Update arena SDK version

components/arena/python/arena/_arena_distributed_tf_op.py

@@ -20,16 +20,16 @@
 
 
 def estimator_op(name, image, command, 
-                      chief_cpu, chief_memory, chief_port,
-                      workers, worker_image, worker_cpu, worker_memory,
-                      parameter_servers, ps_image, ps_cpu, ps_memory, ps_port, 
+                      chief_cpu_limit, chief_memory_limit, chief_port,
+                      workers, worker_image, worker_cpu_limit, worker_memory_limit,
+                      parameter_servers, ps_image, ps_cpu_limit, ps_memory_limit, ps_port, 
                       gpus, rdma, 
                       tensorboard, 
                       worker_port, annotations=[],
-                      evaluator=False, evaluator_cpu=0, evaluator_memory=0, 
+                      evaluator=False, evaluator_cpu_limit=0, evaluator_memory_limit=0, 
                       env=[], data=[], sync_source=None,
                       metrics=['Train-accuracy:PERCENTAGE'],
-                      arena_image='cheyang/arena_launcher:v0.3',
+                      arena_image='cheyang/arena_launcher:v0.5',
                       timeout_hours=240):
 
     """This function submits Distributed TFJob in Estimator mode.
@@ -41,11 +41,11 @@ def estimator_op(name, image, command,
             command: the command to run
           """
     return distributed_tf_op(name=name, image=image, command=command, envs=envs, data=data, sync_source=sync_source,
-                      workers=workers, worker_image=worker_image, worker_cpu=worker_cpu, worker_memory=worker_memory,
-                      parameter_servers=parameter_servers, ps_image=ps_image, ps_cpu=ps_cpu, ps_memory=ps_memory,
+                      workers=workers, worker_image=worker_image, worker_cpu_limit=worker_cpu_limit, worker_memory_limit=worker_memory,
+                      parameter_servers=parameter_servers, ps_image=ps_image, ps_cpu_limit=ps_cpu_limit, ps_memory_limit=ps_memory_limit,
                       gpus=gpus, rdma=rdma,
                       chief=True, 
-                      chief_cpu=chief_cpu,
+                      chief_cpu_limit=chief_cpu_limit,
                       worker_port=worker_port,
                       ps_port=ps_port,
                       tensorboard=tensorboard, 
@@ -56,13 +56,13 @@ def estimator_op(name, image, command,
 # def DistributeTFOp(name, image, gpus: int, ):
 
 def parameter_servers_op(name, image, command, env, data, sync_source, annotations,
-                      workers, worker_image, worker_cpu, worker_memory,
-                      parameter_servers, ps_image, ps_cpu, ps_memory,
+                      workers, worker_image, worker_cpu_limit, worker_memory,
+                      parameter_servers, ps_image, ps_cpu_limit, ps_memory_limit,
                       gpus, rdma, 
                       tensorboard, 
                       worker_port, ps_port, 
                       metrics=['Train-accuracy:PERCENTAGE'],
-                      arena_image='cheyang/arena_launcher:v0.4',
+                      arena_image='cheyang/arena_launcher:v0.5',
                       timeout_hours=240):
 
     """This function submits Distributed TFJob in Parameter Servers mode.
@@ -74,8 +74,8 @@ def parameter_servers_op(name, image, command, env, data, sync_source, annotatio
             command: the command to run
           """
     return distributed_tf_op(name=name, image=image, command=command, envs=envs, data=data, sync_source=sync_source,
-                      workers=workers, worker_image=worker_image, worker_cpu=worker_cpu, worker_memory=worker_memory,
-                      parameter_servers=parameter_servers, ps_image=ps_image, ps_cpu=ps_cpu, ps_memory=ps_memory,
+                      workers=workers, worker_image=worker_image, worker_cpu_limit=worker_cpu_limit, worker_memory_limit=worker_memory,
+                      parameter_servers=parameter_servers, ps_image=ps_image, ps_cpu_limit=ps_cpu_limit, ps_memory_limit=ps_memory_limit,
                       gpus=gpus, rdma=rdma, 
                       worker_port=worker_port,
                       ps_port=ps_port,
@@ -87,17 +87,17 @@ def parameter_servers_op(name, image, command, env, data, sync_source, annotatio
 
 
 def distributed_tf_op(name, image, command, env=[], data=[], sync_source=None,
-                      chief=False, chief_cpu=0, chief_memory=0, 
-                      workers=0, worker_image=None, worker_cpu=0, worker_memory=0,
-                      parameter_servers=0, ps_image=None, ps_cpu=0, ps_memory=0,
-                      evaluator=False, evaluator_cpu=0, evaluator_memory=0, 
+                      chief=False, chief_cpu_limit=0, chief_memory_limit=0, 
+                      workers=0, worker_image=None, worker_cpu_limit=0, worker_memory_limit=0,
+                      parameter_servers=0, ps_image=None, ps_cpu_limit=0, ps_memory_limit=0,
+                      evaluator=False, evaluator_cpu_limit=0, evaluator_memory_limit=0, 
                       gpus=0, rdma=False, 
                       chief_port=22222,
                       worker_port=22222,
                       ps_port=22224,
                       tensorboard=False, 
                       metrics=['Train-accuracy:PERCENTAGE'],
-                      arena_image='cheyang/arena_launcher:v0.3',
+                      arena_image='cheyang/arena_launcher:v0.5',
                       timeout_hours=240):
           """This function submits Distributed TFJob in Distributed mode.
 
@@ -118,8 +118,8 @@ def distributed_tf_op(name, image, command, env=[], data=[], sync_source=None,
                       "--output-data", output_data,
                       "--image", image,
                       "--gpus", gpus,
-                      "--cpu", cpu,
-                      "--memory", memory,
+                      "--worker-cpu", worker_cpu_limit,
+                      "--worker-memory", worker_memory_limit,
                       "--timeout-hours", timeout_hours,
                       "--metric-name", metric_name,
                       "--metric-unit", metric_unit,

components/arena/python/arena/_arena_mpi_op.py

@@ -18,12 +18,12 @@
 import datetime
 import logging
 
-def mpi_job_op(name, image, command, workers=1, gpus=0, cpu=0, memory=0, env=[],annotations=[],
+def mpi_job_op(name, image, command, workers=1, gpus=0, cpu_limit=0, memory_limit=0, env=[], annotations=[],
           data=[], sync_source=None,
           rdma=False,
           tensorboard=False,  tensorboard_image=None, 
           metrics=['Train-accuracy:PERCENTAGE'],
-          arenaImage='cheyang/arena_launcher:v0.4',
+          arenaImage='cheyang/arena_launcher:v0.5',
           timeout_hours=240):
     """This function submits MPI Job, it can run Allreduce-style Distributed Training.
 
@@ -72,8 +72,8 @@ def mpi_job_op(name, image, command, workers=1, gpus=0, cpu=0, memory=0, env=[],
                       "--rdma", str(rdma),
                       "--image", str(image),
                       "--gpus", str(gpus),
-                      "--cpu", str(cpu),
-                      "--memory", str(memory),
+                      "--cpu", str(cpu_limit),
+                      "--memory", str(memory_limit),
                       "--step-name", '{{pod.name}}',
                       "--workflow-name", '{{workflow.name}}',
                       "--workers", str(workers),

components/arena/python/arena/_arena_standalone_op.py

@@ -19,11 +19,11 @@
 import logging
 
 
-def standalone_job_op(name, image, command, gpus=0, cpu=0, memory=0, env=[],
+def standalone_job_op(name, image, command, gpus=0, cpu_limit=0, memory_limit=0, env=[],
           tensorboard=False, tensorboard_image=None,
           data=[], sync_source=None, annotations=[],
           metrics=['Train-accuracy:PERCENTAGE'],
-          arena_image='cheyang/arena_launcher:v0.4',
+          arena_image='cheyang/arena_launcher:v0.5',
           timeout_hours=240):
 
     """This function submits a standalone training Job 
@@ -72,10 +72,10 @@ def standalone_job_op(name, image, command, gpus=0, cpu=0, memory=0, env=[],
                       "--tensorboard", str(tensorboard),
                       "--image", str(image),
                       "--gpus", str(gpus),
-                      "--cpu", str(cpu),
+                      "--cpu", str(cpu_limit),
                       "--step-name", '{{pod.name}}',
                       "--workflow-name", '{{workflow.name}}',
-                      "--memory", str(memory),
+                      "--memory", str(memory_limit),
                       "--timeout-hours", str(timeout_hours),
                       ] + options +
                       [

components/arena/python/build.sh

@@ -5,7 +5,7 @@ get_abs_filename() {
   echo "$(cd "$(dirname "$1")" && pwd)/$(basename "$1")"
 }
 
-target_archive_file=${1:-kfp-arena-0.4.tar.gz}
+target_archive_file=${1:-kfp-arena-0.5.tar.gz}
 target_archive_file=$(get_abs_filename "$target_archive_file")
 
 DIR=$(mktemp -d)

samples/arena-samples/standalonejob/README.md

@@ -73,7 +73,7 @@ spec:
 First, install the necessary Python Packages
 ```shell
 pip3 install http://kubeflow.oss-cn-beijing.aliyuncs.com/kfp/0.1.14/kfp.tar.gz --upgrade
-pip3 install http://kubeflow.oss-cn-beijing.aliyuncs.com/kfp-arena/kfp-arena-0.4.tar.gz --upgrade
+pip3 install http://kubeflow.oss-cn-beijing.aliyuncs.com/kfp-arena/kfp-arena-0.5.tar.gz --upgrade
 ```
 
 Then run [standalone_pipeline.py](standalone_pipeline.py) with different parameters.