[AIR] Move Constants from tune/results.py to air/constants.py (ray-pr…

…oject#35404)

Currently we have result-related constants stored in `ray/tune/result.py`, However, our Result object is defined in `ray/air/result.py`. When we are trying to import constants from `ray/tune/result.py` in `ray/air/result.py`, there will be a cyclic import error:
```
import Result -> import ray.tune.result -> ray.tune.init -> import ResultGrid -> import Result
```

This PR only moves the following constants and changed the import path in all affected files accordingly. In the future, we will gradually move the rest constants along with the module class.

Signed-off-by: woshiyyya <xiaoyunxuan1998@gmail.com>
Signed-off-by: Yunxuan Xiao <xiaoyunxuan1998@gmail.com>
Signed-off-by: e428265 <arvind.chandramouli@lmco.com>

python/ray/air/constants.py

@@ -45,6 +45,36 @@
 # checkpointing.
 LAZY_CHECKPOINT_MARKER_FILE = ".lazy_checkpoint_marker"
 
+
+# The timestamp of when the result is generated.
+# Default to when the result is processed by tune.
+TIMESTAMP = "timestamp"
+
+# (Auto-filled) Time in seconds this iteration took to run.
+# This may be overridden to override the system-computed time difference.
+TIME_THIS_ITER_S = "time_this_iter_s"
+
+# (Auto-filled) The index of this training iteration.
+TRAINING_ITERATION = "training_iteration"
+
+# File that stores parameters of the trial.
+EXPR_PARAM_FILE = "params.json"
+
+# Pickle File that stores parameters of the trial.
+EXPR_PARAM_PICKLE_FILE = "params.pkl"
+
+# File that stores the progress of the trial.
+EXPR_PROGRESS_FILE = "progress.csv"
+
+# File that stores results of the trial.
+EXPR_RESULT_FILE = "result.json"
+
+# File that stores the pickled error file
+EXPR_ERROR_PICKLE_FILE = "error.pkl"
+
+# File that stores the error file
+EXPR_ERROR_FILE = "error.txt"
+
 # ==================================================
 #               Environment Variables
 # ==================================================

python/ray/air/integrations/mlflow.py

@@ -6,8 +6,9 @@
 from ray.air import session
 from ray.air._internal.mlflow import _MLflowLoggerUtil
 from ray.air._internal import usage as air_usage
+from ray.air.constants import TRAINING_ITERATION
 from ray.tune.logger import LoggerCallback
-from ray.tune.result import TIMESTEPS_TOTAL, TRAINING_ITERATION
+from ray.tune.result import TIMESTEPS_TOTAL
 from ray.tune.experiment import Trial
 from ray.util.annotations import PublicAPI
 

python/ray/train/_internal/checkpoint.py

@@ -13,13 +13,13 @@
 from ray.train._internal.utils import construct_path
 from ray.train.constants import (
     CHECKPOINT_RANK_KEY,
-    TIMESTAMP,
     TRAIN_CHECKPOINT_SUBDIR,
     TUNE_CHECKPOINT_ID,
     TUNE_INSTALLED,
     CHECKPOINT_METADATA_KEY,
     LAZY_CHECKPOINT_MARKER_FILE,
 )
+from ray.air.constants import TIMESTAMP
 
 if TUNE_INSTALLED:
     from ray import tune

python/ray/train/_internal/session.py

@@ -15,7 +15,12 @@
 import ray
 from ray.air._internal.util import StartTraceback, RunnerThread
 from ray.air.checkpoint import Checkpoint
-from ray.air.constants import _RESULT_FETCH_TIMEOUT, _ERROR_FETCH_TIMEOUT
+from ray.air.constants import (
+    _RESULT_FETCH_TIMEOUT,
+    _ERROR_FETCH_TIMEOUT,
+    TIMESTAMP,
+    TIME_THIS_ITER_S,
+)
 from ray.data import Dataset, DatasetPipeline
 from ray.train._internal.accelerator import Accelerator
 from ray.train.constants import (
@@ -26,11 +31,10 @@
     WORKER_HOSTNAME,
     WORKER_NODE_IP,
     WORKER_PID,
-    TIME_THIS_ITER_S,
     TIME_TOTAL_S,
-    TIMESTAMP,
     LAZY_CHECKPOINT_MARKER_FILE,
 )
+
 from ray.train.error import SessionMisuseError
 from ray.train.session import _TrainSessionImpl
 from ray.util.annotations import DeveloperAPI

python/ray/train/constants.py

@@ -18,13 +18,6 @@
 )
 
 # Autofilled session.report() metrics. Keys should be consistent with Tune.
-# The train provided `TIME_THIS_ITER_S` and `TIMESTAMP` will triumph what's
-# auto-filled by Tune session.
-# TODO: Combine the following two with tune's, once there is a centralized
-#  file for both tune/train constants.
-TIMESTAMP = "timestamp"
-TIME_THIS_ITER_S = "time_this_iter_s"
-
 TIME_TOTAL_S = "_time_total_s"
 
 WORKER_HOSTNAME = "_hostname"

python/ray/train/tests/test_examples.py

@@ -1,7 +1,7 @@
 import pytest
 
 from ray.air.config import ScalingConfig
-from ray.tune.result import TRAINING_ITERATION
+from ray.air.constants import TRAINING_ITERATION
 
 from ray.train.examples.horovod.horovod_example import (
     train_func as horovod_torch_train_func,

python/ray/train/tests/test_gpu_examples.py

@@ -4,6 +4,7 @@
 from ray.air import Checkpoint, session
 
 from ray.air.config import ScalingConfig
+from ray.air.constants import TRAINING_ITERATION
 from ray.train.examples.horovod.horovod_example import (
     train_func as horovod_torch_train_func,
 )
@@ -20,7 +21,6 @@
 )
 from ray.train.tensorflow.tensorflow_trainer import TensorflowTrainer
 from ray.train.torch.torch_trainer import TorchTrainer
-from ray.tune.result import TRAINING_ITERATION
 
 
 def test_tensorflow_mnist_gpu(ray_start_4_cpus_2_gpus):

python/ray/train/tests/test_mosaic_trainer.py

@@ -8,9 +8,9 @@
 
 from ray.air import session
 from ray.air.config import ScalingConfig
+from ray.air.constants import TRAINING_ITERATION
 import ray.train as train
 from ray.train.trainer import TrainingFailedError
-from ray.tune.result import TRAINING_ITERATION
 
 
 scaling_config = ScalingConfig(num_workers=2, use_gpu=False)

python/ray/tune/analysis/experiment_analysis.py

@@ -15,6 +15,12 @@
 )
 from ray.air._internal.uri_utils import _join_path_or_uri, URI
 from ray.air.checkpoint import Checkpoint
+from ray.air.constants import (
+    EXPR_PROGRESS_FILE,
+    EXPR_RESULT_FILE,
+    EXPR_PARAM_FILE,
+    TRAINING_ITERATION,
+)
 from ray.tune.syncer import SyncConfig
 from ray.tune.utils import flatten_dict
 from ray.tune.utils.serialization import TuneFunctionDecoder
@@ -31,11 +37,7 @@
 from ray.tune.error import TuneError
 from ray.tune.result import (
     DEFAULT_METRIC,
-    EXPR_PROGRESS_FILE,
-    EXPR_RESULT_FILE,
-    EXPR_PARAM_FILE,
     CONFIG_PREFIX,
-    TRAINING_ITERATION,
 )
 from ray.tune.experiment import Trial
 from ray.tune.execution.trial_runner import _find_newest_experiment_checkpoint

python/ray/tune/automl/board/backend/collector.py

@@ -4,6 +4,10 @@
 
 from threading import Thread
 
+from ray.air.constants import (
+    EXPR_PARAM_FILE,
+    EXPR_RESULT_FILE,
+)
 from ray.tune.automl.board.common.exception import CollectorError
 from ray.tune.automl.board.common.utils import (
     parse_json,
@@ -14,8 +18,6 @@
 from ray.tune.result import (
     DEFAULT_RESULTS_DIR,
     JOB_META_FILE,
-    EXPR_PARAM_FILE,
-    EXPR_RESULT_FILE,
     EXPR_META_FILE,
 )
 

python/ray/tune/cli/commands.py

@@ -10,6 +10,7 @@
 
 import pandas as pd
 from pandas.api.types import is_string_dtype, is_numeric_dtype
+from ray.air.constants import EXPR_RESULT_FILE
 from ray.tune.result import (
     DEFAULT_EXPERIMENT_INFO_KEYS,
     DEFAULT_RESULT_KEYS,
@@ -223,7 +224,7 @@ def list_experiments(
 
     for experiment_dir in experiment_folders:
         num_trials = sum(
-            "result.json" in files
+            EXPR_RESULT_FILE in files
             for _, _, files in os.walk(os.path.join(base, experiment_dir))
         )
 

python/ray/tune/execution/trial_runner.py

@@ -12,8 +12,9 @@
 
 import ray
 from ray.air._internal.uri_utils import URI
-from ray.air.config import CheckpointConfig
 from ray.air._internal.checkpoint_manager import CheckpointStorage, _TrackedCheckpoint
+from ray.air.config import CheckpointConfig
+from ray.air.constants import TIME_THIS_ITER_S
 from ray.exceptions import RayTaskError
 from ray.tune.error import _TuneStopTrialError, _TuneRestoreError
 from ray.tune.execution.experiment_state import (
@@ -38,7 +39,6 @@
     DEBUG_METRICS,
     DEFAULT_METRIC,
     DONE,
-    TIME_THIS_ITER_S,
     RESULT_DUPLICATE,
     SHOULD_CHECKPOINT,
     _get_defaults_results_dir,

python/ray/tune/experiment/trial.py

@@ -20,6 +20,12 @@
 from ray.air import CheckpointConfig
 from ray.air._internal.uri_utils import URI
 from ray.air._internal.checkpoint_manager import _TrackedCheckpoint, CheckpointStorage
+from ray.air.constants import (
+    EXPR_ERROR_PICKLE_FILE,
+    EXPR_ERROR_FILE,
+    TRAINING_ITERATION,
+)
+
 import ray.cloudpickle as cloudpickle
 from ray.exceptions import RayActorError, RayTaskError
 from ray.tune import TuneError
@@ -35,7 +41,6 @@
     DONE,
     NODE_IP,
     PID,
-    TRAINING_ITERATION,
     TRIAL_ID,
     DEBUG_METRICS,
     TRIAL_INFO,
@@ -924,10 +929,10 @@ def handle_error(self, exc: Optional[Union[TuneError, RayTaskError]] = None):
             self.num_failures += 1
 
         if self.local_path:
-            self.error_filename = "error.txt"
+            self.error_filename = EXPR_ERROR_FILE
             if isinstance(exc, RayTaskError):
                 # Piping through the actual error to result grid.
-                self.pickled_error_filename = "error.pkl"
+                self.pickled_error_filename = EXPR_ERROR_PICKLE_FILE
                 with open(self.pickled_error_file, "wb") as f:
                     cloudpickle.dump(exc, f)
             with open(self.error_file, "a+") as f:

python/ray/tune/experimental/output.py

@@ -44,6 +44,7 @@
     DataRow,
 )
 from ray.air._internal.checkpoint_manager import _TrackedCheckpoint
+from ray.air.constants import TRAINING_ITERATION
 from ray.tune.callback import Callback
 from ray.tune.result import (
     AUTO_RESULT_KEYS,
@@ -52,7 +53,6 @@
     MEAN_LOSS,
     TIME_TOTAL_S,
     TIMESTEPS_TOTAL,
-    TRAINING_ITERATION,
 )
 from ray.tune.experiment.trial import Trial
 

python/ray/tune/logger/aim.py

@@ -3,9 +3,9 @@
 import numpy as np
 from typing import TYPE_CHECKING, Dict, Optional, List, Union
 
+from ray.air.constants import TRAINING_ITERATION
 from ray.tune.logger.logger import LoggerCallback
 from ray.tune.result import (
-    TRAINING_ITERATION,
     TIME_TOTAL_S,
     TIMESTEPS_TOTAL,
 )

python/ray/tune/logger/csv.py

@@ -4,8 +4,8 @@
 
 from typing import TYPE_CHECKING, Dict, TextIO
 
+from ray.air.constants import EXPR_PROGRESS_FILE
 from ray.tune.logger.logger import _LOGGER_DEPRECATION_WARNING, Logger, LoggerCallback
-from ray.tune.result import EXPR_PROGRESS_FILE
 from ray.tune.utils import flatten_dict
 from ray.util.annotations import Deprecated, PublicAPI
 

python/ray/tune/logger/json.py

@@ -5,15 +5,14 @@
 
 from typing import TYPE_CHECKING, Dict, TextIO
 
-import ray.cloudpickle as cloudpickle
-
-from ray.tune.logger.logger import _LOGGER_DEPRECATION_WARNING, Logger, LoggerCallback
-from ray.tune.utils.util import SafeFallbackEncoder
-from ray.tune.result import (
+from ray.air.constants import (
     EXPR_PARAM_FILE,
     EXPR_PARAM_PICKLE_FILE,
     EXPR_RESULT_FILE,
 )
+import ray.cloudpickle as cloudpickle
+from ray.tune.logger.logger import _LOGGER_DEPRECATION_WARNING, Logger, LoggerCallback
+from ray.tune.utils.util import SafeFallbackEncoder
 from ray.util.annotations import Deprecated, PublicAPI
 
 if TYPE_CHECKING:

python/ray/tune/logger/tensorboardx.py

@@ -3,10 +3,10 @@
 
 from typing import TYPE_CHECKING, Dict
 
+from ray.air.constants import TRAINING_ITERATION
 from ray.tune.logger.logger import _LOGGER_DEPRECATION_WARNING, Logger, LoggerCallback
 from ray.util.debug import log_once
 from ray.tune.result import (
-    TRAINING_ITERATION,
     TIME_TOTAL_S,
     TIMESTEPS_TOTAL,
 )

python/ray/tune/progress_reporter.py

@@ -19,6 +19,7 @@
 from ray._private.thirdparty.tabulate.tabulate import tabulate
 from ray.experimental.tqdm_ray import safe_print
 from ray.air.util.node import _force_on_current_node
+from ray.air.constants import EXPR_ERROR_FILE, TRAINING_ITERATION
 from ray.tune.callback import Callback
 from ray.tune.logger import pretty_print
 from ray.tune.result import (
@@ -33,7 +34,6 @@
     PID,
     TIME_TOTAL_S,
     TIMESTEPS_TOTAL,
-    TRAINING_ITERATION,
     TRIAL_ID,
 )
 from ray.tune.experiment.trial import DEBUG_PRINT_INTERVAL, Trial, _Location
@@ -1373,7 +1373,7 @@ def print_result(self, trial: Trial, result: Dict, error: bool, done: bool):
         elif has_verbosity(Verbosity.V2_TRIAL_NORM):
             metric_name = self._metric or "_metric"
             metric_value = result.get(metric_name, -99.0)
-            error_file = os.path.join(trial.local_path, "error.txt")
+            error_file = os.path.join(trial.local_path, EXPR_ERROR_FILE)
 
             info = ""
             if done: