Comments (7)
I'm trying to run ParallelRunStep:
parallel_run_config = ParallelRunConfig( source_directory=experiment_folder, entry_script='training_file.py', mini_batch_size="1KB", error_threshold=10, output_action="append_row", environment=registered_env, compute_target=pipeline_cluster, node_count=2, process_count_per_node=4) train_step = ParallelRunStep( name="Paralle Run step", parallel_run_config=parallel_run_config, inputs = [data_dataset.as_named_input('inp_data')], output = output_dir, allow_reuse=False)
But getting following error:
Traceback (most recent call last): File "driver/amlbi_main.py", line 275, in <module> main() File "driver/amlbi_main.py", line 226, in main sys.exit(exitcode_candidate) SystemExit: 42
Can anyone please help me with this issue?
@umangkhare @juichiache i met the same problem. Eventually i could find the error inside output+logs --> logs/, inside the folder there're joberror_xxx.txt & jobresult.txt, which contain the real error message
from machinelearningnotebooks.
ParallelRunStep doesn't show the outputs like usual PythonRunStep. I'd suggest to read the file 'readme.txt' under logs folder to understand how the logging works in ParallelRunStep. It's somewhat tricky to read these logs here. Also, you should consider analyzing CPU utilization and memory usage under 'Monitoring' of the step to get a better understanding.
You will always get 'SystemExit: 42' error irrespective of your pipeline success or failure. You should try tuning the parameters- 'no of processes' and 'mini_batch_size' to run your pipeline properly.
Note- It may take you some time, just have patience.😁
from machinelearningnotebooks.
I have the same issue. Inference was working a couple months back.
Check the log files in output+logs -> user -> stdout -> 1 -> process000.std.txt for detailed error.
from machinelearningnotebooks.
I think this is not an error which results in failing the pipeline. It eventually results in output logs every time when ParallelRunStep is used. I tried after correcting my other problems in code, and it worked out. Thanks for the support.
from machinelearningnotebooks.
Hi, I am facing the same issue. Kindly help where should we look at to resolve this.
from machinelearningnotebooks.
Hi, I am facing the same issue. Kindly help where should we look at to resolve this.
same for me. Parallel jobs did not start, indeed it was pending for some time and then died. However, the job was shown as Successful eventually
from machinelearningnotebooks.
How can I see the real error? You have any idea?
from machinelearningnotebooks.
Related Issues (20)
- azureml-pipeline-steps issue HOT 2
- Datastore.upload method is working, but impossible to delete files after HOT 2
- PipelineRun.getSteps returns list of Step objects, not StepRun HOT 1
- Using compute target name as a PipelineParameter
- Model doesn't run after submit - local compute
- ErrorResponseException: Invalid Enpoint
- Error importing PipelineComponentBatchDeployment from 'azure.ai.ml.entities'
- Diabetes catalog link might be broken
- import error
- AttributeError: '_OfflineRun' object has no attribute 'experiment'
- Improve the documentation
- Bug: `exclude_*` credentials in DefaultAzureCredential in azureml-mlflow
- model.predict_proba is not working in scoring script for classification
- Typo "a Azure"→"an Azure"
- Pyarrow<=11 upper bound
- Empezando
- Feature Request: Pipelines support for Ray Distribution
- TypeError: argument of type 'azureml.dataprep.rslex.StreamInfo' is not iterable HOT 4
- duplicates
- Parallel run example fail when executing with deserialization error HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from machinelearningnotebooks.