Known Issues and Limitations
- Known Issues with Model Builds and Deployed Models
Re-deploying or re-building models results in model downtime (usually brief).
Re-starting Cloudera Machine Learning does not automatically restart active models. These models must be manually restarted so they can serve requests again.
Cloudera Bug: DSE-4950
Model builds will fail if your project filesystem includes a
.gitdirectory (likely hidden or nested). Typical build stage errors include:
Error: 2 UNKNOWN: Unable to schedule build: [Unable to create a checkpoint of current source: [Unable to push sources to git server: ...
To work around this, rename the
.gitdirectory (for example,
NO.git) and re-build the model.
Cloudera Bug: DSE-4657
JSON requests made to active models should not be more than 5 MB in size. This is because JSON is not suitable for very large requests and has high overhead for binary objects such as images or video. Call the model with a reference to the image or video, such as a URL, instead of the object itself.
Any external connections, for example, a database connection or a Spark context, must be managed by the model's code. Models that require such connections are responsible for their own setup, teardown, and refresh.
Model logs and statistics are only preserved so long as the individual replica is active. Cloudera Machine Learning may restart a replica at any time it is deemed necessary (such as bad input to the model).
(MLLib) The MLLib
model.save()function fails with the following sample error. This occurs because the Spark executors on CML all share a mount of
/home/cdswwhich results in a race condition as multiple executors attempt to write to it at the same time.
Caused by: java.io.IOException: Mkdirs failed to create file:/home/cdsw/model.mllib/metadata/_temporary ....Recommended workarounds:
- Save the model to
/tmp, then move it into
/home/cdswon the driver/session.
- Save the model to either an S3 URL or any other explicit external URL.
- Save the model to
Scala models are not supported.
Spawning worker threads are not supported with models.
- Models deployed using Cloudera Machine Learning Private Cloud
are highly available subject to the following limitations:
- Model high availability is dependent on the high availability of the Kubernetes service. If using a third-party Kubernetes service to host CDP Private Cloud, please refer to your chosen provider for precise SLAs.
- In the event that the Kubernetes pod running either the model proxy service or the load balancer becomes unavailable, the Model may be unavailable for multiple seconds during failover.
Dynamic scaling and auto-scaling are not currently supported. To change the number of replicas in service, you will have to re-deploy the build.