- The MLRun Client (in version >= 1.5.2) does not support OS Windows,
see ML-4907. Know issues:
- mistake in datatime conversion under python 3.9 and OS Windows, see issue and RedisNoSqlTarget - OSError: [Errno 22] Invalid argument
- it is necessary to use Linux path (not Windows path) for some cases e.g. for CSVTarget
- missing MLRun tests under OS Windows
- probably others (without full tracking and addition details)
NOTE: Solution, it is necessity to use WSL2 under OS Windows
- The data read (via feature vector) accept only ONE on-line and
one off-line target in FeatureSet, see Slack discussion
- in case of e.g. more on-line targets, it is not possible to choose relevant target for FeatureVector
- Issue with support date type
NOTE: It is in preview version, very limited with focus on MySQL only, see detail below
-
SQLTarget limits
- missing support MORE primary keys (only ONE primary key is supported right now)
- schema for mapping FeatureStore to Table must be defined manually (not automatically)
- see detail mlrun/mlrun#5051
-
SqlTarget is limited to MySql, if you need to create table (SqlTarget is in Technical Preview)
- see the detail mlrun/mlrun#5231
- NOTE: It is possible to use work-arround, create table before the ingest
-
SqlTarget issue with save/load content mapping for SqlTarget
- see the detail mlrun/mlrun#5238
- It is not possible to use feature vector operations in KafkaTarget
- but it is possible to do ingest to the KafkaTarget or consume data from KafkaSource (with triggers)
- it means, test scenarios TS401, TS501, TS502, TS601 and TS701 for KafkaTarget was switch-off
- It is necessery to ingest sample data to featureset, before kafka ingest
- known issue from MLRun version v1.1.0, see ML-2407
- It is necessery to ingest sample data to featureset, before http ingest
- CSVSource supports only default CSV setting, it means sep=',', decimal='.'. It is not
possible to use e.g. na_filter=True (it is the issue for nan value or empty strings).
- in case of different setting, it is better to use Pandas/DataFrame source (it has bigger variability)
-
The step DateExtractor, in case of part e.g. 'is_month_end', it generated the warning Converting input from bool to <class 'numpy.uint8'> for compatibility.
-
Relation MapValues and storey.Filter see
- Workarround: Use MapValues with parameter with_original_features=True
- Clarification of issues with relation to the source/target and setting of InferOptions. See, full detail
-
Not to use the engine
pandas
- this
pandas
engine is useful only for test purpose (see the first info about that in change log for MLRun version 1.6.0)
- this
-
Issue with project delete
- see mistake in MLRun versions 1.6.0, 1.6.1, 1.6.2, 1.6.3
- expected solution in MLRun 1.7.0
- It is really tricky to use functions in Preview state
- it makes sense to wait for full version delivery or contribute changes in Github