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License: MIT License
Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops
License: MIT License
Seems to me there could be value in showing the serialized models in minio (http://localhost:9000/) which can be accessed using creds in the .env file. This would show the serliazed model and the metadata:
You can get the same thing from the artifacts view under the mlfow experiments but seeing it in minio too might help show how the architecture hangs together.
Im working through your instructions and I get the following error in my Jenkins pipeline build:
/var/jenkins_home/workspace/CD4ML-Scenarios_master@tmp/durable-01e5a6c2/script.sh: line 1: pip3: not found script returned exit code 127
I've tried changing pip3 to pip, but I get the same error with pip: not found
.
Today when I run the pipeline in Jenkins I get this error:
[2022-05-26T11:09:07.506Z] Traceback (most recent call last):
[2022-05-26T11:09:07.506Z] File "/var/jenkins_home/workspace/CD4ML-Scenarios_master/run_python_script.py", line 50, in <module>
[2022-05-26T11:09:07.506Z] run_python_script(script, arguments, profiler=profiler)
[2022-05-26T11:09:07.506Z] File "/var/jenkins_home/workspace/CD4ML-Scenarios_master/run_python_script.py", line 26, in run_python_script
[2022-05-26T11:09:07.506Z] from scripts import register_model as executable_script
[2022-05-26T11:09:07.506Z] File "/var/jenkins_home/workspace/CD4ML-Scenarios_master/scripts/register_model.py", line 3, in <module>
[2022-05-26T11:09:07.506Z] from cd4ml.register_model import register_model
[2022-05-26T11:09:07.506Z] File "/var/jenkins_home/workspace/CD4ML-Scenarios_master/cd4ml/register_model.py", line 4, in <module>
[2022-05-26T11:09:07.506Z] import mlflow
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/__init__.py", line 34, in <module>
[2022-05-26T11:09:07.506Z] import mlflow.tracking._model_registry.fluent
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/tracking/__init__.py", line 8, in <module>
[2022-05-26T11:09:07.506Z] from mlflow.tracking.client import MlflowClient
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/tracking/client.py", line 16, in <module>
[2022-05-26T11:09:07.506Z] from mlflow.entities import Experiment, Run, RunInfo, Param, Metric, RunTag, FileInfo, ViewType
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/entities/__init__.py", line 6, in <module>
[2022-05-26T11:09:07.506Z] from mlflow.entities.experiment import Experiment
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/entities/experiment.py", line 2, in <module>
[2022-05-26T11:09:07.506Z] from mlflow.entities.experiment_tag import ExperimentTag
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/entities/experiment_tag.py", line 2, in <module>
[2022-05-26T11:09:07.506Z] from mlflow.protos.service_pb2 import ExperimentTag as ProtoExperimentTag
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/protos/service_pb2.py", line 18, in <module>
[2022-05-26T11:09:07.506Z] from .scalapb import scalapb_pb2 as scalapb_dot_scalapb__pb2
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/mlflow/protos/scalapb/scalapb_pb2.py", line 29, in <module>
[2022-05-26T11:09:07.506Z] options = _descriptor.FieldDescriptor(
[2022-05-26T11:09:07.506Z] File "/usr/local/lib/python3.9/dist-packages/google/protobuf/descriptor.py", line 560, in __new__
[2022-05-26T11:09:07.506Z] _message.Message._CheckCalledFromGeneratedFile()
[2022-05-26T11:09:07.506Z] TypeError: Descriptors cannot not be created directly.
But it was working for me yesterday.
There were commits yesterday and also I am now using rancher desktop but I suspect this is about pinning versions just like databrickslabs/dbx#257 for which the solution is databrickslabs/dbx@bf56196
Hi there I am preparing for the CD4ML workshop to be conducted today and I have an issue with building the project in my jenkins environment. I get the following error during the build
org.codehaus.groovy.control.MultipleCompilationErrorsException: startup failed:
WorkflowScript: 9: Invalid option type "timestamps". Valid option types: [authorizationMatrix, buildDiscarder, catchError, checkoutToSubdirectory, disableConcurrentBuilds, disableResume, durabilityHint, newContainerPerStage, overrideIndexTriggers, parallelsAlwaysFailFast, preserveStashes, quietPeriod, rateLimitBuilds, retry, script, skipDefaultCheckout, skipStagesAfterUnstable, timeout, waitUntil, warnError, withContext, withCredentials, withEnv, ws] @ line 9, column 8.
timestamps()
In 1-SystemSetup.md it says “you could run into an SSL error when attempting to run the download data scripts later”. What download data scripts? Is that for a different problem and not housing? I ignored this section for housing and was fine. I've created a PR for other clarifications but was not sure about how to clarify this one.
I tried running with rancher desktop. The docker-compose up gave me:
error getting credentials - err: exec: "docker-credential-desktop": executable file not found in $PATH, out
So then I installed docker-credential-helper with:
brew install docker-credential-helper
Actually to get the installation to work I had to make a docker dir as I had uninstalled docker desktop.
Then I had docker-credential-helper. But that alone did not fix my issue. Perhaps because I didn't change credsStore in the docker config.json at that point.
What I then did was change credsStore to credStore in the docker config.json and then docker-compose up worked. Possibly that change alone might have been enough, not sure.
The experiments UI works fine at http://localhost:12000/#/
Not so much models at http://localhost:12000/#/models
Issue seems to match https://stackoverflow.com/questions/63255631/mlflow-invalid-parameter-value-unsupported-uri-mlruns-for-model-registry-s
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