I was doing an experiment with open pulse on caliberation of a qubit and I stumbled upon this error:
Traceback (most recent call last):
Input In [73] in <cell line: 1>
frequency_sweep_results = job.result(timeout=120) # timeout parameter set to 120 seconds
File /opt/conda/lib/python3.8/site-packages/qiskit/providers/ibmq/job/ibmqjob.py:290 in result
raise IBMQJobFailureError(
IBMQJobFailureError: 'Unable to retrieve result for job 627bcffafd267c3dbc4f42f7. Job has failed: The Qobj pulse type is not supported by the selected backend. Error code: 1108.'
Error code 1108 being :
Run the job on a backend that supports open pulse. Whether a backend supports open pulse can be found in its configuration data.
Use %tb to get the full traceback
I have used ibmq_santiago,ibmq_manila and ibmq_lima so far all giving me the same error.
Could someone suggest a backend that supports qiskit pulse?
You can check out the backends with pulse support in the table view of IBM service list:
Programmatically, you can list the backends with pulse support you have access to in the following way:
from qiskit import IBMQ
provider = IBMQ.load_account()
backends_supporting_openpulse = provider.backends(filters=lambda b: b.configuration().open_pulse)
Related
I'm a beginner on pyflink framework and I would like to know if my use case is possible with it ...
I need to make a tumbling windows and apply a python udf (scikit learn clustering model) on it.
The use case is : every 30 seconds I want to apply my udf on the previous 30 seconds of data.
For the moment I succeeded in consume data from a kafka in streaming but then I'm not able to create a 30seconds window on a non-keyed stream with the python API.
Do you know some example for my use case ? Do you know if the pyflink API allow this ?
Here my first shot :
from pyflink.common import Row
from pyflink.common.serialization import JsonRowDeserializationSchema, JsonRowSerializationSchema
from pyflink.common.typeinfo import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.connectors import FlinkKafkaConsumer, FlinkKafkaProducer
from pyflink.common.watermark_strategy import TimestampAssigner, WatermarkStrategy
from pyflink.common import Duration
import time
from utils.selector import Selector
from utils.timestampAssigner import KafkaRowTimestampAssigner
# 1. create a StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()
# the sql connector for kafka is used here as it's a fat jar and could avoid dependency issues
env.add_jars("file:///flink-sql-connector-kafka_2.11-1.14.0.jar")
deserialization_schema = JsonRowDeserializationSchema.builder() \
.type_info(type_info=Types.ROW_NAMED(["labelId","freq","timestamp"],[Types.STRING(),Types.DOUBLE(),Types.STRING()])).build()
kafka_consumer = FlinkKafkaConsumer(
topics='events',
deserialization_schema=deserialization_schema,
properties={'bootstrap.servers': 'localhost:9092'})
# watermark_strategy = WatermarkStrategy.for_bounded_out_of_orderness(Duration.of_seconds(5))\
# .with_timestamp_assigner(KafkaRowTimestampAssigner())
ds = env.add_source(kafka_consumer)
ds.print()
ds = ds.windowAll()
# ds.print()
env.execute()
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.flink.api.java.ClosureCleaner (file:/home/dorian/dataScience/pyflink/pyflink_env/lib/python3.6/site-packages/pyflink/lib/flink-dist_2.11-1.14.0.jar) to field java.util.Properties.serialVersionUID
WARNING: Please consider reporting this to the maintainers of org.apache.flink.api.java.ClosureCleaner
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
Traceback (most recent call last):
File "/home/dorian/dataScience/pyflink/project/__main__.py", line 35, in <module>
ds = ds.windowAll()
AttributeError: 'DataStream' object has no attribute 'windowAll'
Thx
I recently got myself a new PC(Predator Helios 300) and I wanted to start using aws there but when I try to perform amplify init I get the error below even though I already did all the other steps such as configuration.
× Root stack creation failed
init failed
{ SignatureDoesNotMatch: Signature expired: 20190427T235724Z is now earlier than 20190428T094952Z (20190428T095452Z - 5 min.)
at Request.extractError (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\protocol\query.js:50:29)
at Request.callListeners (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\sequential_executor.js:106:20)
at Request.emit (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\sequential_executor.js:78:10)
at Request.emit (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\request.js:683:14)
at Request.transition (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\request.js:22:10)
at AcceptorStateMachine.runTo (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\state_machine.js:14:12)
at C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\state_machine.js:26:10
at Request.<anonymous> (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\request.js:38:9)
at Request.<anonymous> (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\request.js:685:12)
at Request.callListeners (C:\Users\sahve\AppData\Roaming\npm\node_modules\#aws-amplify\cli\node_modules\aws-sdk\lib\sequential_executor.js:116:18)
message:
'Signature expired: 20190427T235724Z is now earlier than 20190428T094952Z (20190428T095452Z - 5 min.)',
code: 'SignatureDoesNotMatch',
time: 2019-04-27T23:57:24.753Z,
requestId: 'ab179ef3-699b-11e9-bfe3-4ddc7ceb66ee',
statusCode: 403,
retryable: true }
After doing some research It seems to be a verification problem. Does anyone has experience with this or knows how to resolve this issue. Thanks a lot!
Any time you see an error like "is now earlier than" around some numbers that look like timestamps (20190427T235724Z -> 2019-04-27 23:57:24 UTC), that's an indicator that the error is time related. Time matters for cryptography in order to validate certificates (so that an attacker cannot break a certificate and use it after its expiration, among other reasons) [1]. In this case, either your clock or the remote server clock is wrongly set. Since the remote server in this case is AWS, it is highly unlikely that they have any significant clock drift, leaving you as the possible outlier.
Given that you mentioned a new computer, it is even more likely that this is due to an incorrectly set system clock.
Reset/synchronize your system clock and the error should disappear.
Reference [1]: https://security.stackexchange.com/q/72866/47422
Kiwi version 6.0, tcms-api 5.0.
Given that 82 is a valid test run_id and 7 is a valid build_id for the test run's product in the Kiwi instance, run this Python snippet:
from tcms_api import TCMS
kiwi = TCMS()
kiwi.exec.TestRun.update(82, {'build' : 7})
Expect:
The test run's product build is updated from 1 (unspecified) to 7.
Result:
Exception has occurred: xmlrpc.client.Fault
<Fault -32603: "Internal error: 'status'">
There's no other call stack information, so I'm at a loss to further debug. I've tried updating a couple of different fields (manager and status) with the same result. I also get the same result if the value I'm trying to update is unknown/invalid.
Additional information: the equivalent call to TestCaseRun.update() API works. I.e., I can update the build information on a TestCaseRun instance.
#s-manke. This is a genuine bug. I've implemented a hot-fix here: https://github.com/kiwitcms/Kiwi/pull/553
so you can at least continue to use the API.
I am in the process of cutting a new version anyway so this hot-fix will go in. However the API will not process status or stop_date fields at the moment.
I'm trying to automatically run an r script to download a private Google Sheet every hour. It always works fine when I'm interactively using R. It also works fine during the first hour after I automate the script with launchd.
It stops working an hour after I start automating it with launchd. I think the problem is that after one hour the access token changes, and the non-interactive version isn’t waiting for the auto refreshing of the OAuth token. Here is the error that I get from the error report:
Auto-refreshing stale OAuth token.
Error in gzfile(file, mode) : cannot open the connection
Calls: gs_auth ... -> -> cache_token -> saveRDS -> gzfile
In addition: Warning message:
In gzfile(file, mode) :
cannot open compressed file '.httr-oauth', probable reason 'Permission denied'
Execution halted
I'm using Jenny Bryan's googlesheets package. Here is the code that I initially use to register the sheet, and then save the oAuth token:
gToken <- gs_auth() # Run this the first time to get the oAuth information
saveRDS(gToken, "/Users/…/gToken.rds") # Save the oAuth information for non-interactive use
I then use the following script in the file that I automate with launchd:
gs_auth(token = "/Users/…/gToken.rds")
How can I avoid this error when running the script automatically with launchd?
I don't know about launchd but I had the same problem when I wanted to run a R script automatically from the Windows task planer. Changing the 'cache' attribute value to FALSE did the trick for me [1]: https://i.stack.imgur.com/pprlC.png
You can find the solution here: https://github.com/jennybc/googlesheets/issues/262
To authenticate once in the browser in order to get a token file, I did this:
token_file <- gs_auth(new_user = TRUE, cache = FALSE)
saveRDS(token_file, "googlesheets_token.rds")
Automatic login afterwards via:
gs_auth(token = paste0(path_scripts, "googlesheets_token.rds"),
verbose = TRUE, cache = FALSE)
I have built a pipeline on AppEngine that loads data from Cloud Storage to BigQuery. This works fine, ..until there is any error. How can I can loading exceptions by BigQuery from my AppEngine code?
The code in the pipeline looks like this:
#Run the job
credentials = AppAssertionCredentials(scope=SCOPE)
http = credentials.authorize(httplib2.Http())
bigquery_service = build("bigquery", "v2", http=http)
jobCollection = bigquery_service.jobs()
result = jobCollection.insert(projectId=PROJECT_ID,
body=build_job_data(table_name, cloud_storage_files))
#Get the status
while (not allDone and not runtime.is_shutting_down()):
try:
job = jobCollection.get(projectId=PROJECT_ID,
jobId=insertResponse).execute()
#Do something with job.get('status')
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
logging.error(traceback.format_exception(exc_type, exc_value, exc_traceback))
time.sleep(30)
This gives me status error, or major connectivity errors, but what I am looking for is functional errors from BigQuery, like fields formats conversion errors, schema structure issues, or other issues BigQuery may have while trying to insert rows to tables.
If any "functional" error on BigQuery's side happens, this code will run successfully and complete normally, but no table will be written on BigQuery. Not easy to debug when this happens...
You can use the HTTP error code from the exception. BigQuery is a REST API, so the response codes that are returned match the description of HTTP error codes here.
Here is some code that handles retryable errors (connection, rate limit, etc), but re-raises when it is an error type that it doesn't expect.
except HttpError, err:
# If the error is a rate limit or connection error, wait and
# try again.
# 403: Forbidden: Both access denied and rate limits.
# 408: Timeout
# 500: Internal Service Error
# 503: Service Unavailable
if err.resp.status in [403, 408, 500, 503]:
print '%s: Retryable error %s, waiting' % (
self.thread_id, err.resp.status,)
time.sleep(5)
else: raise
If you want even better error handling, check out the BigqueryError class in the bq command line client (this used to be available on code.google.com, but with the recent switch to gCloud, it isn't any more. But if you have gcloud installed, the bq.py and bigquery_client.py files should be in the installation).
The key here is this part of the pasted code:
except:
exc_type, exc_value, exc_traceback = sys.exc_info()
logging.error(traceback.format_exception(exc_type, exc_value, exc_traceback))
time.sleep(30)
This "except" is catching every exception, logging it, and letting the process continue without any consideration for re-trying.
The question is, what would you like to do instead? At least the intention is there with the "#Do something" comment.
As a suggestion, consider App Engine's task queues to check the status, instead of a loop with a 30 second wait. When tasks get an exception, they are automatically retried - and you can tune that behavior.