k-lyda opened a new issue #12236:
URL: https://github.com/apache/airflow/issues/12236
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**Apache Airflow version**:
Apache Airflow [1.10.12]
**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):
Client Version: version.Info{Major:"1", Minor:"18", GitVersion:"v1.18.2",
GitCommit:"52c56ce7a8272c798dbc29846288d7cd9fbae032", GitTreeState:"clean",
BuildDate:"2020-04-16T11:56:40Z", GoVersion:"go1.13.9", Compiler:"gc",
Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"13", GitVersion:"v1.13.12",
GitCommit:"a8b52209ee172232b6db7a6e0ce2adc77458829f", GitTreeState:"clean",
BuildDate:"2019-10-15T12:04:30Z", GoVersion:"go1.11.13", Compiler:"gc",
Platform:"linux/amd64"}
**Environment**:
- **Cloud provider or hardware configuration**:
- **OS** (e.g. from /etc/os-release):
- **Kernel** (e.g. `uname -a`):
- **Install tools**:
- **Others**:
**What happened**:
I want to save logs on S3 storage. I've added proper configuration. Logging
is working fine, unless I add import of MLFlow library in any of the files.
I don't even have to use this tool, just
`from mlflow.tracking import MlflowClient`
is enough to break the logging to S3.
**What you expected to happen**:
There is probably some mismatch in s3 credentials, but I don't see any
specific error message in logs.
**How to reproduce it**:
1. Setup logging to s3 with proper configuration entries or env vars
```
AIRFLOW__CORE__REMOTE_LOGGING: "True"
AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER: "s3://bucket/airflow/logs"
AIRFLOW__CORE__REMOTE_LOG_CONN_ID: "s3_connection_id"
AIRFLOW__CORE__ENCRYPT_S3_LOGS: "False"
```
2. Logging to s3 should work fine in sample dag.
3. Add `from mlflow.tracking import MlflowClient` to DAG file - logging to
s3 is now not working.
**Anything else we need to know**:
This problem occurs every time, when MLFlow is imported to any file
processed by Airflow.
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