No such luck, the file doesn't seem to exist. Here's the output on my local:* "ls: /tmp/model/0001/model.joblib: No such file or directory"*
and *"/tmp/model/0001/model.joblib: cannot open `/tmp/model/0001/model.joblib' (No such file or directory)"* and on the cloud shell: *"ls: cannot access '/tmp/model/0001/model.joblib': No such file or directory"* and *"/bin/sh: 1: file: not found".* On Fri, Jan 25, 2019 at 1:29 PM Bill Ross <r...@cgl.ucsf.edu> wrote: > Dumb generic cross-check from supporting compchem code in the day: What do > these give? Might yield a clue, e.g. all model files seeing this got > corrupted somehow. > > $ file */tmp/model/0001/model.joblib* > > *$ ls -l **/tmp/model/0001/model.joblib* > > > On 1/25/19 9:26 AM, Liam Geron wrote: > > Hi scikit learn contributors, > > I am currently attempting to transfer our preexisting models into cloud ML > for scalability, however I am encountering bugs while running through some > tutorial code found here ( > https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/sklearn/notebooks/Online%20Prediction%20with%20scikit-learn.ipynb > ). > > On both my local machine in a virtual environment and on the cloud shell > I'm encountering errors when it comes to version creation and online > prediction. For version creation on my local machine and on the cloud shell > I'm encountering this error: *"ERROR: (gcloud.ml-engine.versions.create) > Bad model detected with error: "Failed to load model: Could not load the > model: /tmp/model/0001/model.joblib. 32. (Error code: 0)""* with Python > 3.6.4 (local) and Python 3.5.6 (cloud shell) when running the command: > > *"gcloud ml-engine versions create $VERSION_NAME \* > * --model $MODEL_NAME \* > * --config config.yaml"* > > Any help would be greatly appreciated. > > Thank you, > Liam Geron > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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