diff --git a/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py b/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py
index 6e20239c8185561217aa5e38e6fb4c2ac0d30677..6375cd4510bfebadd4df529a6884c1eb1632f162 100644
--- a/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py
+++ b/downloads-generation/models_class1_allele_specific_single_kim2014_only/models.py
@@ -3,13 +3,13 @@ from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
import json
models = HYPERPARAMETER_DEFAULTS.models_grid(
- impute=[False, True],
+ #impute=[False, True],
+ impute=[False],
activation=["tanh"],
layer_sizes=[[12], [64], [128]],
embedding_output_dim=[8, 32, 64],
dropout_probability=[0, .1, .25],
- pretrain_decay=["1 / (1+epoch)**2"],
- fraction_negative=[0, .1, .2],
+ # fraction_negative=[0, .1, .2],
n_training_epochs=[250])
sys.stderr.write("Models: %d\n" % len(models))
diff --git a/mhcflurry/class1_allele_specific/cross_validation.py b/mhcflurry/class1_allele_specific/cross_validation.py
index 63852548670970e1ac90816b7934cebc1ad54ec8..6bcd092cbe2b4c4fe11e4bff273c6c3e8d04a909 100644
--- a/mhcflurry/class1_allele_specific/cross_validation.py
+++ b/mhcflurry/class1_allele_specific/cross_validation.py
@@ -20,11 +20,10 @@ from __future__ import (
import collections
import logging
-
import pepdata
from .train import impute_and_select_allele, AlleleSpecificTrainTestFold
-from ..parallelism import get_default_executor
+from ..parallelism import get_default_backend
gbmr4_transformer = pepdata.reduced_alphabet.make_alphabet_transformer("gbmr4")
@@ -100,7 +99,7 @@ def cross_validation_folds(
'min_observations_per_peptide': 2,
'min_observations_per_allele': 2,
},
- executor=None):
+ parallel_backend=None):
'''
Split a Dataset into n_folds cross validation folds for each allele,
optionally performing imputation.
@@ -136,8 +135,8 @@ def cross_validation_folds(
list of AlleleSpecificTrainTestFold of length num alleles * n_folds
'''
- if executor is None:
- executor = get_default_executor()
+ if parallel_backend is None:
+ parallel_backend = get_default_backend()
if alleles is None:
alleles = train_data.unique_alleles()
@@ -166,7 +165,7 @@ def cross_validation_folds(
test_split = full_test_split
if imputer is not None:
- imputation_future = executor.submit(
+ imputation_future = parallel_backend.submit(
impute_and_select_allele,
all_allele_train_split,
imputer=imputer,
diff --git a/mhcflurry/class1_allele_specific/cv_and_train_command.py b/mhcflurry/class1_allele_specific/cv_and_train_command.py
index cda07b7e9218c155837b2b75918816a10ed573ff..fea31a9558ab13424d08ca20e1d16eac2c590409 100644
--- a/mhcflurry/class1_allele_specific/cv_and_train_command.py
+++ b/mhcflurry/class1_allele_specific/cv_and_train_command.py
@@ -48,9 +48,7 @@ import pickle
import numpy
-from dask import distributed
-
-from ..parallelism import set_default_executor, get_default_executor
+from .. import parallelism
from ..dataset import Dataset
from ..imputation_helpers import imputer_from_name
from .cross_validation import cross_validation_folds
@@ -138,6 +136,19 @@ parser.add_argument(
metavar="HOST:PORT",
help="Host and port of dask distributed scheduler")
+parser.add_argument(
+ "--num-local-processes",
+ metavar="N",
+ type=int,
+ help="Processes (exclusive with --dask-scheduler and --num-local-threads)")
+
+parser.add_argument(
+ "--num-local-threads",
+ metavar="N",
+ type=int,
+ default=1,
+ help="Threads (exclusive with --dask-scheduler and --num-local-processes)")
+
parser.add_argument(
"--min-samples-per-allele",
default=100,
@@ -161,22 +172,36 @@ parser.add_argument(
def run(argv=sys.argv[1:]):
args = parser.parse_args(argv)
- if not args.quiet:
- logging.basicConfig(level="INFO")
if args.verbose:
- logging.basicConfig(level="DEBUG")
+ logging.root.setLevel(level="DEBUG")
+ elif not args.quiet:
+ logging.root.setLevel(level="INFO")
+
+ logging.info("Running with arguments: %s" % args)
+ print("Past logging")
+
+
+ # Set parallel backend
if args.dask_scheduler:
- executor = distributed.Executor(args.dask_scheduler)
- set_default_executor(executor)
- logging.info(
- "Running with dask scheduler: %s [%s cores]" % (
- args.dask_scheduler,
- sum(executor.ncores().values())))
+ backend = parallelism.DaskDistributedParallelBackend(
+ args.dask_scheduler)
+ else:
+ if args.num_local_processes:
+ backend = parallelism.ConcurrentFuturesParallelBackend(
+ args.num_local_processes,
+ processes=True)
+ else:
+ backend = parallelism.ConcurrentFuturesParallelBackend(
+ args.num_local_threads,
+ processes=False)
+
+ parallelism.set_default_backend(backend)
+ logging.info("Using parallel backend: %s" % backend)
go(args)
def go(args):
- executor = get_default_executor()
+ backend = parallelism.get_default_backend()
model_architectures = json.loads(args.model_architectures.read())
logging.info("Read %d model architectures" % len(model_architectures))
@@ -292,7 +317,7 @@ def go(args):
(allele, best_index, architecture))
if architecture['impute']:
- imputation_future = executor.submit(
+ imputation_future = backend.submit(
impute_and_select_allele,
train_data,
imputer=imputer,
diff --git a/mhcflurry/class1_allele_specific/train.py b/mhcflurry/class1_allele_specific/train.py
index 73937f7a06144267367b75e5728a580574e03e69..c4f29e4936adb83d235c5bdba1683a8c181f34a2 100644
--- a/mhcflurry/class1_allele_specific/train.py
+++ b/mhcflurry/class1_allele_specific/train.py
@@ -31,7 +31,7 @@ import mhcflurry
from .scoring import make_scores
from .class1_binding_predictor import Class1BindingPredictor
from ..hyperparameters import HyperparameterDefaults
-from ..parallelism import get_default_executor, map_throw_fast
+from ..parallelism import get_default_backend
TRAIN_HYPERPARAMETER_DEFAULTS = HyperparameterDefaults(impute=False)
@@ -239,7 +239,7 @@ def train_across_models_and_folds(
cartesian_product_of_folds_and_models=True,
return_predictors=False,
folds_per_task=1,
- executor=None):
+ parallel_backend=None):
'''
Train and optionally test any number of models across any number of folds.
@@ -259,14 +259,14 @@ def train_across_models_and_folds(
return_predictors : boolean, optional
Include the trained predictors in the result.
- executor :
+ parallel_backend : parallel backend, optional
Returns
-----------
pandas.DataFrame
'''
- if executor is None:
- executor = get_default_executor()
+ if parallel_backend is None:
+ parallel_backend = get_default_backend()
if cartesian_product_of_folds_and_models:
tasks_per_model = int(math.ceil(float(len(folds)) / folds_per_task))
@@ -303,8 +303,7 @@ def train_across_models_and_folds(
[folds[i] for i in fold_nums],
return_predictor=return_predictors)
- task_results = map_throw_fast(
- executor,
+ task_results = parallel_backend.map(
train_and_test_one_model_task,
task_model_and_fold_indices)
diff --git a/mhcflurry/parallelism.py b/mhcflurry/parallelism.py
index 73e9cfce34cd686a99beb2b307e2ad7c8d584ecd..d22473bb4803485c4cd762f82689ccd41318dc43 100644
--- a/mhcflurry/parallelism.py
+++ b/mhcflurry/parallelism.py
@@ -1,34 +1,72 @@
from concurrent import futures
import logging
-DEFAULT_EXECUTOR = None
+DEFAULT_BACKEND = None
-def set_default_executor(executor):
- global DEFAULT_EXECUTOR
- DEFAULT_EXECUTOR = executor
+class ParallelBackend(object):
+ def __init__(self, executor, module, verbose=1):
+ self.executor = executor
+ self.module = module
+ self.verbose = verbose
+ def submit(self, func, *args, **kwargs):
+ if self.verbose > 0:
+ logging.debug("Submitting: %s %s %s" % (func, args, kwargs))
+ return self.executor.submit(func, *args, **kwargs)
-def get_default_executor():
- global DEFAULT_EXECUTOR
- if DEFAULT_EXECUTOR is None:
- DEFAULT_EXECUTOR = futures.ThreadPoolExecutor(max_workers=1)
- return DEFAULT_EXECUTOR
+ def map(self, func, iterable):
+ fs = [
+ self.executor.submit(func, arg) for arg in iterable
+ ]
+ return self.wait(fs)
+ def wait(self, fs):
+ result_dict = {}
+ for finished_future in self.module.as_completed(fs):
+ result = finished_future.result()
+ logging.info("%3d / %3d tasks completed" % (
+ len(result_dict), len(fs)))
+ result_dict[finished_future] = result
-def map_throw_fast(executor, func, iterable):
- futures = [
- executor.submit(func, arg) for arg in iterable
- ]
- return wait_all_throw_fast(futures)
+ return [result_dict[future] for future in fs]
-def wait_all_throw_fast(fs):
- result_dict = {}
- for finished_future in futures.as_completed(fs):
- result = finished_future.result()
- logging.info("%3d / %3d tasks completed" % (
- len(result_dict), len(fs)))
- result_dict[finished_future] = result
+class DaskDistributedParallelBackend(ParallelBackend):
+ def __init__(self, scheduler_ip_and_port, verbose=1):
+ from dask import distributed
+ executor = distributed.Executor(scheduler_ip_and_port)
+ ParallelBackend.__init__(self, executor, distributed, verbose=verbose)
+ self.scheduler_ip_and_port = scheduler_ip_and_port
- return [result_dict[future] for future in fs]
+ def __str__(self):
+ return "<Dask distributed backend, scheduler=%s, total_cores=%d>" % (
+ self.scheduler_ip_and_port,
+ sum(self.executor.ncores().values()))
+
+
+class ConcurrentFuturesParallelBackend(ParallelBackend):
+ def __init__(self, num_workers=1, processes=False, verbose=1):
+ if processes:
+ executor = futures.ProcessPoolExecutor(num_workers)
+ else:
+ executor = futures.ThreadPoolExecutor(num_workers)
+ ParallelBackend.__init__(self, executor, futures, verbose=verbose)
+ self.num_workers = num_workers
+ self.processes = processes
+
+ def __str__(self):
+ return "<Concurrent futures %s parallel backend, num workers = %d>" % (
+ ("processes" if self.processes else "threads"), self.num_workers)
+
+
+def set_default_backend(backend):
+ global DEFAULT_BACKEND
+ DEFAULT_BACKEND = backend
+
+
+def get_default_backend():
+ global DEFAULT_BACKEND
+ if DEFAULT_BACKEND is None:
+ set_default_backend(ConcurrentFuturesParallelBackend())
+ return DEFAULT_BACKEND
diff --git a/test/test_class1_allele_specific_cv_and_train_command.py b/test/test_class1_allele_specific_cv_and_train_command.py
index a439b5151977414f6e8f4f9cb7addec2086159b5..fdc123be44d31c1602c0b971b9cbd91247183b38 100644
--- a/test/test_class1_allele_specific_cv_and_train_command.py
+++ b/test/test_class1_allele_specific_cv_and_train_command.py
@@ -60,6 +60,7 @@ def test_small_run():
"--cv-num-folds", "2",
"--alleles", "HLA-A0201", "HLA-A0301",
"--verbose",
+ "--num-local-threads", "2",
]
print("Running cv_and_train_command with args: %s " % str(args))