Fix docker image versioning

MANIFEST.in

@@ -1,3 +1,4 @@
 #include the license file
 include LICENSE.txt
 include README.md
+include VERSION.txt

NoopBenchDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 COPY containers/python/noop_container.py /container/
 COPY bench/setup_noop_bench_docker.sh /bench/

NoopDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 MAINTAINER Dan Crankshaw <dscrankshaw@gmail.com>
 

PySparkContainerDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 MAINTAINER Dan Crankshaw <dscrankshaw@gmail.com>
 
@@ -14,6 +15,7 @@ RUN curl -o /spark.tgz https://d3kbcqa49mib13.cloudfront.net/spark-2.1.1-bin-had
 
 COPY containers/python/pyspark_container.py containers/python/pyspark_container_entry.sh /container/
 COPY clipper_admin/ /lib/clipper_admin/
+COPY VERSION.txt /lib/
 
 ENV SPARK_HOME="/spark"
 

PythonContainerDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 MAINTAINER Dan Crankshaw <dscrankshaw@gmail.com>
 
@@ -9,6 +10,7 @@ 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/
+COPY VERSION.txt /lib/
 
 
 CMD ["/container/python_container_entry.sh"]

RPythonDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 ## Use Debian unstable via pinning -- new style via APT::Default-Release
 RUN echo "deb http://http.debian.net/debian sid main" > /etc/apt/sources.list.d/debian-unstable.list \

SklearnCifarDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 MAINTAINER Dan Crankshaw <dscrankshaw@gmail.com>
 

SumBenchDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 COPY containers/python/sum_container.py /container/
 COPY bench/setup_sum_bench_docker.sh /bench/

SumDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 COPY containers/python/sum_container.py /container/
 

TensorFlowCifarDockerfile

@@ -1,4 +1,5 @@
-FROM clipper/py-rpc:latest
+ARG CODE_VERSION=0.2-rc1
+FROM clipper/py-rpc:${CODE_VERSION}
 
 MAINTAINER Dan Crankshaw <dscrankshaw@gmail.com>
 

VERSION.txt

@@ -0,0 +1 @@
+develop

bench/build_bench_docker_images.sh

@@ -10,26 +10,31 @@ unset CDPATH
 # the script.
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 
+
 # Let the user start this script from anywhere in the filesystem.
 cd $DIR/..
 
+tag=$(<VERSION.txt)
+
 if [ $# -ne 0 ] && [ $# -ne 4 ]; then
 	echo "Usage: ./build_bench_docker_images.sh [<sum_model_name> <sum_model_version> <noop_model_name> <noop_model_version>]"
 	exit 1
 fi
 
 # Build the Clipper Docker images
 # Assume local clipper/py-rpc base image (if exists) or pulled image is correct
+
+docker build -t clipper/py-rpc:$tag -f ./RPCDockerfile ./
 if [ $# -eq 0 ]; then
-	time docker build -t clipper/sum-bench -f SumBenchDockerfile ./
-	time docker build  -t clipper/noop-bench -f NoopBenchDockerfile ./
+	time docker build build --build-arg CODE_VERSION=$tag -t clipper/sum-bench:$tag -f SumBenchDockerfile ./
+	time docker build build --build-arg CODE_VERSION=$tag -t clipper/noop-bench:$tag -f NoopBenchDockerfile ./
 else
 	echo $1
 	echo $2
 	echo $3
 	echo $4
-	time docker build -t clipper/sum-bench -f SumBenchDockerfile ./ --build-arg MODEL_NAME="$1" --build-arg MODEL_VERSION="$2"
-	time docker build  -t clipper/noop-bench -f NoopBenchDockerfile ./ --build-arg MODEL_NAME="$3" --build-arg MODEL_VERSION="$4"
+	time docker build build --build-arg CODE_VERSION=$tag -t clipper/sum-bench:$tag -f SumBenchDockerfile ./ --build-arg MODEL_NAME="$1" --build-arg MODEL_VERSION="$2"
+	time docker build build --build-arg CODE_VERSION=$tag -t clipper/noop-bench:$tag -f NoopBenchDockerfile ./ --build-arg MODEL_NAME="$3" --build-arg MODEL_VERSION="$4"
 fi
 
 cd -

bin/build_docker_images.sh

@@ -10,29 +10,32 @@ unset CDPATH
 # the script.
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 
+
 # Let the user start this script from anywhere in the filesystem.
 cd $DIR/..
 
+tag=$(<VERSION.txt)
+
 # Build the Clipper Docker images
-time docker build -t clipper/query_frontend -f QueryFrontendDockerfile ./
-time docker build -t clipper/management_frontend -f ManagementFrontendDockerfile ./
+time docker build -t clipper/query_frontend:$tag -f QueryFrontendDockerfile ./
+time docker build -t clipper/management_frontend:$tag -f ManagementFrontendDockerfile ./
 cd -
 
 # Build Spark JVM Container
 cd $DIR/../containers/jvm
-time docker build -t clipper/spark-scala-container -f SparkScalaContainerDockerfile ./
+time docker build -t clipper/spark-scala-container:$tag -f SparkScalaContainerDockerfile ./
 cd -
 
 # Build the Python model containers
 cd $DIR/..
 
 # first build base image
-docker build -t clipper/py-rpc -f ./RPCDockerfile ./
-time docker build -t clipper/sum-container -f ./SumDockerfile ./
-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 ./
-time docker build -t clipper/r_python_container -f ./RPythonDockerfile ./
+docker build -t clipper/py-rpc:$tag -f ./RPCDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/sum-container:$tag -f ./SumDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/noop-container:$tag -f ./NoopDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/python-container:$tag -f ./PythonContainerDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/pyspark-container:$tag -f ./PySparkContainerDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/sklearn_cifar_container:$tag -f ./SklearnCifarDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/tf_cifar_container:$tag -f ./TensorFlowCifarDockerfile ./
+time docker build --build-arg CODE_VERSION=$tag -t clipper/r_python_container:$tag -f ./RPythonDockerfile ./
 cd -

clipper_admin/__init__.py

@@ -6,3 +6,4 @@
     sys.exit(1)
 
 from clipper_manager import Clipper
+from version import version as __version__

clipper_admin/clipper_manager.py

@@ -18,6 +18,7 @@
 import time
 import re
 from .module_dependency import ModuleDependencyAnalyzer
+from .version import version as code_version
 
 __all__ = ['Clipper']
 
@@ -99,7 +100,7 @@ class Clipper:
         on `host` at the port specified by `redis_port`.
     redis_persistence_path : string, optional
         The directory path to which redis data should be persisted. The directory
-        should not already exist. If unspecified, redis will not persist data to disk. 
+        should not already exist. If unspecified, redis will not persist data to disk.
     restart_containers : bool, optional
         If true, containers will restart on failure. If false, containers
         will not restart automatically.
@@ -134,7 +135,7 @@ def __init__(self,
                         '--redis_port=%d' % self.redis_port
                     ],
                     'image':
-                    'clipper/management_frontend:latest',
+                    'clipper/management_frontend:{}'.format(code_version),
                     'ports': [
                         '%d:%d' % (CLIPPER_MANAGEMENT_PORT,
                                    CLIPPER_MANAGEMENT_PORT)
@@ -150,7 +151,7 @@ def __init__(self,
                     ],
                     'depends_on': ['mgmt_frontend'],
                     'image':
-                    'clipper/query_frontend:latest',
+                    'clipper/query_frontend:{}'.format(code_version),
                     'ports': [
                         '%d:%d' % (CLIPPER_RPC_PORT, CLIPPER_RPC_PORT),
                         '%d:%d' % (CLIPPER_QUERY_PORT, CLIPPER_QUERY_PORT)
@@ -340,8 +341,8 @@ def register_application(self, name, input_type, default_output,
             by the end of the latency objective.
         slo_micros : int
             The query latency objective for the application in microseconds.
-            This is the processing latency between Clipper receiving a request 
-            and sending a response. It does not account for network latencies 
+            This is the processing latency between Clipper receiving a request
+            and sending a response. It does not account for network latencies
             before a request is received or after a response is sent.
 
             If Clipper cannot process a query within the latency objective,
@@ -830,7 +831,7 @@ def deploy_pyspark_model(self,
             print("Error saving spark model: %s" % e)
             raise e
 
-        pyspark_container = "clipper/pyspark-container"
+        pyspark_container = "clipper/pyspark-container:{}".format(code_version)
 
         # extract the pyspark class name. This will be something like
         # pyspark.mllib.classification.LogisticRegressionModel
@@ -910,7 +911,8 @@ def centered_predict(inputs):
                 num_containers=1)
         """
 
-        default_python_container = "clipper/python-container"
+        default_python_container = "clipper/python-container:{}".format(
+            code_version)
         serialization_dir = self._save_python_function(name, predict_function)
 
         # Deploy function
@@ -970,8 +972,8 @@ def register_app_and_deploy_predict_function(
             The version to assign the deployed model.
         slo_micros : int
             The query latency objective for the application in microseconds.
-            This is the processing latency between Clipper receiving a request 
-            and sending a response. It does not account for network latencies 
+            This is the processing latency between Clipper receiving a request
+            and sending a response. It does not account for network latencies
             before a request is received or after a response is sent.
         labels : list of str, optional
             A list of strings annotating the model.
@@ -1037,8 +1039,8 @@ def register_app_and_deploy_pyspark_model(
             The version to assign the deployed model.
         slo_micros : int, optional
             The query latency objective for the application in microseconds.
-            This is the processing latency between Clipper receiving a request 
-            and sending a response. It does not account for network latencies 
+            This is the processing latency between Clipper receiving a request
+            and sending a response. It does not account for network latencies
             before a request is received or after a response is sent.
         labels : list of str, optional
             A list of strings annotating the model.
@@ -1242,7 +1244,7 @@ def _check_and_write_dependencies(self, environment_path, directory,
         If packages listed in specified conda environment file have conflicting dependencies,
         this function will warn the user and return False.
 
-        If there are no conflicting package dependencies, existence of the packages in the 
+        If there are no conflicting package dependencies, existence of the packages in the
         container conda channel is tested. The user is warned about any missing packages.
         All existing conda packages are written out to `conda_dep_fname` and pip packages
         to `pip_dep_fname` in the given `directory`. This function then returns True.
@@ -1588,13 +1590,13 @@ def deploy_R_model(self,
             The name to assign this model.
         version : int
             The version to assign this model.
-        model_data : 
+        model_data :
             The trained model to add to Clipper.The type has to be rpy2.robjects.vectors.ListVector,
             this is how python's rpy2 encapsulates any given R model.This model will be loaded
             into the Clipper model container and provided as an argument to the
-            predict function each time it is called. 
+            predict function each time it is called.
         labels : list of str, optional
-            A set of strings annotating the model 
+            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.
@@ -1606,7 +1608,7 @@ def deploy_R_model(self,
         base = importr('base')
 
         input_type = "strings"
-        container_name = "clipper/r_python_container"
+        container_name = "clipper/r_python_container:{}".format(code_version)
 
         with hide("warnings", "output", "running"):
             fname = name.replace("/", "_")

clipper_admin/version.py

@@ -0,0 +1,5 @@
+import sys
+import os
+cur_dir = os.path.dirname(os.path.abspath(__file__))
+with open(os.path.abspath(os.path.join(cur_dir, "../VERSION.txt")), 'r') as f:
+    version = f.read().strip()