From 5e96719fdcb3ac9add109e588a605b727f9537bf Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Sun, 28 Jan 2018 15:34:45 -0500
Subject: [PATCH] attempt fix race condition
---
mhcflurry/class1_neural_network.py | 7 +++++++
mhcflurry/train_allele_specific_models_command.py | 5 ++++-
2 files changed, 11 insertions(+), 1 deletion(-)
diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index 73405fdb..043a5c0c 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -125,6 +125,13 @@ class Class1NeuralNetwork(object):
(Keras model, existing network weights)
"""
+ @classmethod
+ def clear_model_cache(klass):
+ """
+ Clear the Keras model cache.
+ """
+ klass.KERAS_MODELS_CACHE.clear()
+
@classmethod
def borrow_cached_network(klass, network_json, network_weights):
"""
diff --git a/mhcflurry/train_allele_specific_models_command.py b/mhcflurry/train_allele_specific_models_command.py
index 3d72b32a..c2b6ed5d 100644
--- a/mhcflurry/train_allele_specific_models_command.py
+++ b/mhcflurry/train_allele_specific_models_command.py
@@ -17,6 +17,7 @@ from mhcnames import normalize_allele_name
import tqdm # progress bar
from .class1_affinity_predictor import Class1AffinityPredictor
+from .class1_neural_network import Class1NeuralNetwork
from .common import configure_logging, set_keras_backend
@@ -281,10 +282,11 @@ def run(argv=sys.argv[1:]):
# Store peptides in global variable so they are in shared memory
# after fork, instead of needing to be pickled.
GLOBAL_DATA["calibration_peptides"] = encoded_peptides
+ Class1NeuralNetwork.clear_model_cache()
worker_pool = Pool(
processes=(
args.calibration_num_jobs
- if args.train_num_jobs else None))
+ if args.calibration_num_jobs else None))
print("Using worker pool: %s" % str(worker_pool))
results = worker_pool.imap_unordered(
partial(
@@ -376,6 +378,7 @@ def calibrate_percentile_ranks(allele, predictor, peptides=None):
"""
Private helper function.
"""
+ global GLOBAL_DATA
if peptides is None:
peptides = GLOBAL_DATA["calibration_peptides"]
predictor.calibrate_percentile_ranks(
--
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