Merge branch 'master' of https://github.com/rigdenlab/ample

ample/ensembler/__init__.py

@@ -36,6 +36,8 @@
 from ample.util import pdb_edit
 from ample.util import printTable
 
+from pyjob import Script
+
 logger = logging.getLogger(__name__)
 
 
@@ -57,15 +59,12 @@ def cluster_script(amoptd, python_path="ccp4-python"):
     """
     # write out script
     work_dir = amoptd['work_dir']
-    script_path = os.path.join(work_dir, "submit_ensemble.sh")
-    with open(script_path, "w") as job_script:
-        job_script.write(ample_util.SCRIPT_HEADER + os.linesep)
-        job_script.write("export CCP4_SCR=${TMPDIR}" + os.linesep)  # Added by Ronan after issues on CCP4online server
-        job_script.write("ccp4-python -m ample.ensembler -restart_pkl {0}".format(amoptd['results_path']) + os.linesep)
-
-    # Make executable
-    os.chmod(script_path, 0o777)
-    return script_path
+    script = Script(directory=work_dir, stem="submit_ensemble")
+    script.append("export CCP4_SCR=${TMPDIR}")
+    script.append("ccp4-python -m ample.ensembler -restart_pkl {0}".format(amoptd['results_path']))
+    script.write()
+
+    return script
 
 
 def create_ensembles(amoptd):

ample/main.py

@@ -8,6 +8,7 @@
 import shutil
 import sys
 import time
+import warnings
 
 from ample import ensembler
 from ample.ensembler.constants import UNMODIFIED
@@ -24,9 +25,10 @@
 from ample.util import process_models
 from ample.util import pyrvapi_results
 from ample.util import reference_manager
-from ample.util import workers_util
 from ample.util import version
 
+from pyjob.factory import TaskFactory
+
 __author__ = "Jens Thomas, Felix Simkovic, Adam Simpkin, Ronan Keegan, and Jaclyn Bibby"
 __credits__ = "Daniel Rigden, Martyn Winn, and Olga Mayans"
 __email__ = "drigden@liverpool.ac.uk"
@@ -79,6 +81,17 @@ def monitor():
         else:
             monitor = None
 
+        # Highlight deprecated command line arguments
+        if amopt.d['submit_cluster']:
+            message = "-%s has been deprecated and will be removed in version %s!" % ('submit_cluster', 1.6)
+            warnings.warn(message, DeprecationWarning)
+        if amopt.d["submit_pe_lsf"]:
+            message = "-%s has been deprecated and will be removed in version %s! Use -submit_pe instead" % ('submit_pe_lsf', 1.6)
+            warnings.warn(message, DeprecationWarning)
+        if amopt.d["submit_pe_sge"]:
+            message = "-%s has been deprecated and will be removed in version %s! Use -submit_pe instead" % ('submit_pe_sge', 1.6)
+            warnings.warn(message, DeprecationWarning)
+
         # Process any files we may have been given
         model_results = process_models.extract_and_validate_models(amopt.d)
         if model_results:
@@ -147,24 +160,25 @@ def monitor():
         return
 
     def benchmarking(self, optd):
-        if optd['submit_cluster']:
+        if optd['submit_qtype'] != 'local':
             # Pickle dictionary so it can be opened by the job to get the parameters
             ample_util.save_amoptd(optd)
             script = benchmark_util.cluster_script(optd)
-            workers_util.run_scripts(
-                job_scripts=[script],
-                monitor=monitor,
-                nproc=optd['nproc'],
-                job_time=43200,
-                job_name='benchmark',
-                submit_cluster=optd['submit_cluster'],
-                submit_qtype=optd['submit_qtype'],
-                submit_queue=optd['submit_queue'],
-                submit_pe_lsf=optd['submit_pe_lsf'],
-                submit_pe_sge=optd['submit_pe_sge'],
-                submit_array=optd['submit_array'],
-                submit_max_array=optd['submit_max_array'],
-            )
+            with TaskFactory(
+                    optd['submit_qtype'],
+                    script,
+                    cwd=optd['work_dir'],
+                    environment=optd['submit_pe'],
+                    run_time=43200,
+                    name='benchmark',
+                    nprocesses=optd['nproc'],
+                    max_array_size=optd['submit_max_array'],
+                    queue=optd['submit_queue'],
+                    shell="/bin/bash",
+            ) as task:
+                task.run()
+                task.wait(interval=5, monitor_f=monitor)
+
             # queue finished so unpickle results
             optd.update(ample_util.read_amoptd(optd['results_path']))
         else:
@@ -255,25 +269,25 @@ def ensembling(self, optd):
                 msg = "ERROR! Cannot find any pdb files in: {0}".format(optd['models_dir'])
                 exit_util.exit_error(msg)
             optd['ensemble_ok'] = os.path.join(optd['work_dir'], 'ensemble.ok')
-            if optd['submit_cluster']:
+            if optd['submit_qtype'] != 'local':
                 # Pickle dictionary so it can be opened by the job to get the parameters
                 ample_util.save_amoptd(optd)
                 script = ensembler.cluster_script(optd)
                 ensembler_timeout = ensembler.get_ensembler_timeout(optd)
-                workers_util.run_scripts(
-                    job_scripts=[script],
-                    monitor=monitor,
-                    nproc=optd['nproc'],
-                    job_time=ensembler_timeout,
-                    job_name='ensemble',
-                    submit_cluster=optd['submit_cluster'],
-                    submit_qtype=optd['submit_qtype'],
-                    submit_queue=optd['submit_queue'],
-                    submit_pe_lsf=optd['submit_pe_lsf'],
-                    submit_pe_sge=optd['submit_pe_sge'],
-                    submit_array=optd['submit_array'],
-                    submit_max_array=optd['submit_max_array'],
-                )
+                with TaskFactory(
+                        optd['submit_qtype'],
+                        script,
+                        cwd=optd['work_dir'],
+                        environment=optd['submit_pe'],
+                        run_time=ensembler_timeout,
+                        name='benchmark',
+                        nprocesses=optd['nproc'],
+                        max_array_size=optd['submit_max_array'],
+                        queue=optd['submit_queue'],
+                        shell="/bin/bash",
+                ) as task:
+                    task.run()
+                    task.wait(interval=5, monitor_f=monitor)
                 # queue finished so unpickle results
                 optd.update(ample_util.read_amoptd(optd['results_path']))
             else:
@@ -402,24 +416,27 @@ def monitor():
 
         # Change to mrbump directory before running
         os.chdir(optd['mrbump_dir'])
-        ok = workers_util.run_scripts(
-            job_scripts=optd['mrbump_scripts'],
-            monitor=monitor,
-            check_success=mrbump_util.checkSuccess,
-            early_terminate=optd['early_terminate'],
-            nproc=optd['nproc'],
-            job_time=mrbump_util.MRBUMP_RUNTIME,
-            job_name='mrbump',
-            submit_cluster=optd['submit_cluster'],
-            submit_qtype=optd['submit_qtype'],
-            submit_queue=optd['submit_queue'],
-            submit_pe_lsf=optd['submit_pe_lsf'],
-            submit_pe_sge=optd['submit_pe_sge'],
-            submit_array=optd['submit_array'],
-            submit_max_array=optd['submit_max_array'],
-        )
-
-        if not ok:
+
+        with TaskFactory(
+                optd['submit_qtype'],
+                optd['mrbump_scripts'],
+                cwd=bump_dir,
+                environment=optd['submit_pe'],
+                run_time=mrbump_util.MRBUMP_RUNTIME,
+                name="mrbump",
+                nprocesses=optd['nproc'],
+                max_array_size=optd['submit_max_array'],
+                queue=optd['submit_queue'],
+                shell="/bin/bash",
+        ) as task:
+            task.run()
+
+            if optd['early_terminate']:
+                task.wait(interval=5, monitor_f=monitor, success_f=mrbump_util.checkSuccess)
+            else:
+                task.wait(interval=5, monitor_f=monitor)
+
+        if not task.completed:
             msg = (
                     "An error code was returned after running MRBUMP on the ensembles!\n"
                     + "For further information check the logs in directory: {0}".format(optd['mrbump_dir'])

ample/modelling/__main__.py

@@ -19,7 +19,7 @@ def process_args(args):
     if args.rosetta_flagsfile:
         args.rosetta_flagsfile = os.path.abspath(args.rosetta_flagsfile)
     if args.nproc is None:
-        if args.submit_cluster:
+        if args.submit_qtype != 'local':
             args.nproc = 1
         else:
             args.nproc = multiprocessing.cpu_count()
@@ -37,7 +37,7 @@ def process_args(args):
 argparse_util.add_cluster_submit_options(parser)
 
 work_dir = os.path.abspath('rosetta_modelling')
-parser.set_defaults(submit_cluster=False, submit_qtype='SGE', submit_array=True, nmodels=1000, work_dir=work_dir)
+parser.set_defaults(submit_qtype='SGE', submit_array=True, nmodels=1000, work_dir=work_dir)
 args = parser.parse_args()
 process_args(args)
 
@@ -66,7 +66,7 @@ def process_args(args):
 rm.nchains = args.nchains
 
 rm.nproc = args.nproc
-rm.submit_cluster = args.submit_cluster
+rm.submit_cluster = args.submit_qtype != "local"
 rm.submit_qtype = args.submit_qtype
 rm.submit_queue = args.submit_queue
 rm.submit_array = args.submit_array