Update evaluation pipeline

Author: nastaran78

docs/reference/evaluate_pipeline.md

@@ -2,7 +2,7 @@
 
 > **TL;DR**
 > *Sync*: return a scalar or a dict ⟶ NePS records it automatically.
-> *Async*: return `None`, launch a job, and call `neps.save_callback()` when the job finishes.
+> *Async*: return `None`, launch a job, and call `neps.save_pipeline_results()` when the job finishes.
 
 ---
 
@@ -57,7 +57,6 @@ All other values raise a `TypeError` inside NePS.
 
 ```python
 from pathlib import Path
-import subprocess
 import neps
 
 def evaluate_pipeline(
@@ -67,9 +66,8 @@ def evaluate_pipeline(
     learning_rate: float,
     optimizer: str,
 ):
-    # 1) write a Slurm script into the trial dir
-    sh = pipeline_directory / "run.sh"
-    sh.write_text(f"""#!/bin/bash
+    # 1) write a Slurm script
+    script = f"""#!/bin/bash
 #SBATCH --time=0-00:10
 #SBATCH --job-name=trial_{pipeline_id}
 #SBATCH --partition=bosch_cpu-cascadelake
@@ -82,10 +80,9 @@ python run_pipeline.py \
        --pipeline_id {pipeline_id} \
        --root_dir {root_directory}
 """)
-    sh.chmod(0o755)
 
     # 2) submit and RETURN None (async)
-    subprocess.check_call(["sbatch", str(sh)])
+    sumit_job(script)
     return None  # ⟵ signals async mode
 ```
 
@@ -101,17 +98,22 @@ parser.add_argument("--optimizer")
 parser.add_argument("--pipeline_id")
 parser.add_argument("--root_dir")
 args = parser.parse_args()
-
-# … do heavy training …
-val_loss = 0.1234
-wall_clock_cost = 180  # seconds
-
-result = {
-    "objective_to_minimize": val_loss,
-    "cost": wall_clock_cost,
-}
-
-neps.save_callback(
+try:
+    # … do heavy training …
+    val_loss = 0.1234
+    wall_clock_cost = 180  # seconds
+    result = {
+        "objective_to_minimize": val_loss,
+        "cost": wall_clock_cost,
+    }
+except Exception as e:
+    result = {
+        "objective_to_minimize": val_loss,
+        "cost": wall_clock_cost,
+        "exception": e
+    }
+
+neps.save_pipeline_results(
     user_result=result,
     pipeline_id=args.pipeline_id,
     root_directory=Path(args.root_dir),
@@ -143,6 +145,6 @@ Use them to handle warm‑starts, logging and result persistence.
 
 * [x] Return scalar **or** dict **or** `None`.
 * [x] Include `cost` when using cost budgets.
-* [x] When returning `None`, make sure **exactly one** call to `neps.save_callback` happens.
+* [x] When returning `None`, make sure **exactly one** call to `neps.save_pipeline_results` happens.
 * [x] Save checkpoints and artefacts in `pipeline_directory`.
 * [x] Handle resume via `previous_pipeline_directory`.

neps/runtime.py

@@ -38,8 +38,8 @@
     OnErrorPossibilities,
     OptimizationState,
     SeedSnapshot,
-    State,
     Trial,
+    UserResult,
     WorkerSettings,
     evaluate_trial,
 )
@@ -674,9 +674,9 @@ def _save_results(
         raise RuntimeError(f"Trial '{trial_id}' not found in '{root_directory}'")
 
     report = trial.set_complete(
-        report_as=State.SUCCESS.value
+        report_as=Trial.State.SUCCESS.value
         if result.exception is None
-        else State.CRASHED.value,
+        else Trial.State.CRASHED.value,
         objective_to_minimize=result.objective_to_minimize,
         cost=result.cost,
         learning_curve=result.learning_curve,

neps/state/pipeline_eval.py

@@ -378,15 +378,12 @@ def _eval_trial(
     default_report_values: DefaultReportValues,
     fn: Callable[..., Any],
     **kwargs: Any,
-) -> Report:
+) -> Report | None:
     start = time.monotonic()
     try:
         user_result = fn(**kwargs, **trial.config)
-<<<<<<< HEAD
-=======
         if user_result is None:
             return None
->>>>>>> 2e3dfbce (add async evaluation)
     # Something went wrong in evaluation
     except Exception as e:
         duration = time.monotonic() - start
@@ -435,11 +432,7 @@ def evaluate_trial(
     *,
     evaluation_fn: Callable[..., Any],
     default_report_values: DefaultReportValues,
-<<<<<<< HEAD
-) -> tuple[Trial, Report]:
-=======
 ) -> tuple[Trial, Report | None]:
->>>>>>> 2e3dfbce (add async evaluation)
     """Evaluates a trial from a user and parses the results into a `Report`."""
     trial_location = Path(trial.metadata.location)
     prev_trial_location = (