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PR #571: Fix bp2vec TensorFlow crash — graceful fallback + add TF to requirements #440

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Merged
jaayslaughter-cpu merged 1 commit into from
May 15, 2026
Merged

PR #571: Fix bp2vec TensorFlow crash — graceful fallback + add TF to requirements #440

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7 changes: 5 additions & 2 deletions bp2vec_train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -262,8 +262,8 @@ def build_model(num_batters: int, num_pitchers: int, num_outcomes: int):
from tensorflow import keras
from tensorflow.keras import layers
except ImportError:
log.error("TensorFlow not installed. Run: pip install tensorflow")
sys.exit(1)
log.error("TensorFlow not installed — bp2vec training skipped. Add tensorflow to requirements_army.txt")
return None
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@gemini-code-assist gemini-code-assist Bot May 15, 2026

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high

The transition from sys.exit(1) to return None correctly addresses the issue of disrupting the scheduler's event loop. However, please note that load_statcast_seasons (line 130) still contains a sys.exit(1) call on pybaseball import failure. To fully achieve the goal of a graceful fallback and prevent process termination in a shared environment, that instance should also be updated to return or raise an exception instead of exiting.


batter_idx_in = keras.Input(shape=(1,), dtype="int32", name="batter_idx")
pitcher_idx_in = keras.Input(shape=(1,), dtype="int32", name="pitcher_idx")
Expand Down Expand Up @@ -312,6 +312,9 @@ def train(seasons: list[int]) -> None:
log.info("Building model: %d batters, %d pitchers, %d outcomes",
num_batters, num_pitchers, num_outcomes)
model = build_model(num_batters, num_pitchers, num_outcomes)
if model is None:
log.warning("bp2vec training aborted — TensorFlow unavailable. Add tensorflow to requirements_army.txt")
return
Comment on lines +315 to +317
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@gemini-code-assist gemini-code-assist Bot May 15, 2026

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medium

This check correctly handles the case where TensorFlow is missing. However, it occurs after load_statcast_seasons and build_indices, which can be time-consuming (the docstring mentions 10-20 minutes). Consider performing a quick check for TensorFlow availability at the very beginning of the train function to "fail fast" and avoid unnecessary data processing when the dependency is unavailable.

model.summary()

log.info("Training for %d epochs...", NUM_EPOCHS)
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1 change: 1 addition & 0 deletions requirements_army.txt
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Expand Up @@ -41,3 +41,4 @@ pyarrow
pybaseball>=2.2.7
mlb-statsapi>=1.7.2
curl_cffi>=0.6.0
tensorflow>=2.15.0