CreateSession still waiting for response from worker: /job:worker/replica:0/task:0

We are trying run mnist_spark.py using  Spark on yarn cluster on CDH 5.11.0 cluster. The cluster has 4 machines and each machine has 2 GPUs.
Below are the cluster details
Version information:
 pip list | grep tensor
tensorflow-gpu (1.5.0)
tensorflow-tensorboard (1.5.1)
tensorflowonspark (1.2.0)

python --version
Python 2.7.10 :: Continuum Analytics, Inc.

Job ran successfully when i specified number of executors as 2 but when i increase the number of executors to any value greater than 2 it is going to a hang state.
Below is the shell script that am triggering:
set -x
export PYTHON_ROOT=/apps/anaconda/2.3
export MASTER=yarn
export HADOOP_HDFS_HOME=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib/hadoop-hdfs
export TFoS_HOME=/data1/tensorflow-software/TensorFlowOnSpark
export hdfs_path=hdfs://<Hostname>:8020/user/vap_admin_fid/tensorflow
export LIB_HDFS=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib64
export LIB_JVM=/usr/java/jdk1.8.0_60/jre/lib/amd64/server
export LIB_CUDA=/usr/local/cuda-9.0/lib64
export PYSPARK_PYTHON=/apps/anaconda/2.3/bin/python

spark-submit \
--master ${MASTER} \
--deploy-mode client \
--executor-memory 4G \
--executor-cores 1 \
--driver-memory 2G \
--num-executors 4 \
--py-files ${TFoS_HOME}/examples/mnist/spark/mnist_dist.py \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--conf spark.yarn.executor.memoryOverhead=16 \
--conf spark.executorEnv.LD_LIBRARY_PATH="${LIB_CUDA}:${LIB_JVM}:${LIB_HDFS}" \
--conf spark.executorEnv.HADOOP_HDFS_HOME=$HADOOP_HDFS_HOME \
${TFoS_HOME}/examples/mnist/spark/mnist_spark.py \
--steps 10 \
--images ${hdfs_path}/images \
--labels ${hdfs_path}/labels \
--format csv \
--mode train \
--model ${hdfs_path}/mnist_model \
--cluster_size 4

Driver Log:
+ export PYTHON_ROOT=/apps/anaconda/2.3
+ PYTHON_ROOT=/apps/anaconda/2.3
+ export MASTER=yarn
+ MASTER=yarn
+ export HADOOP_HDFS_HOME=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib/hadoop-hdfs
+ HADOOP_HDFS_HOME=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib/hadoop-hdfs
+ export TFoS_HOME=/data1/tensorflow-software/TensorFlowOnSpark
+ TFoS_HOME=/data1/tensorflow-software/TensorFlowOnSpark
+ export hdfs_path=hdfs://node194215:8020/user/vap_admin_fid/tensorflow
+ hdfs_path=hdfs://node194215:8020/user/vap_admin_fid/tensorflow
+ export LIB_HDFS=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib64
+ LIB_HDFS=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib64
+ export LIB_JVM=/usr/java/jdk1.8.0_60/jre/lib/amd64/server
+ LIB_JVM=/usr/java/jdk1.8.0_60/jre/lib/amd64/server
+ export LIB_CUDA=/usr/local/cuda-9.0/lib64
+ LIB_CUDA=/usr/local/cuda-9.0/lib64
+ export PYSPARK_PYTHON=/apps/anaconda/2.3/bin/python
+ PYSPARK_PYTHON=/apps/anaconda/2.3/bin/python
+ spark-submit --master yarn --deploy-mode client --executor-memory 4G --executor-cores 1 --driver-memory 2G --num-executors 4 --py-files /data1/tensorflow-software/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py --conf spark.dynamicAllocation.enabled=false --conf spark.yarn.maxAppAttempts=1 --conf spark.yarn.executor.memoryOverhead=16 --conf spark.executorEnv.LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64/:/usr/java/jdk1.8.0_60/jre/lib/amd64/server/:/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib64/ --conf spark.executorEnv.HADOOP_HDFS_HOME=/opt/cloudera/parcels/CDH-5.11.1-1.cdh5.11.1.p0.4/lib/hadoop-hdfs --archives hdfs://node194215:8020/user/vap_admin_fid/tensorflow/Python.zip#Python /data1/tensorflow-software/TensorFlowOnSpark/examples/mnist/spark/mnist_spark.py --steps 10 --images hdfs://node194215:8020/user/vap_admin_fid/tensorflow/images --labels hdfs://node194215:8020/user/vap_admin_fid/tensorflow/labels --format csv --mode train --model hdfs://node194215:8020/user/vap_admin_fid/tensorflow/mnist_model
18/03/12 13:18:13 INFO spark.SparkContext: Running Spark version 1.6.0
18/03/12 13:18:13 INFO spark.SecurityManager: Changing view acls to: root
18/03/12 13:18:13 INFO spark.SecurityManager: Changing modify acls to: root
18/03/12 13:18:13 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
18/03/12 13:18:13 INFO util.Utils: Successfully started service 'sparkDriver' on port 37199.
18/03/12 13:18:14 INFO slf4j.Slf4jLogger: Slf4jLogger started
18/03/12 13:18:14 INFO Remoting: Starting remoting
18/03/12 13:18:14 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@<IP_ADDRESS>:46236]
18/03/12 13:18:14 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkDriverActorSystem@<IP_ADDRESS>:46236]
18/03/12 13:18:14 INFO util.Utils: Successfully started service 'sparkDriverActorSystem' on port 46236.
18/03/12 13:18:14 INFO spark.SparkEnv: Registering MapOutputTracker
18/03/12 13:18:14 INFO spark.SparkEnv: Registering BlockManagerMaster
18/03/12 13:18:14 INFO storage.DiskBlockManager: Created local directory at /tmp/blockmgr-0483b8d5-3a34-442d-92dc-2a2ace61b808
18/03/12 13:18:14 INFO storage.MemoryStore: MemoryStore started with capacity 1060.0 MB
18/03/12 13:18:14 INFO spark.SparkEnv: Registering OutputCommitCoordinator
18/03/12 13:18:14 INFO util.Utils: Successfully started service 'SparkUI' on port 4040.
18/03/12 13:18:14 INFO ui.SparkUI: Started SparkUI at http://<IP_ADDRESS>:4040
18/03/12 13:18:14 INFO client.RMProxy: Connecting to ResourceManager at node194090/<IP_ADDRESS>:8032
18/03/12 13:18:14 INFO yarn.Client: Requesting a new application from cluster with 4 NodeManagers
18/03/12 13:18:14 INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster (495192 MB per container)
18/03/12 13:18:14 INFO yarn.Client: Will allocate AM container, with 896 MB memory including 384 MB overhead
18/03/12 13:18:14 INFO yarn.Client: Setting up container launch context for our AM
18/03/12 13:18:14 INFO yarn.Client: Setting up the launch environment for our AM container
18/03/12 13:18:14 INFO yarn.Client: Preparing resources for our AM container
18/03/12 13:18:15 INFO yarn.Client: Source and destination file systems are the same. Not copying hdfs://node194215:8020/user/vap_admin_fid/tensorflow/Python.zip#Python
18/03/12 13:18:15 INFO yarn.Client: Uploading resource file:/data1/tensorflow-software/TensorFlowOnSpark/examples/mnist/spark/mnist_dist.py -> hdfs://node194215:8020/user/root/.sparkStaging/application_1520874763502_0012/mnist_dist.py
18/03/12 13:18:15 INFO yarn.Client: Uploading resource file:/tmp/spark-b42c9811-b323-407a-bbc9-4d3996647850/__spark_conf__6309647882202470623.zip -> hdfs://node194215:8020/user/root/.sparkStaging/application_1520874763502_0012/__spark_conf__6309647882202470623.zip
18/03/12 13:18:15 INFO spark.SecurityManager: Changing view acls to: root
18/03/12 13:18:15 INFO spark.SecurityManager: Changing modify acls to: root
18/03/12 13:18:15 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
18/03/12 13:18:15 INFO yarn.Client: Submitting application 12 to ResourceManager
18/03/12 13:18:16 INFO impl.YarnClientImpl: Submitted application application_1520874763502_0012
18/03/12 13:18:17 INFO yarn.Client: Application report for application_1520874763502_0012 (state: ACCEPTED)
18/03/12 13:18:17 INFO yarn.Client:
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: N/A
         ApplicationMaster RPC port: -1
         queue: root.users.root
         start time:<PHONE_NUMBER>671
         final status: UNDEFINED
         tracking URL: http://node194090:8088/proxy/application_1520874763502_0012/
         user: root
18/03/12 13:18:18 INFO yarn.Client: Application report for application_1520874763502_0012 (state: ACCEPTED)
18/03/12 13:18:18 INFO cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: ApplicationMaster registered as NettyRpcEndpointRef(null)
18/03/12 13:18:18 INFO cluster.YarnClientSchedulerBackend: Add WebUI Filter. org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter, Map(PROXY_HOSTS -> node194090, PROXY_URI_BASES -> http://node194090:8088/proxy/application_1520874763502_0012), /proxy/application_1520874763502_0012
18/03/12 13:18:18 INFO ui.JettyUtils: Adding filter: org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter
18/03/12 13:18:19 INFO yarn.Client: Application report for application_1520874763502_0012 (state: ACCEPTED)
18/03/12 13:18:20 INFO yarn.Client: Application report for application_1520874763502_0012 (state: RUNNING)
18/03/12 13:18:20 INFO yarn.Client:
         client token: N/A
         diagnostics: N/A
         ApplicationMaster host: <IP_ADDRESS>
         ApplicationMaster RPC port: 0
         queue: root.users.root
         start time:<PHONE_NUMBER>671
         final status: UNDEFINED
         tracking URL: http://node194090:8088/proxy/application_1520874763502_0012/
         user: root
18/03/12 13:18:20 INFO cluster.YarnClientSchedulerBackend: Application application_1520874763502_0012 has started running.
18/03/12 13:18:20 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 39801.
18/03/12 13:18:20 INFO netty.NettyBlockTransferService: Server created on 39801
18/03/12 13:18:20 INFO storage.BlockManager: external shuffle service port = 7337
18/03/12 13:18:20 INFO storage.BlockManagerMaster: Trying to register BlockManager
18/03/12 13:18:20 INFO storage.BlockManagerMasterEndpoint: Registering block manager <IP_ADDRESS>:39801 with 1060.0 MB RAM, BlockManagerId(driver, <IP_ADDRESS>, 39801)
18/03/12 13:18:20 INFO storage.BlockManagerMaster: Registered BlockManager
18/03/12 13:18:20 INFO scheduler.EventLoggingListener: Logging events to hdfs://node194215:8020/user/spark/applicationHistory/application_1520874763502_0012
18/03/12 13:18:20 INFO spark.SparkContext: Registered listener com.cloudera.spark.lineage.ClouderaNavigatorListener
18/03/12 13:18:22 INFO cluster.YarnClientSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (node194215:40102) with ID 1
18/03/12 13:18:22 INFO storage.BlockManagerMasterEndpoint: Registering block manager node194215:39167 with 2.1 GB RAM, BlockManagerId(1, node194215, 39167)
18/03/12 13:18:22 INFO cluster.YarnClientSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (node194090:49448) with ID 2
18/03/12 13:18:22 INFO cluster.YarnClientSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (node194090:49446) with ID 3
18/03/12 13:18:22 INFO storage.BlockManagerMasterEndpoint: Registering block manager node194090:43201 with 2.1 GB RAM, BlockManagerId(2, node194090, 43201)
18/03/12 13:18:22 INFO storage.BlockManagerMasterEndpoint: Registering block manager node194090:34955 with 2.1 GB RAM, BlockManagerId(3, node194090, 34955)
18/03/12 13:18:22 INFO cluster.YarnClientSchedulerBackend: Registered executor NettyRpcEndpointRef(null) (node194217:48856) with ID 4
18/03/12 13:18:22 INFO storage.BlockManagerMasterEndpoint: Registering block manager node194217:36373 with 2.1 GB RAM, BlockManagerId(4, node194217, 36373)
18/03/12 13:18:22 INFO cluster.YarnClientSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.8
args: Namespace(batch_size=100, cluster_size=4, epochs=1, format='csv', images='hdfs://node194215:8020/user/vap_admin_fid/tensorflow/images', labels='hdfs://node194215:8020/user/vap_admin_fid/tensorflow/labels', mode='train', model='hdfs://node194215:8020/user/vap_admin_fid/tensorflow/mnist_model', output='predictions', rdma=False, readers=1, steps=10, tensorboard=False)
2018-03-12T13:18:22.911420 ===== Start
18/03/12 13:18:23 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 290.6 KB, free 1059.7 MB)
18/03/12 13:18:23 INFO storage.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 25.4 KB, free 1059.7 MB)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on <IP_ADDRESS>:39801 (size: 25.4 KB, free: 1060.0 MB)
18/03/12 13:18:23 INFO spark.SparkContext: Created broadcast 0 from textFile at NativeMethodAccessorImpl.java:-2
18/03/12 13:18:23 INFO storage.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 290.7 KB, free 1059.4 MB)
18/03/12 13:18:23 INFO storage.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 25.4 KB, free 1059.4 MB)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on <IP_ADDRESS>:39801 (size: 25.4 KB, free: 1060.0 MB)
18/03/12 13:18:23 INFO spark.SparkContext: Created broadcast 1 from textFile at NativeMethodAccessorImpl.java:-2
zipping images and labels
18/03/12 13:18:23 INFO mapred.FileInputFormat: Total input paths to process : 10
18/03/12 13:18:23 INFO mapred.FileInputFormat: Total input paths to process : 10
2018-03-12 13:18:23,484 INFO (MainThread-76707) Reserving TFSparkNodes
2018-03-12 13:18:23,484 INFO (MainThread-76707) worker node range [1, 2, 3], ps node range [0]
2018-03-12 13:18:23,486 INFO (MainThread-76707) listening for reservations at ('<IP_ADDRESS>', 42756)
2018-03-12 13:18:23,486 INFO (MainThread-76707) Starting TensorFlow on executors
2018-03-12 13:18:23,492 INFO (MainThread-76707) Waiting for TFSparkNodes to start
2018-03-12 13:18:23,493 INFO (MainThread-76707) waiting for 4 reservations
18/03/12 13:18:23 INFO spark.SparkContext: Starting job: foreachPartition at /apps/anaconda/2.3/lib/python2.7/site-packages/tensorflowonspark/TFCluster.py:271
18/03/12 13:18:23 INFO scheduler.DAGScheduler: Got job 0 (foreachPartition at /apps/anaconda/2.3/lib/python2.7/site-packages/tensorflowonspark/TFCluster.py:271) with 4 output partitions
18/03/12 13:18:23 INFO scheduler.DAGScheduler: Final stage: ResultStage 0 (foreachPartition at /apps/anaconda/2.3/lib/python2.7/site-packages/tensorflowonspark/TFCluster.py:271)
18/03/12 13:18:23 INFO scheduler.DAGScheduler: Parents of final stage: List()
18/03/12 13:18:23 INFO scheduler.DAGScheduler: Missing parents: List()
18/03/12 13:18:23 INFO scheduler.DAGScheduler: Submitting ResultStage 0 (PythonRDD[8] at foreachPartition at /apps/anaconda/2.3/lib/python2.7/site-packages/tensorflowonspark/TFCluster.py:271), which has no missing parents
18/03/12 13:18:23 INFO storage.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 11.7 KB, free 1059.4 MB)
18/03/12 13:18:23 INFO storage.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 8.0 KB, free 1059.4 MB)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on <IP_ADDRESS>:39801 (size: 8.0 KB, free: 1060.0 MB)
18/03/12 13:18:23 INFO spark.SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1004
18/03/12 13:18:23 INFO scheduler.DAGScheduler: Submitting 4 missing tasks from ResultStage 0 (PythonRDD[8] at foreachPartition at /apps/anaconda/2.3/lib/python2.7/site-packages/tensorflowonspark/TFCluster.py:271) (first 15 tasks are for partitions Vector(0, 1, 2, 3))
18/03/12 13:18:23 INFO cluster.YarnScheduler: Adding task set 0.0 with 4 tasks
18/03/12 13:18:23 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, node194090, executor 2, partition 0, PROCESS_LOCAL, 1990 bytes)
18/03/12 13:18:23 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, node194215, executor 1, partition 1, PROCESS_LOCAL, 1990 bytes)
18/03/12 13:18:23 INFO scheduler.TaskSetManager: Starting task 2.0 in stage 0.0 (TID 2, node194090, executor 3, partition 2, PROCESS_LOCAL, 1990 bytes)
18/03/12 13:18:23 INFO scheduler.TaskSetManager: Starting task 3.0 in stage 0.0 (TID 3, node194217, executor 4, partition 3, PROCESS_LOCAL, 1990 bytes)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on node194215:39167 (size: 8.0 KB, free: 2.1 GB)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on node194090:43201 (size: 8.0 KB, free: 2.1 GB)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on node194090:34955 (size: 8.0 KB, free: 2.1 GB)
18/03/12 13:18:23 INFO storage.BlockManagerInfo: Added broadcast_2_piece0 in memory on node194217:36373 (size: 8.0 KB, free: 2.1 GB)
2018-03-12 13:18:24,494 INFO (MainThread-76707) waiting for 4 reservations
2018-03-12 13:18:25,495 INFO (MainThread-76707) waiting for 1 reservations
2018-03-12 13:18:26,496 INFO (MainThread-76707) all reservations completed
2018-03-12 13:18:26,497 INFO (MainThread-76707) All TFSparkNodes started
2018-03-12 13:18:26,497 INFO (MainThread-76707) {'addr': '/tmp/pymp-g_uEG1/listener-nFtokO', 'task_index': 0, 'port': 38993, 'authkey': '\xc4y\x85\xc8\xb6!G3\xacm\x13!\xfb\xca\x82\xe6', 'worker_num': 1, 'host': '<IP_ADDRESS>', 'ppid': 62585, 'job_name': 'worker', 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:26,497 INFO (MainThread-76707) {'addr': '/tmp/pymp-h267A9/listener-FAcTff', 'task_index': 1, 'port': 46096, 'authkey': 'F\xa0\nge\xeeJ\xb5\xa0\xf8\xbfr.f\xc0K', 'worker_num': 2, 'host': '<IP_ADDRESS>', 'ppid': 75053, 'job_name': 'worker', 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:26,497 INFO (MainThread-76707) {'addr': ('<IP_ADDRESS>', 41401), 'task_index': 0, 'port': 37805, 'authkey': '\xf5\xc9*C\x84EC\xf2\xb5\xd7j\x8d\x04\x8b\x80\xea', 'worker_num': 0, 'host': '<IP_ADDRESS>', 'ppid': 75051, 'job_name': 'ps', 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:26,497 INFO (MainThread-76707) {'addr': '/tmp/pymp-wkzZQO/listener-xo8DCy', 'task_index': 2, 'port': 42757, 'authkey': '\x89\xf1~j\xae\xacM\xfc\xb2\xb8\x14\xa8\xcd\x81jt', 'worker_num': 3, 'host': '<IP_ADDRESS>', 'ppid': 78242, 'job_name': 'worker', 'tb_pid': 0, 'tb_port': 0}
----------<tensorflowonspark.TFCluster.TFCluster object at 0x7f37551ae550>
2018-03-12 13:18:26,497 INFO (MainThread-76707) Feeding training data
18/03/12 13:18:26 INFO spark.SparkContext: Starting job: collect at PythonRDD.scala:405
18/03/12 13:18:26 INFO scheduler.DAGScheduler: Got job 1 (collect at PythonRDD.scala:405) with 10 output partitions
18/03/12 13:18:26 INFO scheduler.DAGScheduler: Final stage: ResultStage 1 (collect at PythonRDD.scala:405)
18/03/12 13:18:26 INFO scheduler.DAGScheduler: Parents of final stage: List()
18/03/12 13:18:26 INFO scheduler.DAGScheduler: Missing parents: List()
18/03/12 13:18:26 INFO scheduler.DAGScheduler: Submitting ResultStage 1 (PythonRDD[10] at RDD at PythonRDD.scala:43), which has no missing parents
18/03/12 13:18:26 INFO storage.MemoryStore: Block broadcast_3 stored as values in memory (estimated size 14.0 KB, free 1059.4 MB)
18/03/12 13:18:26 INFO storage.MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated size 7.2 KB, free 1059.4 MB)
18/03/12 13:18:26 INFO storage.BlockManagerInfo: Added broadcast_3_piece0 in memory on <IP_ADDRESS>:39801 (size: 7.2 KB, free: 1060.0 MB)
18/03/12 13:18:26 INFO spark.SparkContext: Created broadcast 3 from broadcast at DAGScheduler.scala:1004
18/03/12 13:18:26 INFO scheduler.DAGScheduler: Submitting 10 missing tasks from ResultStage 1 (PythonRDD[10] at RDD at PythonRDD.scala:43) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9))
18/03/12 13:18:26 INFO cluster.YarnScheduler: Adding task set 1.0 with 10 tasks
18/03/12 13:18:27 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 1.0 (TID 4, node194217, executor 4, partition 0, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:27 INFO scheduler.TaskSetManager: Finished task 3.0 in stage 0.0 (TID 3) in 3399 ms on node194217 (executor 4) (1/4)
18/03/12 13:18:27 INFO storage.BlockManagerInfo: Added broadcast_3_piece0 in memory on node194217:36373 (size: 7.2 KB, free: 2.1 GB)
18/03/12 13:18:27 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on node194217:36373 (size: 25.4 KB, free: 2.1 GB)
18/03/12 13:18:27 INFO scheduler.TaskSetManager: Starting task 1.0 in stage 1.0 (TID 5, node194215, executor 1, partition 1, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:27 INFO scheduler.TaskSetManager: Finished task 1.0 in stage 0.0 (TID 1) in 4197 ms on node194215 (executor 1) (2/4)
18/03/12 13:18:27 INFO scheduler.TaskSetManager: Finished task 2.0 in stage 0.0 (TID 2) in 4213 ms on node194090 (executor 3) (3/4)
18/03/12 13:18:27 INFO storage.BlockManagerInfo: Added broadcast_3_piece0 in memory on node194215:39167 (size: 7.2 KB, free: 2.1 GB)
18/03/12 13:18:27 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on node194215:39167 (size: 25.4 KB, free: 2.1 GB)
18/03/12 13:18:28 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on node194217:36373 (size: 25.4 KB, free: 2.1 GB)
18/03/12 13:18:28 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on node194215:39167 (size: 25.4 KB, free: 2.1 GB)
18/03/12 13:18:31 INFO scheduler.TaskSetManager: Starting task 2.0 in stage 1.0 (TID 6, node194090, executor 3, partition 2, RACK_LOCAL, 2682 bytes)
18/03/12 13:18:31 INFO storage.BlockManagerInfo: Added broadcast_3_piece0 in memory on node194090:34955 (size: 7.2 KB, free: 2.1 GB)
18/03/12 13:18:31 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on node194090:34955 (size: 25.4 KB, free: 2.1 GB)
18/03/12 13:18:31 INFO storage.BlockManagerInfo: Added broadcast_1_piece0 in memory on node194090:34955 (size: 25.4 KB, free: 2.1 GB)
2018-03-12 13:18:50,075 INFO (Thread-2-76707) setting server.done
18/03/12 13:18:50 INFO scheduler.TaskSetManager: Starting task 3.0 in stage 1.0 (TID 7, node194215, executor 1, partition 3, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:50 INFO scheduler.TaskSetManager: Finished task 1.0 in stage 1.0 (TID 5) in 22294 ms on node194215 (executor 1) (1/10)
18/03/12 13:18:50 INFO scheduler.TaskSetManager: Starting task 4.0 in stage 1.0 (TID 8, node194215, executor 1, partition 4, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:50 INFO scheduler.TaskSetManager: Finished task 3.0 in stage 1.0 (TID 7) in 398 ms on node194215 (executor 1) (2/10)
18/03/12 13:18:50 INFO scheduler.TaskSetManager: Starting task 5.0 in stage 1.0 (TID 9, node194215, executor 1, partition 5, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:50 INFO scheduler.TaskSetManager: Finished task 4.0 in stage 1.0 (TID 8) in 373 ms on node194215 (executor 1) (3/10)
18/03/12 13:18:51 INFO scheduler.TaskSetManager: Starting task 6.0 in stage 1.0 (TID 10, node194215, executor 1, partition 6, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:51 INFO scheduler.TaskSetManager: Finished task 5.0 in stage 1.0 (TID 9) in 378 ms on node194215 (executor 1) (4/10)
18/03/12 13:18:51 INFO scheduler.TaskSetManager: Starting task 7.0 in stage 1.0 (TID 11, node194215, executor 1, partition 7, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:51 INFO scheduler.TaskSetManager: Finished task 6.0 in stage 1.0 (TID 10) in 387 ms on node194215 (executor 1) (5/10)
18/03/12 13:18:52 INFO scheduler.TaskSetManager: Starting task 8.0 in stage 1.0 (TID 12, node194215, executor 1, partition 8, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:52 INFO scheduler.TaskSetManager: Finished task 7.0 in stage 1.0 (TID 11) in 401 ms on node194215 (executor 1) (6/10)
18/03/12 13:18:52 INFO scheduler.TaskSetManager: Starting task 9.0 in stage 1.0 (TID 13, node194215, executor 1, partition 9, NODE_LOCAL, 2682 bytes)
18/03/12 13:18:52 INFO scheduler.TaskSetManager: Finished task 8.0 in stage 1.0 (TID 12) in 398 ms on node194215 (executor 1) (7/10)
18/03/12 13:18:52 INFO scheduler.TaskSetManager: Finished task 9.0 in stage 1.0 (TID 13) in 395 ms on node194215 (executor 1) (8/10)

ps Log:
18/03/12 13:18:09 INFO executor.CoarseGrainedExecutorBackend: Started daemon with process name: 74403@node194090
18/03/12 13:18:09 INFO executor.CoarseGrainedExecutorBackend: Registered signal handlers for [TERM, HUP, INT]
18/03/12 13:18:09 INFO spark.SecurityManager: Changing view acls to: yarn,root
18/03/12 13:18:09 INFO spark.SecurityManager: Changing modify acls to: yarn,root
18/03/12 13:18:09 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yarn, root); users with modify permissions: Set(yarn, root)
18/03/12 13:18:10 INFO spark.SecurityManager: Changing view acls to: yarn,root
18/03/12 13:18:10 INFO spark.SecurityManager: Changing modify acls to: yarn,root
18/03/12 13:18:10 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yarn, root); users with modify permissions: Set(yarn, root)
18/03/12 13:18:10 INFO slf4j.Slf4jLogger: Slf4jLogger started
18/03/12 13:18:10 INFO Remoting: Starting remoting
18/03/12 13:18:10 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkExecutorActorSystem@node194090:39440]
18/03/12 13:18:10 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkExecutorActorSystem@node194090:39440]
18/03/12 13:18:10 INFO util.Utils: Successfully started service 'sparkExecutorActorSystem' on port 39440.
18/03/12 13:18:10 INFO storage.DiskBlockManager: Created local directory at /data1/yarn/nm/usercache/root/appcache/application_1520874763502_0012/blockmgr-086a4998-4b05-4aae-beee-dda2b122c936
18/03/12 13:18:10 INFO storage.MemoryStore: MemoryStore started with capacity 2.1 GB
18/03/12 13:18:10 INFO executor.CoarseGrainedExecutorBackend: Connecting to driver: spark://CoarseGrainedScheduler@<IP_ADDRESS>:37199
18/03/12 13:18:10 INFO executor.CoarseGrainedExecutorBackend: Successfully registered with driver
18/03/12 13:18:10 INFO executor.Executor: Starting executor ID 2 on host node194090
18/03/12 13:18:10 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 43201.
18/03/12 13:18:10 INFO netty.NettyBlockTransferService: Server created on 43201
18/03/12 13:18:10 INFO storage.BlockManager: external shuffle service port = 7337
18/03/12 13:18:10 INFO storage.BlockManagerMaster: Trying to register BlockManager
18/03/12 13:18:10 INFO storage.BlockManagerMaster: Registered BlockManager
18/03/12 13:18:10 INFO storage.BlockManager: Registering executor with local external shuffle service.
18/03/12 13:18:12 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 0
18/03/12 13:18:12 INFO executor.Executor: Running task 0.0 in stage 0.0 (TID 0)
18/03/12 13:18:12 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 2
18/03/12 13:18:12 INFO storage.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 8.0 KB, free 2.1 GB)
18/03/12 13:18:12 INFO broadcast.TorrentBroadcast: Reading broadcast variable 2 took 100 ms
18/03/12 13:18:12 INFO storage.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 11.7 KB, free 2.1 GB)
2018-03-12 13:18:13,624 INFO (MainThread-75240) connected to server at ('<IP_ADDRESS>', 42756)
2018-03-12 13:18:13,626 INFO (MainThread-75240) TFSparkNode.reserve: {'authkey': '\xf5\xc9*C\x84EC\xf2\xb5\xd7j\x8d\x04\x8b\x80\xea', 'worker_num': 0, 'host': '<IP_ADDRESS>', 'tb_port': 0, 'addr': ('<IP_ADDRESS>', 41401), 'ppid': 75051, 'task_index': 0, 'job_name': 'ps', 'tb_pid': 0, 'port': 37805}
2018-03-12 13:18:16,631 INFO (MainThread-75240) node: {'addr': ('<IP_ADDRESS>', 41401), 'task_index': 0, 'job_name': 'ps', 'authkey': '\xf5\xc9*C\x84EC\xf2\xb5\xd7j\x8d\x04\x8b\x80\xea', 'worker_num': 0, 'host': '<IP_ADDRESS>', 'ppid': 75051, 'port': 37805, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,632 INFO (MainThread-75240) node: {'addr': '/tmp/pymp-g_uEG1/listener-nFtokO', 'task_index': 0, 'job_name': 'worker', 'authkey': '\xc4y\x85\xc8\xb6!G3\xacm\x13!\xfb\xca\x82\xe6', 'worker_num': 1, 'host': '<IP_ADDRESS>', 'ppid': 62585, 'port': 38993, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,632 INFO (MainThread-75240) node: {'addr': '/tmp/pymp-h267A9/listener-FAcTff', 'task_index': 1, 'job_name': 'worker', 'authkey': 'F\xa0\nge\xeeJ\xb5\xa0\xf8\xbfr.f\xc0K', 'worker_num': 2, 'host': '<IP_ADDRESS>', 'ppid': 75053, 'port': 46096, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,632 INFO (MainThread-75240) node: {'addr': '/tmp/pymp-wkzZQO/listener-xo8DCy', 'task_index': 2, 'job_name': 'worker', 'authkey': '\x89\xf1~j\xae\xacM\xfc\xb2\xb8\x14\xa8\xcd\x81jt', 'worker_num': 3, 'host': '<IP_ADDRESS>', 'ppid': 78242, 'port': 42757, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,632 INFO (MainThread-75240) Starting TensorFlow ps:0 as ps on cluster node 0 on background process
2018-03-12 13:18:22,268 INFO (MainThread-75450) 0: ======== ps:0 ========
2018-03-12 13:18:22,268 INFO (MainThread-75450) 0: Cluster spec: {'ps': ['<IP_ADDRESS>:37805'], 'worker': ['<IP_ADDRESS>:38993', '<IP_ADDRESS>:46096', '<IP_ADDRESS>:42757']}
2018-03-12 13:18:30,656 INFO (MainThread-75450) 0: Using GPU: 1
2018-03-12 13:18:30.662680: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-03-12 13:18:33.773353: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] Found device 0 with properties:
name: Tesla M40 major: 5 minor: 2 memoryClockRate(GHz): 1.112
pciBusID: 0000:83:00.0
totalMemory: 11.17GiB freeMemory: 10.95GiB
2018-03-12 13:18:33.773397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla M40, pci bus id: 0000:83:00.0, compute capability: 5.2)
2018-03-12 13:18:34.015885: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job ps -> {0 -> localhost:37805}
2018-03-12 13:18:34.015935: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job worker -> {0 -> <IP_ADDRESS>:38993, 1 -> <IP_ADDRESS>:46096, 2 -> <IP_ADDRESS>:42757}
2018-03-12 13:18:34.018356: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:324] Started server with target: grpc://localhost:37805
======== PS SERVER===========
18/03/12 13:18:36 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

Worker 1 where we see the below waiting message :
18/03/12 13:18:09 INFO executor.CoarseGrainedExecutorBackend: Started daemon with process name: 74402@node194090
18/03/12 13:18:09 INFO executor.CoarseGrainedExecutorBackend: Registered signal handlers for [TERM, HUP, INT]
18/03/12 13:18:09 INFO spark.SecurityManager: Changing view acls to: yarn,root
18/03/12 13:18:09 INFO spark.SecurityManager: Changing modify acls to: yarn,root
18/03/12 13:18:09 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yarn, root); users with modify permissions: Set(yarn, root)
18/03/12 13:18:10 INFO spark.SecurityManager: Changing view acls to: yarn,root
18/03/12 13:18:10 INFO spark.SecurityManager: Changing modify acls to: yarn,root
18/03/12 13:18:10 INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(yarn, root); users with modify permissions: Set(yarn, root)
18/03/12 13:18:10 INFO slf4j.Slf4jLogger: Slf4jLogger started
18/03/12 13:18:10 INFO Remoting: Starting remoting
18/03/12 13:18:10 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkExecutorActorSystem@node194090:36506]
18/03/12 13:18:10 INFO Remoting: Remoting now listens on addresses: [akka.tcp://sparkExecutorActorSystem@node194090:36506]
18/03/12 13:18:10 INFO util.Utils: Successfully started service 'sparkExecutorActorSystem' on port 36506.
18/03/12 13:18:10 INFO storage.DiskBlockManager: Created local directory at /data1/yarn/nm/usercache/root/appcache/application_1520874763502_0012/blockmgr-a6cdb7fe-6dda-4597-8380-f958764e26bb
18/03/12 13:18:10 INFO storage.MemoryStore: MemoryStore started with capacity 2.1 GB
18/03/12 13:18:10 INFO executor.CoarseGrainedExecutorBackend: Connecting to driver: spark://CoarseGrainedScheduler@<IP_ADDRESS>:37199
18/03/12 13:18:10 INFO executor.CoarseGrainedExecutorBackend: Successfully registered with driver
18/03/12 13:18:10 INFO executor.Executor: Starting executor ID 3 on host node194090
18/03/12 13:18:10 INFO util.Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 34955.
18/03/12 13:18:10 INFO netty.NettyBlockTransferService: Server created on 34955
18/03/12 13:18:10 INFO storage.BlockManager: external shuffle service port = 7337
18/03/12 13:18:10 INFO storage.BlockManagerMaster: Trying to register BlockManager
18/03/12 13:18:10 INFO storage.BlockManagerMaster: Registered BlockManager
18/03/12 13:18:10 INFO storage.BlockManager: Registering executor with local external shuffle service.
18/03/12 13:18:12 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 2
18/03/12 13:18:12 INFO executor.Executor: Running task 2.0 in stage 0.0 (TID 2)
18/03/12 13:18:12 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 2
18/03/12 13:18:12 INFO storage.MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 8.0 KB, free 2.1 GB)
18/03/12 13:18:12 INFO broadcast.TorrentBroadcast: Reading broadcast variable 2 took 95 ms
18/03/12 13:18:12 INFO storage.MemoryStore: Block broadcast_2 stored as values in memory (estimated size 11.7 KB, free 2.1 GB)
2018-03-12 13:18:13,474 INFO (MainThread-75245) connected to server at ('<IP_ADDRESS>', 42756)
2018-03-12 13:18:13,475 INFO (MainThread-75245) TFSparkNode.reserve: {'authkey': 'F\xa0\nge\xeeJ\xb5\xa0\xf8\xbfr.f\xc0K', 'worker_num': 2, 'host': '<IP_ADDRESS>', 'tb_port': 0, 'addr': '/tmp/pymp-h267A9/listener-FAcTff', 'ppid': 75053, 'task_index': 1, 'job_name': 'worker', 'tb_pid': 0, 'port': 46096}
2018-03-12 13:18:16,481 INFO (MainThread-75245) node: {'addr': ('<IP_ADDRESS>', 41401), 'task_index': 0, 'job_name': 'ps', 'authkey': '\xf5\xc9*C\x84EC\xf2\xb5\xd7j\x8d\x04\x8b\x80\xea', 'worker_num': 0, 'host': '<IP_ADDRESS>', 'ppid': 75051, 'port': 37805, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,482 INFO (MainThread-75245) node: {'addr': '/tmp/pymp-g_uEG1/listener-nFtokO', 'task_index': 0, 'job_name': 'worker', 'authkey': '\xc4y\x85\xc8\xb6!G3\xacm\x13!\xfb\xca\x82\xe6', 'worker_num': 1, 'host': '<IP_ADDRESS>', 'ppid': 62585, 'port': 38993, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,482 INFO (MainThread-75245) node: {'addr': '/tmp/pymp-h267A9/listener-FAcTff', 'task_index': 1, 'job_name': 'worker', 'authkey': 'F\xa0\nge\xeeJ\xb5\xa0\xf8\xbfr.f\xc0K', 'worker_num': 2, 'host': '<IP_ADDRESS>', 'ppid': 75053, 'port': 46096, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,482 INFO (MainThread-75245) node: {'addr': '/tmp/pymp-wkzZQO/listener-xo8DCy', 'task_index': 2, 'job_name': 'worker', 'authkey': '\x89\xf1~j\xae\xacM\xfc\xb2\xb8\x14\xa8\xcd\x81jt', 'worker_num': 3, 'host': '<IP_ADDRESS>', 'ppid': 78242, 'port': 42757, 'tb_pid': 0, 'tb_port': 0}
2018-03-12 13:18:16,482 INFO (MainThread-75245) Starting TensorFlow worker:1 as worker on cluster node 2 on background process
18/03/12 13:18:16 INFO python.PythonRunner: Times: total = 3788, boot = 575, init = 17, finish = 3196
18/03/12 13:18:16 INFO executor.Executor: Finished task 2.0 in stage 0.0 (TID 2). 977 bytes result sent to driver
2018-03-12 13:18:17,123 INFO (MainThread-75449) 2: ======== worker:1 ========
2018-03-12 13:18:17,123 INFO (MainThread-75449) 2: Cluster spec: {'ps': ['<IP_ADDRESS>:37805'], 'worker': ['<IP_ADDRESS>:38993', '<IP_ADDRESS>:46096', '<IP_ADDRESS>:42757']}
18/03/12 13:18:19 INFO executor.CoarseGrainedExecutorBackend: Got assigned task 6
18/03/12 13:18:19 INFO executor.Executor: Running task 2.0 in stage 1.0 (TID 6)
18/03/12 13:18:19 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 3
18/03/12 13:18:19 INFO storage.MemoryStore: Block broadcast_3_piece0 stored as bytes in memory (estimated size 7.2 KB, free 2.1 GB)
18/03/12 13:18:19 INFO broadcast.TorrentBroadcast: Reading broadcast variable 3 took 46 ms
18/03/12 13:18:19 INFO storage.MemoryStore: Block broadcast_3 stored as values in memory (estimated size 14.0 KB, free 2.1 GB)
18/03/12 13:18:19 INFO rdd.HadoopRDD: Input split: hdfs://node194215:8020/user/vap_admin_fid/tensorflow/images/part-00002:0+1811128
18/03/12 13:18:19 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 0
18/03/12 13:18:19 INFO storage.MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 25.4 KB, free 2.1 GB)
18/03/12 13:18:19 INFO broadcast.TorrentBroadcast: Reading broadcast variable 0 took 15 ms
18/03/12 13:18:19 INFO storage.MemoryStore: Block broadcast_0 stored as values in memory (estimated size 377.5 KB, free 2.1 GB)
18/03/12 13:18:20 INFO Configuration.deprecation: mapred.tip.id is deprecated. Instead, use mapreduce.task.id
18/03/12 13:18:20 INFO Configuration.deprecation: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
18/03/12 13:18:20 INFO Configuration.deprecation: mapred.task.is.map is deprecated. Instead, use mapreduce.task.ismap
18/03/12 13:18:20 INFO Configuration.deprecation: mapred.task.partition is deprecated. Instead, use mapreduce.task.partition
18/03/12 13:18:20 INFO Configuration.deprecation: mapred.job.id is deprecated. Instead, use mapreduce.job.id
18/03/12 13:18:20 INFO rdd.HadoopRDD: Input split: hdfs://node194215:8020/user/vap_admin_fid/tensorflow/labels/part-00002:0+40000
18/03/12 13:18:20 INFO broadcast.TorrentBroadcast: Started reading broadcast variable 1
18/03/12 13:18:20 INFO storage.MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 25.4 KB, free 2.1 GB)
18/03/12 13:18:20 INFO broadcast.TorrentBroadcast: Reading broadcast variable 1 took 11 ms
18/03/12 13:18:20 INFO storage.MemoryStore: Block broadcast_1 stored as values in memory (estimated size 377.5 KB, free 2.1 GB)
2018-03-12 13:18:20,719 INFO (MainThread-75474) ********[{'addr': '/tmp/pymp-g_uEG1/listener-nFtokO', 'task_index': 0, 'job_name': 'worker', 'authkey': '\xc4y\x85\xc8\xb6!G3\xacm\x13!\xfb\xca\x82\xe6', 'worker_num': 1, 'host': '<IP_ADDRESS>', 'ppid': 62585, 'port': 38993, 'tb_pid': 0, 'tb_port': 0}, {'addr': '/tmp/pymp-h267A9/listener-FAcTff', 'task_index': 1, 'job_name': 'worker', 'authkey': 'F\xa0\nge\xeeJ\xb5\xa0\xf8\xbfr.f\xc0K', 'worker_num': 2, 'host': '<IP_ADDRESS>', 'ppid': 75053, 'port': 46096, 'tb_pid': 0, 'tb_port': 0}, {'addr': ('<IP_ADDRESS>', 41401), 'task_index': 0, 'job_name': 'ps', 'authkey': '\xf5\xc9*C\x84EC\xf2\xb5\xd7j\x8d\x04\x8b\x80\xea', 'worker_num': 0, 'host': '<IP_ADDRESS>', 'ppid': 75051, 'port': 37805, 'tb_pid': 0, 'tb_port': 0}, {'addr': '/tmp/pymp-wkzZQO/listener-xo8DCy', 'task_index': 2, 'job_name': 'worker', 'authkey': '\x89\xf1~j\xae\xacM\xfc\xb2\xb8\x14\xa8\xcd\x81jt', 'worker_num': 3, 'host': '<IP_ADDRESS>', 'ppid': 78242, 'port': 42757, 'tb_pid': 0, 'tb_port': 0}]
2018-03-12 13:18:20,720 INFO (MainThread-75474) ********<IP_ADDRESS>
2018-03-12 13:18:20,720 INFO (MainThread-75474) ********75053
2018-03-12 13:18:20,720 INFO (MainThread-75474) ----------------------------------
2018-03-12 13:18:20,725 INFO (MainThread-75474) Connected to TFSparkNode.mgr on <IP_ADDRESS>, ppid=75053, state='running'
2018-03-12 13:18:20,728 INFO (MainThread-75474) mgr.state='running'
2018-03-12 13:18:20,728 INFO (MainThread-75474) Feeding partition <generator object load_stream at 0x7fb62621dfa0> into input queue <multiprocessing.queues.JoinableQueue object at 0x7fb6261afa50>
18/03/12 13:18:20 INFO python.PythonRunner: Times: total = 404, boot = -4034, init = 4077, finish = 361
18/03/12 13:18:20 INFO python.PythonRunner: Times: total = 15, boot = 2, init = 6, finish = 7
2018-03-12 13:18:28,377 INFO (MainThread-75449) 2: Using GPU: 1
2018-03-12 13:18:28.379503: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-03-12 13:18:33.730500: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] Found device 0 with properties:
name: Tesla M40 major: 5 minor: 2 memoryClockRate(GHz): 1.112
pciBusID: 0000:83:00.0
totalMemory: 11.17GiB freeMemory: 11.06GiB
2018-03-12 13:18:33.730588: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Tesla M40, pci bus id: 0000:83:00.0, compute capability: 5.2)
2018-03-12 13:18:34.140095: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job ps -> {0 -> <IP_ADDRESS>:37805}
2018-03-12 13:18:34.140131: I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:215] Initialize GrpcChannelCache for job worker -> {0 -> <IP_ADDRESS>:38993, 1 -> localhost:46096, 2 -> <IP_ADDRESS>:42757}
2018-03-12 13:18:34.142438: I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:324] Started server with target: grpc://localhost:46096
tensorflow model path: hdfs://node194215:8020/user/vap_admin_fid/tensorflow/mnist_model
2018-03-12 13:18:34,477 WARNING (MainThread-75449) From /data1/yarn/nm/usercache/root/appcache/application_1520874763502_0012/container_1520874763502_0012_01_000004/__pyfiles__/mnist_dist.py:125: __init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.MonitoredTrainingSession
2018-03-12 13:18:34.653706: I tensorflow/core/distributed_runtime/master_session.cc:1017] Start master session bfd00b4d908af7d4 with config:
2018-03-12 13:18:34,672 INFO (MainThread-75449) Waiting for model to be ready.  Ready_for_local_init_op:  None, ready: Variables not initialized: hid_w, hid_b, sm_w, sm_b, Variable, hid_w/Adagrad, hid_b/Adagrad, sm_w/Adagrad, sm_b/Adagrad
2018-03-12 13:19:14.748934: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:19:24.749101: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:19:34.749245: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:19:44.749423: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:19:54.749576: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:20:04.749778: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:20:14.750193: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:20:24.750365: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:20:34.750553: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:20:44.750743: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:20:54.750937: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:21:04.751136: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:21:14.751305: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:21:24.751452: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:21:34.751600: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:21:44.751747: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:21:54.751902: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:22:04.752167: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:22:14.752332: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:22:24.752496: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:22:34.752667: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:22:44.752811: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0
2018-03-12 13:22:54.753002: I tensorflow/core/distributed_runtime/master.cc:221] CreateSession still waiting for response from worker: /job:worker/replica:0/task:0

What could be the reason why worker 1 is not able to establish a session Let me know if am missing anything?

Can you try --steps 10000 --epochs 10?   When you use --steps 10, it's entirely possible that one node "finishes" before the other nodes even start, so they'll be unable to correctly form the cluster.

@leewyang : Thank you for the reply. I was able to resolve the above issue by adding a clean up statement to clear  ${hdfs_path}/mnist_model  model hdfs directory in the script before triggering the spark-submit command (# hadoop fs -rm -r ${hdfs_path}/mnist_model). I did run the script multiple times which where successful. But at times am seeing some random errors where the the job is going into hang state with below error message in one of the worker log. When i re triggered the same job again it is getting  succeed. So basically the error am seeing is very random and in-consistent.
2018-03-13 13:35:25.168932: E tensorflow/stream_executor/cuda/cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-03-13 13:35:25.188304: E tensorflow/stream_executor/cuda/cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-03-13 13:35:25.207410: E tensorflow/stream_executor/cuda/cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-03-13 13:35:25.225475: E tensorflow/stream_executor/cuda/cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-03-13 13:35:25.243833: E tensorflow/stream_executor/cuda/cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-03-13 13:35:25.270560: E tensorflow/stream_executor/cuda/cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-03-13 13:35:25.270591: W tensorflow/stream_executor/stream.cc:1901] attempting to perform BLAS operation using StreamExecutor without BLAS support
2018-03-13 13:35:25.270836: I tensorflow/stream_executor/stream.cc:1851] stream 0xf5960b0 did not wait for stream: 0x4389f50
2018-03-13 13:35:25.270976: F tensorflow/core/common_runtime/gpu/gpu_util.cc:343] CPU->GPU Memcpy failed

Any Thoughts?

Are your GPUs configured in "exclusive" mode?

@leewyang : No currently i checked all the machines and i see compute mode for GPU's configured to default mode.
nvidia-smi -q -d compute

==============NVSMI LOG==============

Timestamp                           : Wed Mar 14 08:24:06 2018
Driver Version                      : 384.81

Attached GPUs                       : 2
GPU 00000000:03:00.0
    Compute Mode                    : Default

GPU 00000000:83:00.0
    Compute Mode                    : Default

Yeah, I think you want "Exclusive Process".  In "Default" mode, a GPU can be shared by multiple processes, so if two TF nodes land on the same box, it's possible that they're contending for the same GPU (esp. GPU memory per your log message: CPU->GPU Memcpy failed).

Closing due to inactivity.  Feel free to open if this is still an issue.


Refactor shared async sequence

Dependent on #7.

Refactored to use actor rather than NSLock.
Uses PassthroughAsyncSequence's for it's sub sequences.

Nice!


Add option to host_game to retry binding port, in an attempt to work around Kubernetes pod restarting.

Going to merge this with the understanding that the OS blocking should be removed in a future version.


WIP Add Banners (ENGTASKS-3665)

Description Of Changes
TBD.
Motivation and Context
TBD.
Testing
TBD.
Operating Systems Testing
n/a
Change Types Made

[ ] Bug fix (non-breaking change).
[ ] Feature / Enhancement (non-breaking change).
[ ] Breaking change (fix or feature that could cause existing functionality to change).
[ ] Documentation changes.
[ ] PowerShell code changes.

Change Checklist

[ ] Requires a change to the documentation.
[ ] Documentation has been updated.
[ ] Tests to cover my changes, have been added.
[ ] All new and existing tests passed?
[ ] PowerShell code changes: PowerShell v2 compatibility checked?

Related Issue
TBD.

This is in draft because work is still to be done on the repository it will be deployed to.


Upgrade helix jackson

Fixes #8306 and #8144

Codecov Report

Merging #8524 (2ebf386) into master (e4b1d05) will decrease coverage by 41.54%.
The diff coverage is 0.00%.

@@              Coverage Diff              @@
##             master    #8524       +/-   ##
=============================================
- Coverage     70.65%   29.10%   -41.55%
=============================================
  Files          1674     1663       -11
  Lines         87618    87373      -245
  Branches      13272    13242       -30
=============================================
- Hits          61905    25431    -36474
- Misses        21417    59628    +38211
+ Partials       4296     2314     -1982

Flag
Coverage Δ

integration1
26.87% <0.00%> (-0.04%)
:arrow_down:

integration2
25.79% <0.00%> (-0.17%)
:arrow_down:

unittests1
?

unittests2
?

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

...e/pinot/broker/broker/helix/BaseBrokerStarter.java
76.84% <ø> (-2.96%)
:arrow_down:

.../BrokerResourceOnlineOfflineStateModelFactory.java
55.81% <ø> (ø)

...quota/HelixExternalViewBasedQueryQuotaManager.java
44.39% <ø> (-24.67%)
:arrow_down:

...che/pinot/broker/routing/BrokerRoutingManager.java
87.62% <ø> (ø)

...elector/SegmentLineageBasedSegmentPreSelector.java
100.00% <ø> (ø)

.../segmentpreselector/SegmentPreSelectorFactory.java
100.00% <ø> (ø)

...oker/routing/segmentpruner/EmptySegmentPruner.java
0.00% <ø> (-87.94%)
:arrow_down:

.../routing/segmentpruner/PartitionSegmentPruner.java
47.28% <ø> (-20.94%)
:arrow_down:

...er/routing/segmentpruner/SegmentPrunerFactory.java
46.26% <ø> (-46.27%)
:arrow_down:

...roker/routing/segmentpruner/TimeSegmentPruner.java
0.00% <ø> (-80.23%)
:arrow_down:

... and 1249 more

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apologies - unit test set 2 logs are so big that I can't really access them from my laptop - it's running out of space as it is

Hi @pjfanning, thanks for making the code change! I do have a draft PR to bump up Helix version to 1.0.2 and currently I'm working with Helix team to test out the new Helix release, including the aspects of functionality correctness and performance. So there is no need to bump up the Helix version in this PR. Sorry didn't give you a heads up earlier.

thanks @jackjlli - it would be great to get helix bumped up because the current 0.9.8 version has dependencies on jackson 1 (no longer supported)


feat(inspector): Allow including/excluding safelist in REPL

Closes: #1530
Excluding safelist:

Including safelist:

P.S. There are no recommended extensions listed in .vscode. Moreover, if there's ESLint I suggest adding your eslint config as well.

Thanks for the suggestions!
Done 👍🏻

Can you help testing if the new changes works for you?

testing if the new changes work for you

I linked unocss to fresh vuejs project and used this config:
Unocss({
    safelist: ['bg-red', 'opacity-50'],
    blocklist: ['bg-red'],
})

REPL:

Yes, It's working

Thanks


Fix outdated images in User Guide

Resolves #205 #196

Small visual nitpick, but can we standardize images of the StudyTracker window to show a drop shadow? 😅
The way to take screenshots with drop shadow on macOS should be Shift + Command + 4 + Space bar when you select the window, or check with @mslevis

Codecov Report

Merging #228 (337501b) into master (67e5144) will increase coverage by 2.67%.
The diff coverage is n/a.

@@             Coverage Diff              @@
##             master     #228      +/-   ##
============================================
+ Coverage     62.30%   64.98%   +2.67%
- Complexity      663      717      +54
============================================
  Files            94       94
  Lines          2573     2590      +17
  Branches        335      348      +13
============================================
+ Hits           1603     1683      +80
+ Misses          870      818      -52
+ Partials        100       89      -11

Impacted Files
Coverage Δ

src/main/java/seedu/address/model/tag/Tag.java
91.66% <0.00%> (-8.34%)
:arrow_down:

...in/java/seedu/address/logic/parser/ParserUtil.java
78.12% <0.00%> (-2.73%)
:arrow_down:

...seedu/address/logic/parser/AliasCommandParser.java
100.00% <0.00%> (ø)

...ava/seedu/address/logic/commands/AliasCommand.java
96.15% <0.00%> (+0.32%)
:arrow_up:

.../seedu/address/model/studyspot/OperatingHours.java
90.24% <0.00%> (+2.43%)
:arrow_up:

...u/address/model/studyspot/UniqueStudySpotList.java
97.56% <0.00%> (+2.43%)
:arrow_up:

.../java/seedu/address/logic/commands/LogCommand.java
96.00% <0.00%> (+3.57%)
:arrow_up:

...rc/main/java/seedu/address/model/StudyTracker.java
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:arrow_up:

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:arrow_up:

.../main/java/seedu/address/model/studyspot/Name.java
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:arrow_up:

... and 10 more

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Non-plist filename extensions

Since this doesn't actually add a new filetype, setting ft to plist doesn't work on a plist file with a different extension (".strings"), for instance. How would I got about getting the plugin to treat such a file as a binary plist?

That's a very valid use case and something this plugin should accommodate. I do not know the best way to implement support for this. I'm far from a VimL guru, so I am not aware of any solutions from the top of my head.
A temporary, but subpar solution, would be to add the following to your .vimrc:
autocmd BufReadCmd *.strings call plist#Read(1) | call plist#ReadPost()
autocmd BufWriteCmd,FileWriteCmd *.strings call plist#Write()


fix: docs URL not showing up correctly

Related issues 🔗
Closes #1377
Description ℹ
The documentation links were already present but because of the environment file changes meant that these were not being rendered.
Demo 📺

Merging as status checks are for an app not affected by this PR. These status checks have been removed in netlify


[Dashboard] No amount shown for multisig transaction

v0.13.6
WTR:

Initiate transaction from multisig account (10 XEM)

Result: you can see N/A on Dashboard instead of 10 XEM

@NikolaiB that's because amounts are only displayed on transfer transactions

@NikolaiB that's because amounts are only displayed on transfer transactions

@bassemmagdy is about Transfer Transaction send as an Aggregate. Please fix :)

@bassemmagdy is about Transfer Transaction send as an Aggregate. Please fix :)

@cryptoBeliever yes, we only have access to inner tx in transaction details.

Ok. Maybe it can be improved somehow by changing the rest API too. I'm chaning to enhancement.

Related with https://github.com/nemgrouplimited/symbol-desktop-wallet/issues/856


Title fix (Issues #182 and #89)

This PR is an attempt at fixing both issues #182 and #89.
Attached is a test script to reproduce the issues in old and new code.
ImportExcelPRSample.zip

hey @jeremytbrun this is what I get running your changes. Is this right?

@dfinke Well that's odd. That is definitely not what I get when I run the sample script I originally attached. Can you provide the exact command you are running?

The sample script I attached should yield this.

OK, I'll clone it again and double check my local machine has no other modules.

Weird, git clone https://github.com/jeremytbrun/ImportExcel.git. My machine has no other installs and I get the results I posted.

Are you cloning the master repository (copy of yours) or the TitleFix repository?

Sorry, I mean TitleFix branch. That is what contains the fixes. Master branch is merely a clone of yours.

Sorry, my fault. Looks good.  I'll take it through it paces a bit more.
Please could you also update the readme.md?

This is great, it's looking good.

Sure, I'll update the readme tonight or tomorrow.
Jeremy
On Mon, Oct 2, 2017, 17:52 Doug Finke<EMAIL_ADDRESS>wrote:

This is great, it's looking good.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/dfinke/ImportExcel/pull/228#issuecomment-333675981,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AbABimb_m2njPgi6eGWe7xn4oge5MPqjks5soVscgaJpZM4PqsoM
.

@dfinke  README.md updated. Let me know if there is anything else I should add. Thanks for the pointers!

@jeremytbrun, @DarkLite1 I published a new version to the gallery

Good job guys! :+1:


oldest compatible MacOS ?

---- fixes of MacOS DMG generation (support for older version of MacOS, package signature) ----
That's what it says in the description of v1.99.4.
High Sierra aka 10.13 is not compatible.
Is the next in line, Mojave 10.14 OK ?
What is the oldest I could use ?

Catalina (10.15) is the oldest supported version. Mojave (10.14) could be supported if someone would be willing to test it, High Sierra is not possible because this version is not supported by Qt6.
Do you have Mojave (10.14) on your Mac and can you test the application?

Mojave will be supported in next pre-release 1.99.5


[BUG] BSOD when launching bloxtrap

Acknowledgement of preliminary instructions

[X] I have read the preliminary instructions, and I am certain that my problem has not already been addressed.
[X] I have thoroughly looked through the available Wiki articles and could not find a solution to my problem.
[X] I am using the latest version of Bloxstrap.
[X] I did not answer truthfully to all the above checkboxes.

Bloxstrap Version
v2.8.4
What problem did you encounter?
every time i launch bloxtrap i'm getting BSOD,pls fix it
Bloxstrap Log
N/A

I don't think this is a bloxstrap issue


Import of scene which uses Forest Pack causes scene building exception

This issue was reported in discord by @rbrune:
https://discordapp.com/channels/430063582777049089/430067119963504641/559685855229509672

When trying to use appleseed with a scene that uses forest pack appleseed will run into an exception during scene building: "foundation::Exception" when parsing the scene objects 'XYZforestlibrary[]' or 'XYZforesttemplate[]'.
I assume scene building tries to access some property that might not exist.
XYZ__forest_library_[]
XYZ__forest_template_[]

Deleting the template solved the problem.

Opening a scene file with Forest Pack content leads to an exception in the Max scripting system.
########################################################################
-- MAXScript Callback script Exception:
-- Known system exception
-- ########################################################################
-- Address: 0x1e3f6fa7; nCode: 0x00000000C0000005
-- Desc: EXCEPTION_ACCESS_VIOLATION The thread tried to read from or write to a virtual address for which it does not have the appropriate access.
--       Read of Address: 0x0000000000000000
-- ########################################################################

The reason is that the MassFX plugin tries to establish if the objects from the Forest Pack templates are rigid bodies and fails doing so. The function call to
PxIsRB: Global:PxIsRB : PxIsRB()
--			owner: undefined

gives an error.
Nonetheless, the scene renders without issues in appleseed (see screenshot below) and switching the render engine to Arnold does not change the exception being thrown on scene loading.
Test render of scattered stones from a Forest Pack template

Thanks for testing @Mango-3. I guess we can close this issue then?

Agree, seems like this is not something on appleseed's side which needs to be fixed now.
For engines which natively support Forest Pack this issue may not show up. Hence, Rafael's observation that Corona does not show an error with his scene.
On the long term we may be able to add support for Forest Pack, which would need additions/changes to the mesh export routine and having access to their SDK.


"Hădărău" is listed in the drop-down menu

Description:
"Hădărău" is listed in the drop-down menu alongside its corresponding county and commune, according to the law
Precondition:
The website is up an running.

Step 1
Type "Hădărău" in the search bar.

Expected results
The location is listed in the drop-down menu as "Hădărău, Alba (Lupșa)".


feat: harbor extension

Harbor 🚢

See which ports are exposed by your devlopment processes and act on them.

Imagine you're a developer that needs to set up multiple devservers to run his app locally. E.g: a server that serves the client side app, and another for the API server etc.
With Harbor you can easily see which devservers are running right in your Menu Bar!
Preferences

hideByArgs: command separated string list.

Allows you to hide certain processes by their command line args.
Useful for long running processes that are not related to your devservers.

Images

Note: uses lsof to query for open ports.
Repo: https://github.com/dlvhdr/harbor
Signed-off-by: Dolev Hadar<EMAIL_ADDRESS>

Thanks for your contribution @dlvhdr 🔥
Would it make sense to build into Port Manager instead, they are quite similar, it feels like a very good enhancement to that one.

@lucaschultz wdyt? The menu bar extension I wrote puts more emphasis on locally exposed node processes but can be extended to prioritize more.

I agree with @pernielsentikaer, while the two extensions each provide a different UI, they offer seemingly the same functionality and their potential user base is probably very similar. Integrating this in Port Manager would give users of the existing extension access two the Menu Bar command, instead of having two discover and install another extension.
This might be an opportunity to polish PortManager up a bit, perhaps adding the possibility too emphasize node processes a bit more (I'm thinking List.Section) and unifying which information is shown.


Heatmap that becomes a scatterplot matrix on zoom?

It just occurred to me that this kind of thing might be a good fit for lightning.
When looking at pairwise relationships between a number of variables there is a tradeoff between the number of variables you can see and the amount of information you get about the relationship for each pairing. So typically, if n variables is smaller than some threshold (~6) you can make a scatterplot matrix, but larger than that and you basically are stuck with heatmaps, which collapse a lot of information.
But with an interactive tool, it occurs to me that you could make a plot that starts out as a heatmap, but when when the user zooms in on a certain number of cells, they are dynamically redrawn as scatterplots. Then zooming back out would return to the heatmap. So you could quickly examine lots of interesting structure in a dataset having only plotted once.
This probably exists out there in some form and I am ignorant of it; but, it seems like it would cool to have in lightning.

This might belong in the https://github.com/lightning-viz/lightning repo I guess...I don't fully understand the organization organization.

This is would be a great thing to include in lightning. It fits in to a larger category of visualizations with  "adaptive zooming" features that @freeman-lab and I have been discussing for some time. The main idea being that they can change their form based on the granularity of view desired by the user.
I can put together a proof of concept for what this would look like. If anyone has examples of this technique being used in the wild it would be awesome to collect them somewhere.

As for where this kind of issue belongs, with the current org structure maybe on https://github.com/lightning-viz/lightning-default-index (this list of visualizations) or else just on the main server would probably be best in the future

Seconded! Would also be fun to bounce around some mocks here. A couple things I'm wondering: approximately when to go from matrix -> scatter plot (when we're looking at 3x3, 4x4, or whenever you zoom in?), and should it actually be a smooth zoom or something more clicky and discrete. I sort of like the interactivity here
http://www.htmlwidgets.org/showcase_d3heatmap.html
at least the click-and-drag with snapping to select cells. Except here, making a selection could turn it into scatter plots instead of zooming.

And yeah, that makes sense for repo location. Could create a lightning-viz/viz-ideas repo purely for this purpose -- i've seen similar things on other organizations -- but that might be a overkill =)

That is cool. Seems like if you are going to go to the trouble to cluster the matrix, clicking on the dendrogram should automatically zoom the matrix to the branch that you selected :)

Yes, agreed =) Awhile ago I was hacking on an interactive version of http://stanford.edu/~mwaskom/software/seaborn/examples/structured_heatmap.html where the dendrogram part was clickable, should definitely revisit! (probably separate from the zoom-to-scatter)


[QUESTION] Glitch? Lost restriction

Steps:

The input ontology in the file RestrictionGlitch.Input.ttl includes an individual with a related restriction (maxQualifiedCardinality 1).
Reading the file, creating the output ontology and saving it immediately to the output file RestrictionGlitch.RDFSharpOutput.ttl:

OWLOntology.FromRDFGraph(RDFGraph.FromFile(RDFFormats.Turtle, inputPath))
           .ToRDFGraph()
           .ToFile(RDFFormats.Turtle, outputPath);

The output ontology includes the original restriction as an anonimous class, but it's relation to the individual is lost.

Files:
RestrictionGlitch.Input.ttl.txt
RestrictionGlitch.RDFSharpOutput.ttl.txt

Hi,
thanks for the detailed report.
I'll check in the next days the root cause of this behavior. It may be related to the fact that the restriction class is anonymous and so it is not declared to the model, but I'll confirm this after analysis.
With this (and the 0-cardinality restrictions) we have 2 reports which may be find way in a 3.2.1 minor fix release.
Regards,
Marco


Update SG meta typing to dict[str, Any]

This updates the sequencing group model to allow the meta dict to take any type of value, not just strings.
While most of the other models with meta fields (e.g. participant, sample) are typed with meta: dict, the meta dict for the model assay is typed: dict[str, Any]. This would match that typing for sequencing group meta.
If we should limit sequencing group meta dicts to only contain string values it would be good to know why.
Also not sure if this would require a bumpversion to release.

I encountered this when trying to upsert a sequencing group with a meta dict that included {"haplotag": true}. The HTTP request returned a 422 exception:
File ".../venv/lib/python3.11/site-packages/metamist/rest.py", line 225, in request
    raise ApiException(http_resp=r)
metamist.exceptions.ApiException: (422)
...
HTTP response body: {"detail":[
    {
     "type":"string_type",
     "loc":["body",0,"samples",0,"sequencing_groups",0,"meta","dict[str,str]","haplotag"],
     "msg":"Input should be a valid string",
     "input":true
    }, ...
]

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 77.03%. Comparing base (c02ecc3) to head (ac08fe1).

Additional details and impacted files
@@           Coverage Diff           @@
##              dev     #799   +/-   ##
=======================================
  Coverage   77.03%   77.03%
=======================================
  Files         157      157
  Lines       13067    13068    +1
=======================================
+ Hits        10066    10067    +1
  Misses       3001     3001

:umbrella: View full report in Codecov by Sentry.
:loudspeaker: Have feedback on the report? Share it here.


Added id to cluster Update / PUT endpoint

The id (UUID) of a Cluster is needed in the request URL for a PUT to update it using the REST API - missing in the current docs release.
Was noticed by a few folks - and have added it in 👍

(I'm going to go ahead and merge since it's a clear error we're fixing)


Added Reference to CSS quesitons

Added reference to CSS questions:

Explain CSS sprites, and how you would implement them on a page or site.
What is the difference between classes and IDs in CSS?

Thank you for your contribution!  It's a good way to contribute to Hacktoberfest 😎


Error:OpenSSL SSL_read: error:14094418:SSL routines:ssl3_read_bytes:tlsv1 alert unknown ca, errno 0

I'm getting SSL error after using the following command you provided but it was working fine last time I used.

I think your openssl got upgraded after your 'last time', with the latest openssl(after 3.0), you'd better run
openssl pkcs12 -legacy -in voip_services.p12 -out voip_services.pem -nodes -clcerts
you can check it out here:
Legacy and loading legacy PKCS#12 files that involve, for example, RC2-40-CBC, try using the -legacy option


Trace leak with odeint

Bug
This is a follow-up to #7142: I have since moved from haiku to stax to avoid the statefulness and transforms, but I am still trying to backpropagate through jax.experimental.ode.odeint. My actual code runs fine until we try to take a gradient, in which case it fails due to leaked tracers. When I set the JAX_CHECK_TRACER_LEAKS=1 flag, all my test cases involving odeint (and not just backprop) start to fail. Since my code is too big to be distilled into a minimal repro, I tried building one from ground up, including operations which I find relevant. I arrive at the code below, which runs fine with JAX_CHECK_TRACER_LEAKS=0 (unlike my actual codebase, gradients get computed without erroring out) but similarly to it, with JAX_CHECK_TRACER_LEAKS=1 it fails at the forward computation (and backward too, if you comment out the forward computation).
In #7142 I was told that the tracer checker may give false positives, but I am unable to judge this myself. Is the case below enough to diagnose some bugs? I can potentially open my actual codebase, but I'd prefer to avoid that.
Code
import jax
import jax.numpy as jnp
import jax.experimental.stax as sx
import numpy as np
from jax.experimental.ode import odeint

def dense(n):
    '''
    A simple example definition of the function f in `dy = f(y, t)`.
    We just concatenate `y` and `t` into a single vector and apply a
    dense layer.
    '''

    d_init_fn, d_apply_fn = sx.Dense(n)

    def init_fn(rng, input_shape):
        y_shape, t_shape, = input_shape

        d_input_shape = (*y_shape[:-1], y_shape[-1] + 1)
        output_shape, params = d_init_fn(rng, d_input_shape)
        return output_shape, params

    def apply_fn(params, y, t):
        assert len(y.shape) == 1
        assert len(t.shape) == 0
        yt = jnp.concatenate([y, t[None]])

        return d_apply_fn(params, yt)

    return init_fn, apply_fn

def solve_ode(f, params, y0):
    ''' solves the ode defined by f over t between 0 and 1 '''
    t = jnp.linspace(0, 1, 10)
    fun = lambda y, t: f(params, y, t)
    y = odeint(fun, y0, t)
    return y

def grad_ode(f, params, y0):
    def loss(params):
        ''' an example way to define some loss which we can backprop on '''
        d_t = solve_ode(f, params, y0)
        return d_t[-1].mean()

    return jax.value_and_grad(loss)(params)

rng_key = jax.random.PRNGKey(42)
N, C = 5, 3

y0 = np.random.randn(N)
t = np.array(0.5)

d_init, d_apply = dense(N)
_, params = d_init(rng_key, (y0.shape, t.shape))

d_t = solve_ode(d_apply, params, y0)
print(d_t.shape)

loss, g = grad_ode(d_apply, params, y0)

print(loss)
print(g)

Error stack trace
jatentaki@drozd:~/Programs/jflow/debugging$ JAX_CHECK_TRACER_LEAKS=1 python test.py
WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
Traceback (most recent call last):
  File "test.py", line 56, in <module>
    d_t = solve_ode(d_apply, params, y0)
  File "test.py", line 36, in solve_ode
    y = odeint(fun, y0, t)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/experimental/ode.py", line 173, in odeint
    return _odeint_wrapper(converted, rtol, atol, mxstep, y0, t, *args, *consts)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 183, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/_src/api.py", line 424, in cache_miss
    out_flat = xla.xla_call(
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/core.py", line 1560, in bind
    return call_bind(self, fun, *args, **params)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/core.py", line 1551, in call_bind
    outs = primitive.process(top_trace, fun, tracers, params)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/core.py", line 1563, in process
    return trace.process_call(self, fun, tracers, params)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/core.py", line 606, in process_call
    return primitive.impl(f, *tracers, **params)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/interpreters/xla.py", line 592, in _xla_call_impl
    compiled_fun = _xla_callable(fun, device, backend, name, donated_invars,
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/linear_util.py", line 262, in memoized_fun
    ans = call(fun, *args)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/interpreters/xla.py", line 667, in _xla_callable
    jaxpr, out_avals, consts = pe.trace_to_jaxpr_final(
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/interpreters/partial_eval.py", line 1284, in trace_to_jaxpr_final
    jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, main, in_avals)
  File "/home/jatentaki/miniconda3/lib/python3.8/contextlib.py", line 120, in __exit__
    next(self.gen)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/core.py", line 806, in new_sublevel
    raise Exception(f'Leaked sublevel {t()}.')
jax._src.traceback_util.UnfilteredStackTrace: Exception: Leaked sublevel 1.

The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.

--------------------

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "test.py", line 56, in <module>
    d_t = solve_ode(d_apply, params, y0)
  File "test.py", line 36, in solve_ode
    y = odeint(fun, y0, t)
  File "/home/jatentaki/miniconda3/lib/python3.8/site-packages/jax/experimental/ode.py", line 173, in odeint
    return _odeint_wrapper(converted, rtol, atol, mxstep, y0, t, *args, *consts)
  File "/home/jatentaki/miniconda3/lib/python3.8/contextlib.py", line 120, in __exit__
    next(self.gen)
Exception: Leaked sublevel 1.

Library versions:
jatentaki@drozd:~/Programs/jflow$ python -c "import jax, jaxlib; print(jax.__version__, jaxlib.__version__)"
0.2.16 0.1.68

Thanks for the report & clear repro, @jatentaki. Hopefully someone will be able to take a closer look soon.

Hi @jatentaki
It looks like this issue has been fixed in the latest JAX versions. I executed the mentioned code in JAX version 0.4.23 without any errors. However, note that jax.experimental.stax has been deprecated and replaced by jax.example_libraries.stax.
import jax
import jax.numpy as jnp
# jax.experimental.stax is removed; it has long been a deprecated alias of jax.example_libraries.stax.
# import jax.experimental.stax as sx
import jax.example_libraries.stax as sx
import numpy as np
from jax.experimental.ode import odeint

def dense(n):
    '''
    A simple example definition of the function f in `dy = f(y, t)`.
    We just concatenate `y` and `t` into a single vector and apply a
    dense layer.
    '''

    d_init_fn, d_apply_fn = sx.Dense(n)

    def init_fn(rng, input_shape):
        y_shape, t_shape, = input_shape

        d_input_shape = (*y_shape[:-1], y_shape[-1] + 1)
        output_shape, params = d_init_fn(rng, d_input_shape)
        return output_shape, params

    def apply_fn(params, y, t):
        assert len(y.shape) == 1
        assert len(t.shape) == 0
        yt = jnp.concatenate([y, t[None]])

        return d_apply_fn(params, yt)

    return init_fn, apply_fn

def solve_ode(f, params, y0):
    ''' solves the ode defined by f over t between 0 and 1 '''
    t = jnp.linspace(0, 1, 10)
    fun = lambda y, t: f(params, y, t)
    y = odeint(fun, y0, t)
    return y

def grad_ode(f, params, y0):
    def loss(params):
        ''' an example way to define some loss which we can backprop on '''
        d_t = solve_ode(f, params, y0)
        return d_t[-1].mean()

    return jax.value_and_grad(loss)(params)

rng_key = jax.random.PRNGKey(42)
N, C = 5, 3

y0 = np.random.randn(N)
t = np.array(0.5)

d_init, d_apply = dense(N)
_, params = d_init(rng_key, (y0.shape, t.shape))

d_t = solve_ode(d_apply, params, y0)
print(d_t.shape)

loss, g = grad_ode(d_apply, params, y0)

print(loss)
print(g)

Output:
(10, 5)
0.045054007
(Array([[ 0.00094843, -0.17762688, -0.09329863, -0.02836601, -0.08737083],
       [-0.01951531, -0.09566554, -0.05993164, -0.02846049, -0.05375667],
       [-0.04377968,  0.43775326,  0.20988415,  0.04158223,  0.2004192 ],
       [-0.01857239,  0.5370345 ,  0.27447653,  0.07459338,  0.25807694],
       [ 0.0255919 , -0.33067527, -0.16216335, -0.03688037, -0.15443903],
       [ 0.03454172,  0.15016206,  0.09596114,  0.04780234,  0.08623691]],      dtype=float32), Array([-0.00179814,  0.3366217 ,  0.17680681,  0.05351925,  0.16534077],      dtype=float32))

I've included a Gist here for your reference.

Thanks for the followup! It looks like we can close this.


ci: release 0.17.0

:robot: I have created a release beep boop
0.17.0 (2024-09-04)
Features

support lookup_secret method, offer user to regenerate the set if 3 or less codes are left (45738a8)

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/canonical/identity-platform-login-ui/releases/tag/v0.17.0 :sunflower:


z-index being improperly set

I'm using bootstrap-datepicker in a project where it sits next to another widget.
In Safari, the z-index is being manually set to 10.  However, the widget that it sits next to is set to 999.  So whenever the date picker drop down is displayed, it shows up UNDER the widget next to it.
In Firefox, the z-index is not set, so this behavior is not apparent.
Solution:
you are already attaching a .datepicker-dropdown class to the drop down.  Simply let us developers customize that class with our own z-index value so that we can make sure it is properly positioned on our pages.
Don't manually set it in code.

This PR #1242 could fix this issue!

I would like to test the PR, I've some cases like bs-dp inside modals. If it's working without my tweaks for the old version I can rebase it.
That said, @appdevdesigns have a point I agree with, souldn't bs-dp have the z-index set in css or rather not set at all (or have an option to disable it)? and let devs set it properly in css. There is many cases where the calculated z-index is a bad design.

Looks like the PR is not working.

I'm curious what the conditions are around the test above not applying. I tested this change, and have it live in a web app supporting FF, Chrome, and IE 8+. But there could be a condition I didn't catch in testing.
There is an alternative as mentioned in the OP, and that is to simply comment out the z-index in the placement and expect that the user has all CSS in place to handle it.

So far my workaround have been to stay on the old version.
Do you want me to run new tests ? It might have been fixed since then.
I'm not fan of workaround which consist of editing lib code ;)

Tried on the last version and it looks fine.

I have a situation where a sibling element (not parent) is absolutely positioned.  In this rare case, hardcoding the z-index with !important works i.e.
.datepicker-dropdown {
  z-index: 1036 !important;
}


Cache Windows system dependencies in CI

Proposed changes:
System dependencies installation take ~7min on Windows, accounting for ~12% of build time for Windows tests workflow. This PR uses a cache folder for Windows system dependencies, that choco can automatically pickup during our workflows.
Current state
Currently stuck at the end of the installation of wget package with the following error:
Logs from CI artifact
2021-02-25 16:58:30,066 3276 [DEBUG] -  [ShimGen] [WARN ] Could not extract icon from associated program. Using default. Error:
2021-02-25 16:58:30,067 3276 [DEBUG] -  [ShimGen]   Format not recognized by IconLib
2021-02-25 16:58:30,143 3276 [DEBUG] -  [ShimGen] Microsoft (R) Visual C# Compiler version 4.8.3761.0
2021-02-25 16:58:30,144 3276 [DEBUG] -  [ShimGen] for C# 5
2021-02-25 16:58:30,145 3276 [DEBUG] -  [ShimGen] Copyright (C) Microsoft Corporation. All rights reserved.
2021-02-25 16:58:30,145 3276 [DEBUG] -  [ShimGen] This compiler is provided as part of the Microsoft (R) .NET Framework, but only supports language versions up to C# 5, which is no longer the latest version. For compilers that support newer versions of the C# programming language, see http://go.microsoft.com/fwlink/?LinkID=533240
2021-02-25 16:58:30,145 3276 [DEBUG] -  [ShimGen] error CS0016: Could not write to output file 'c:\ProgramData\chocolatey\bin\wget.exe' -- 'The directory name is invalid. '
2021-02-25 16:58:30,148 3276 [DEBUG] -  [ShimGen] [ERROR] ShimGen had an issue creating 'C:\ProgramData\chocolatey\bin\wget.exe'. Please check the logs above.
2021-02-25 16:58:30,155 3276 [DEBUG] - Command ['"C:\ProgramData\chocolatey\tools\shimgen.exe" --path="..\\lib\Wget\tools\wget.exe" --output="C:\ProgramData\chocolatey\bin\wget.exe"  --iconpath="C:\ProgramData\chocolatey\lib\Wget\tools\wget.exe"'] exited with '1'

Status (please check what you already did):

[ ] added some tests for the functionality
[ ] updated the documentation
[x] updated the changelog (please check changelog for instructions)
[ ] reformat files using black (please check Readme for instructions)

@wochinge docstring checks are failing on this PR, but I haven't changed any python files. Any idea what's going on?

Do you have a link to the failing check?

@wochinge here (from the commit before the last)

I think the issue is because your changes were outdated and it pulled in the diff from master against your branch. Some of these warnings are still weird though.

will fix it here

@m-vdb What's the state of this one?

For reference: this PR is taken over by the infrastructure squad, as I won't have enough time to wrap it up in a timely fashion

@m-vdb I think it's ready to review, do you wanna take a look? :D

yes! thanks a lot for taking over 🙏🏻

It looks like the feature to use cache is available in pro+ version of choco.
2021-03-04T08:08:53.2395335Z Chocolatey v0.10.15
2021-03-04T08:08:53.7402524Z Feature 'downloadCache' not found

and https://docs.chocolatey.org/en-us/features/private-cdn

Closing this PR as there's not an easy way of leveraging cache with choco. We could revisit that later if it becomes more priority. Thanks a lot @tczekajlo for jumping on this so quickly and disentangling the issues 😄 💯


Docs link on website is broken

The link in the footer attempts to access https://www.kinovea.org/help/en/index.htm instead of https://www.kinovea.org/help/en/

Thanks. Fixed.


Any.Instance should avoid copy-constructor if possible

Check this with a test, currently I think this throws exception

The real problem is when copy constructor is public and there

Added better heuristics


🐛 Mobile | RedeemPage | The Get button is not clickable in Carousel

Cc: @camilla

Describe the Bug
Expected Behavior
The RedeemReward popup appears
Tasks

[ ] Investigate
[ ] Fix

Acceptance Criteria

[ ] Meets Definition of Done - https://github.com/SSWConsulting/SSW.Rewards.Mobile/blob/main/_docs/Definition-of-Done.md
[ ] ...

More Information

Environment

Device: [e.g. iPhone 12]
OS: [e.g. iOS]

Screenshots

Thanks!

Done - this issue has been resolved.


Different RoPE settings for local attention layers

Changes
This PR adds support for different RoPE settings for local attention layers via the local_attn_rotary_emb_base and local_attn_rotary_emb_dim flags. It builds on #99, which should be merged first.
Tests

[x] Is the new feature tested? (Not always necessary for all changes -- just adding to the checklist to keep track)
[x] Have you ran all the tests?
[x] Do the tests all pass?

Merging as I've trained multiple models with this PR without issue


2 samples not functional because "Typeson is not defined"

The Samples page contains 4 jsfiddle examples, but 2 of them don't start out of the box because of the same error "92:12 Uncaught ReferenceError: Typeson is not defined":

Storing Binary Date
Preserving custom types using typeson

Thanks! I've removed those two samples. The Typeson library has evolved a lot since those samples where created and I don't have time to figure out how to use it properly in this case. Don't think these samples are essential anyway.

I'm probably going to experiment with Typeson one of these days, so I'll add "Update Dexie/Typeson samples" to my nice-to-have list. (If you're interested in having them updated at least)
You're 100% right that it's not essential to Dexie, but pointing out the library in the docs with a working sample could provide people with a very useful abstraction layer. Not having to think about (de)serializing Date objects, for instance, would help me a lot already in my project.

IndexedDB has a built-in support for storing Date, Blob, ArrayBuffer even without Typeson, but to serialize other types, like custom classes other types not supported by structured clone algorithm, it could be useful.


nicotineplus: init at 1.4.1

Added myself (6AA4FD) to maintainer list
Motivation for this change
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[ ] NixOS
[ ] macOS
[x] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nox --run "nox-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Assured whether relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

i'm happy to do that, but i've just realized that #53717 predates this, and seems to be better put together, so perhaps that should be tested+merged instead?

Looks like that was merged. Closing.


Authentication header required?

I have been reading about jwt and a few times there has been mention of using the http header Authentication to pass the jwt token to the server. This is especially true when reading about angular and jwt. Yet, I've been using a custom header: x-access-token.
I'm wondering if there is a reason to switch to this header. The documentation about the Authentication header http://tools.ietf.org/html/rfc1945#section-10.2 suggests it's to be used for specific reasons, but I can't tell if it does anything special like encrypt it first.

The Authorization header is part of the standard so it makes it easier for others to consume your API with prebuilt plugins. On my current project I allow either headers. I'm using the format Authorization: Bearer TOKEN. The only encryption is done when you use TLS, which should be requires when using JWT on your server.


docs(README.md): remove mention of adding yourself as a maintainer

We don't want to encourage new contributors to immediately become maintainers. We can suggest the idea of becoming a maintainer to active contributors when the time is right imo.

perhaps we should follow this up with a rough guide on the steps to taking over a style?

Eh I think that can be explained in Discord or as we go, its hard to document that as it isn't some sort of exact procedure.


Calico-vpp-node agent restarts

Environment

Calico/VPP version: v0.18.1-calicov3.21.2
Kubernetes version: v1.21.1
kind cluster

Issue description
Sometimes I see calico-vpp-nodes restarts.
Logs:
...
time="2022-02-14T11:12:20Z" level=info msg="Main client watcher loop"
time="2022-02-14T11:13:28Z" level=info msg="Terminating main client watcher loop"
time="2022-02-14T11:13:41Z" level=info msg="Terminating main client watcher loop"
panic: runtime error: invalid memory address or nil pointer dereference
[signal SIGSEGV: segmentation violation code=0x1 addr=0x0 pc=0x1b8b84a]

goroutine 47 [running]:
github.com/projectcalico/vpp-dataplane/calico-vpp-agent/watchers.nodeSpecCopy(0x0, 0xc000ceedd8)
	/home/nskrzypc/vpp-dataplane/calico-vpp-agent/watchers/nodes_watcher.go:50 +0x6a
github.com/projectcalico/vpp-dataplane/calico-vpp-agent/watchers.(*NodeWatcher).WatchNodes(0xc00005d080, 0x347c120, 0x0, 0x0)
	/home/nskrzypc/vpp-dataplane/calico-vpp-agent/watchers/nodes_watcher.go:120 +0x19c
main.Go.func1(0x0, 0x0)
	/home/nskrzypc/vpp-dataplane/calico-vpp-agent/cmd/calico_vpp_dataplane.go:69 +0x35
gopkg.in/tomb%2ev2.(*Tomb).run(0x347c120, 0xc000a937c0)
	/home/nskrzypc/.go/pkg/mod/gopkg.in/tomb.v2@v2.0.0-20161208151619-d5d1b5820637/tomb.go:163 +0x38
created by gopkg.in/tomb%2ev2.(*Tomb).Go
	/home/nskrzypc/.go/pkg/mod/gopkg.in/tomb.v2@v2.0.0-20161208151619-d5d1b5820637/tomb.go:159 +0xbc

Steps

Start Calico-VPP on kind cluster
Wait 30-60 minutes
kubectl get pods --all-namespaces
See restarts

NAMESPACE              NAME                                         READY   STATUS    RESTARTS   AGE
calico-vpp-dataplane   calico-vpp-node-bklxg                        2/2     Running   1          68m
calico-vpp-dataplane   calico-vpp-node-pj8zt                        2/2     Running   1          69m
calico-vpp-dataplane   calico-vpp-node-wmtxr                        2/2     Running   1          68m
kube-system            calico-kube-controllers-6b9fbfff44-rwkps     1/1     Running   0          7h18m
...

Full log:
calico_vpp_agent_log.txt

Hi @glazychev-art
Thanks for the report !
This issue should be covered by #298 before, we were indeed not handling the channel closing
correctly, and we could end up with node being nil
You can test with the :latest images which should contain this fix

@sknat
Thank you!

I think we can close this issue, I haven't seen this issue again. Thanks!


bug #3 fix

bug #3 fix, update pedantic to lints, fix analyzer complains

Completed. Please review.

You can do something like @GZGavinZhao at the end so that GitHub will notify me that the PR is ready.


Race condition leading to text resources not loading

Description of the bug
Originally reported on Slack. On rare occasions, possibly in this particular app only, the text resources fail to load for some reason, leaving the user without proper texts.

Steps To Reproduce

Open the app
Observe raw text resource identifiers instead of resolved ones

The fix for this is now in production. Calling out @StianVestli to report back if he notices the problem again.


x/website:

What version of Go are you using (go version)?

$ go version
go version go1.13.8 linux/amd64

Does this issue reproduce with the latest release?
yes (latest website)
What operating system and processor architecture are you using (go env)?
What did you do?
On https://golang.org/doc/ clicked the link "Writing Web Applications"
What did you expect to see?
Link should have opened
What did you see instead?
Page https://golang.org/doc/articles/wiki/
Error message: "open REDACTED: file does not exist"

Duplicate of #44314


rate_timer.h allow user specified pulse time

I want to collect my second "pulse" time from a source other than the current time on the local clock, this change defaults to the current behavior but allows the user to specify their own time.
With the default behavior this also has the convenient side effect of collecting the time before starting the function call.

Can you give a bit of context what you want to do, i.e. where will the functionality be used?

I'm using this class to collect converted time stamps from hardware clocks on an optical tracker and robot arm in relation to a local CPU clock, and I'm collecting the timestamp immediately before and after my driver receives the data, but I don't want my driver to depend on cartographer. For that reason I'd like to supply both time stamps myself.

If you're using this outside of the cartographer library itself, I suggest forking. We want to move towards a public API and my guess is that we will stop exposing rate_timer.h before a 1.0 release.

I installed the cartographer API using the CMake scripts and I'm including it from my own code.
The following is very preliminary and subject to change. I'm working on improving timestamp estimates as a way to improve localization for an optical tracker, computer, and robot arm. I expect very similar time correction algorithms incorporating physical measurements could provide similar benefits for the IMU, LIDAR, and RGBD hardware used by cartographer applications to better estimate and minimize mapping error in slam software. I found cartographer to be well written with relatively few dependencies, especially since ROS is optional. This makes for a very good starting point for testing across platforms and minimizing infrastructure work.
I hoped some of this might become directly useful to cartographer users, so I was hoping to minimize the difference between my fork and cartographer by pulling useful changes upstream early in a collaborative fashion. However, if you think it is best that I simply make a permanent fork I will understand and close my PRs accordingly.

Just to be clear: I was suggesting you fork the RateTimer class and put it in your code base, not forking all of Cartographer. The way I see it, RateTimer is currently only used internally for logging/debugging and will not become part of the public API. The public API will focus on SLAM.
I think it would make sense for helper classes that are useful specifically for SLAM users to become part of the public API. So if you can make that case for the RateTimer, I'm happy to reconsider.

Thank your for your explanation, your motivation makes sense to me since you're focusing on SLAM end users. In my own words, Cartographer is not middleware for algorithm developers and extending the logging/debug components is not part of the current mission of Cartographer. It may even prove distracting!
Since you mentioned you're definitely interested in something that proves concretely useful for SLAM I'll keep that in mind in case I eventually end up with something worthwhile. In the meantime I'll just put any useful cartographer utilities I employ and modify directly in my own code base. Thanks for your time & feedback! :-)


User interface and .data

Right now, there's a .data interface to a PSM object, where you can see the results that were reported across records.
In the past, it would be populated and managed by pipestat, even when backed by a db.
With change to sqlmodel, this went away, and so tests and this interface are broken.
The important principle is that the User API should be the same regardless of back-end. But, now using sqlmodel, we don't really want to have local in-memory representations of things, we want to retrieve from the db. So, we need some way to show the data that works for both back-ends.
Two possible solutions:

Some new class that uses dict accessors, and translates those into .retrieve queries
deprecate a .data interface that expects nested dict objects; instead, use .retrieve or implement .list() or something else that can work with both.

I'm leaning toward option 2

Pytests failures regarding this issue have been rectified but the proposed solutions have not yet been implemented.