diff --git a/PySparkContainerDockerfile b/PySparkContainerDockerfile new file mode 100644 index 000000000..1e0e4d163 --- /dev/null +++ b/PySparkContainerDockerfile @@ -0,0 +1,22 @@ +FROM clipper/py-rpc:latest + +MAINTAINER Dan Crankshaw + +COPY containers/python/python_container_conda_deps.txt /lib/ + +RUN curl -o /spark.tgz https://d3kbcqa49mib13.cloudfront.net/spark-2.1.1-bin-hadoop2.7.tgz \ + && cd / && tar zxf /spark.tgz && mv /spark-2.1.1-bin-hadoop2.7 /spark \ + && echo deb http://ftp.de.debian.org/debian jessie-backports main >> /etc/apt/sources.list \ + && apt-get update --fix-missing \ + && apt-get install -yqq -t jessie-backports openjdk-8-jdk \ + && conda install -y --file /lib/python_container_conda_deps.txt \ + && pip install findspark + +COPY containers/python/pyspark_container.py containers/python/pyspark_container_entry.sh /container/ +COPY clipper_admin/ /lib/clipper_admin/ + +ENV SPARK_HOME="/spark" + +CMD ["/container/pyspark_container_entry.sh"] + +# vim: set filetype=dockerfile: diff --git a/PythonContainerDockerfile b/PythonContainerDockerfile index 705584d32..c99f7b071 100644 --- a/PythonContainerDockerfile +++ b/PythonContainerDockerfile @@ -2,11 +2,12 @@ FROM clipper/py-rpc:latest MAINTAINER Dan Crankshaw -COPY containers/python/python_container.py containers/python/python_container_entry.sh /container/ COPY containers/python/python_container_conda_deps.txt /lib/ +RUN conda install -y --file /lib/python_container_conda_deps.txt + +COPY containers/python/python_container.py containers/python/python_container_entry.sh /container/ COPY clipper_admin/ /lib/clipper_admin/ -RUN conda install -y --file /lib/python_container_conda_deps.txt CMD ["/container/python_container_entry.sh"] diff --git a/bin/build_docker_images.sh b/bin/build_docker_images.sh index 1c5e812f2..cd4ba0093 100755 --- a/bin/build_docker_images.sh +++ b/bin/build_docker_images.sh @@ -30,6 +30,7 @@ cd $DIR/.. docker build -t clipper/py-rpc -f ./RPCDockerfile ./ time docker build -t clipper/noop-container -f ./NoopDockerfile ./ time docker build -t clipper/python-container -f ./PythonContainerDockerfile ./ +time docker build -t clipper/pyspark-container -f ./PySparkContainerDockerfile ./ time docker build -t clipper/sklearn_cifar_container -f ./SklearnCifarDockerfile ./ time docker build -t clipper/tf_cifar_container -f ./TensorFlowCifarDockerfile ./ cd - diff --git a/bin/run_unittests.sh b/bin/run_unittests.sh index 215cfa4e6..f4c0e9877 100755 --- a/bin/run_unittests.sh +++ b/bin/run_unittests.sh @@ -120,7 +120,18 @@ function run_frontend_tests { function run_integration_tests { echo -e "\nRunning integration tests\n\n" cd $DIR + if [ -z ${SPARK_HOME+x} ]; then + echo "Downloading Spark" + curl -o spark.tgz https://d3kbcqa49mib13.cloudfront.net/spark-2.1.1-bin-hadoop2.7.tgz + tar zxf spark.tgz && mv spark-2.1.1-bin-hadoop2.7 spark + export SPARK_HOME=`pwd`/spark + else + echo "Found Spark at $SPARK_HOME" + fi + pip install findspark python ../integration-tests/clipper_manager_tests.py + python ../integration-tests/deploy_pyspark_models.py + python ../integration-tests/deploy_pyspark_pipeline_models.py python ../integration-tests/many_apps_many_models.py 2 3 } @@ -131,11 +142,12 @@ function run_all_tests { redis-cli -p $REDIS_PORT "flushall" run_management_tests redis-cli -p $REDIS_PORT "flushall" + run_integration_tests + redis-cli -p $REDIS_PORT "flushall" run_jvm_container_tests redis-cli -p $REDIS_PORT "flushall" run_rpc_container_tests redis-cli -p $REDIS_PORT "flushall" - run_integration_tests } if [ "$#" == 0 ] diff --git a/clipper_admin/clipper_manager.py b/clipper_admin/clipper_manager.py index ff9581c87..3df066360 100644 --- a/clipper_admin/clipper_manager.py +++ b/clipper_admin/clipper_manager.py @@ -1,6 +1,6 @@ """Clipper Management Utilities""" -from __future__ import print_function, with_statement +from __future__ import print_function, with_statement, absolute_import from fabric.api import * from fabric.contrib.files import append import os @@ -14,8 +14,9 @@ from sklearn.externals import joblib from cStringIO import StringIO import sys -from pywrencloudpickle import CloudPickler +from .cloudpickle import CloudPickler import time +import re __all__ = ['Clipper'] @@ -286,7 +287,13 @@ def _execute_put(self, local_path, remote_path, *args, **kwargs): # We should only copy data if the paths are different if local_path != remote_path: if os.path.isdir(local_path): - self._copytree(local_path, remote_path) + remote_path = os.path.join(remote_path, + os.path.basename(local_path)) + # if remote_path exists, delete it because shutil.copytree requires + # that the dst path doesn't exist + if os.path.exists(remote_path): + shutil.rmtree(remote_path) + shutil.copytree(local_path, remote_path) else: shutil.copy2(local_path, remote_path) else: @@ -296,38 +303,6 @@ def _execute_put(self, local_path, remote_path, *args, **kwargs): *args, **kwargs) - # Taken from http://stackoverflow.com/a/12514470 - # Recursively copies a directory from src to dst, - # where dst may or may not exist. We cannot use - # shutil.copytree() alone because it stipulates that - # dst cannot already exist - def _copytree(self, src, dst, symlinks=False, ignore=None): - # Appends the final directory in the tree specified by "src" to - # the tree specified by "dst". This is consistent with fabric's - # put() behavior - final_dst_char = dst[len(dst) - 1] - if final_dst_char != "/": - dst = dst + "/" - nested_dir_names = src.split("/") - dst = dst + nested_dir_names[len(nested_dir_names) - 1] - - if not os.path.exists(dst): - os.makedirs(dst) - for item in os.listdir(src): - s = os.path.join(src, item) - d = os.path.join(dst, item) - if os.path.isdir(s): - self._copytree(s, d, symlinks, ignore) - else: - if not os.path.exists( - d) or os.stat(s).st_mtime - os.stat(d).st_mtime > 1: - try: - shutil.copy2(s, d) - except Exception as e: - print( - "Error copying {source} to {dest}: {error}. File will be skipped.". - format(source=s, dest=d, error=e)) - def start(self): """Start a Clipper instance. @@ -490,6 +465,7 @@ def deploy_model(self, elif isinstance(model_data, str): # assume that model_data is a path to the serialized model model_data_path = model_data + print("model_data_path is: %s" % model_data_path) else: warn("%s is invalid model format" % str(type(model_data))) return False @@ -513,36 +489,7 @@ def deploy_model(self, with cd(vol): with hide("warnings", "output", "running"): - if model_data_path.startswith("s3://"): - with hide("warnings", "output", "running"): - aws_cli_installed = self._execute_standard( - "dpkg-query -Wf'${db:Status-abbrev}' awscli 2>/dev/null | grep -q '^i'", - warn_only=True).return_code == 0 - if not aws_cli_installed: - self._execute_root("apt-get update -qq") - self._execute_root( - "apt-get install -yqq awscli") - if self._execute_root( - "stat ~/.aws/config", - warn_only=True).return_code != 0: - self._execute_standard("mkdir -p ~/.aws") - self._execute_append( - "~/.aws/config", - aws_cli_config.format( - access_key=os.environ[ - "AWS_ACCESS_KEY_ID"], - secret_key=os.environ[ - "AWS_SECRET_ACCESS_KEY"])) - - self._execute_standard( - "aws s3 cp {model_data_path} {dl_path} --recursive". - format( - model_data_path=model_data_path, - dl_path=os.path.join( - vol, os.path.basename(model_data_path)))) - else: - with hide("output", "running"): - self._execute_put(model_data_path, vol) + self._execute_put(model_data_path, vol) print("Copied model data to host") # aggregate results of starting all containers @@ -573,6 +520,147 @@ def register_external_model(self, EXTERNALLY_MANAGED_MODEL, EXTERNALLY_MANAGED_MODEL) + def _save_python_function(self, name, predict_function): + relative_base_serializations_dir = "predict_serializations" + predict_fname = "predict_func.pkl" + environment_fname = "environment.yml" + conda_dep_fname = "conda_dependencies.txt" + pip_dep_fname = "pip_dependencies.txt" + + # Serialize function + s = StringIO() + c = CloudPickler(s, 2) + c.dump(predict_function) + serialized_prediction_function = s.getvalue() + + # Set up serialization directory + serialization_dir = os.path.join( + '/tmp', relative_base_serializations_dir, name) + if not os.path.exists(serialization_dir): + os.makedirs(serialization_dir) + + # Export Anaconda environment + environment_file_abs_path = os.path.join(serialization_dir, + environment_fname) + + conda_env_exported = self._export_conda_env(environment_file_abs_path) + + if conda_env_exported: + print("Anaconda environment found. Verifying packages.") + + # Confirm that packages installed through conda are solvable + # Write out conda and pip dependency files to be supplied to container + if not (self._check_and_write_dependencies( + environment_file_abs_path, serialization_dir, + conda_dep_fname, pip_dep_fname)): + return False + + print("Supplied environment details") + else: + print( + "Warning: Anaconda environment was either not found or exporting the environment " + "failed. Your function will still be serialized and deployed, but may fail due to " + "missing dependencies. In this case, please re-run inside an Anaconda environment. " + "See http://clipper.ai/documentation/python_model_deployment/ for more information." + ) + + # Write out function serialization + func_file_path = os.path.join(serialization_dir, predict_fname) + with open(func_file_path, "w") as serialized_function_file: + serialized_function_file.write(serialized_prediction_function) + print("Serialized and supplied predict function") + return serialization_dir + + def deploy_pyspark_model(self, + name, + version, + predict_function, + pyspark_model, + sc, + input_type, + labels=DEFAULT_LABEL, + num_containers=1): + """Deploy a Spark MLLib model to Clipper. + + Parameters + ---------- + name : str + The name to assign this model. + version : int + The version to assign this model. + predict_function : function + A function that takes three arguments, a SparkContext, the ``model`` parameter and + a list of inputs of the type specified by the ``input_type`` argument. + Any state associated with the function other than the Spark model should + be captured via closure capture. Note that the function must not capture + the SparkContext or the model implicitly, as these objects are not pickleable + and therefore will prevent the ``predict_function`` from being serialized. + pyspark_model : pyspark.mllib.util.Saveable + An object that mixes in the pyspark Saveable mixin. Generally this + is either an mllib model or transformer. This model will be loaded + into the Clipper model container and provided as an argument to the + predict function each time it is called. + sc : SparkContext + The SparkContext associated with the model. This is needed + to save the model for pyspark.mllib models. + input_type : str + One of "integers", "floats", "doubles", "bytes", or "strings". + labels : list of str, optional + A set of strings annotating the model + num_containers : int, optional + The number of replicas of the model to create. More replicas can be + created later as well. Defaults to 1. + + Returns + ------- + bool + True if the model was successfully deployed. False otherwise. + """ + + model_class = re.search("pyspark.*'", + str(type(pyspark_model))).group(0).strip("'") + if model_class is None: + raise ClipperManagerException( + "pyspark_model argument was not a pyspark object") + + # save predict function + serialization_dir = self._save_python_function(name, predict_function) + # save Spark model + spark_model_save_loc = os.path.join(serialization_dir, + "pyspark_model_data") + try: + # we only import pyspark here so that if the caller of the library does + # not want to use this function, clipper_manager does not have a dependency + # on pyspark + import pyspark + if isinstance(pyspark_model, pyspark.ml.pipeline.PipelineModel): + pyspark_model.save(spark_model_save_loc) + else: + pyspark_model.save(sc, spark_model_save_loc) + except Exception as e: + print("Error saving spark model: %s" % e) + raise e + + pyspark_container = "clipper/pyspark-container" + + # extract the pyspark class name. This will be something like + # pyspark.mllib.classification.LogisticRegressionModel + with open(os.path.join(serialization_dir, "metadata.json"), + "w") as metadata_file: + json.dump({"model_class": model_class}, metadata_file) + + print("Spark model saved") + + # Deploy model + deploy_result = self.deploy_model(name, version, serialization_dir, + pyspark_container, input_type, + labels, num_containers) + + # Remove temp files + shutil.rmtree(serialization_dir) + + return deploy_result + def deploy_predict_function(self, name, version, @@ -583,8 +671,8 @@ def deploy_predict_function(self, """Deploy an arbitrary Python function to Clipper. The function should take a list of inputs of the type specified by `input_type` and - return a Python or numpy array of predictions. All dependencies for the function must - be installed with Anaconda or Pip and this function must be called from within an Anaconda + return a Python or numpy array of predictions as strings. All dependencies for the function + must be installed with Anaconda or Pip and this function must be called from within an Anaconda environment. Parameters @@ -604,6 +692,11 @@ def deploy_predict_function(self, The number of replicas of the model to create. More replicas can be created later as well. Defaults to 1. + Returns + ------- + bool + True if the model was successfully deployed. False otherwise. + Example ------- Define a feature function ``center()`` and train a model on the featurized input:: @@ -628,54 +721,8 @@ def centered_predict(inputs): num_containers=1) """ - relative_base_serializations_dir = "predict_serializations" default_python_container = "clipper/python-container" - predict_fname = "predict_func.pkl" - environment_fname = "environment.yml" - conda_dep_fname = "conda_dependencies.txt" - pip_dep_fname = "pip_dependencies.txt" - - # Serialize function - s = StringIO() - c = CloudPickler(s, 2) - c.dump(predict_function) - serialized_prediction_function = s.getvalue() - - # Set up serialization directory - serialization_dir = os.path.join( - '/tmp', relative_base_serializations_dir, name) - if not os.path.exists(serialization_dir): - os.makedirs(serialization_dir) - - # Attempt to export Anaconda environment - environment_file_abs_path = os.path.join(serialization_dir, - environment_fname) - conda_env_exported = self._export_conda_env(environment_file_abs_path) - - if conda_env_exported: - print("Anaconda environment found. Verifying packages.") - - # Confirm that packages installed through conda are solvable - # Write out conda and pip dependency files to be supplied to container - if not (self._check_and_write_dependencies( - environment_file_abs_path, serialization_dir, - conda_dep_fname, pip_dep_fname)): - return False - - print("Supplied environment details") - else: - print( - "Warning: Anaconda environment was either not found or exporting the environment " - "failed. Your function will still be serialized deployed, but may fail due to " - "missing dependencies. In this case, please re-run inside an Anaconda environment. " - "See http://clipper.ai/documentation/python_model_deployment/ for more information." - ) - - # Write out function serialization - func_file_path = os.path.join(serialization_dir, predict_fname) - with open(func_file_path, "w") as serialized_function_file: - serialized_function_file.write(serialized_prediction_function) - print("Serialized and supplied predict function") + serialization_dir = self._save_python_function(name, predict_function) # Deploy function deploy_result = self.deploy_model(name, version, serialization_dir, diff --git a/clipper_admin/pywrencloudpickle.py b/clipper_admin/cloudpickle.py similarity index 100% rename from clipper_admin/pywrencloudpickle.py rename to clipper_admin/cloudpickle.py diff --git a/containers/python/pyspark_container.py b/containers/python/pyspark_container.py new file mode 100644 index 000000000..f5fe20c20 --- /dev/null +++ b/containers/python/pyspark_container.py @@ -0,0 +1,134 @@ +from __future__ import print_function +import rpc +import os +import sys +import json + +import numpy as np +np.set_printoptions(threshold=np.nan) + +sys.path.append(os.path.abspath("/lib/")) +from clipper_admin import cloudpickle + +import findspark +findspark.init() +import pyspark +from pyspark import SparkConf, SparkContext +from pyspark.sql import SparkSession +import importlib + +IMPORT_ERROR_RETURN_CODE = 3 + + +def load_predict_func(file_path): + with open(file_path, 'r') as serialized_func_file: + return cloudpickle.load(serialized_func_file) + + +def load_pyspark_model(metadata_path, spark, model_path): + with open(metadata_path, "r") as metadata: + metadata = json.load(metadata) + if "model_class" not in metadata: + print("Malformed metadata file.") + sys.exit(1) + model_class = metadata["model_class"] + + print("Loading %s model from %s" % (model_class, model_path)) + splits = model_class.split(".") + module = ".".join(splits[:-1]) + class_name = splits[-1] + ModelClass = getattr(importlib.import_module(module), class_name) + if model_class == "pyspark.ml.pipeline.PipelineModel": + model = ModelClass.load(model_path) + else: + model = ModelClass.load(spark.sparkContext, model_path) + return model + + +class PySparkContainer(rpc.ModelContainerBase): + def __init__(self, path, input_type): + self.input_type = rpc.string_to_input_type(input_type) + predict_fname = "predict_func.pkl" + predict_path = "{dir}/{predict_fname}".format( + dir=path, predict_fname=predict_fname) + self.predict_func = load_predict_func(predict_path) + self.spark = SparkSession\ + .builder\ + .appName("clipper-pyspark")\ + .getOrCreate() + metadata_path = os.path.join(path, "metadata.json") + spark_model_path = os.path.join(path, "pyspark_model_data") + self.model = load_pyspark_model(metadata_path, self.spark, + spark_model_path) + + def predict_ints(self, inputs): + preds = self.predict_func(self.spark, self.model, inputs) + return [str(p) for p in preds] + + def predict_floats(self, inputs): + preds = self.predict_func(self.spark, self.model, inputs) + return [str(p) for p in preds] + + def predict_doubles(self, inputs): + preds = self.predict_func(self.spark, self.model, inputs) + return [str(p) for p in preds] + + def predict_bytes(self, inputs): + preds = self.predict_func(self.spark, self.model, inputs) + return [str(p) for p in preds] + + def predict_strings(self, inputs): + preds = self.predict_func(self.spark, self.model, inputs) + return [str(p) for p in preds] + + +if __name__ == "__main__": + print("Starting PySparkContainer container") + try: + model_name = os.environ["CLIPPER_MODEL_NAME"] + except KeyError: + print( + "ERROR: CLIPPER_MODEL_NAME environment variable must be set", + file=sys.stdout) + sys.exit(1) + try: + model_version = os.environ["CLIPPER_MODEL_VERSION"] + except KeyError: + print( + "ERROR: CLIPPER_MODEL_VERSION environment variable must be set", + file=sys.stdout) + sys.exit(1) + + ip = "127.0.0.1" + if "CLIPPER_IP" in os.environ: + ip = os.environ["CLIPPER_IP"] + else: + print("Connecting to Clipper on localhost") + + port = 7000 + if "CLIPPER_PORT" in os.environ: + port = int(os.environ["CLIPPER_PORT"]) + else: + print("Connecting to Clipper with default port: {port}".format( + port=port)) + + input_type = "doubles" + if "CLIPPER_INPUT_TYPE" in os.environ: + input_type = os.environ["CLIPPER_INPUT_TYPE"] + else: + print("Using default input type: doubles") + + model_path = os.environ["CLIPPER_MODEL_PATH"] + print(model_path) + + print("Initializing PySpark function container") + sys.stdout.flush() + sys.stderr.flush() + + try: + model = PySparkContainer(model_path, input_type) + rpc_service = rpc.RPCService() + rpc_service.start(model, ip, port, model_name, model_version, + input_type) + except ImportError: + sys.exit(IMPORT_ERROR_RETURN_CODE) diff --git a/containers/python/pyspark_container_entry.sh b/containers/python/pyspark_container_entry.sh new file mode 100755 index 000000000..670d0179e --- /dev/null +++ b/containers/python/pyspark_container_entry.sh @@ -0,0 +1,15 @@ +#!/usr/bin/env sh + +IMPORT_ERROR_RETURN_CODE=3 + +echo "Attempting to run PySpark container without installing dependencies" +echo "Contents of /model" +ls /model/ +/bin/bash -c "exec python /container/pyspark_container.py" +if [ $? -eq $IMPORT_ERROR_RETURN_CODE ]; then + echo "Running PySpark container without installing dependencies fails" + echo "Will install dependencies and try again" + conda install -y --file /model/conda_dependencies.txt + pip install -r /model/pip_dependencies.txt + /bin/bash -c "exec python /container/pyspark_container.py" +fi diff --git a/containers/python/python_container.py b/containers/python/python_container.py index b29525ddd..008fa1850 100644 --- a/containers/python/python_container.py +++ b/containers/python/python_container.py @@ -7,14 +7,14 @@ np.set_printoptions(threshold=np.nan) sys.path.append(os.path.abspath("/lib/")) -from clipper_admin import pywrencloudpickle +from clipper_admin import cloudpickle IMPORT_ERROR_RETURN_CODE = 3 def load_predict_func(file_path): with open(file_path, 'r') as serialized_func_file: - return pywrencloudpickle.load(serialized_func_file) + return cloudpickle.load(serialized_func_file) class PythonContainer(rpc.ModelContainerBase): @@ -26,50 +26,25 @@ def __init__(self, path, input_type): self.predict_func = load_predict_func(predict_path) def predict_ints(self, inputs): - if self.input_type != rpc.INPUT_TYPE_INTS: - self._log_incorrect_input_type(rpc.INPUT_TYPE_INTS) - return preds = self.predict_func(inputs) return [str(p) for p in preds] def predict_floats(self, inputs): - if self.input_type != rpc.INPUT_TYPE_FLOATS: - self._log_incorrect_input_type(rpc.INPUT_TYPE_FLOATS) - return preds = self.predict_func(inputs) return [str(p) for p in preds] def predict_doubles(self, inputs): - if self.input_type != rpc.INPUT_TYPE_DOUBLES: - self._log_incorrect_input_type(rpc.INPUT_TYPE_DOUBLES) - return preds = self.predict_func(inputs) return [str(p) for p in preds] def predict_bytes(self, inputs): - if self.input_type != rpc.INPUT_TYPE_BYTES: - self._log_incorrect_input_type(rpc.INPUT_TYPE_BYTES) - return preds = self.predict_func(inputs) return [str(p) for p in preds] def predict_strings(self, inputs): - if self.input_type != rpc.INPUT_TYPE_STRINGS: - self._log_incorrect_input_type(rpc.INPUT_TYPE_STRINGS) - return preds = self.predict_func(inputs) return [str(p) for p in preds] - def _log_incorrect_input_type(self, input_type): - incorrect_input_type = rpc.input_type_to_string(input_type) - correct_input_type = rpc.input_type_to_string(self.input_type) - print( - "Attempted to use prediction function for input type {incorrect_input_type}.\ - This model-container was configured accept data for input type {correct_input_type}" - .format( - incorrect_input_type=incorrect_input_type, - correct_input_type=correct_input_type)) - if __name__ == "__main__": print("Starting PythonContainer container") diff --git a/examples/basic_query/example_client.py b/examples/basic_query/example_client.py index 4150f8d61..97cf5297b 100644 --- a/examples/basic_query/example_client.py +++ b/examples/basic_query/example_client.py @@ -11,7 +11,7 @@ def predict(host, uid, x): url = "http://%s:1337/example_app/predict" % host - req_json = json.dumps({'uid': uid, 'input': list(x)}) + req_json = json.dumps({'input': list(x)}) headers = {'Content-type': 'application/json'} start = datetime.now() r = requests.post(url, headers=headers, data=req_json) diff --git a/integration-tests/data/train.data b/integration-tests/data/train.data new file mode 100644 index 000000000..4652c0292 --- /dev/null +++ b/integration-tests/data/train.data @@ -0,0 +1,104 @@ + 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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as np +cur_dir = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, os.path.abspath("%s/.." % cur_dir)) +from clipper_admin import Clipper +import time +import subprocess32 as subprocess +import pprint +import random +import socket + +import findspark +findspark.init() +import pyspark +# from pyspark import SparkConf, SparkContext +from pyspark.mllib.classification import LogisticRegressionWithSGD +from pyspark.mllib.classification import SVMWithSGD +from pyspark.mllib.tree import RandomForest +from pyspark.mllib.regression import LabeledPoint +from pyspark.sql import SparkSession + +headers = {'Content-type': 'application/json'} +app_name = "pyspark_test" +model_name = "pyspark_model" + + +class BenchmarkException(Exception): + def __init__(self, value): + self.parameter = value + + def __str__(self): + return repr(self.parameter) + + +# range of ports where available ports can be found +PORT_RANGE = [34256, 40000] + + +def find_unbound_port(): + """ + Returns an unbound port number on 127.0.0.1. + """ + while True: + port = random.randint(*PORT_RANGE) + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + sock.bind(("127.0.0.1", port)) + return port + except socket.error: + print("randomly generated port %d is bound. Trying again." % port) + + +def init_clipper(): + clipper = Clipper("localhost", redis_port=find_unbound_port()) + clipper.stop_all() + clipper.start() + time.sleep(1) + return clipper + + +def normalize(x): + return x.astype(np.double) / 255.0 + + +def objective(y, pos_label): + # prediction objective + if y == pos_label: + return 1 + else: + return 0 + + +def parseData(line, obj, pos_label): + fields = line.strip().split(',') + return LabeledPoint( + obj(int(fields[0]), pos_label), normalize(np.array(fields[1:]))) + + +def predict(spark, model, xs): + return [str(model.predict(normalize(x))) for x in xs] + + +def deploy_and_test_model(sc, clipper, model, version): + clipper.deploy_pyspark_model(model_name, version, predict, model, sc, + "ints") + time.sleep(25) + num_preds = 25 + num_defaults = 0 + for i in range(num_preds): + response = requests.post( + "http://localhost:1337/%s/predict" % app_name, + headers=headers, + data=json.dumps({ + 'input': get_test_point() + })) + result = response.json() + if response.status_code == requests.codes.ok and result["default"] == True: + num_defaults += 1 + elif response.status_code != requests.codes.ok: + print(result) + raise BenchmarkException(response.text) + + if num_defaults > 0: + print("Error: %d/%d predictions were default" % (num_defaults, + num_preds)) + if num_defaults > num_preds / 2: + raise BenchmarkException("Error querying APP %s, MODEL %s:%d" % + (app_name, model_name, version)) + + +def train_logistic_regression(trainRDD): + return LogisticRegressionWithSGD.train(trainRDD, iterations=10) + + +def train_svm(trainRDD): + return SVMWithSGD.train(trainRDD) + + +def train_random_forest(trainRDD, num_trees, max_depth): + return RandomForest.trainClassifier( + trainRDD, 2, {}, num_trees, maxDepth=max_depth) + + +def get_test_point(): + return [np.random.randint(255) for _ in range(784)] + + +if __name__ == "__main__": + pos_label = 3 + try: + spark = SparkSession\ + .builder\ + .appName("clipper-pyspark")\ + .getOrCreate() + sc = spark.sparkContext + clipper = init_clipper() + + train_path = os.path.join(cur_dir, "data/train.data") + trainRDD = sc.textFile(train_path).map( + lambda line: parseData(line, objective, pos_label)).cache() + + try: + clipper.register_application(app_name, model_name, "ints", + "default_pred", 100000) + time.sleep(1) + response = requests.post( + "http://localhost:1337/%s/predict" % app_name, + headers=headers, + data=json.dumps({ + 'input': get_test_point() + })) + result = response.json() + if response.status_code != requests.codes.ok: + print("Error: %s" % response.text) + raise BenchmarkException("Error creating app %s" % app_name) + + version = 1 + lr_model = train_logistic_regression(trainRDD) + deploy_and_test_model(sc, clipper, lr_model, version) + + version += 1 + svm_model = train_svm(trainRDD) + deploy_and_test_model(sc, clipper, svm_model, version) + + version += 1 + rf_model = train_random_forest(trainRDD, 20, 16) + deploy_and_test_model(sc, clipper, svm_model, version) + except BenchmarkException as e: + print(e) + clipper.stop_all() + spark.stop() + sys.exit(1) + else: + spark.stop() + clipper.stop_all() + print("ALL TESTS PASSED") + except Exception as e: + print(e) + clipper = Clipper("localhost") + clipper.stop_all() + sys.exit(1) diff --git a/integration-tests/deploy_pyspark_pipeline_models.py b/integration-tests/deploy_pyspark_pipeline_models.py new file mode 100644 index 000000000..2a1c3bf46 --- /dev/null +++ b/integration-tests/deploy_pyspark_pipeline_models.py @@ -0,0 +1,205 @@ +from __future__ import absolute_import, print_function +import os +import sys +import requests +import json +import numpy as np +cur_dir = os.path.dirname(os.path.abspath(__file__)) +sys.path.insert(0, os.path.abspath("%s/.." % cur_dir)) +from clipper_admin import Clipper +import time +import subprocess32 as subprocess +import pprint +import random +import socket + +import findspark +findspark.init() +import pyspark +from pyspark.ml import Pipeline +from pyspark.ml.classification import LogisticRegression +from pyspark.ml.feature import HashingTF, Tokenizer +from pyspark.sql import SparkSession + +headers = {'Content-type': 'application/json'} +app_name = "pyspark_pipeline_test" +model_name = "pyspark_pipeline" + + +class BenchmarkException(Exception): + def __init__(self, value): + self.parameter = value + + def __str__(self): + return repr(self.parameter) + + +# range of ports where available ports can be found +PORT_RANGE = [34256, 40000] + + +def find_unbound_port(): + """ + Returns an unbound port number on 127.0.0.1. + """ + while True: + port = random.randint(*PORT_RANGE) + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + sock.bind(("127.0.0.1", port)) + return port + except socket.error: + print("randomly generated port %d is bound. Trying again." % port) + + +def init_clipper(): + clipper = Clipper("localhost", redis_port=find_unbound_port()) + clipper.stop_all() + clipper.start() + time.sleep(1) + return clipper + + +columns = ["id", "text"] + + +def json_to_dataframe(spark_session, xs): + tuples = [tuple(json.loads(x)) for x in xs] + df = spark_session.createDataFrame(tuples, columns) + return df + + +def predict(spark, pipeline, xs): + df = json_to_dataframe(spark, xs) + preds = pipeline.transform(df) + selected = preds.select("probability", "prediction") + outputs = [] + for row in selected.collect(): + prob, prediction = row + outputs.append( + json.dumps({ + "prob": str(prob), + "prediction": prediction + })) + return outputs + + +if __name__ == "__main__": + spark = SparkSession\ + .builder\ + .appName("clipper-pyspark")\ + .getOrCreate() + + training = spark.createDataFrame([(0, "a b c d e spark", 1.0), ( + 1, "b d", 0.0), (2, "spark f g h", 1.0), (3, "hadoop mapreduce", 0.0)], + columns + ["label"]) + + # Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr. + tokenizer = Tokenizer(inputCol="text", outputCol="words") + hashingTF = HashingTF( + inputCol=tokenizer.getOutputCol(), outputCol="features") + lr = LogisticRegression(maxIter=10, regParam=0.001) + pipeline = Pipeline(stages=[tokenizer, hashingTF, lr]) + + # Fit the pipeline to training documents. + model = pipeline.fit(training) + + # Prepare test documents, which are unlabeled (id, text) tuples. + test = spark.createDataFrame([(4, "spark i j k"), (5, "l m n"), ( + 6, "spark hadoop spark"), (7, "apache hadoop")], columns) + + # Make predictions on test documents and print columns of interest. + prediction = model.transform(test) + selected = prediction.select("id", "text", "probability", "prediction") + for row in selected.collect(): + rid, text, prob, prediction = row + print("(%d, %s) --> prob=%s, prediction=%f" % (rid, text, str(prob), + prediction)) + + # test predict function + print(predict(spark, model, + [json.dumps((np.random.randint(1000), "spark abcd"))])) + + try: + clipper = init_clipper() + + try: + clipper.register_application(app_name, model_name, "strings", + "default_pred", 10000000) + time.sleep(1) + response = requests.post( + "http://localhost:1337/%s/predict" % app_name, + headers=headers, + data=json.dumps({ + 'input': + json.dumps((np.random.randint(1000), "spark abcd")) + })) + result = response.json() + if response.status_code != requests.codes.ok: + print("Error: %s" % response.text) + raise BenchmarkException("Error creating app %s" % app_name) + + version = 1 + clipper.deploy_pyspark_model(model_name, version, predict, model, + spark.sparkContext, "strings") + time.sleep(10) + num_preds = 25 + num_defaults = 0 + for i in range(num_preds): + response = requests.post( + "http://localhost:1337/%s/predict" % app_name, + headers=headers, + data=json.dumps({ + 'input': + json.dumps((np.random.randint(1000), "spark abcd")) + })) + result = response.json() + if response.status_code == requests.codes.ok and result["default"] == True: + num_defaults += 1 + if num_defaults > 0: + print("Error: %d/%d predictions were default" % (num_defaults, + num_preds)) + if num_defaults > num_preds / 2: + raise BenchmarkException("Error querying APP %s, MODEL %s:%d" % + (app_name, model_name, version)) + + version += 1 + clipper.deploy_pyspark_model(model_name, version, predict, model, + spark.sparkContext, "strings") + time.sleep(10) + num_preds = 25 + num_defaults = 0 + for i in range(num_preds): + response = requests.post( + "http://localhost:1337/%s/predict" % app_name, + headers=headers, + data=json.dumps({ + 'input': + json.dumps((np.random.randint(1000), "spark abcd")) + })) + result = response.json() + if response.status_code == requests.codes.ok and result["default"] == True: + num_defaults += 1 + if num_defaults > 0: + print("Error: %d/%d predictions were default" % (num_defaults, + num_preds)) + if num_defaults > num_preds / 2: + raise BenchmarkException("Error querying APP %s, MODEL %s:%d" % + (app_name, model_name, version)) + + except BenchmarkException as e: + print(e) + clipper.stop_all() + spark.stop() + sys.exit(1) + else: + spark.stop() + clipper.stop_all() + print("ALL TESTS PASSED") + + except Exception as e: + print(e) + clipper = Clipper("localhost") + clipper.stop_all() + spark.stop() + sys.exit(1) diff --git a/integration-tests/many_apps_many_models.py b/integration-tests/many_apps_many_models.py index 6a1d9fed3..5b9bd4cf6 100644 --- a/integration-tests/many_apps_many_models.py +++ b/integration-tests/many_apps_many_models.py @@ -5,8 +5,8 @@ import json import numpy as np cur_dir = os.path.dirname(os.path.abspath(__file__)) -sys.path.insert(0, os.path.abspath('%s/../' % cur_dir)) -from clipper_admin.clipper_manager import Clipper +sys.path.insert(0, os.path.abspath("%s/.." % cur_dir)) +from clipper_admin import Clipper import time import subprocess32 as subprocess import pprint