worker."
)
if self.use_gpu and self.num_gpus_per_worker == 0:
raise ValueError(
"`use_gpu` is True but `GPU` is set to 0 in "
"`resources_per_worker`. Either set `use_gpu` to False or "
"request a positive number of `GPU` in "
"`resources_per_worker."
)
def __repr__(self):
return _repr_dataclass(self)
def _repr_html_(self) -> str:
return make_table_html_repr(obj=self, title=type(self).__name__)
def __eq__(self, o: "ScalingConfig") -> bool:
if not isinstance(o, type(self)):
return False
return self.as_placement_group_factory() == o.as_placement_group_factory()
@property
def _resources_per_worker_not_none(self):
if self.resources_per_worker is None:
if self.use_gpu:
# Note that we don't request any CPUs, which avoids possible
# scheduling contention. Generally nodes have many more CPUs than
# GPUs, so not requesting a CPU does not lead to oversubscription.
resources_per_worker = {"GPU": 1}
else:
resources_per_worker = {"CPU": 1}
else:
resources_per_worker = {
k: v for k, v in self.resources_per_worker.items() if v != 0
}
if self.use_gpu:
resources_per_worker.setdefault("GPU", 1)
if self.accelerator_type:
accelerator = f"{RESOURCE_CONSTRAINT_PREFIX}{self.accelerator_type}"
resources_per_worker.setdefault(accelerator, 0.001)
return resources_per_worker
@property
def _trainer_resources_not_none(self):
if self.trainer_resources is None:
if self.num_workers:
# For Google Colab, don't allocate resources to the base Trainer.
# Colab only has 2 CPUs, and because of this resource scarcity,
# we have to be careful on where we allocate resources. Since Colab
# is not distributed, the concern about many parallel Ray Tune trials
# leading to all Trainers being scheduled on the head node if we set
# `trainer_resources` to 0 is no longer applicable.
try:
import google.colab # noqa: F401
trainer_num_cpus = 0
except ImportError:
trainer_num_cpus = 1
else:
# If there are no additional workers, then always reserve 1 CPU for
# the Trainer.
trainer_num_cpus = 1
trainer_resources = {"CPU": trainer_num_cpus}
else:
trainer_resources = {
k: v for k, v in self.trainer_resources.items() if v != 0
}
return trainer_resources
@property
def total_resources(self):
"""Map of total resources required for the trainer."""
total_resource_map = defaultdict(float, self._trainer_resources_not_none)
for k, value in self._resources_per_worker_not_none.items():
total_resource_map[k] += value * self.num_workers
return dict(total_resource_map)
@property
def num_cpus_per_worker(self):
"""The number of CPUs to set per worker."""
return self._resources_per_worker_not_none.get("CPU", 0)
@property
def num_gpus_per_worker(self):
"""The number of GPUs to set per worker."""
return self._resources_per_worker_not_none.get("GPU", 0)
@property
def additional_resources_per_worker(self):
"""Resources per worker, not including CPU or GPU resources."""
return {
k: v
for k, v in self._resources_per_worker_not_none.items()
if k not in ["CPU", "GPU"]
}
[docs] def as_placement_group_factory(self) -> "PlacementGroupFactory":
"""Returns a PlacementGroupFactory to specify resources for Tune."""
from ray.tune.execution.placement_groups import PlacementGroupFactory
trainer_bundle = self._trainer_resources_not_none
worker_bundle = self._resources_per_worker_not_none
# Colocate Trainer and rank0 worker by merging their bundles
# Note: This empty bundle is required so that the Tune actor manager schedules
# the Trainable onto the combined bundle while taking none of its resources,
# rather than a non-empty head bundle.
combined_bundle = dict(Counter(trainer_bundle) + Counter(worker_bundle))
bundles = [{}, combined_bundle] + [worker_bundle] * (self.num_workers - 1)
return PlacementGroupFactory(bundles, strategy=self.placement_strategy)
[docs] @classmethod
def from_placement_group_factory(
cls, pgf: "PlacementGroupFactory"
) -> "ScalingConfig":
"""Create a ScalingConfig from a Tune's PlacementGroupFactory
Note that this is only needed for ResourceChangingScheduler, which
modifies a trial's PlacementGroupFactory but doesn't propagate
the changes to ScalingConfig. TrainTrainable needs to reconstruct
a ScalingConfig from on the trial's PlacementGroupFactory.
"""
# pgf.bundles = [{trainer + worker}, {worker}, ..., {worker}]
num_workers = len(pgf.bundles)
combined_resources = pgf.bundles[0]
resources_per_worker = pgf.bundles[-1]
use_gpu = bool(resources_per_worker.get("GPU", False))
placement_strategy = pgf.strategy
# In `as_placement_group_factory`, we merged the trainer resource into the
# first worker resources bundle. We need to calculate the resources diff to
# get the trainer resources.
# Note: If there's only one worker, we won't be able to calculate the diff.
# We'll have empty trainer bundle and assign all resources to the worker.
trainer_resources = dict(
Counter(combined_resources) - Counter(resources_per_worker)
)
return ScalingConfig(
trainer_resources=trainer_resources,
num_workers=num_workers,
use_gpu=use_gpu,
resources_per_worker=resources_per_worker,
placement_strategy=placement_strategy,
)
@dataclass
@Deprecated(DATASET_CONFIG_DEPRECATION_MSG)
class DatasetConfig:
"""Configuration for ingest of a single Dataset.
See :ref:`the AIR Dataset configuration guide <data-ingest-torch>` for
usage examples.
This config defines how the Dataset should be read into the DataParallelTrainer.
It configures the preprocessing, splitting, and ingest strategy per-dataset.
DataParallelTrainers declare default DatasetConfigs for each dataset passed in the
``datasets`` argument. Users have the opportunity to selectively override these
configs by passing the ``dataset_config`` argument. Trainers can also define user
customizable values (e.g., XGBoostTrainer doesn't support streaming ingest).
Args:
fit: Whether to fit preprocessors on this dataset. This can be set on at most
one dataset at a time. True by default for the "train" dataset only.
split: Whether the dataset should be split across multiple workers.
True by default for the "train" dataset only.
required: Whether to raise an error if the Dataset isn't provided by the user.
False by default.
transform: Whether to transform the dataset with the fitted preprocessor.
This must be enabled at least for the dataset that is fit.
True by default.
max_object_store_memory_fraction [Experimental]: The maximum fraction
of Ray's shared-memory object store to use for the dataset. The
default value is -1, meaning that the preprocessed dataset should
be cached, which may cause spilling if its size is larger than the
object store's capacity. Pipelined ingest (all other values, 0 or
higher) is experimental. Note that the absolute memory capacity
used is based on the object store capacity at invocation time; this
does not currently cover autoscaling cases where the size of the
cluster may change.
global_shuffle: Whether to enable global shuffle (per pipeline window
in streaming mode). Note that this is an expensive all-to-all operation,
and most likely you want to use local shuffle instead.
See https://docs.ray.io/en/master/data/faq.html and
https://docs.ray.io/en/master/ray-air/check-ingest.html.
False by default.
randomize_block_order: Whether to randomize the iteration order over blocks.
The main purpose of this is to prevent data fetching hotspots in the
cluster when running many parallel workers / trials on the same data.
We recommend enabling it always. True by default.
per_epoch_preprocessor [Experimental]: A preprocessor to re-apply on
each pass of the dataset. The main use case for this is to apply a
random transform on a training dataset on each epoch. The
per-epoch preprocessor will be applied *after* all other
preprocessors and in parallel with the dataset consumer.
use_stream_api: Deprecated. Use max_object_store_memory_fraction instead.
stream_window_size: Deprecated. Use max_object_store_memory_fraction instead.
"""
# TODO(ekl) could we unify DataParallelTrainer and Trainer so the same data ingest
# strategy applies to all Trainers?
fit: Optional[bool] = None
split: Optional[bool] = None
required: Optional[bool] = None
transform: Optional[bool] = None
max_object_store_memory_fraction: Optional[float] = None
global_shuffle: Optional[bool] = None
randomize_block_order: Optional[bool] = None
per_epoch_preprocessor: Optional["Preprocessor"] = None
# Deprecated.
use_stream_api: Optional[int] = None
stream_window_size: Optional[int] = None
def __post_init__(self):
raise DeprecationWarning(DATASET_CONFIG_DEPRECATION_MSG)
@dataclass
@PublicAPI(stability="stable")
class FailureConfig:
"""Configuration related to failure handling of each training/tuning run.
Args:
max_failures: Tries to recover a run at least this many times.
Will recover from the latest checkpoint if present.
Setting to -1 will lead to infinite recovery retries.
Setting to 0 will disable retries. Defaults to 0.
fail_fast: Whether to fail upon the first error.
If fail_fast='raise' provided, the original error during training will be
immediately raised. fail_fast='raise' can easily leak resources and
should be used with caution.
"""
max_failures: int = 0
fail_fast: Union[bool, str] = False
def __post_init__(self):
# Same check as in TuneController
if not (isinstance(self.fail_fast, bool) or self.fail_fast.upper() == "RAISE"):
raise ValueError(
"fail_fast must be one of {bool, 'raise'}. " f"Got {self.fail_fast}."
)
# Same check as in tune.run
if self.fail_fast and self.max_failures != 0:
raise ValueError(
f"max_failures must be 0 if fail_fast={repr(self.fail_fast)}."
)
def __repr__(self):
return _repr_dataclass(self)
def _repr_html_(self):
return Template("scrollableTable.html.j2").render(
table=tabulate(
{
"Setting": ["Max failures", "Fail fast"],
"Value": [self.max_failures, self.fail_fast],
},
tablefmt="html",
showindex=False,
headers="keys",
),
max_height="none",
)
@dataclass
@PublicAPI(stability="stable")
class CheckpointConfig:
"""Configurable parameters for defining the checkpointing strategy.
Default behavior is to persist all checkpoints to disk. If
``num_to_keep`` is set, the default retention policy is to keep the
checkpoints with maximum timestamp, i.e. the most recent checkpoints.
Args:
num_to_keep: The number of checkpoints to keep
on disk for this run. If a checkpoint is persisted to disk after
there are already this many checkpoints, then an existing
checkpoint will be deleted. If this is ``None`` then checkpoints
will not be deleted. Must be >= 1.
checkpoint_score_attribute: The attribute that will be used to
score checkpoints to determine which checkpoints should be kept
on disk when there are greater than ``num_to_keep`` checkpoints.
This attribute must be a key from the checkpoint
dictionary which has a numerical value. Per default, the last
checkpoints will be kept.
checkpoint_score_order: Either "max" or "min".
If "max", then checkpoints with highest values of
``checkpoint_score_attribute`` will be kept.
If "min", then checkpoints with lowest values of
``checkpoint_score_attribute`` will be kept.
checkpoint_frequency: Number of iterations between checkpoints. If 0
this will disable checkpointing.
Please note that most trainers will still save one checkpoint at
the end of training.
This attribute is only supported
by trainers that don't take in custom training loops.
checkpoint_at_end: If True, will save a checkpoint at the end of training.
This attribute is only supported by trainers that don't take in
custom training loops. Defaults to True for trainers that support it
and False for generic function trainables.
_checkpoint_keep_all_ranks: This experimental config is deprecated.
This behavior is now controlled by reporting `checkpoint=None`
in the workers that shouldn't persist a checkpoint.
For example, if you only want the rank 0 worker to persist a checkpoint
(e.g., in standard data parallel training), then you should save and
report a checkpoint if `ray.train.get_context().get_world_rank() == 0`
and `None` otherwise.
_checkpoint_upload_from_workers: This experimental config is deprecated.
Uploading checkpoint directly from the worker is now the default behavior.
"""
num_to_keep: Optional[int] = None
checkpoint_score_attribute: Optional[str] = None
checkpoint_score_order: Optional[str] = MAX
checkpoint_frequency: Optional[int] = 0
checkpoint_at_end: Optional[bool] = None
_checkpoint_keep_all_ranks: Optional[bool] = _DEPRECATED_VALUE
_checkpoint_upload_from_workers: Optional[bool] = _DEPRECATED_VALUE
def __post_init__(self):
if self._checkpoint_keep_all_ranks != _DEPRECATED_VALUE:
raise DeprecationWarning(
"The experimental `_checkpoint_keep_all_ranks` config is deprecated. "
"This behavior is now controlled by reporting `checkpoint=None` "
"in the workers that shouldn't persist a checkpoint. "
"For example, if you only want the rank 0 worker to persist a "
"checkpoint (e.g., in standard data parallel training), "
"then you should save and report a checkpoint if "
"`ray.train.get_context().get_world_rank() == 0` "
"and `None` otherwise."
)
if self._checkpoint_upload_from_workers != _DEPRECATED_VALUE:
raise DeprecationWarning(
"The experimental `_checkpoint_upload_from_workers` config is "
"deprecated. Uploading checkpoint directly from the worker is "
"now the default behavior."
)
if self.num_to_keep is not None and self.num_to_keep <= 0:
raise ValueError(
f"Received invalid num_to_keep: "
f"{self.num_to_keep}. "
f"Must be None or an integer >= 1."
)
if self.checkpoint_score_order not in (MAX, MIN):
raise ValueError(
f"checkpoint_score_order must be either " f'"{MAX}" or "{MIN}".'
)
if self.checkpoint_frequency < 0:
raise ValueError(
f"checkpoint_frequency must be >=0, got {self.checkpoint_frequency}"
)
def __repr__(self):
return _repr_dataclass(self)
def _repr_html_(self) -> str:
if self.num_to_keep is None:
num_to_keep_repr = "All"
else:
num_to_keep_repr = self.num_to_keep
if self.checkpoint_score_attribute is None:
checkpoint_score_attribute_repr = "Most recent"
else:
checkpoint_score_attribute_repr = self.checkpoint_score_attribute
if self.checkpoint_at_end is None:
checkpoint_at_end_repr = ""
else:
checkpoint_at_end_repr = self.checkpoint_at_end
return Template("scrollableTable.html.j2").render(
table=tabulate(
{
"Setting": [
"Number of checkpoints to keep",
"Checkpoint score attribute",
"Checkpoint score order",
"Checkpoint frequency",
"Checkpoint at end",
],
"Value": [
num_to_keep_repr,
checkpoint_score_attribute_repr,
self.checkpoint_score_order,
self.checkpoint_frequency,
checkpoint_at_end_repr,
],
},
tablefmt="html",
showindex=False,
headers="keys",
),
max_height="none",
)
@property
def _tune_legacy_checkpoint_score_attr(self) -> Optional[str]:
"""Same as ``checkpoint_score_attr`` in ``tune.run``.
Only used for Legacy API compatibility.
"""
if self.checkpoint_score_attribute is None:
return self.checkpoint_score_attribute
prefix = ""
if self.checkpoint_score_order == MIN:
prefix = "min-"
return f"{prefix}{self.checkpoint_score_attribute}"
@dataclass
@PublicAPI(stability="stable")
class RunConfig:
"""Runtime configuration for training and tuning runs.
Upon resuming from a training or tuning run checkpoint,
Ray Train/Tune will automatically apply the RunConfig from
the previously checkpointed run.
Args:
name: Name of the trial or experiment. If not provided, will be deduced
from the Trainable.
storage_path: [Beta] Path where all results and checkpoints are persisted.
Can be a local directory or a destination on cloud storage.
For multi-node training/tuning runs, this must be set to a
shared storage location (e.g., S3, NFS).
This defaults to the local ``~/ray_results`` directory.
storage_filesystem: [Beta] A custom filesystem to use for storage.
If this is provided, `storage_path` should be a path with its
prefix stripped (e.g., `s3://bucket/path` -> `bucket/path`).
failure_config: Failure mode configuration.
checkpoint_config: Checkpointing configuration.
sync_config: Configuration object for syncing. See train.SyncConfig.
verbose: 0, 1, or 2. Verbosity mode.
0 = silent, 1 = default, 2 = verbose. Defaults to 1.
If the ``RAY_AIR_NEW_OUTPUT=1`` environment variable is set,
uses the old verbosity settings:
0 = silent, 1 = only status updates, 2 = status and brief
results, 3 = status and detailed results.
stop: Stop conditions to consider. Refer to ray.tune.stopper.Stopper
for more info. Stoppers should be serializable.
callbacks: [DeveloperAPI] Callbacks to invoke.
Refer to ray.tune.callback.Callback for more info.
Callbacks should be serializable.
Currently only stateless callbacks are supported for resumed runs.
(any state of the callback will not be checkpointed by Tune
and thus will not take effect in resumed runs).
progress_reporter: [DeveloperAPI] Progress reporter for reporting
intermediate experiment progress. Defaults to CLIReporter if
running in command-line, or JupyterNotebookReporter if running in
a Jupyter notebook.
log_to_file: [DeveloperAPI] Log stdout and stderr to files in
trial directories. If this is `False` (default), no files
are written. If `true`, outputs are written to `trialdir/stdout`
and `trialdir/stderr`, respectively. If this is a single string,
this is interpreted as a file relative to the trialdir, to which
both streams are written. If this is a Sequence (e.g. a Tuple),
it has to have length 2 and the elements indicate the files to
which stdout and stderr are written, respectively.
"""
name: Optional[str] = None
storage_path: Optional[str] = None
storage_filesystem: Optional[pyarrow.fs.FileSystem] = None
failure_config: Optional[FailureConfig] = None
checkpoint_config: Optional[CheckpointConfig] = None
sync_config: Optional["SyncConfig"] = None
verbose: Optional[Union[int, "AirVerbosity", "Verbosity"]] = None
stop: Optional[Union[Mapping, "Stopper", Callable[[str, Mapping], bool]]] = None
callbacks: Optional[List["Callback"]] = None
progress_reporter: Optional["ProgressReporter"] = None
log_to_file: Union[bool, str, Tuple[str, str]] = False
# Deprecated
local_dir: Optional[str] = None
def __post_init__(self):
from ray.train import SyncConfig
from ray.train.constants import DEFAULT_STORAGE_PATH
from ray.tune.experimental.output import AirVerbosity, get_air_verbosity
if self.storage_path is None:
# TODO(justinvyu): [Deprecated] Remove fallback to local dir.
self.storage_path = self.local_dir or DEFAULT_STORAGE_PATH
# If no remote path is set, try to get Ray Storage URI
ray_storage_uri: Optional[str] = _get_storage_uri()
if ray_storage_uri is not None:
logger.info(
"Using configured Ray Storage URI as the `storage_path`: "
f"{ray_storage_uri}"
)
self.storage_path = ray_storage_uri
if not self.failure_config:
self.failure_config = FailureConfig()
if not self.sync_config:
self.sync_config = SyncConfig()
if not self.checkpoint_config:
self.checkpoint_config = CheckpointConfig()
if self.verbose is None:
# Default `verbose` value. For new output engine,
# this is AirVerbosity.DEFAULT.
# For old output engine, this is Verbosity.V3_TRIAL_DETAILS
# Todo (krfricke): Currently uses number to pass test_configs::test_repr
self.verbose = get_air_verbosity(AirVerbosity.DEFAULT) or 3
# Convert Paths to strings
if isinstance(self.local_dir, Path):
self.local_dir = self.local_dir.as_posix()
if isinstance(self.storage_path, Path):
self.storage_path = self.storage_path.as_posix()
def __repr__(self):
from ray.train import SyncConfig
return _repr_dataclass(
self,
default_values={
"failure_config": FailureConfig(),
"sync_config": SyncConfig(),
"checkpoint_config": CheckpointConfig(),
},
)
def _repr_html_(self) -> str:
reprs = []
if self.failure_config is not None:
reprs.append(
Template("title_data_mini.html.j2").render(
title="Failure Config", data=self.failure_config._repr_html_()
)
)
if self.sync_config is not None:
reprs.append(
Template("title_data_mini.html.j2").render(
title="Sync Config", data=self.sync_config._repr_html_()
)
)
if self.checkpoint_config is not None:
reprs.append(
Template("title_data_mini.html.j2").render(
title="Checkpoint Config", data=self.checkpoint_config._repr_html_()
)
)
# Create a divider between each displayed repr
subconfigs = [Template("divider.html.j2").render()] * (2 * len(reprs) - 1)
subconfigs[::2] = reprs
settings = Template("scrollableTable.html.j2").render(
table=tabulate(
{
"Name": self.name,
"Local results directory": self.local_dir,
"Verbosity": self.verbose,
"Log to file": self.log_to_file,
}.items(),
tablefmt="html",
headers=["Setting", "Value"],
showindex=False,
),
max_height="300px",
)
return Template("title_data.html.j2").render(
title="RunConfig",
data=Template("run_config.html.j2").render(
subconfigs=subconfigs,
settings=settings,
),
)