def test_local(self):
@ray.remote
class TestRay:
def hostname(self):
import socket
return socket.gethostname()
sc = init_spark_on_local(cores=4)
ray_ctx = RayContext(sc=sc, object_store_memory="1g")
address_info = ray_ctx.init()
assert "object_store_address" in address_info
actors = [TestRay.remote() for i in range(0, 4)]
print(ray.get([actor.hostname.remote() for actor in actors]))
ray_ctx.stop()
sc.stop()
(options, args) = parser.parse_args(sys.argv)
# Prepare csv files
df = pd.read_csv(options.file_path)
sc = init_spark_on_local(cores="*")
sqlContext = SQLContext(sc)
num_nodes, num_cores = get_node_and_core_number()
df_spark = sqlContext.createDataFrame(df)
df_spark.printSchema()
df_spark.repartition(num_cores).write.\
format('json').mode("overwrite").save("/tmp/ray-pandas-example")
# init ray context
ray_ctx = RayContext(sc=sc, object_store_memory="5g")
ray_ctx.init(object_store_memory="5g")
# read data
data_shard = zoo.xshard.pandas.read_json("/tmp/ray-pandas-example",
ray_ctx)
# collect data
data = data_shard.collect()
print("collected data :")
print(data[0].head())
# repartition
partitions = data_shard.get_partitions()
print("get %d partitions" % len(partitions))
data_shard.repartition(2)
new_partitions = data_shard.get_partitions()
parser.add_argument('-n',
'--num_workers',
type=int,
default=2,
help='The number of workers to be launched.')
opt = parser.parse_args()
if opt.hadoop_conf:
assert opt.conda_name is not None, "conda_name must be specified for yarn mode"
sc = init_spark_on_yarn(hadoop_conf=opt.hadoop_conf,
conda_name=opt.conda_name,
num_executors=opt.num_workers,
executor_cores=opt.executor_cores)
else:
sc = init_spark_on_local(cores="*")
ray_ctx = RayContext(sc=sc)
ray_ctx.init()
import pandas as pd
df = pd.read_csv(opt.path)
feature_cols = [
"FIPS", "Lower 95% Confidence Interval",
"Upper 95% Confidence Interval", "Average Annual Count",
"Recent 5-Year Trend"
]
target_col = "Age-Adjusted Incidence Rate"
train_df, val_df = train_test_split(df, test_size=0.2, random_state=2)
config = {'random_state': 2, 'min_child_weight': 3, 'n_jobs': 2}
estimator = AutoXGBoost().regressor(feature_cols=feature_cols,
target_col=target_col,
config=config)
def init_orca_context(cluster_mode="local",
cores=2,
memory="2g",
num_nodes=1,
init_ray_on_spark=False,
**kwargs):
"""
Creates or gets a SparkContext for different Spark cluster modes (and launch Ray services
across the cluster if necessary).
:param cluster_mode: The mode for the Spark cluster. One of "local", "yarn-client",
"standalone" and "spark-submit". Default to be "local".
For "spark-submit", you are supposed to use spark-submit to submit the application.
In this case, please set the Spark configurations through command line options or
the properties file. You need to use "spark-submit" for yarn-cluster mode.
To make things easier, you are recommended to use the launching scripts under
`analytics-zoo/scripts`.
For other cluster modes, you are recommended to install and run analytics-zoo through
pip, which is more convenient.
:param cores: The number of cores to be used on each node. Default to be 2.
:param memory: The memory allocated for each node. Default to be '2g'.
:param num_nodes: The number of nodes to be used in the cluster. Default to be 1.
For Spark local, num_nodes should always be 1 and you don't need to change it.
:param init_ray_on_spark: Whether to launch Ray services across the cluster.
Default to be False and in this case the Ray cluster would be launched lazily when
Ray is involved in Project Orca.
:param kwargs: The extra keyword arguments used for creating SparkContext and
launching Ray if any.
:return: An instance of SparkContext.
"""
cluster_mode = cluster_mode.lower()
spark_args = {}
for key in ["conf", "spark_log_level", "redirect_spark_log"]:
if key in kwargs:
spark_args[key] = kwargs[key]
if cluster_mode == "spark-submit":
from zoo import init_nncontext
sc = init_nncontext(**spark_args)
elif cluster_mode == "local":
assert num_nodes == 1, "For Spark local mode, num_nodes should be 1"
os.environ["SPARK_DRIVER_MEMORY"] = memory
if "python_location" in kwargs:
spark_args["python_location"] = kwargs["python_location"]
from zoo import init_spark_on_local
sc = init_spark_on_local(cores, **spark_args)
elif cluster_mode.startswith("yarn"): # yarn or yarn-client
if cluster_mode == "yarn-cluster":
raise ValueError(
'For yarn-cluster mode, please set cluster_mode to "spark-submit" '
'and submit the application via spark-submit instead')
hadoop_conf = os.environ.get("HADOOP_CONF_DIR")
if not hadoop_conf:
assert "hadoop_conf" in kwargs,\
"Directory path to hadoop conf not found for yarn-client mode. Please either " \
"specify argument hadoop_conf or set the environment variable HADOOP_CONF_DIR"
hadoop_conf = kwargs["hadoop_conf"]
from zoo.util.utils import detect_python_location
python_location = detect_python_location(
) # /path/to/conda/envs/conda_name/bin/python
assert "envs" in python_location, "You must use a conda environment for yarn-client mode"
for key in [
"driver_cores", "driver_memory",
"extra_executor_memory_for_ray", "extra_python_lib",
"penv_archive", "additional_archive", "hadoop_user_name",
"spark_yarn_archive", "jars"
]:
if key in kwargs:
spark_args[key] = kwargs[key]
from zoo import init_spark_on_yarn
sc = init_spark_on_yarn(hadoop_conf=hadoop_conf,
conda_name=python_location.split("/")[-3],
num_executors=num_nodes,
executor_cores=cores,
executor_memory=memory,
**spark_args)
elif cluster_mode == "standalone":
for key in [
"driver_cores", "driver_memory",
"extra_executor_memory_for_ray", "extra_python_lib", "jars",
"master", "enable_numa_binding"
]:
if key in kwargs:
spark_args[key] = kwargs[key]
from zoo import init_spark_standalone
sc = init_spark_standalone(num_executors=num_nodes,
executor_cores=cores,
executor_memory=memory,
**spark_args)
else:
raise ValueError(
"cluster_mode can only be local, yarn-client, standalone or spark-submit, "
"but got: %s".format(cluster_mode))
ray_args = {}
for key in [
"redis_port", "password", "object_store_memory", "verbose", "env",
"extra_params", "num_ray_nodes", "ray_node_cpu_cores"
]:
if key in kwargs:
ray_args[key] = kwargs[key]
from zoo.ray import RayContext
ray_ctx = RayContext(sc, **ray_args)
if init_ray_on_spark:
driver_cores = 0 # This is the default value.
if "driver_cores" in kwargs:
driver_cores = kwargs["driver_cores"]
ray_ctx.init(driver_cores=driver_cores)
return sc
num_executor=slave_num,
executor_cores=28,
executor_memory="10g",
driver_memory="2g",
driver_cores=4,
extra_executor_memory_for_ray="30g",
spark_conf={"hello": "world"})
ray_ctx = RayContext(sc=sc,
object_store_memory="25g",
extra_params={"temp-dir": "/tmp/hello/"},
env={
"http_proxy": "http://child-prc.intel.com:913",
"http_proxys": "http://child-prc.intel.com:913"
})
ray_ctx.init(object_store_memory="2g", num_cores=0, labels="", extra_params={})
@ray.remote
class TestRay():
def hostname(self):
import socket
return socket.gethostname()
def check_cv2(self):
# conda install -c conda-forge opencv==3.4.2
import cv2
return cv2.__version__
def ip(self):
import ray.services as rservices
