def test_call_async(self):
pw = pywren.ibm_cf_executor()
pw.call_async(self.hello_world, "")
result = pw.get_result()
self.assertEqual(result, "Hello World!")
pw = pywren.ibm_cf_executor()
pw.call_async(self.simple_map_function, [4, 6])
result = pw.get_result()
self.assertEqual(result, 10)
pw = pywren.ibm_cf_executor()
pw.call_async(self.simple_map_function, {'x': 2, 'y': 8})
result = pw.get_result()
self.assertEqual(result, 10)
def pywren_test():
runtime = "spitfiredd/pywren-flaskapi-runtime:3.7"
iterdata = [1, 2, 3, 4]
PYWREN_CONFIG = {
'pywren': {
'storage_bucket': os.environ.get('PYWREN_STORAGE_BUCKET')
},
'ibm_cf': {
'endpoint': os.environ.get('PYWREN_IBM_CF_ENDPOINT'),
'namespace': os.environ.get('PYWREN_IBM_CF_NAMESPACE'),
'api_key': os.environ.get('PYWREN_IBM_CF_API_KEY')
},
'ibm_cos': {
'endpoint': os.environ.get('PYWREN_IBM_COS_ENDPOINT'),
'api_key': os.environ.get('PYWREN_IBM_COS_API_KEY')
}
}
def my_map_function(x):
return x + 7
def my_reduce_function(results):
total = 0
for map_result in results:
total = total + map_result
return total
pw = pywren.ibm_cf_executor(config=PYWREN_CONFIG, runtime=runtime)
pw.map_reduce(my_map_function, iterdata, my_reduce_function)
result = pw.get_result()
return {'result': result}
def test_map_reduce(self):
iterdata = [[1, 1], [2, 2], [3, 3], [4, 4]]
pw = pywren.ibm_cf_executor()
pw.map_reduce(self.simple_map_function, iterdata,
self.simple_reduce_function)
result = pw.get_result()
self.assertEqual(result, 20)
def partitioner(map_func_args, chunk_size, storage_handler):
logger.info('Starting partitioner() function')
map_func_keys = map_func_args[0].keys()
if 'bucket' in map_func_keys and not 'key' in map_func_keys:
partitions = split_objects_from_bucket(map_func_args,
chunk_size,
storage_handler)
elif 'key' in map_func_keys:
partitions = split_object_from_key(map_func_args, chunk_size,
storage_handler)
elif 'url' in map_func_keys:
partitions = split_object_from_url(map_func_args, chunk_size)
else:
raise ValueError(
'You did not provide any bucket or object key/url')
# logger.info(partitions)
pw = pywren.ibm_cf_executor()
reduce_future = pw.map_reduce(map_func,
partitions,
reduce_function,
reducer_wait_local=False,
throw_except=throw_except,
extra_meta=extra_meta)
return reduce_future
def test_map_reduce_cos_key(self):
bucket_name = STORAGE_CONFIG['storage_bucket']
iterdata = [bucket_name + '/' + key for key in list_test_keys()]
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_key, iterdata, my_reduce_function)
result = pw.get_result()
self.checkResult(result)
def main():
path = None
if len(sys.argv) >= 2:
path = sys.argv[1]
backend = cos.Backend(pywren_config['ibm_cos'],
pywren_config['pywren']['storage_bucket'])
pw = pywren.ibm_cf_executor(config=pywren_config)
if path == None:
execute(pw, backend, N_SLAVES, MASTER_DELAY, SLAVES_DELAY)
return
num_slaves = N_SLAVES
num_samples = N_SAMPLES
if len(sys.argv) >= 3:
num_slaves = int(sys.argv[2])
if len(sys.argv) >= 4:
num_samples = int(sys.argv[3])
with open(path, "w") as f:
f.write("slaves,time\n")
for slaves in range(1, num_slaves + 1, int(num_slaves / num_samples)):
correct, execution_time = execute(pw, backend, slaves,
MASTER_DELAY, SLAVES_DELAY)
print(slaves, getRequests(backend))
if not correct:
print("ERROR: Incorrect execution sequence", file=sys.stderr)
exit(1)
f.write("{},{}\n".format(slaves, execution_time))
def test_cloudobject(self):
print('Testing cloudobjects...')
data_prefix = STORAGE_CONFIG['bucket'] + '/' + PREFIX + '/'
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_cloudobject_put, data_prefix, my_cloudobject_get)
result = pw.get_result()
self.checkResult(result)
def test_map_reduce_cos_bucket_one_reducer_per_object(self):
print('Testing map_reduce() over a COS bucket with one reducer per object...')
data_prefix = STORAGE_CONFIG['bucket'] + '/' + PREFIX + '/'
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_obj, data_prefix, my_reduce_function, reducer_one_per_object=True)
result = pw.get_result()
self.checkResult(result)
def test_map_reduce(self):
print('Testing map_reduce()...')
iterdata = [[1, 1], [2, 2], [3, 3], [4, 4]]
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(simple_map_function, iterdata, simple_reduce_function)
result = pw.get_result()
self.assertEqual(result, 20)
def test_internal_executions(self):
print('Testing internal executions...')
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map(pywren_inside_pywren_map_function1, range(1, 11))
result = pw.get_result()
self.assertEqual(result, [0] + [list(range(i)) for i in range(2, 11)])
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.call_async(pywren_inside_pywren_map_function2, 10)
result = pw.get_result()
self.assertEqual(result, 10)
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map(pywren_inside_pywren_map_function3, range(1, 11))
result = pw.get_result()
self.assertEqual(result, [[0, 0]] + [[list(range(i)), list(range(i))] for i in range(2, 11)])
def test_map_reduce_url(self):
chunk_size = 4 * 1024**2 # 4MB
pw = pywren.ibm_cf_executor()
pw.map_reduce(self.my_map_function_url, TEST_FILES_URLS,
self.my_reduce_function, chunk_size)
result = pw.get_result()
self.checkResult(initCos(), result + 1)
def test_call_async(self):
print('Testing call_async()...')
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.call_async(hello_world, "")
result = pw.get_result()
self.assertEqual(result, "Hello World!")
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.call_async(simple_map_function, [4, 6])
result = pw.get_result()
self.assertEqual(result, 10)
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.call_async(simple_map_function, {'x': 2, 'y': 8})
result = pw.get_result()
self.assertEqual(result, 10)
def test_storage_handler(self):
print('Testing ibm_cos function arg...')
iterdata = [[key, STORAGE_CONFIG['bucket']] for key in list_test_keys()]
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_ibm_cos, iterdata, my_reduce_function)
result = pw.get_result()
self.checkResult(result)
def pywren_inside_pywren_map_function2(x):
def _func(x):
return x
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.call_async(_func, x)
return pw.get_result()
def pywren_inside_pywren_map_function1(x):
def _func(x):
return x
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map(_func, range(x))
return pw.get_result()
def test_chunks_bucket(self):
print('Testing cunk_size on a bucket...')
data_prefix = STORAGE_CONFIG['bucket'] + '/' + PREFIX + '/'
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_obj, data_prefix, my_reduce_function, chunk_size=1*1024**2)
result = pw.get_result()
self.checkResult(result)
def object_partitioner_function(map_func_args, chunk_size, storage):
"""
Partitioner is a function executed in the Cloud to create partitions from objects
"""
logger.info('Starting partitioner() function')
map_func_keys = map_func_args[0].keys()
if 'bucket' in map_func_keys and 'key' not in map_func_keys:
partitions = split_objects_from_bucket(map_func_args, chunk_size, storage)
if not partitions:
raise Exception('No objects available within bucket: {}'.format(map_func_args[0]['bucket']))
elif 'key' in map_func_keys:
partitions = split_object_from_key(map_func_args, chunk_size, storage)
elif 'url' in map_func_keys:
partitions = split_object_from_url(map_func_args, chunk_size)
else:
raise ValueError('You did not provide any bucket or object key/url')
#logger.info(partitions)
pw = pywren.ibm_cf_executor()
futures = pw.map_reduce(map_function_wrapper, partitions,
reduce_function,
reducer_wait_local=False,
extra_env=extra_env,
extra_meta=extra_meta)
return futures
def remote_invoker(input_data):
pw = pywren.ibm_cf_executor()
return pw.map(map_function,
input_data,
runtime_memory=runtime_memory,
invoke_pool_threads=invoke_pool_threads,
extra_env=extra_env)
def test_map_reduce_cos_bucket(self):
data_prefix = CONFIG['pywren']['storage_bucket'] + '/' + PREFIX
chunk_size = 4 * 1024**2 # 4MB
pw = pywren.ibm_cf_executor()
pw.map_reduce(self.my_map_function_bucket, data_prefix,
self.my_reduce_function, chunk_size)
result = pw.get_result()
self.checkResult(initCos(), result)
def remote_invoker(input_data):
pw = pywren.ibm_cf_executor(runtime=runtime_name,
runtime_memory=runtime_memory,
rabbitmq_monitor=rabbitmq_monitor)
return pw.map(map_function, input_data,
invoke_pool_threads=invoke_pool_threads,
extra_env=extra_env,
extra_meta=extra_meta)
def pywren_return_futures_map_function3(x):
def _func(x):
return x + 1
pw = pywren.ibm_cf_executor()
fut1 = pw.map(_func, range(x))
fut2 = pw.map(_func, range(x))
return fut1 + fut2
def test_map_reduce_cos_key_one_reducer_per_object(self):
print('Testing map_reduce() over COS keys with one reducer per object...')
bucket_name = STORAGE_CONFIG['bucket']
iterdata = [bucket_name + '/' + key for key in list_test_keys()]
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_obj, iterdata, my_reduce_function, reducer_one_per_object=True)
result = pw.get_result()
self.checkResult(result)
def pywren_inside_pywren_map_function3(x):
def _func(x):
return x
pw = pywren.ibm_cf_executor(config=CONFIG)
fut1 = pw.map(_func, range(x))
fut2 = pw.map(_func, range(x))
return [pw.get_result(fut1), pw.get_result(fut2)]
def test_map_reduce_cos_bucket_one_reducer_per_object(self):
data_prefix = STORAGE_CONFIG['storage_bucket'] + '/' + PREFIX
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_bucket,
data_prefix,
my_reduce_function,
reducer_one_per_object=True)
result = pw.get_result()
self.checkResult(result)
def test_chunks_bucket(self):
data_prefix = STORAGE_CONFIG['storage_bucket'] + '/' + PREFIX
pw = pywren.ibm_cf_executor(config=CONFIG)
pw.map_reduce(my_map_function_bucket,
data_prefix,
my_reduce_function,
chunk_size=1 * 1024**2)
result = pw.get_result()
self.checkResult(result)
def main():
try:
nmaps = int(sys.argv[1])
except:
print('Usage:\n\tpython3 main.py NUM_MAPS\n')
exit(2)
if nmaps < 1:
print('ERROR: the number of maps must be higher than 1\n')
exit(3)
pw = pywren.ibm_cf_executor(rabbitmq_monitor=True)
params = pika.URLParameters(pw.config['rabbitmq']['amqp_url'])
connection = pika.BlockingConnection(params)
channel = connection.channel()
channel.exchange_declare(exchange='fan_logs', exchange_type='fanout')
pw.call_async(master, nmaps)
slave_list = []
for i in range(nmaps):
slave_list.append([nmaps, i])
pw_s = pywren.ibm_cf_executor(rabbitmq_monitor=True)
pw_s.map(slave, slave_list)
results = pw_s.get_result()
i = 0
equal = len(set(map(tuple, results))) == 1
for res_list in results:
print(f'List {i}: {res_list}')
i += 1
if equal:
print('\nIT WORKS!')
else:
print('\nThe lists don\'t match...')
connection.close()
def post(self):
data = api.payload
iterdata = data['iterdata']
pw = pywren.ibm_cf_executor(config=c_app.config.get('PYWREN_CONFIG'),
runtime=c_app.config.get('PYWREN_RUNTIME'))
pw.map_reduce(my_map_function, iterdata, my_reduce_function)
result = pw.get_result()
return jsonify({'result': result})
def main():
args = []
if len(sys.argv) != 2:
print("ERROR: Insuficientes agrumentos")
exit(1)
try:
N = int(sys.argv[1])
except:
print("ERROR: El parámetro ha de ser un entero")
exit(2)
pw = pywren.ibm_cf_executor(rabbitmq_monitor=True)
params = pika.URLParameters(pw.config['rabbitmq']['amqp_url'])
connection = pika.BlockingConnection(params)
channel = connection.channel() # start a channel
channel.exchange_declare(exchange='publish_subscribe',
exchange_type='fanout')
pw.call_async(my_function_leader, N)
pw = pywren.ibm_cf_executor(rabbitmq_monitor=True)
d = {'N': N}
for i in range(N):
d['id'] = i
args.append(d.copy())
pw.map(my_function_slave, args)
results = pw.get_result()
print(results)
channel.exchange_delete(exchange='publish_subscribe', if_unused=False)
connection.close()
exit(0)
def test_map_reduce_cos_key(self):
cos = initCos()
bucket_name = CONFIG['pywren']['storage_bucket']
iterdata = [
bucket_name + '/' + key
for key in getFilenamesFromCOS(cos, bucket_name, PREFIX)
]
chunk_size = 4 * 1024**2 # 4MB
pw = pywren.ibm_cf_executor()
pw.map_reduce(self.my_map_function_key, iterdata,
self.my_reduce_function, chunk_size)
result = pw.get_result()
self.checkResult(cos, result)
def calculate_centroids(config, input_db, polarity='+', isocalc_sigma=0.001238):
bucket = config["storage"]["db_bucket"]
formulas_chunks_prefix = input_db["formulas_chunks"]
centroids_chunks_prefix = input_db["centroids_chunks"]
clean_from_cos(config, bucket, centroids_chunks_prefix)
def calculate_peaks_for_formula(formula_i, formula):
mzs, ints = isocalc_wrapper.centroids(formula)
if mzs is not None:
return list(zip(repeat(formula_i), range(len(mzs)), mzs, ints))
else:
return []
def calculate_peaks_chunk(obj, id, storage):
print(f'Calculating peaks from formulas chunk {obj.key}')
chunk_df = pd.read_msgpack(obj.data_stream._raw_stream)
peaks = [peak for formula_i, formula in chunk_df.formula.items()
for peak in calculate_peaks_for_formula(formula_i, formula)]
peaks_df = pd.DataFrame(peaks, columns=['formula_i', 'peak_i', 'mz', 'int'])
peaks_df.set_index('formula_i', inplace=True)
print(f'Storing centroids chunk {id}')
centroids_chunk_key = f'{centroids_chunks_prefix}/{id}.msgpack'
storage.put_object(Bucket=bucket, Key=centroids_chunk_key, Body=peaks_df.to_msgpack())
return peaks_df.shape[0]
from annotation_pipeline.isocalc_wrapper import IsocalcWrapper # Import lazily so that the rest of the pipeline still works if the dependency is missing
isocalc_wrapper = IsocalcWrapper({
# These instrument settings are usually customized on a per-dataset basis out of a set of
# 18 possible combinations, but most of EMBL's datasets are compatible with the following settings:
'charge': {
'polarity': polarity,
'n_charges': 1,
},
'isocalc_sigma': float(f"{isocalc_sigma:f}") # Rounding to match production implementation
})
pw = pywren.ibm_cf_executor(config=config)
memory_capacity_mb = 2048
futures = pw.map(calculate_peaks_chunk, f'cos://{bucket}/{formulas_chunks_prefix}/', runtime_memory=memory_capacity_mb)
centroids_chunks_n = pw.get_result(futures)
append_pywren_stats(futures, memory_mb=memory_capacity_mb, cloud_objects_n=len(futures))
num_centroids = sum(centroids_chunks_n)
n_centroids_chunks = len(centroids_chunks_n)
logger.info(f'Calculated {num_centroids} centroids in {n_centroids_chunks} chunks')
return num_centroids, n_centroids_chunks
