class MyDaskClient():
def __init__(self, address=None):
self._client = Client(address)
def _who_has(self, key):
who_has_dict = self._client.who_has()
if key in who_has_dict:
return {"key": key, "worker": who_has_dict[key]}
def get_status(self, key):
# first we check if a worker has it
processing_dict = self._client.processing()
for worker in processing_dict.keys():
if key in processing_dict[worker]:
return {"status": "running", "worker": worker}
# then we check if the task is in the stream
for task in reversed(self._client.get_task_stream()):
if task["key"] == key:
return {"status": "done", "dask_status": task["status"]}
def main():
#print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
parser = argparse.ArgumentParser("rapidssample")
parser.add_argument("--data_dir", type=str, help="location of data")
parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
parser.add_argument("--part_count", type=int, help="Number of data files to train against", default=2)
parser.add_argument("--end_year", type=int, help="Year to end the data load", default=2000)
parser.add_argument("--cpu_predictor", type=str, help="Flag to use CPU for prediction", default='False')
parser.add_argument('-f', type=str, default='') # added for notebook execution scenarios
args = parser.parse_args()
data_dir = args.data_dir
num_gpu = args.num_gpu
part_count = args.part_count
end_year = args.end_year
cpu_predictor = args.cpu_predictor.lower() in ('yes', 'true', 't', 'y', '1')
if cpu_predictor:
print('Training with CPUs require num gpu = 1')
num_gpu = 1
print('data_dir = {0}'.format(data_dir))
print('num_gpu = {0}'.format(num_gpu))
print('part_count = {0}'.format(part_count))
#part_count = part_count + 1 # adding one because the usage below is not inclusive
print('end_year = {0}'.format(end_year))
print('cpu_predictor = {0}'.format(cpu_predictor))
import subprocess
cmd = "hostname --all-ip-addresses"
process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
output, error = process.communicate()
IPADDR = str(output.decode()).split()[0]
cluster = LocalCUDACluster(ip=IPADDR,n_workers=num_gpu)
client = Client(cluster)
client
print(client.ncores())
# to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
start_year = 2000
#end_year = 2000 # end_year is inclusive -- converted to parameter
#part_count = 2 # the number of data files to train against -- converted to parameter
client.run(initialize_rmm_pool)
client
print(client.ncores())
# NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
# This can be optimized to avoid calculating the dropped features.
print("Reading ...")
t1 = datetime.datetime.now()
gpu_dfs = []
gpu_time = 0
quarter = 1
year = start_year
count = 0
while year <= end_year:
for file in glob(os.path.join(perf_data_path + "/Performance_" + str(year) + "Q" + str(quarter) + "*")):
if count < part_count:
gpu_dfs.append(process_quarter_gpu(client, col_names_path, acq_data_path, year=year, quarter=quarter, perf_file=file))
count += 1
print('file: {0}'.format(file))
print('count: {0}'.format(count))
quarter += 1
if quarter == 5:
year += 1
quarter = 1
wait(gpu_dfs)
t2 = datetime.datetime.now()
print("Reading time ...")
print(t2-t1)
print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
client.run(cudf._gdf.rmm_finalize)
client.run(initialize_rmm_no_pool)
client
print(client.ncores())
dxgb_gpu_params = {
'nround': 100,
'max_depth': 8,
'max_leaves': 2**8,
'alpha': 0.9,
'eta': 0.1,
'gamma': 0.1,
'learning_rate': 0.1,
'subsample': 1,
'reg_lambda': 1,
'scale_pos_weight': 2,
'min_child_weight': 30,
'tree_method': 'gpu_hist',
'n_gpus': 1,
'distributed_dask': True,
'loss': 'ls',
'objective': 'gpu:reg:linear',
'max_features': 'auto',
'criterion': 'friedman_mse',
'grow_policy': 'lossguide',
'verbose': True
}
if cpu_predictor:
print('Training using CPUs')
dxgb_gpu_params['predictor'] = 'cpu_predictor'
dxgb_gpu_params['tree_method'] = 'hist'
dxgb_gpu_params['objective'] = 'reg:linear'
else:
print('Training using GPUs')
print('Training parameters are {0}'.format(dxgb_gpu_params))
gpu_dfs = [delayed(DataFrame.from_arrow)(gpu_df) for gpu_df in gpu_dfs[:part_count]]
gpu_dfs = [gpu_df for gpu_df in gpu_dfs]
wait(gpu_dfs)
tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0]) for gpu_df in gpu_dfs]
new_map = {}
for key, value in tmp_map:
if value not in new_map:
new_map[value] = [key]
else:
new_map[value].append(key)
del(tmp_map)
gpu_dfs = []
for list_delayed in new_map.values():
gpu_dfs.append(delayed(cudf.concat)(list_delayed))
del(new_map)
gpu_dfs = [(gpu_df[['delinquency_12']], gpu_df[delayed(list)(gpu_df.columns.difference(['delinquency_12']))]) for gpu_df in gpu_dfs]
gpu_dfs = [(gpu_df[0].persist(), gpu_df[1].persist()) for gpu_df in gpu_dfs]
gpu_dfs = [dask.delayed(xgb.DMatrix)(gpu_df[1], gpu_df[0]) for gpu_df in gpu_dfs]
gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
gc.collect()
wait(gpu_dfs)
labels = None
t1 = datetime.datetime.now()
bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
t2 = datetime.datetime.now()
print("Training time ...")
print(t2-t1)
print('str(bst) is {0}'.format(str(bst)))
print('Exiting script')
}
# #### Load the data from host memory, and convert to CSR
# In[ ]:
# %%time
gpu_dfs = [
delayed(DataFrame.from_arrow)(gpu_df)
for gpu_df in gpu_dfs[:part_count]
]
gpu_dfs = [gpu_df for gpu_df in gpu_dfs]
wait(gpu_dfs)
tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0])
for gpu_df in gpu_dfs]
new_map = {}
for key, value in tmp_map:
if value not in new_map:
new_map[value] = [key]
else:
new_map[value].append(key)
del (tmp_map)
gpu_dfs = []
for list_delayed in new_map.values():
gpu_dfs.append(delayed(cudf.concat)(list_delayed))
del (new_map)
gpu_dfs = [
c.gather(c.map(lambda x: get_worker().array_cache.clear(),workers,workers=workers))
# start
c.get_task_stream()
# print(get_mll_hist(chunks[0]))
t0 = time.time()
futures = c.map(get_mll_hist,chunks)
results = c.gather(futures)
t1 = time.time()
print(len(results),"results")
print(t1-t0)
task_stream = c.get_task_stream(start=t0,stop=t1)
print("task_stream length",len(task_stream))
pd.DataFrame(task_stream).drop("type",axis=1).to_json("data/dask_cold_{}.json".format(trial))
d = c.who_has(futures)
# chunk_workers = list(zip(chunks,[d[f.key] for f in futures]))
workers = [d[f.key][0] for f in futures]
print(workers)
c.get_task_stream()
t0 = time.time()
# pure=False to avoid caching of the *results*
futures = [c.submit(get_mll_hist,chunk,pure=False,workers=worker,allow_other_workers=True) for chunk,worker in zip(chunks,workers)]
# futures = c.map(get_mll_hist,chunks,workers=workers,pure=False)
results = c.gather(futures)
t1 = time.time()
print(len(results),"results")
print(t1-t0)
task_stream = c.get_task_stream(start=t0,stop=t1)
print("task_stream length",len(task_stream))
