It was written in the Microsoft AzureML documentation, "A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial" and A Run object is also created when you submit or start_logging with the Experiment class."
Related to start_logging, as far as I know, when we have simply started the run by executing this start logging method. We have to stop, or complete by complete method when the run is completed. This is because start_logging is a synchronized way of creating an experiment. However, Run object created from start_logging is to monitor the asynchronous execution of a trial.
Can anyone clarify whether start_logging will start asynchronous execution or synchronous execution?
start_logging will be considered as asynchronous execution as this generates the multiple interactive run sessions. In a specific experiment, there is a chance of multiple interactive sessions, that work parallelly and there will be no scenario to be followed in sequential.
The individual operation can be performed and recognized based on the parameters like args and kwargs.
When the start_logging is called, then an interactive run like jupyter notebook was created. The complete metrics and components which are created when the start_logging was called will be utilized. When the output directory was mentioned for each interactive run, based on the args value, the output folder will be called seamlessly.
The following code block will help to define the operation of start_logging
experiment = Experiment(your_workspace, "your_experiment_name")
run = experiment.start_logging(outputs=None, snapshot_directory=".", display_name="test")
...
run.log_metric("Accuracy_Value", accuracy)
run.complete()
the below code block will be defining the basic syntax of start_logging
start_logging(*args, **kwargs)
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I am currently using Python's Threading to parallelize the execution of multiple Databricks notebooks. These are long-running notebooks, and I need to add some logic for killing the threads in the case where I want to restart the execution with new changes. When re-executing the master notebook without killing the threads, the cluster is quickly filled with computational heavy, long-lived threads, leaving little space for the actually required computations.
I have tried these suggestions without luck. Furthermore, I have tried getting the runId from dbutils.notebook.run() and killing the thread with dbutils.notebook.exit(runId), but since the call to dbutils.notebook.run() is synchronous, I am unable to obtain the runId before the notebook has executed.
I would appreciate any suggestion on how to solve this issue!
Hello #sondrelv and thank you for your question. I would like to clarify that dbutils.notebook.exit(value) is not used for killing other threads by runId. dbutils.notebook.exit(value) is used to cause the current (this) thread to exit and return a value.
I see the difficulty of management without an available interrupt inside notebook code. Given this limitation, I have tried to look for other ways to cancel the threads.
One way is to use other utilities to kill the thread/run.
Part of the difficulty in solving this, is that threads/runs created through dbutils.notebook.run() are ephemeral runs. The Databricks CLI databricks runs get --run-id <ephemeral_run_id> can fetch details of an ephemeral run. If details can be fetched, then the cancel should also work (databricks runs cancel ...).
The remaining difficulty is getting the run ids. Ephemeral runs are excluded from the CLI runs list operation databricks runs list.
As you noted, the dbutils.notebook.run() is synchronous, and does not return a value to code until it finishes.
However, in the notbook UI, the run ID and link is printed when it starts. There must be a way to capture these. I have not yet found how.
Another possible solution, would be to create some endpoint or resources for the child notebooks check whether they should continue execution or exit early using dbutils.notebooks.exit(). Doing this check between every few cells would be similar to the approaches in the article you linked, just applied to a notebook instead of a thread.
I see that Origen supports passing jobs to the program command in this video. What would be the preferred method to run the program command in a job loop (i.e. job == 'ws' then job == 'ft', etc.).
thx
The job is a runtime concept, not a compile/generate time concept, so it doesn't really make sense to run the program command (i.e. generate the program) against different settings of job.
Origen doesn't currently provide any mechanism to pass define-type arguments through to the program generator from the command line, though you could implement that in your app easily enough by overriding the program command - i.e. capture and store them somewhere in your app and then continue with the regular command.
The 'Origen-way' of doing things like this is to setup different target files with different variables set within them, then execute the program command for the different targets.
I have a SoapUI test which uses an input file to read lines as input of requests. So there is a loop which reads data and execute request and write output to file. Response times are too long, so processing of this file should be done asynchronously, but I am not sure, how SoapUI can handle this. There is file attachment in SOAP requests, which is not handled by current version of JMeter.
As per the SoapUI's documentation below, both test cases or test suites can be executed in Parallel mode.
In the case of TestSuites and TestCases these can be executed either in sequence or parallell, as configured with the corresponding toolbar buttons.
In the above image, first one in the marked image stands for sequential execution and the second one (with multiple parallel arrows) stands for Parallel execution mode.
User can select either of the one before executing the tests.
Hope this helps.
Note that SOAPUI does not allows test steps to be executed in parallel. If you need any custom execution i.e., same test case and steps to be executed in Parallel, here is sample project done for that. It can be used as reference and apply it to your case.
I understood this question as requiring the ability to call a service asynchronously due to the time it takes to process. So, by this, I mean SoapUI makes a request to a web service and instead of waiting for it, it carries on. At some point later, SoapUI receives the response.
SoapUI can handle this, I haven't tried it myself, but when reading some guides recently, I noticed it can be done.
See....
Blog Guide
SoapUI Forum
In short, it involves setting up a mock service to receive the response, which can then be validated.
I have a trigger on one of the DB2 tables. What i need is, every time that trigger runs, it should invoke a Linux shell script.
How to do this-- same as any other process? If my trigger was to invoke a Java process instead of a shell script, putting the bytecode (.class file) of that process to ..SQLLIB/function and defining a function for it would do the job.
Is this much different for Linux script, any subtleties?
I don't have Linux for another few days but deployment is right around the corner and nervous at it just the same.
TIA.
You cannot invoke a shell script from SQL, including from a trigger. What you can do is create a Java UDF the way you described, then use Runtime.exec() to invoke the script.
Note that your approach, apart from introducing security risks, can affect data consistency (a transaction can be rolled back, but you cannot "unrun" the script) and concurrency (a transaction with all its acquired locks will have to wait until your script returns).
A better approach would be to use an asynchronous process to decouple the external action from the database transaction. As an example, your trigger might insert a record into a log table, and the external process would read that log table on schedule, perform the required action when a new record is found, then delete the processed record.
There is an good article about
Making Operating System Calls from SQL
which includes sample code.
I'm developing an Objective C application using Parse. I understand why, when I make a Parse query to the server, that I would need to perform the query in background and run code in a completion handler / callback when that call has completed. And this is what I do when I initially launch the app and download some data tables.
However, I then pin all these Parse objects locally and subsequently make queries to this data using the LocalDataStore option. Do I still need to execute these calls in background? If I remove the background option with these calls, the code runs fine but I still get the warning in the console:
Warning: A long-running operation is being executed on the main thread.
Break on warnBlockingOperationOnMainThread() to debug.
If I'm performing local Parse queries can I treat this simply as a warning (and ignore it) or do I still need to treat these queries as operations that should be performed in a background thread ? Any advice would be appreciated. Thanks.