Error message I am trying to create a staking pool for cardano, i got the node up and running but cardano-cli is giving me a hard time. I have it installed as when i type cardano-cli version it returns infocardano-cli version.
However when i enter cardano-cli query utxo --mainnet --address $RECEIVER i get this error:
cardano-cli: Network.Socket.connect: <socket: 11>: does not exist (No such file or directory)root#vmi803461:~#
Could it be because the blockchain isn't fully synched?
I am running windows 10 with vs
node
Yes ivan p is correct however its also important to mention that there have been cardano node example scripts involving stakepools such as creating a shelley blockchain from scratch that had a bug for quite some time essentially causing multiple nodes to point to the same socket. If you're attempting to run multiple nodes - which you should be while running a stakepool, double check the precise path and location of the socket
cardano-cli requires both CARDANO_NODE_SOCKET_PATH variables installed in the shell and node server running to access it with the socket.
export CARDANO_NODE_SOCKET_PATH="/root/db/node.socket"
To query the balance of an address we need a running node and the
environment variable CARDANO_NODE_SOCKET_PATH set to the path of the
node.socket:
Related
I am following the tutorial on
Center for High Throughput Computing and Introduction to Configuration in the HTCondor website to set up a Partitionable slot. Before any configuration I run
condor_status
and get the following output.
I update the file 00-minicondor in /etc/condor/config.d by adding the following lines at the end of the file.
NUM_SLOTS = 1
NUM_SLOTS_TYPE_1 = 1
SLOT_TYPE_1 = cpus=4
SLOT_TYPE_1_PARTITIONABLE = TRUE
and reconfigure
sudo condor_reconfig
Now with
condor_status
I get this output as expected. Now, I run the following command to check everything is fine
condor_status -af Name Slotype Cpus
and find slot1#ip-172-31-54-214.ec2.internal undefined 1 instead of slot1#ip-172-31-54-214.ec2.internal Partitionable 4 61295 that is what I would expect. Moreover, when I try to summit a job that asks for more than 1 cpu it does not allocate space for it (It stays waiting forever) as it should.
I don't know if I made some mistake during the installation process or what could be happening. I would really appreciate any help!
EXTRA INFO: If it can be of any help have have installed HTCondor with the command
curl -fsSL https://get.htcondor.org | sudo /bin/bash -s – –no-dry-run
on Ubuntu 18.04 running on an old p2.xlarge instance (it has 4 cores).
UPDATE: After rebooting the whole thing it seems to be working. I can now send jobs with different CPUs requests and it will start them properly.
The only issue I would say persists is that Memory allocation is not showing properly, for example:
But in reality it is allocating enough memory for the job (in this case around 12 GB).
If I run again
condor_status -af Name Slotype Cpus
I still get something I am not supposed to
But at least it is showing the correct number of CPUs (even if it just says undefined).
What is the output of condor_q -better when the job is idle?
I am trying to connect a C++ application (using MQCONNX) based on a PaaS IBM MQ client to two different queue managers, each one based on a different server (one in a PaaS server and the other one in a Unix server). Unfortunately I am not able to do it as I am getting a message when I try to connect to the second server saying that it is not possible as it is connected to the first queue manager. I am using two different MQHCONN connections, one for each queue manager, but the problem is still there.
I have taken a look into this link, but I still have some doubts, as for example, from which server should I copy the CCDT to the client?
https://www.ibm.com/support/pages/connecting-mq-clients-multiple-queue-managers-client-channel-definition-table-ccdt
Any help would be much appreciated, or even a quick sample of how to use CCDT, as right now I am completely stuck.
Many thanks in advance for any help.
Assuming Queue Manager 1 is called MQG1 and Queue Manager 2 is called MQG2 and these can be found using connection names of machine1.com(1701) and machine2.com(1702) respectively, and using channel names MQG1.SVRCONN and MQG2.SVRCONN respectively, you can create your CCDT, on your client application machine, thus:-
runmqsc -n
issue these commands into runmqsc:-
DEFINE CHANNEL(MQG1.SVRCONN) CHLTYPE(CLNTCONN) CONNAME('machine1.com(1701)') QMNAME(MQG1)
DEFINE CHANNEL(MQG2.SVRCONN) CHLTYPE(CLNTCONN) CONNAME('machine2.com(1702)') QMNAME(MQG2)
Then you can code your 2 x MQCONN (or MQCONNX if you need to specify any additional things on the connection) thus:-
#include <cmqc.h> /* Includes for MQI constants */
#include <cmqstrc.h> /* Convert MQRC into string */
MQHCONN hConn1 = MQHC_UNUSABLE_HCONN;
MQHCONN hConn2 = MQHC_UNUSABLE_HCONN;
MQCHAR QMName1[MQ_Q_MGR_NAME_LENGTH] = "MQG1";
MQCHAR QMName2[MQ_Q_MGR_NAME_LENGTH] = "MQG2";
MQLONG CompCode, Reason;
MQCONN(QMName1,
&hConn1,
&CompCode,
&Reason);
if (CompCode)
{
printf("MQCONN to %s failed with reason code %s (%d)\n", QMName1, MQRC_STR(Reason), Reason);
}
MQCONN(QMName2,
&hConn2,
&CompCode,
&Reason);
if (CompCode)
{
printf("MQCONN to %s failed with reason code %s (%d)\n", QMName2, MQRC_STR(Reason), Reason);
}
Take care with how you are linking your program. If you try to make two local connections, you will get a return code of MQRC_ANOTHER_Q_MGR_CONNECTED. Ensure you either link with the client library, set connection option MQCNO_CLIENT (which means you must use MQCONNX) or set the environment variable MQ_CONNECT_TYPE=CLIENT.
You might find the following blog post useful additional reading:-
IBM MQ Little Gem #30: MQ_CONNECT_TYPE
I’m trying to run a model using TPUEstimator locally on a CPU first to validate that it works by setting use_tpu=False on the estimator initialization. When running train I get this error.
InternalError: failed to synchronously memcpy host-to-device: host 0x7fcc7e4d4000 to device 0x1deffc002 size 4096: Failed precondition: Unable to enqueue when not opened, queue: [0000:00:04.0 PE0 C0 MC0 TN0 Queue HBM_WRITE]. State is: CLOSED
[[Node: optimizer/gradients/neural_network/fully_connected_2/BiasAdd_grad/BiasAddGrad_G14 = _Recv[client_terminated=false, recv_device="/job:worker/replica:0/task:0/device:TPU:0", send_device="/job:worker/replica:0/task:0/device:CPU:0", send_device_incarnation=-7832507818616568453, tensor_name="edge_42_op...iasAddGrad", tensor_type=DT_FLOAT, _device="/job:worker/replica:0/task:0/device:TPU:0"]()]]
It looks like it’s still trying to use the TPU, as it says recv_device="/job:worker/replica:0/task:0/device:TPU:0". Why is it trying to use the TPU when use_tpu is set to False?
What optimizer are you using? This type of error can happen if you use a tf.contrib.tpu.CrossShardOptimizer and use_tpu is set to False. The optimizer is trying to shard the work across TPU cores but can’t because you’re running on your CPU.
It’s common practice to have a command line flag that sets whether the TPU is being used. This flag is used to toggle things like CrossShardOptimizer and use_tpu. For example, in the MNIST reference model:
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
https://github.com/tensorflow/models/blob/ad3526a98e7d5e9e57c029b8857ef7b15c903ca2/official/mnist/mnist_tpu.py#L102
I was build example simpleswitch with registry node in Qt5.9. It's work fine, but when i replace QUrl("local.registry") and QUrl("local.replica") in definition QRemoteObjectRegistryHost and QRemoteObjectHost to QUrl("tcp://localhost:9999") or "tcp://127.0.0.1:9999", or paste current host address to defniton... i have error after run app...
qt.remoteobjects: Listen failed for URL: QUrl("tcp://127.0.0.1:9999")
qt.remoteobjects: QAbstractSocket::AddressInUseError
qt.remoteobjects: Could not create ClientIoDevice for client. Invalid url/scheme provided? QUrl("")
The valid value, use instead of QUrl("local.registry"):
QUrl("tcp://192.168.0.3:-1");
The valid value, use instead of QUrl("local.replica"):
QUrl("tcp://192.168.0.3:9999");
The IP address 192.168.0.3 - used for example (it value valid for my workstation in our office network) in your case IP address can contain other digits.
If Qt is indicating that 'the address is in use' then that is likely the source of the problem, literally the socket at IP:PORT is being used. You may check your development environment to see if you have multiple processes running of the same app - this is often the case and it leads to a collision of address spaces. We see this error in our environment consistently, and the root cause is simply as stated: 'the address is in use'.
I have a django application that does some heavy computation. It works very good with less data on my machine and on 'aws -elasticbeanstalk' as well. But When the data becomes large it on aws, gives, internal server error, and in the logs it shows:
[core:error]End of script output before headers: wsgi.py
However works fine on my machine
The code where it constantly gives this error is :
[my_big_lst[int(i[0][1])-1].appendleft((int(i[0][0]) - i[1])) for i in itertools.product(zipped_list,temp_list)]
where:
my_big_lst is a big list of deques
zipped_list is a large list of tuples
temp_list is a large list of numbers
It is notable, that as data grows large, the processing time also increases, and also that this problem is only coming on aws when data is large, and on my machine, it always works fine.
Update:
I worked out, that this error happens when the processing time exceeds 60 seconds, I also changed the Idle Loadbalancer time to 3600, but no effect, still error is there
Please anyone suggest a solution ?
If you are using a c-extension module, You could try setting
WSGIApplicationGroup %{GLOBAL}
in your virtualhost.
Something about python subinterpreters not working with c-extension modules. However since your code works for a smaller data set, your problem might be solved by setting memory-specific directives.
https://code.google.com/archive/p/modwsgi/wikis/ApplicationIssues.wiki#Python_Simplified_GIL_State_API