Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR contains the following updates:
==1.20.0
->==1.30.1
Release Notes
mlflow/mlflow (mlflow)
v1.30.0
Compare Source
MLflow 1.30.0 includes several major features and improvements
Features:
Delta
tables as a datasource in the ingest step (#7010, @sunishsheth2009)run_name
attribute forcreate_run
,get_run
andupdate_run
APIs (#6782, #6798 @apurva-koti)creation_time
andlast_update_time
for thesearch_experiments
API (#6979, @harupy)run_id IN
andrun ID NOT IN
for thesearch_runs
API (#6945, @harupy)user_id
andend_time
for thesearch_runs
API (#6881, #6880 @subramaniam02)run_name
andrun_id
for thesearch_runs
API (#6899, @harupy; #6952, @alexacole)name
attribute andmlflow.runName
tag (#6971, @BenWilson2)update_run()
API for modifying thestatus
andname
attributes of existing runs (#7013, @gabrielfu)mlflow gc
cli API (#6977, @shaikmoeed)evaluate()
API (#6728, @jerrylian-db)evaluate()
API (#7077, @dbczumar)BooleanType
tomlflow.pyfunc.spark_udf()
(#6913, @BenWilson2)Pool
class options forSqlAlchemyStore
(#6883, @mingyu89)Bug fixes:
SparkSession
if one does not exist (#6846, @prithvikannan)bool
column types in Step Card data profiles (#6907, @sunishsheth2009)mlflow.pyspark.ml.autolog()
(#6831, @harupy)mlflow-skinny
package to serve as base requirement inMLmodel
requirements (#6974, @BenWilson2)pos_label
tosklearn.metrics.precision_recall_curve
inmlflow.evaluate()
(#6854, @dbczumar)SqlAlchemyStore
whereset_tag()
updates the incorrect tags (#7027, @gabrielfu)Documentation updates:
Keras
serialization format (#7022, @balvisio)Small bug fixes and documentation updates:
#7093, #7095, #7092, #7064, #7049, #6921, #6920, #6940, #6926, #6923, #6862, @jerrylian-db; #6946, #6954, #6938, @mingyu89; #7047, #7087, #7056, #6936, #6925, #6892, #6860, #6828, @sunishsheth2009; #7061, #7058, #7098, #7071, #7073, #7057, #7038, #7029, #6918, #6993, #6944, #6976, #6960, #6933, #6943, #6941, #6900, #6901, #6898, #6890, #6888, #6886, #6887, #6885, #6884, #6849, #6835, #6834, @harupy; #7094, #7065, #7053, #7026, #7034, #7021, #7020, #6999, #6998, #6996, #6990, #6989, #6934, #6924, #6896, #6895, #6876, #6875, #6861, @prithvikannan; #7081, #7030, #7031, #6965, #6750, @bbarnes52; #7080, #7069, #7051, #7039, #7012, #7004, @dbczumar; #7054, @jinzhang21; #7055, #7037, #7036, #6949, #6951, @apurva-koti; #6815, @michaguenther; #6897, @chaturvedakash; #7025, #6981, #6950, #6948, #6937, #6829, #6830, @BenWilson2; #6982, @vadim; #6985, #6927, @kriscon-db; #6917, #6919, #6872, #6855, @WeichenXu123; #6980, @utkarsh867; #6973, #6935, @wentinghu; #6930, @mingyangge-db; #6956, @RohanBha1; #6916, @av-maslov; #6824, @shrinath-suresh; #6732, @oojo12; #6807, @ikrizanic; #7066, @subramaniam20jan; #7043, @AvikantSrivastava; #6879, @jspablo
v1.29.0
Compare Source
MLflow 1.29.0 includes several major features and improvements
Features:
mlflow pipelines get-artifact
CLI for retrieving Pipeline artifacts (#6517, @prithvikannan)mlflow pipelines
CLI command for reproducing a Pipeline run in the MLflow UI (#6376, @hubertzub-db)load_text()
,load_image()
andload_dict()
fluent APIs for convenient artifact loading (#6475, @subramaniam02)creation_time
andlast_update_time
attributes to the Experiment class (#6756, @subramaniam02)searchExperiments
API to Java client and deprecatelistExperiments
(#6561, @dbczumar)mlflow_search_experiments
API to R client and deprecatemlflow_list_experiments
(#6576, @dbczumar)mlflow.models.add_libraries_to_model()
API for adding libraries to an MLflow Model (#6586, @arjundc-db)mlflow.evaluate()
(#6582, @jerrylian-db)sample_weights
support tomlflow.evaluate()
(#6806, @dbczumar)pos_label
support tomlflow.evaluate()
for identifying the positive class (#6696, @harupy)mlflow.evaluate()
(#6593, @dbczumar)predict_proba()
support to the pyfunc representation of scikit-learn models (#6631, @skylarbpayne)/health
endpoint to scoring server (#6574, @gabriel-milan)variant_name
during Sagemaker deployment (#6486, @nfarley-soaren)data_capture_config
during SageMaker deployment (#6423, @jonwiggins)Bug fixes:
__main__
module when loading model code (#6647, @Jooakim)mlflow server
compatibility issue with HDFS when running in--serve-artifacts
mode (#6482, @shidianshifen)Documentation updates:
list_run_infos()
APIs (#6550, @dbczumar)mlflow.sagemaker.deploy()
in favor ofSageMakerDeploymentClient.create()
(#6651, @dbczumar)Small bug fixes and documentation updates:
#6803, #6804, #6801, #6791, #6772, #6745, #6762, #6760, #6761, #6741, #6725, #6720, #6666, #6708, #6717, #6704, #6711, #6710, #6706, #6699, #6700, #6702, #6701, #6685, #6664, #6644, #6653, #6629, #6639, #6624, #6565, #6558, #6557, #6552, #6549, #6534, #6533, #6516, #6514, #6506, #6509, #6505, #6492, #6490, #6478, #6481, #6464, #6463, #6460, #6461, @harupy; #6810, #6809, #6727, #6648, @BenWilson2; #6808, #6766, #6729, @jerrylian-db; #6781, #6694, @marijncv; #6580, #6661, @bbarnes52; #6778, #6687, #6623, @shraddhafalane; #6662, #6737, #6612, #6595, @sunishsheth2009; #6777, @aviralsharma07; #6665, #6743, #6573, @liangz1; #6784, @apurva-koti; #6753, #6751, @mingyu89; #6690, #6455, #6484, @kriscon-db; #6465, #6689, @hubertzub-db; #6721, @WeichenXu123; #6722, #6718, #6668, #6663, #6621, #6547, #6508, #6474, #6452, @dbczumar; #6555, #6584, #6543, #6542, #6521, @dsgibbons; #6634, #6596, #6563, #6495, @prithvikannan; #6571, @smurching; #6630, #6483, @serena-ruan; #6642, @thinkall; #6614, #6597, @jinzhang21; #6457, @cnphil; #6570, #6559, @kumaryogesh17; #6560, #6540, @iamthen0ise; #6544, @Monkero; #6438, @ahlag; #3292, @dolfinus; #6637, @ninabacc-db; #6632, @arpitjasa-db
v1.28.0
Compare Source
MLflow 1.28.0 includes several major features and improvements:
Features:
pipeline.yaml
configurations to specify the Model Registry backend used for model registration (#6284, @sunishsheth2009)transform
step of the scikit-learn regression pipeline (#6362, @sunishsheth2009)mlflow.search_experiments()
API for searching experiments by name and by tags (#6333, @WeichenXu123; #6227, #6172, #6154, @harupy)--older-than
flag tomlflow gc
for removing runs based on deletion time (#6354, @Jason-CKY)MLFLOW_SQLALCHEMYSTORE_POOL_RECYCLE
environment variable for recycling SQLAlchemy connections (#6344, @postrational)MlflowClient
importable asmlflow.MlflowClient
(#6085, @subramaniam02)stage
parameter toset_model_version_tag()
(#6185, @subramaniam02)--registry-store-uri
flag tomlflow server
for specifying the Model Registry backend URI (#6142, @Secbone)model_uri
optional inmlflow models build-docker
to support building generic model serving images (#6302, @harupy)Bug fixes and documentation updates:
xdg-open
instead ofopen
for viewing Pipeline results on Linux systems (#6326, @strangiato)mlflow.pyspark.ml.autolog()
to only log model signatures for supported input / output data types (#6365, @harupy)mlflow.tensorflow.autolog()
to log TensorFlow early stopping callback info whenlog_models=False
is specified (#6170, @WeichenXu123)mlflow.sklearn.autolog()
for models containing transformers (#6230, @dbczumar)mlflow gc
that occurred when removing a run whose artifacts had been previously deleted (#6165, @dbczumar)sqlparse
library to MLflow Skinny client, which is required for search support (#6174, @dbczumar)mlflow server
bug that rejected parameters and tags with empty string values (#6179, @dbczumar)--serve-arifacts
enabled (#6355, @abbas123456)mlflow deployments predict
CLI (#6323, @dbczumar)mlflow.pyfunc.spark_udf()
(#6244, @harupy)MlflowClient
frommlflow.tracking
tomlflow.client
(#6405, @dbczumar)CONTRIBUTING.rst
(#6330, @ahlag)Small bug fixes and doc updates (#6322, #6321, #6213, @KarthikKothareddy; #6409, #6408, #6396, #6402, #6399, #6398, #6397, #6390, #6381, #6386, #6385, #6373, #6375, #6380, #6374, #6372, #6363, #6353, #6352, #6350, #6351, #6349, #6347, #6287, #6341, #6342, #6340, #6338, #6319, #6314, #6316, #6317, #6318, #6315, #6313, #6311, #6300, #6292, #6291, #6289, #6290, #6278, #6279, #6276, #6272, #6252, #6243, #6250, #6242, #6241, #6240, #6224, #6220, #6208, #6219, #6207, #6171, #6206, #6199, #6196, #6191, #6190, #6175, #6167, #6161, #6160, #6153, @harupy; #6193, @jwgwalton; #6304, #6239, #6234, #6229, @sunishsheth2009; #6258, @xanderwebs; #6106, @balvisio; #6303, @bbarnes52; #6117, @wenfeiy-db; #6389, #6214, @apurva-koti; #6412, #6420, #6277, #6266, #6260, #6148, @WeichenXu123; #6120, @ameya-parab; #6281, @nathaneastwood; #6426, #6415, #6417, #6418, #6257, #6182, #6157, @dbczumar; #6189, @shrinath-suresh; #6309, @SamirPS; #5897, @temporaer; #6251, @herrmann; #6198, @sniafas; #6368, #6158, @jinzhang21; #6236, @subramaniam02; #6036, @serena-ruan; #6430, @ninabacc-db)
v1.27.0
Compare Source
MLflow 1.27.0 includes several major features and improvements:
[Pipelines] With MLflow 1.27.0, we are excited to announce the release of
MLflow Pipelines, an opinionated framework for
structuring MLOps workflows that simplifies and standardizes machine learning application development
and productionization. MLflow Pipelines makes it easy for data scientists to follow best practices
for creating production-ready ML deliverables, allowing them to focus on developing excellent models.
MLflow Pipelines also enables ML engineers and DevOps teams to seamlessly deploy models to production
and incorporate them into applications. To get started with MLflow Pipelines, check out the docs at
https://mlflow.org/docs/latest/pipelines.html. (#6115)
[UI] Introduce UI support for searching and comparing runs across multiple Experiments (#5971, @r3stl355)
More features:
ndarray
and tensor instances as metrics via themlflow.log_metric()
API (#5756, @ntakouris)CatBoostRanker
models to themlflow.catboost
flavor (#6032, @danielgafni)KernelExplainer
withmlflow.evaluate()
, enabling model explanations on categorical data (#6044, #5920, [@WeicheConfiguration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
🔕 Ignore: Close this PR and you won't be reminded about this update again.
This PR was generated by Mend Renovate. View the repository job log.