diff --git a/downloads-generation/models_class1/GENERATE.sh b/downloads-generation/models_class1/GENERATE.sh
index 2722c9fabf599ad24c07a3e6ff055d11d187dcc9..cf77bcf77d667e7b9266d02b6516bb4fe9015964 100755
--- a/downloads-generation/models_class1/GENERATE.sh
+++ b/downloads-generation/models_class1/GENERATE.sh
@@ -37,8 +37,7 @@ time mhcflurry-class1-train-allele-specific-models \
--out-models-dir models \
--percent-rank-calibration-num-peptides-per-length 1000000 \
--min-measurements-per-allele 75 \
- --train-num-jobs 0 \
- --calibration-num-jobs 1
+ --num-jobs 0
cp $SCRIPT_ABSOLUTE_PATH .
bzip2 LOG.txt
diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index 043a5c0c5704533dd7939e26572be9ee56a7bd41..ccd08367f938bd50e10e441b758776588116127f 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -600,7 +600,7 @@ class Class1NeuralNetwork(object):
self.hyperparameters['patience'])
if i > threshold:
print((progress_preamble + " " +
- "Early stopping at epoch %3d / %3d: loss=%g. "
+ "Stopping at epoch %3d / %3d: loss=%g. "
"Min val loss (%s) at epoch %s" % (
i,
self.hyperparameters['max_epochs'],
diff --git a/mhcflurry/train_allele_specific_models_command.py b/mhcflurry/train_allele_specific_models_command.py
index bf8d653c868010c04506e26a02f2e50332cb542e..87c6beef642a7a495efaba654a5eb80f78e481cb 100644
--- a/mhcflurry/train_allele_specific_models_command.py
+++ b/mhcflurry/train_allele_specific_models_command.py
@@ -17,7 +17,6 @@ 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
@@ -29,6 +28,12 @@ from .common import configure_logging, set_keras_backend
GLOBAL_DATA = {}
+# Note on parallelization:
+# It seems essential currently (tensorflow==1.4.1) that no processes are forked
+# after tensorflow has been used at all, which includes merely importing
+# keras.backend. So we must make sure not to use tensorflow in the main process
+# if we are running in parallel.
+
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
@@ -97,26 +102,19 @@ parser.add_argument(
help="Keras verbosity. Default: %(default)s",
default=0)
parser.add_argument(
- "--train-num-jobs",
- default=1,
- type=int,
- metavar="N",
- help="Number of processes to parallelize training over. "
- "Set to 1 for serial run. Set to 0 to use number of cores. Experimental."
- "Default: %(default)s.")
-parser.add_argument(
- "--calibration-num-jobs",
+ "--num-jobs",
default=1,
type=int,
metavar="N",
- help="Number of processes to parallelize percent rank calibration over. "
- "Set to 1 for serial run. Set to 0 to use number of cores. Experimental."
- "Default: %(default)s.")
+ help="Number of processes to parallelize training and percent rank "
+ "calibration over. Experimental. "
+ "Set to 1 for serial run. Set to 0 to use number of cores. Default: %(default)s.")
parser.add_argument(
"--backend",
choices=("tensorflow-gpu", "tensorflow-cpu"),
help="Keras backend. If not specified will use system default.")
+
def run(argv=sys.argv[1:]):
global GLOBAL_DATA
@@ -171,15 +169,15 @@ def run(argv=sys.argv[1:]):
GLOBAL_DATA["train_data"] = df
predictor = Class1AffinityPredictor()
- if args.train_num_jobs == 1:
+ if args.num_jobs == 1:
# Serial run
print("Running in serial.")
worker_pool = None
else:
worker_pool = Pool(
processes=(
- args.train_num_jobs
- if args.train_num_jobs else None))
+ args.num_jobs
+ if args.num_jobs else None))
print("Using worker pool: %s" % str(worker_pool))
if args.out_models_dir and not os.path.exists(args.out_models_dir):
@@ -257,18 +255,22 @@ def run(argv=sys.argv[1:]):
start = time.time()
if args.percent_rank_calibration_num_peptides_per_length > 0:
alleles = list(predictor.supported_alleles)
- first_allele = alleles.pop(0)
- print("Performing percent rank calibration. Calibrating first allele.")
+ print("Performing percent rank calibration. Encoding peptides.")
encoded_peptides = predictor.calibrate_percentile_ranks(
- alleles=[first_allele],
+ alleles=[], # don't actually do any calibration, just return peptides
num_peptides_per_length=args.percent_rank_calibration_num_peptides_per_length)
+
+ # Now we encode the peptides for each neural network, so the encoding
+ # becomes cached.
+ for network in predictor.neural_networks:
+ network.peptides_to_network_input(encoded_peptides)
assert encoded_peptides.encoding_cache # must have cached the encoding
- print("Finished calibrating percent ranks for first allele in %0.2f sec." % (
+ print("Finished encoding peptides for percent ranks in %0.2f sec." % (
time.time() - start))
- print("Calibrating %d additional alleles." % len(alleles))
+ print("Calibrating percent rank calibration for %d alleles." % len(alleles))
- if args.calibration_num_jobs == 1:
+ if args.num_jobs == 1:
# Serial run
print("Running in serial.")
worker_pool = None
@@ -283,21 +285,21 @@ 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.calibration_num_jobs else None))
+ args.num_jobs
+ if args.num_jobs else None))
print("Using worker pool: %s" % str(worker_pool))
results = worker_pool.imap_unordered(
partial(
calibrate_percentile_ranks,
- predictor=args.out_models_dir),
+ predictor=predictor),
alleles,
chunksize=1)
for result in tqdm.tqdm(results, ascii=True, total=len(alleles)):
predictor.allele_to_percent_rank_transform.update(result)
+
print("Done calibrating %d additional alleles." % len(alleles))
predictor.save(args.out_models_dir, model_names_to_write=[])
@@ -306,7 +308,6 @@ def run(argv=sys.argv[1:]):
if worker_pool:
worker_pool.close()
worker_pool.join()
- worker_pool = None
print("Train time: %0.2f min. Percent rank calibration time: %0.2f min." % (
training_time / 60.0, percent_rank_calibration_time / 60.0))
@@ -381,13 +382,8 @@ def calibrate_percentile_ranks(allele, predictor, peptides=None):
Private helper function.
"""
global GLOBAL_DATA
- Class1NeuralNetwork.clear_model_cache()
- import keras.backend as K
- K.clear_session()
if peptides is None:
peptides = GLOBAL_DATA["calibration_peptides"]
- if isinstance(predictor, str):
- predictor = Class1AffinityPredictor.load(predictor)
predictor.calibrate_percentile_ranks(
peptides=peptides,
alleles=[allele])
diff --git a/test/test_train_allele_specific_models_command.py b/test/test_train_allele_specific_models_command.py
index 537aac7ada87e3f8220ef055c25869bf95c4307b..12fcaa3d07614804ff004ee6110c5587163bb70d 100644
--- a/test/test_train_allele_specific_models_command.py
+++ b/test/test_train_allele_specific_models_command.py
@@ -62,8 +62,7 @@ def run_and_check(n_jobs=0):
"--allele", "HLA-A*02:01", "HLA-A*01:01", "HLA-A*03:01",
"--out-models-dir", models_dir,
"--percent-rank-calibration-num-peptides-per-length", "10000",
- "--train-num-jobs", str(n_jobs),
- "--calibration-num-jobs", str(n_jobs),
+ "--num-jobs", str(n_jobs),
"--ignore-inequalities",
]
print("Running with args: %s" % args)