I have a few questions regarding building graph with OTP (OpenTripPlanner) containing GTFS data and OSM data. I am using GTFS and OSM data for Berlin.
I read here that OTP version 2 does not support isochrone and surface analysis features. This is from November 2021. Has this changed since then?
Using the same GTFS and OSM data I am able to build the graph with OTP v2 but not with OTP v1.5. I get an error as shown below while building the graph with OTP v1.5. It seems a field is missing in pathways.txt file. However, before changing any of the GTFS files I would like to know if anyone has had this error and does adding a field will resolve the error?
Exception in thread "main" org.onebusaway.csv_entities.exceptions.CsvEntityIOException: io error: entityType=org.onebusaway.gtfs.model.Pathway path=pathways.txt lineNumber=2
Caused by: org.onebusaway.csv_entities.exceptions.MissingRequiredFieldException: missing required field: pathway_type
Command used to build the graph:
java -Xmx4G -jar otp-1.5.0-shaded.jar --build graphs/current --port 9090
I am also trying to start the OTP v1.5 server by loading an already build graph using the switch --load but, it throws error Unknown option --load. How can I load an already build graph to start the otp server in version 1.5?
Related
I am using mlflow with sqlite backend. started the server with:
mlflow server --backend-store-uri sqlite:///mlruns_db/mlruns.db --default-artifact-root $PWD/mlruns --host 0.0.0.0 -p 5000
in the code, I log the model with signature as such
...
signature = infer_signature(X, y)
mlflow.sklearn.log_model(model, model_name, signature=signature)
...
then I get warnings
2022/05/26 19:52:17 WARNING mlflow.models.model: Logging model metadata to the tracking server has failed, possibly due older server version. The model artifacts have been logged successfully under ./mlruns/1/d4c8f611d3f24986a32d19c7d8b03f06/artifacts. In addition to exporting model artifacts, MLflow clients 1.7.0 and above attempt to record model metadata to the tracking store. If logging to a mlflow server via REST, consider upgrading the server version to MLflow 1.7.0 or above.
I am using mlflow, version 1.24.0, though.
I see that the signature is correctly logged inside MLmodel file, but the nice rendering of mlflow ui is lost.
with logging signature
mlflow ui with logging signature
without logging signature
mlflow ui without logging signature
Does this have any consequence later when serving models with signature enforcement?
Also, I see many blog examples with postgres instead of sqlite, and sftp/minio instead of filestore. maybe changing to those setups will solve this?
Had a similar issue. Even after running both server and local on 1.30
you can import this:
import logging
logging.getLogger("mlflow").setLevel(logging.DEBUG)
The logging told me it was because the size of the paramaters exceeded the character limit of 5000. This was because my model signature was about 10k in length.
I'm using the latest ibm_watson_machine_learning SDK (python)
Until a few days/weeks ago my code was working fine but now I get an error when running
client.repository.store_model(model='./model.tar.gz', meta_props=model_metadata)
Here is some sample code:
https://github.com/IBMDecisionOptimization/oplrunonwml
Exception has occurred: IndexError
list index out of range
File "C:\Temp\oplrunonwml\oprunonwmlv2.py", line 126, in main
model_details = client.repository.store_model(model='./model.tar.gz', meta_props=model_metadata)
File "C:\Temp\oplrunonwml\oprunonwmlv2.py", line 215, in <module>
main(sys.argv[1:])
I get this error while using various different models (OPL/Cplex/Docplex) and they all fail with this error.
What's strange, is that the model is uploaded correctly in the Deployment Space and I can use it without problems in deployment/jobs on the UI or on other scripts.
The code was working fine without any changes a few weeks ago so I assume something's changed on the API side
Update:
I'm using a Cloud Lite account.
I'm also using the latest version of the SDK
client = APIClient(wml_credentials)
print(client.version) # 1.0.29
print(client.version_param) #2020-08-01
I deleted all my IBM services (ObjectStorage,WatsonStudio) and created new ones but I still get the same error.
I would suspect the WML v2 instances deployement.
*** With V2 plan, user need to use updated Python SDK (ibm-watson-machine-learning 1.0.38) ***
If you had a v1 iunstance before and according to your plan, it might have been keeping working withoutmirgation for a while.
May be you reached the end of this compatibility period.
Can you clarify your plan type?
See https://medium.com/#AlainChabrier/migrate-your-python-code-for-do-in-wml-v2-instances-710025796f7
Alain
We have developed two interactive XDP which have some pre-population and binding of data and they are generated in interactive PDF. Whenever we deploy the XDP in our ETE environment everything is perfect and works fine. We have developed a rest API which generate the PDF and bind values from front end.
The problem is whenever we deploy the XDP in QA environment and try to consume and bind dynamic values to XDP and generate the same PDF documents consuming the same rest API we get failure in generating the documents. I check the error logs of AEM instance and I am getting below. Please can somebody help me out here as we are not able to find what is the root cause of this failure specific to QA Environment.
09.07.2019 16:53:13.307 *ERROR* [10.52.160.35 [1562683992994] POST /content/AemFormsSamples/renderpdfform.html HTTP/1.1] com.adobe.fd.readerextensions.service.impl.ReaderExtensionsServiceImpl AEM-REX-001-008: Unable to apply the requested usage rights to the given document.
java.lang.NullPointerException: null
at com.adobe.xfa.form.FormModel.preSave(FormModel.java:7242)
at com.adobe.xfa.form.FormModel.preSave(FormModel.java:7159)
at com.adobe.xfa.form.FormModel.preSave(FormModel.java:7159)
at com.adobe.xfa.form.FormModel.preSave(FormModel.java:7159)
at com.adobe.xfa.form.FormModel.preSave(FormModel.java:7159)
at com.adobe.xfa.form.FormModel.preSave(FormModel.java:7159)
I trained a model using watson machine learning service. The training process has completed so I ran these command lines to deploy it:
bx ml store training-runs model-XXXXXXX
I get the output with the model ID
Starting to store the training-run 'model-XXXXXX' ...
OK
Model store successful. Model-ID is '93sdsdsf05-3ea4-4d9e-a751-5bcfbsdsd3391'.
Then I use the following to deploy it :
bx ml deploy 93sdsdsf05-3ea4-4d9e-a751-5bcfbsdsd3391 "my-test-model"
The problem is that I'm getting an endless message saying:
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
When I check in COS result bucket the model size is ~25MB so it shouldn't be that long to deploy. Am I missing something here ?
Deploying the same model using Python Client API:
from watson_machine_learning_client import WatsonMachineLearningAPIClient
client = WatsonMachineLearningAPIClient(wml_credentials)
deployment_details = client.deployments.create( model_id, "model_name")
This showed me very quickly that there is an error with the deployment. The strange thing is that the error doesn't pop up when deploying with command line interface (CLI).
Os is Mac Os Maverick.
In a jhipster context (last version, 1.2.2), I get an error when I request the default application on an entity I have just generate using yo jhipster:entity generator.
I run a yo jhipster to create a vanilla application with mongodb as database, java 7 and nothing special.
Then I run grunt build and grunt server for hot reload on the client part and mvn spring-boot:run for the server side app.
When I go to the http://localhost:8080/ url, I get the normal page. I can sign in with either the user or admin login.
I run the yo jhipster:entity foo to get an exemple of rest service in the back end.
When I request for the foo resource with the URL http://localhost:8080/#/foo, I get the page to CRUD the resource as it is said on the jhipster website.
But when I try to create a foo item with the modal form, I get an error on the back end server log ([WARN] org.springframework.web.servlet.PageNotFound - Request method 'POST' not supported).
I can't figure out how to solve this.
Do I miss something in the documentation ?
Do you have any idea, hint ?
I have the same issue using H2 as development database instead of mongodb.
Thanks.
Hervé
This might be due to MongoDB, if you have a date field.
We will release very soon a new and improved Entity sub-generator, which should work better for you. While testing it, I had a serialization issue with MongoDB and a date field, and I corrected it in this new version. This is due to Jackson which can't serialize Joda Time dates (the correct annotations were only generated for SQL databases, not NoSQL databases)