背景
mlflow 的更新迭代速度还是很快的，平均一个月一个大版本的更新，截止到11月1号，已经更新到了1.11.0版本
我们查看mlflow release,就能看到早在1.10.0版本，就提供了对model registry的更好的feature支持，以及能够对实验进行逻辑删除操作，
而这些features 在mlflow 1.4.0是没有的，特别是删除实验的特性，如果实验很多的情况下，我们看到的实验是杂乱无章的，很不方便我们进行管理，所以我们进行mlflow的升级
升级以及准备
参照之前mlflow的搭建使用 ，我们先建立mlflow 1.4.0 和mlflow 1.11.0的conda环境
假设你已经建立好了对应的conda环境，且分别为mlflow-1.4.0 和mlflow-1.11.0 则执行：
conda activate mlflow-1.11.0参考mlflow db upgrade ，执行
mlflow db upgrade mysql://user:passwd@host:port/db
如：mlflow db upgrade mysql://root:root@localhost/mlflow其中
| 名词 | 解释 |
|---|---|
| user | 数据库的用户名 |
| passwd | 数据库的密码 |
| host | 数据库的主机地址 |
| port | 数据库的端口，如默认为3306则可以省略 |
| db | 数据库的database |
如果执行成功则会看到如下输出信息:
2020/11/02 10:24:50 INFO mlflow.store.db.utils: Updating database tables
INFO [alembic.runtime.migration] Context impl MySQLImpl.
INFO [alembic.runtime.migration] Will assume non-transactional DDL.
INFO [alembic.runtime.migration] Running upgrade 2b4d017a5e9b -> cfd24bdc0731, Update run status constraint with killed
INFO [alembic.runtime.migration] Running upgrade cfd24bdc0731 -> 0a8213491aaa, drop_duplicate_killed_constraint
WARNI [0a8213491aaa_drop_duplicate_killed_constraint_py] Failed to drop check constraint. Dropping check constraints may not be supported by your SQL database. Exception content: (MySQLdb._exceptions.ProgrammingError) (1064, "You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near 'CHECK status' at line 1")
[SQL: ALTER TABLE runs DROP CHECK status]
(Background on this error at: http://sqlalche.me/e/f405)
INFO [alembic.runtime.migration] Running upgrade 0a8213491aaa -> 728d730b5ebd, add registered model tags table
INFO [alembic.runtime.migration] Running upgrade 728d730b5ebd -> 27a6a02d2cf1, add model version tags table
INFO [alembic.runtime.migration] Running upgrade 27a6a02d2cf1 -> 84291f40a231, add run_link to model_version如果此时再在mlflow 1.4.0的环境下 再执行：
mlflow server \
--backend-store-uri mysql://root:root@localhost/mlflow \
--host 0.0.0.0 -p 5002 \
--default-artifact-root s3://mlflow就会报错：
2020/11/02 10:25:41 ERROR mlflow.cli: Error initializing backend store
2020/11/02 10:25:41 ERROR mlflow.cli: Detected out-of-date database schema (found version 84291f40a231, but expected 2b4d017a5e9b). Take a backup of your database, then run 'mlflow db upgrade ' to migrate your database to the latest schema. NOTE: schema migration may result in database downtime - please consult your database's documentation for more detail.
Traceback (most recent call last):
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/cli.py", line 263, in server
initialize_backend_stores(backend_store_uri, default_artifact_root)
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/server/handlers.py", line 97, in initialize_backend_stores
_get_tracking_store(backend_store_uri, default_artifact_root)
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/server/handlers.py", line 83, in _get_tracking_store
_tracking_store = _tracking_store_registry.get_store(store_uri, artifact_root)
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/tracking/_tracking_service/registry.py", line 37, in get_store
return builder(store_uri=store_uri, artifact_uri=artifact_uri)
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/server/handlers.py", line 54, in _get_sqlalchemy_store
return SqlAlchemyStore(store_uri, artifact_uri)
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/store/tracking/sqlalchemy_store.py", line 99, in __init__
mlflow.store.db.utils._verify_schema(self.engine)
File "/Users/ljh/opt/miniconda3/envs/mlflow-1.4.0-dev/lib/python3.6/site-packages/mlflow/store/db/utils.py", line 52, in _verify_schema
"more detail." % (current_rev, head_revision))
mlflow.exceptions.MlflowException: Detected out-of-date database schema (found version 84291f40a231, but expected 2b4d017a5e9b). Take a backup of your database, then run 'mlflow db upgrade ' to migrate your database to the latest schema. NOTE: schema migration may result in database downtime - please consult your database's documentation for more detail. 这说明升级成功
此时再在mlflow 1.11.0的conda环境下执行：
mlflow server \
--backend-store-uri mysql://root:root@localhost/mlflow \
--host 0.0.0.0 -p 5003 \
--default-artifact-root s3://mlflow就能正常的看到页面,这样mlflow 从1.4.0到1.11.0的升级就完成了
注意事项
如果是线上操作，则先备份数据库，因为该升级不一定能保证升级成功，如升级失败,直接从备份数据库恢复或者参照失败处理进行处理