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Upgrade MLflow client #366

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Nov 9, 2023
Merged

Upgrade MLflow client #366

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093d9ec
Update mlflow and protobuf dependency
krithika369 Nov 9, 2023
f25e12b
Update user docs
krithika369 Nov 9, 2023
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6 changes: 5 additions & 1 deletion docs/how-to/create-a-router/configure-ensembler.md
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Expand Up @@ -104,7 +104,11 @@ Similar to requests sent to a Docker Ensembler, the request payload sent to a Py
original request, responses from all routes, and the treatment configuration (if an Experiment
Engine is selected, in the Configure Experiment Engine step). The ensembler's request headers will contain the original
request headers sent to Turing, merged with the enricher's response headers (if there are duplicates, the value in
the enricher's response headers will take precedence), and an identifier `Turing-Req-Id` that is uniquely assigned to each request received by the Router.
the enricher's response headers will take precedence), and an identifier `Turing-Req-Id` that is uniquely assigned to each request received by the Router.

**Note on compatibility**: The Pyfunc servers are compatible with `protobuf>=3.12.0,<5.0.0`. Users whose ensemblers have a strong dependency on Protobuf `3.x.x` are advised to pin the library version in their conda environment, when submitting the ensembler. If using Protobuf `3.x.x` with the UPI protocol, users can do one of the following:
* Use `protobuf>=3.20.0` - these versions support simplified class definitions and this is the recommended approach.
* If you must use `protobuf>=3.12.0,<3.20.0`, please pin `caraml-upi-protos<=0.3.6` in your ensembler’s conda environment.

To configure your router with a Pyfunc ensembler, simply select from the drop down list your desired ensembler,
registered in your current project. You'll also need to indicate your desired timeout value and resource request values:
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1 change: 0 additions & 1 deletion engines/pyfunc-ensembler-job/requirements.txt
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Expand Up @@ -2,7 +2,6 @@ cloudpickle==2.0.0
google-cloud-storage>=1.37.0
jinjasql==0.1.8
jinja2==3.0.3
mlflow>=1.2.0,<=1.23.0
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numpy==1.21.6
pandas==1.3.5
py4j==0.10.9
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4 changes: 1 addition & 3 deletions sdk/requirements.dev.txt
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Expand Up @@ -6,6 +6,4 @@ pytest
pytest-cov
urllib3-mock>=0.3.3
caraml-upi-protos
grpcio<1.49.0
grpcio-reflection<1.49.0
grpcio-tools<1.49.0
grpcio-reflection
4 changes: 2 additions & 2 deletions sdk/requirements.txt
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Expand Up @@ -2,12 +2,12 @@ cloudpickle==2.0.0
deprecation==2.1.0
fire
google-cloud-storage>=1.19.0
mlflow>=1.2.0,<=1.23.0
mlflow>=1.26.1,<2.0.0
# Numpy >= v1.24.0 is incompatible with our pinned versions of mlflow due to the deprecation of several common numpy
# aliases (see the last bullet point here: https://numpy.org/doc/stable/release/1.24.0-notes.html#expired-deprecations).
numpy<1.24.0
pandas
protobuf>=3.0.0,<4.0.0dev
protobuf>=3.12.0,<5.0.0 # Determined by the mlflow dependency
python_dateutil>=2.5.3
requests
urllib3>=1.25.3
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