Change log

Change log#

v1.7.1 | v1.7.0
v1.6.4 | v1.6.3 | v1.6.2 | v1.6.1 | v1.6.0
v1.5.2 | v1.5.1 | v1.5.0
v1.4.1 | v1.4.0
v1.3.4 | v1.3.3 | v1.3.2 | v1.3.1 | v1.3.0
v1.2.3 | v1.2.2 | v1.2.1 | v1.2.0
v1.1.3 | v1.1.2 | v1.1.1 | v1.1.0
v1.0.6 | v1.0.5 | v1.0.4 | v1.0.3 | v1.0.2 | v1.0.0
Open issues
Limitations
Deprecations and removed code

v1.7.1 (2 December 2024)#

Model monitoring#

ID	Description
ML-7731	Model monitoring can now be run on a larger scale, using MLRun's additional replicas/workers. To benefit from the scale-out: After upgrading to v1.7.1, in projects that already have model monitoring enabled, run `disable model-monitoring` followed by `enable_model_monitoring`.

Breaking changes#

ID	Description
ML-7801	When running a remote/scheduled workflow, the remote workflow pulls/extracts the remote source content to the running pod but loads the project configuration from the MLRun DB and not from the `project.yaml` file in the remote source. See more details in Scheduling a workflow.

Closed issues#

ID	Description
ML-7285	Improved the response time when loading the Feature sets page.
ML-7967	Fixed the `_ApplicationErrorHandler` message.
ML-8001	Job failures are now described in the job details and when hovering over the status (in the Projects > Jobs monitoring > Jobs page).
ML-8006	Fixed the pipelines error message for a failed step (previously included messages from other (skipped) steps of the workflow).
ML-8012	You can now use the same tag on more than one model.
ML-8046	The number of TSDB records is now correct when running model monitoring with a user app.
ML-8080	Projects with many artifact tags can now be deleted without complications.
ML-8082 / 8246	You can now delete projects with large number of resources in TDEngine.
ML-8097	UI: Now displays the pod status in Monitor Jobs when you hover over the status symbol.
ML-8110	Fixed the scheduled jobs counters in the project summary.
ML-8112	Logging a model with a training set that has large number of columns now displays the model feature statistics.
ML-8150	Fixed the 03-model-serving tutorial so that all tutorials can be run sequentially.
ML-8151	Fixed the error message that displays when deploying monitoring without setting the required credentials.
ML-8171	The Projects page does not refresh every time you click in the Search box (the previous behavior).
ML-8195	You can now use `format_` and `limit` flags to all project `list_artifacts` methods, improving queries of large numbers of artifacts.
ML-8215	You can now use an asterisk ( "*") in the project name parameter of `run_db.list_functions()` to retrieve functions for all projects.
ML-8217	The counters in the Projects page now have tooltips.
ML-8224	Kafka topics are now deleted together with the project during project deletion.
ML-8286	The tooltip for failed jobs now differentiates between Error and Reason.

v1.7.0 (1 November 2024)#

Model monitoring#

Note

Model monitoring is in TechPreview.

ID	Description
ML-5459	Improved support for unstructured data handling, including for LLMs.
ML-5460	The model monitoring now has a per-endpoint view that presents information data on the monitoring metrics.
ML-5851	Model monitoring supports Kafka or V3IO as streaming platforms, and TDEngine or V3IO as TSDB platforms. See Selecting the streaming and TSDB platforms.

Alerts#

ID	Description
ML-5287	You can now configure alerts for model monitoring and other possible problem situations. TechPreview.

Projects#

ID	Description
ML-3874	Node selector can now be defined in the project spec.

Artifacts#

ID	Description
ML-2585	When deleting a artifact, you can also delete the data of a single artifact.

Runtimes#

ID	Description
ML-2652	Supports creating an API gateway using the MLRun SDK.
ML-4601	New application runtime where you can provide an image (for example, a web-app) that runs as a Kubernetes deployment.

Workflows#

ID	Description
ML-6885	You can now add run details to the notification by using `{{ runs }}` in the `override_body` section in notifications. See Configuring notifications for runs. Webhook notification now contains run logs when `override_body=True`

Functions#

ID	Description
ML-4248	You can now load models from a json file instead of adding all the models to the function spec.

Serving graph#

ID	Description
ML-6015	Storey/Nuclio serving graph: supports concurrent processing, typically used for serving of deep-learning models, where preparation steps and inference can be CPU/GPU heavy, or involving I/O.

Data store#

ID	Description
ML-5726	Add support for Hadoop/hdfs datastore.

Notifications#

ID	Description
ML-6644	Explicit control of the configuration for the Pipeline started notification.

Breaking changes#

ID	Description
ML-5741/3206	The new flag `allow_cross_project` (`mlrun.projects.get_or_create_project()` and `mlrun.projects.load_project()`) gives you options to manage a situation where you call `get_or_create_project` or `load_project` and there is already a `project.yaml` with a different project name. In v1.7 and v1.8 `allow_cross_project` is set to None by default, meaning if there is a mismatch in the name, you get a warning (that the project name is different than the `project.yaml`) and then the flow continues. If `allow_cross_project` is False, an exception is raised. And if `allow_cross_project` is True, the flow continues. (Prior to v1.9 you should set `allow_cross_project` to either True or False; the None option will be deprecated.)

Description

ML-5741/3206

The new flag allow_cross_project (mlrun.projects.get_or_create_project() and mlrun.projects.load_project()) gives you options to manage a situation where you call get_or_create_project or load_project and there is already a project.yaml with a different project name. In v1.7 and v1.8 allow_cross_project is set to None by default, meaning if there is a mismatch in the name, you get a warning (that the project name is different than the project.yaml) and then the flow continues. If allow_cross_project is False, an exception is raised. And if allow_cross_project is True, the flow continues. (Prior to v1.9 you should set allow_cross_project to either True or False; the None option will be deprecated.)

Feature store#

ID	Description
ML-3303	Optimized the parquet read when the partitioning is on fields other than the timestamp.
ML-5656	Add Snowflake as an offline target store

UI#

ID	Description
ML-4666	The new cross-project view gives a summary of all jobs, workflows, and schedules that ran in the last 24 hours.
ML-5140	Improved responsiveness for runs and functions.
ML-5846	The Filter in the Projects>ML Functions table is now a popup menu.
ML-6275	The Projects dashboard now notifies when MLRun isn't reachable.

Documentation#

ID	Description
ML-6052	New page: Logging artifacts.
ML-7480	New topic: Setup for ARM64 (Apple Silicon) Users
ML-7669	New topic: Setting the log level.
NA	New tutorial: Model monitoring using LLM.
NA	New page: Writing a model monitoring application.
NA	Updated page: Model monitoring architecture.
NA	Updated MLRun ecosystem.

Closed issues#

ID	Description
ML-2223	Can now deploy a function when notebook names contain "."
ML-3143/ML-3432	Can now delete remote (Nuclio) functions from the DB with both the SDK and the UI.
ML-3680	Function specs that are modified before running the workflow are now saved.
ML-3804	A serving step with no class now inherits parameters from the function spec.
ML-4442	You can now add monitoring after a model is deployed (`set_tracking()` was not set on the serving function).
ML-4490	Documentation: Added caveat that MLRun does not support decompressing large Kubeflow pipeline graphs.
ML-4636	A local run that was created via `get_or_create_ctx` can be marked as complete using `commit()` method.
ML-4846	CE: `V3IO_ACCESS_KEY` is no longer required for Parquet target.
ML-4934	Modifying the parameters of a serving-function (for example changing `default_batch_intervals`) that is configured for model-monitoring tracking does not require a specific workflow.
ML-5079	You can now update git remote with `project.create_remote()`.
ML-5204	UI: The Project settings now provide validation rules on labels.
ML-5627	Documentation: Added details about `labels`.
ML-5774	UI: Improved speed of querying for pipelines of specific projects in the Pipelines page.
ML-6020	UI: Copy URI in the Datasets main page now copies the same value as in the detailed Dataset page.
ML-6065	Fixed serving graphs when working with Kafka.
ML-6068	Feature-store Redis-target is now created by default with the project/feature-set in the path.
ML-6194	When running a remote workflow, the client side does not need to contain workflow local files.
ML-6249	Reduced the time that feature sets with a large number of entities take to query (and no longer timeout).
ML-6253	The `project.build_function()`now passes the `requirements_file parameter` when the engine is not `kfp`.
ML-6602/6556	You can now specify a UID when running `artifacts.get_model`.
ML-6800	Resolved spiking of CPU and memory of mlrun-api service.
ML-6991	UI: Now shows KFP pod errors.
ML-7103/7131	UI: By default the UI only retrieves tagged functions: non-tagged functions are only retrieved if explicitly asked for. Added time filters to the Functions page, by default shows only functions that were modified in the last week. The changes improve the overall responsiveness.
ML-7119	When attempting to create a project without the required permissions, the error now reads: You don’t have permission to create a project.
ML-7135	Fixed upgrading mlrun if the `spec.build` was saved incorrectly.
ML-7162	UI: Erroneous "“No data matches…” messages no longer appear.
ML-7192	Improved the liveness and readiness probes responsiveness.
ML-7202	The run state now indicates if a pod failed on OOM.
ML-7226	Exception now raised when using use KFP engine with `pass schedule` argument, which is not supported with the KFP engine.
ML-7232	The run state now indicates when a pod fails on OOM.
ML-7256	Deleting artifact by UID deletes only the artifact with the given UID.
ML-7290	Nuclio functions deployed by MLRun: At the Nuclio service level, node selectors are added by Nuclio. In cases where there is overlap between the node selectors specified at the MLRun service level and those applied by Nuclio, the node selectors from the MLRun side take precedence to prevent any conflicts. See node selection.
ML-7335	Serving functions now support up to 4500 models and the size of the serving specification to 1 MB.
ML-7358	MLRun client now validates SSL certificates by default.
ML-7367	Updated OS packages for MLRun UI.
ML-7404/7405	Documentation: Added the minimum Nuclio version for supporting `disable_default_http_trigger`.
ML-7416	An error message is now displayed upon workflow failure in the "Monitor workflows" tab.
ML-7599	Fixed mlrun-api vulnerabilities.
ML-7613	The pipeline SDK output now displays the logs in addition to the graph animation, and it also raises a warning when a client uses notifications with `engine=kfp`.
ML-7655	The "filter by label" in the Models page is now case insensitive.
ML-7673	Improved performance of `db.list_pipelines()` with project filter in environments with a large number of pipelines.
ML-7674	Modified MLClientCtx.log_level() to change the context logger log level
ML-7706	Changed `sync_functions` to be "best effort" when scheduling workflows or running remote workflows.
ML-7796	New `project.run()` parameter `workflow_runner_node_selector` that defines the node selector for the workflow runner pod when using a remote engine.
ML-7907	Fixed an issue with RabbitMQ event path when using Model serving functions.
ML-8027	Fix periodic log collection to list only runs with pods.
ML-8029	Notifications with secret params now send notifications. See Notification parameters and secrets.

v1.6.4 (2 July 2024)#

UI#

ID	Description
ML-6867	Scalability improvement. The artifacts page (artifacts/datasets/models) now displays a maximum of 1000 items. (Use filters to focus the results.)

Closed issues#

ID	Description
ML-6770	Resolved MLRun workers restart when running many workflows that produce artifacts.
ML-6795	Can now upgrade to v1.6.4 when cluster has artifacts that do not have a `key`.

v1.6.3 (4 June 2024)#

Workflows#

ID	Description
ML-3521,5482	Remote/scheduled workflows can now be performed by a project with a source that is contained on the image. See Scheduling a workflow. Tech Preview.

Infrastructure#

ID	Description
ML-5739	MLRun now supports email-like username.

Documentation#

ID	Description
ML-4620	Updated Realtime monitoring and drift detection tutorial and Model monitoring for the model monitoring feature introduced in v1.6.0.
NA	New Deploying an LLM using MLRun tutorial.
NA	New sections describing gen AI tasks: Gen AI development workflow, Gen AI data management, Developing a gen AI pipeline, Deploying gen AI application serving pipelines.
NA	New page describing Logging artifacts.
NA	New page describing Running multiple functions in parallel.
NA	New page describing Running a conditional workflow.
NA	New page describing Running multiple functions with ExitHandler.

Breaking change#

ID	Description
ML-6098	The `prediction` and `named_predictions` columns (list of all predictions) were removed from the model monitoring parquet files. Each prediction is still available in a column of its own.

Closed issues#

ID	Description
ML-4149	UI: Workflows are now listed from newest to oldest.
ML-5763	The log formatter options can now be changed by an env var.
ML-5772	Resolved: "Projects" screen/counters may show "N/A" or "MySQL server has gone away" transient error.
ML-5776	Concurrent request to project deletion now do not fail.
ML-6000	Improved MLRun startup time on system with large number of runs.
ML-6026	Remote workflows using a large image no longer time-out.
ML-6045	UI: User-filters return all of the matching users.
ML-6048	UI: An admin user can now change its role in the project.
ML-6051	UI: After an admin user deletes itself from a project, the user is redirected.
ML-6188	If a workflow runner pod fails due to an application error, an immediate response returns the error (and not that the workflow does not exist).
ML-6194	When running workflows with a remote engine, functions files are not synced instead they are loaded dynamically during runtime.
ML-6317	Reduced MLRun memory consumption.
ML-6384	Improved resource consumption of list runs with partitioning query
ML-6397	Artifacts are no longer stored in the run body in the DB, instead a map of artifact keys to URIs is maintained.
ML-6489	Resolved jobs transient failures with error 'ClientOSError(104, 'Connection reset by peer')'.

v1.6.2 (29 March 2024)#

Closed issues#

ID	Description
ML-4758	Heavy projects can be deleted successfully.
ML-5808	Fix selecting the project-owner user.
ML-5907	"Invite New Members" now returns the full list of users when there are 100+ users in system.
ML-5749, 6037	After the user removes ownership of the currently displayed project, the UI redirects to the Projects page.
ML-5977	The 'Members' tab in Project settings is now shown for groups with admin privileges.

v1.6.1 (29 February 2024)#

Closed issue#

ID	Description
ML-5799	The artifact `db_key` is not overwritten after upgrade.

v1.6.0 (22 February 2024)#

Data store#

ID	Description
ML-3618	Integrate MLflow: seamlessly integrate and transfer logs from MLflow to MLRun. Tech Preview. See MLflow tracker tutorial.
ML-4343	Datastore profiles (for managing datastore credentials) now support Azure, DBFS, GCS, Kafka, and S3. See Using data store profiles.

Feature store#

ID	Description
ML-4622	Feature set and feature vector APIs are now class methods. See examples in Feature sets and Feature vectors.
ML-5109	You can set `min_replicas` and `max_replicas` for `KafkaSource`. See Consumer function configuration.

Model monitoring#

ID	Description
ML-4620	Model monitoring is now based on monitoring apps that are run on a set of model end-points, see Model monitoring. The Grafana Model Monitoring Applications dashboard now includes charts and KPIs that are relevant to a specific monitoring application (under a specific model endpoint). The graphs are: Draft status by category, Average drift value result, Latest result, Application summary, Result value by time, Drift detection history. See Model Monitoring Applications dashboard.

Description

ML-4620

Model monitoring is now based on monitoring apps that are run on a set of model end-points, see Model monitoring. The Grafana Model Monitoring Applications dashboard now includes charts and KPIs that are relevant to a specific monitoring application (under a specific model endpoint). The graphs are: Draft status by category, Average drift value result, Latest result, Application summary, Result value by time, Drift detection history. See Model Monitoring Applications dashboard.

Runtimes#

ID	Description
ML-3379,4997	New `state_thresholds` used to identify pod status and abort a run. See Preventing stuck pods and `set_state_thresholds()`.
ML-3728	Labels added to pods that are running as part of KFP to facilitate monitoring. View in Git.
ML-4032	You can now disable the automatic HTTP trigger creation in Nuclio and MLRun. See Serving/Nuclio triggers.
ML-4182	Support for notifications on remote pipelines. See Configuring Notifications For Pipelines.
ML-4623	You can now Log a Databricks response as an artifact.

UI#

ID	Description
ML-1855	New Train Model wizard.
ML-2336	You can now delete Jobs in the UI (and not just from the SDK).
ML-4506	You can now delete artifacts, models, and datasets in the UI (and not just from the SDK).
ML-4667	Project monitoring is now the default project view. The previous default page is now named Quick actions, and is the second tab in the Projects page.
ML-4916	You can now add a tag when registering an artifact in the Register Artifact, Register Dataset, and Register Model dialogs.

Infrastructure#

ID	Description
ML-3921	The Docker image for installation of mlrun was modified, resulting in better compatibility with external packages.
ML-5193	Support for Pandas 2.0.

Documentation#

ID	Description
ML-3663	New: How to build a docker image externally using a Dockerfile and then use it. See Building a docker image using a Dockerfile and using it.
ML-4048	New: Creating and using a custom function hub. See Private function hub.
ML-5260	New: Load code at runtime using a non-default source.
ML-5602, ML-5680	Improved feature store documentation including sources and targets, and partitioning. See Sources and targets.
NA	New: MLRun project bootstrapping with project_setup.py.
NA	Improved serving function example, and new example of a serving function with Git integration. See Function of type serving.

Breaking Changes#

ID	Description
ML-4741	The default `target_dir` path of `with_source_archive` is now `/home/mlrun_code`. It was previously `/tmp`, which could be randomly deleted. If you are running a Spark job, and cloning the git repo, with mlrun <1.6.0, run `sj.with_source_archive(source=project.source, pull_at_runtime=False)`, then run: `sj.spec.image_pull_policy = "Always"`, `sj.spec.build.commands = ["mkdir -p /mlrun"]`, `sj.with_source_archive(source=project.source, pull_at_runtime=False, target_dir="/mlrun")`

Description

ML-4741

The default target_dir path of with_source_archive is now /home/mlrun_code. It was previously /tmp, which could be randomly deleted. If you are running a Spark job, and cloning the git repo, with mlrun <1.6.0, run sj.with_source_archive(source=project.source, pull_at_runtime=False), then run: sj.spec.image_pull_policy = "Always", sj.spec.build.commands = ["mkdir -p /mlrun"], sj.with_source_archive(source=project.source, pull_at_runtime=False, target_dir="/mlrun")

Closed issues#

ID	Description
ML-76	Artifacts submitted from a git based project can now be received (after changing the HEAD git commit of the project between different log_artifact calls) .
ML-1373	Incorrect service names do not result in stuck pods during ContainerCreating.
ML-1835	The index record is not duplicated in the datasets metadata.
ML-3714	Runs that complete successfully do not show errors in Kubeflow.
ML-3856	Documentation: Add how to update a feature set with appending ingestion (and not create a new FS on every ingest). See Ingest data locally.
ML-4093	Documentation: Improved description of handlers and Functions.
ML-4370	Hyper-param and single runs no longer generate artifacts with the same name.
ML-4563	Local jobs can now be aborted in the UI.
ML-4585	The `mlrun/mlrun` image now supports mpijob.
ML-4613	UI: Fixed the map type hint in the Batch Inference Parameters.
ML-4642	The UI no longer gets stuck when there is a high number of query results.
ML-4678	When tagging a specific version of a model using the SDK, it does not clear the tags from the rest of the versions.
ML-4690	Enabling the Spark event log (sj.spec.spark_conf["spark.eventLog.enabled"] = True) no longer causes the job to fail.
ML-4920	Documentation: improve description of `log_artifact`. See Artifacts and `log_artifact()`.
ML-4608	The artifact `db_key` is now forwarded when registering an artifact.
ML-4617	Fixed error message when using a feature vector as an input to a job without first calling `get_offline_features` on the vector.
ML-4714	Logs are not truncated in the MLRun UI logs page for jobs that have a high number of logs or run for over day.
ML-4810	Can now rerun a job when the "mlrun/client_version" label has "+" in its value.
ML-4821	Very big projects are deleted successfully.
ML-4855	MLRun now supports TensorFlow up to 2.15.0.
ML-4857	When local runs are aborted in the UI, the actual execution is also aborted.
ML-4858	After aborting a job/run from the UI, the logs are populated.
ML-4922	Preview and Metadata tabs now indicate when there are more columns that are not displayed.
ML-4967	The Deploy button in the Project > Models page now creates a new endpoint/serving function.
ML-4992	Fixed starting a spark job from source archive (using `with_source_archive()`).
ML-5001	The Monitoring workflows page now states that it includes only workflows that have already been run.
ML-5042	After creating and deleting a project, a new project cannot be created in the same folder with the same context.
ML-5048	UI Edit function dialog: When selecting Use an existing image and pressing Deploy, the existing image is used, as expected.
ML-5078	`project.create_remote()` is no longer dependant on setting `init_git=True` on project creation.
ML-5089	When trying to delete a running job, an error opens that a running job cannot be deleted and it needs to be aborted first.
ML-5091	Monitoring does not recreate a deleted run.
ML-5146	Resolved OOM issues by reducing the memory footprint when monitoring runs.
ML-5175	For Nuclio runtimes, MLRun is now installed together with user requirements, to account for MLRun dependencies.
ML-5481	You can now use `build_image` using the project source. See the example in build_image.
ML-5576	FeatureSet can now ingest data that contains single quotes.
ML-5746	Labels no longer create partial projects that cannot be deleted.

v1.5.2 (30 November 2023)#

Closed issues#

ID	Description
ML-4960	Fixed browser caching so the Members tab is always presented for projects.

v1.5.1 (2 November 2023)#

Closed issues#

ID	Description
ML-3480	Add details about `label_feature` parameter. See Creating a feature vector.
ML-4839/4844	Running `project.build_image` now always reads the requirements.txt file.
ML-4860	Fixed creating and running functions with no parameters from the UI.
ML-4872	Fixed synchronizing functions from project yaml.

v1.5.0 (23 October 2023)#

Data store#

ID	Description
ML-2296	Add ability to manage Redis datastore credentials with datastore profiles. See Using data store profiles, view in Git.
ML-3500	Support for DBFS data store (Databricks file system). See Databricks file system, view in Git.

Feature store#

ID	Description
ML-3784	Support for feature vector-defined feature-set relations and join-type (per-join). Tech Preview. See Feature vector with different entities and complex joins and view in Git.

Infrastructure#

ID	Description
ML-3370	Accessing the MLRun hub is now available through a service API. This will enable implementing better function version selection and combining hub functions from different sources. Tech Preview. View in Git.
ML-3644	Support for self-signed docker registries. See Using self-signed registry and view in Git.
ML-4132	The `invoke` function can now receive any parameter supported in the `requests.request` method. See `invoke()` and view in Git.
NA	From v1.5, clients must be running Python 3.9.

Runtimes#

ID	Description
ML-3501	Support for running Spark jobs on Databricks cluster. See Databricks. View in Git.
ML-3854	Support for webhook notification. See webhook in Notification Kinds and view in Git.
ML-4059	Support for adding env vars or secrets to the docker build during runtime. See Extra arguments, `build_config()` and view in Git.

UI#

ID	Description
ML-2811	New Batch Inference wizard. Tech Preview.
ML-2815	New Batch Run wizard that replaces the previous New job page.
ML-3584	The Model Endpoints page now displays the Function Tag.
ML-4066	The Online types list of the Target Store now includes Redis.
ML-4167	The Projects page now supports downloading the .yaml file. Tech Preview.
ML-4571	The Model Endpoints page now displays the drift threshold and the drift actual value.
ML-4756	The Recents list in Jobs and Workflows (Projects pane) now displays a maximum of the last 48 hours.
ML-4511	You can now change the image and add new requirements (such as `xgboost`) in the Batch Infer wizard.

Documentation#

ID	Description
ML-3763	Add description of configuring number of workers per GPU. See updated Number of workers/GPUs.
ML-4420	Add configuration of memory in Spark Operator. See Spark Operator runtime.
ML-2380	Add details of V3IO and Spark runtime. See Spark Operator runtime and `invoke()`.

Breaking changes#

ID	Description
ML-3823	The default format of `list projects` returns project names only. You can either get names or projects (`name_only`) and do a `get` only on the specific project you want (preferable), or get the full list (`full`). View in Git.
ML-4171	The Redis target implementation changed. Features-sets that use Redis as online targets must be recreated. View in Git.
ML-4366	The MLRun images `mlrun/ml-models` and `mlrun/ml-models-gpu` were deprecated and removed. The new image mlrun/mlrun-gpu is added. Additional dependencies must be installed on an as-need basis. See MLRun images.

Deprecations#

See Deprecations and removed code.

Closed issues#

ID	Description
ML-1584	Can now run `code_to_function` when filename contains special characters.
ML-2199	Spark operator job does not fail with default requests arguments.
ML-2380	Spark runtime sustains naive user actions.
ML-4188	Projects are deleted simultaneously in the backend and the UI.
ML-4212	Pipeline filters that have no results now show the labels.
ML-4214	Scheduled workflows with "-" in the name are no longer truncated.
ML-4232	User attempts to create a consumer group with "-" now throws an error.
ML-4316	Fixed: `list_runs` fails with `Read timed out`.
ML-4323	Fixed: pipeline step failed with "Read timed out.: get log"
ML-4391	Consumer group UI now shows complete details.
ML-4501	Fixed: UI shows error after deleting a function, then viewing a related job.
ML-4533	UI: ML functions can now be created with upper-case letters.

v1.4.1 (8 August 2023)#

Closed issues#

ID	Description
ML-4303	Archive out-of-sync leader projects.
ML-4232	Consumer group names cannot include the character "-".

v1.4.0 (23 July 2023)#

New and updated features#

Functions#

ID	Description
ML-3474	New sub-package in MLRun for packing returning outputs, logging them to MLRun and unpacking inputs, parsing data items to their required type. View in Git.

Notifications#

ID	Description
ML-21	Supports job notifications. See full details in Notifications, and View in Git.

Projects#

ID	Description
ML-3375	Two new APIs in the MlrunProject object, used to build an image directly through project API, without creating a function and building an image for it: `build_config` configures the default build for a given project; `build_image` builds a docker image based on the project configuration. See `MlrunProject`, Image build configuration, build_image, and View in Git.
ML-4084	New API to run a setup script to enrich a project, when loading the project. View in Git

Serving#

ID	Description
ML-3654	Updates to `error_handler`: Exceptions are applied to either a graph or a step. In the case of a graph, the graph stops upon an error. In the case of a step, the graph can either stop or complete upon an error. See Error handling. View in Git.

UI#

ID	Description
ML-1248	The engine type now displays in the Feature Set Overview tab.
ML-2083	The Run on spot value now displays in the Jobs Overview tab.
ML-3176	The new Passthrough button in the Create Feature Set enables creating a feature set without ingesting its data, previously supported by SDK.
ML-3549	The new Resource monitoring button in the Jobs Details view opens the Grafana dashboard.
ML-3551	Nested workflows (`ParallelFor`) now fully display in UI.
ML-2922	The Artifacts, Datasets and Models pages have an improved filter. Enhanced look and feel in tables.

Documentation#

ID	Description
ML-3548	Passing parameters between steps using the `outputs` parameter is now described in Write a pipeline.
ML-3763	The relationship between GPUs and remote functions is now explained in Number of GPUs and Example of Nuclio function.

New documentation pages#

Breaking changes#

ID	Description
ML-3733	`mlrun.get_run_db().list_model_endpoints()` returns `list`. Previously, it returned `mlrun.api.schemas.model_endpoints.ModelEndpointList`.
ML-3773	The aggregation mechanism on Redis databases has improved, but the history of the aggregation (from before the upgrade) is lost, as if there were 0 events during that period.
ML-4053	Pre-v1.4.0: When logging artifacts during a runtime (regular artifacts, not models (ModelArtifact via context.log_model) or datasets (DatasetArtifact via context.log_dataset)), they were strings in the RunObject outputs property. The strings were the target path to the file logged in the artifact. From v1.4.0, they are the store path of the artifact, and not the target path. (They now appear the same as the store paths for logging models and datasets.) This is breaking behavior only if you use the output of the run object as a parameter to another runtime and not as an input. View in Git.

 # Set 2 functions:
func1 = project.set_function(...)
func2 = project.set_function(...)

# Run the first function:
run1 = func1.run(...)
# In the function  `func1` we logged a model "my_model" and an artifact "my_artifact"
run1.outputs  
{
    "my_model": "store://...",
    "my_artifact": "store://...",  # Instead of target path: "/User/.../data.csv"
}

# The function `func2` expects a `DataItem` for the logged artifact so passing it through inputs will work as `DataItem` can work with store paths:
run2 = func2.run(..., inputs={"artifact": run1.outputs["my_artifact"]})

# But passing it through a parameter won't work as the string value is now a store path and not a target path:
run2 = func2.run(..., params={"artifact": run1.outputs["my_artifact"]})

Deprecations and future deprecations#

See Deprecations and removed code.

Closed issues#

ID	Description
ML-1787	Optimized distribution of load between chief and workers so that heavy loads do not cause restart of kubelet. View in Git.
ML-2773	Reduced memory footprint for feature vector that joins data from multiple feature sets. View in Git.
ML-3166	New error message when `load_project` uses an invalid URL source. View in Git.
ML-3420	Fix artifacts corruption due to overflowing size. View in Git.
ML-3443	Spark ingestion engine now supports more than 2 keys in online target. Tech Preview. View in Git.
ML-3470	Changes in secrets are now recorded in the audit log of the platform. View in Git.
ML-3508	Improved description of list_runs. See `list_runs` View in Git.
ML-3621	`clear_context()` now does not delete content if the path is relative; and if a subpath exists, only the sub dir is deleted/cleared. View in Git.
ML-3631	MLRun now successfully pulls the source code from GitLab with a personal access token. View in Git.
ML-3633	Fix parsing of date fields when importing a context from dict. View in Git.
ML-3652	V3IO_API is now inferred from the DBPATH. View in Git.
ML-3703	`project.set_secrets()` now throws a `file not found` exception if the file does not exist. View in Git.
ML-3713	Users can now use pipeline parameters in the spec of jobs created within the workflow python file without causing run failure. View in Git.
ML-3761	**kwargs now forward as expected in MLRun jobs and hyper params. View in Git.
ML-3782	The (incorrect) naming of features causes error when getting the feature vector from the online feature service. The fix is an additional restriction in feature names. See Aggregations View in Git.
ML-3806	Mismatch errors now printed when ingesting from Kafka into offline target. In case of errors (due to type mismatch) no errors are printed.View in Git.
ML-3847	`add_code_metadata` now prints error messages when working with git View in Git.
ML-3900	Improved error message when ingesting into a feature set (online target) and no features found on retrieval. View in Git.
ML-4129	Errors from BigQuerySource are now forwarded to MLRun. View in Git.

v1.3.4 (23 August 2023)#

Closed issues#

ID	Description
ML-4409	Importing a project.yaml now does not overwrite the artifacts with older tags.

v1.3.3 (7 Jun 2023)#

Closed issues#

ID	Description
ML-3940	MLRun does not initiate log collection for runs in aborted state. View in Git.

v1.3.2 (4 Jun 2023)#

Closed issues#

ID	Description
ML-3896	Fixed: MLRun API failed to get pod logs. View in Git.
ML-3865	kubectl now returns logs as expected. View in Git.
ML-3917	Reduced number of logs. View in Git.
ML-3934	Logs are no longer collected for run pods in an unknown state. View in Git.

v1.3.1 (18 May 2023)#

Closed issues#

ID	Description
ML-3764	Fixed the scikit-learn to 1.2 in the tutorial 02-model-training. (Previously pointed to 1.0.) View in Git.
ML-3794	Fixed a Mask detection demo notebook (3-automatic-pipeline.ipynb). View in Git.
ML-3819	Reduce overly-verbose logs on the backend side. View in Git. View in Git.
ML-3823	Optimized `/projects` endpoint to work faster. View in Git.

Documentation#

New sections describing Git best practices and an example Nuclio function.

v1.3.0 (22 March 2023)#

Client/server matrix, prerequisites, and installing#

The MLRun server is now based on Python 3.9. It's recommended to move the client to Python 3.9 as well.

MLRun v1.3.0 maintains support for mlrun base images that are based on python 3.7. To differentiate between the images, the images based on python 3.7 have the suffix: -py37. The correct version is automatically chosen for the built-in MLRun images according to the Python version of the MLRun client (for example, a 3.7 Jupyter gets the -py37 images).

MLRun v1.3.x maintains support for mlrun base images that are based on a python 3.7 environment. To differentiate between the images, the images based on python 3.7 have the suffix: -py37. The correct version is automatically chosen for the built-in MLRun images according to the Python version of the MLRun client (for example, a 3.7 Jupyter gets the -py37 images).

For a Python 3.9 environment see Set up a Python 3.9 client environment.

Set up a Python 3.7 client environment (Iguazio versions up to and including v3.5.2)#

Note

There is a known bug with nbformat on the Jupyter version in Iguazio up to and including v3.5.2, which requires upgrading nbformat to 5.7.0. When using an older nbformat, some Jupyter Notebooks do not open.

To install on a Python 3.7 environment (and optionally upgrade to python 3.9 environment):

Configure the Jupyter service with the env variable JUPYTER_PREFER_ENV_PATH=false.
Within the Jupyter service, open a terminal and update conda and pip to have an up-to-date pip resolver.

$CONDA_HOME/bin/conda install -y conda=23.1.0 	
$CONDA_HOME/bin/conda install -y 'pip>=22.0'
$CONDA_HOME/bin/conda install -y nbformat=5.7.0

If you want to upgrade to a Python 3.9 environment, create a new conda env and activate it:

conda create -n python39 python=3.9 ipykernel -y
conda activate python39

Install mlrun:

./align_mlrun.sh

New and updated features#

Feature store#

ID	Description
ML-2592	Offline data can be registered as feature sets (Tech Preview). See Create a feature set without ingesting its data.
ML-2610	Supports SQLSource for batch ingestion (Tech Preview). See SQL data source.
ML-2610	Supports SQLTarget for storey engine (Tech Preview). (Spark is not yet supported.) See SQL target store.
ML-2709	The Spark engine now supports the steps: `MapValues`, `Imputer`, `OneHotEncoder`, `DropFeatures`; and supports extracting the time parts from the date in the `DateExtractor` step. See Data transformations.
ML-2802	`get_offline_features` supports Spark Operator and Remote Spark.
ML-2957	The username and password for the RedisNoSqlTarget are now configured using secrets, as `<prefix_>REDIS_USER <prefix_>REDIS_PASSWORD` where <prefix> is the optional RedisNoSqlTarget `credentials_prefix` parameter. See Redis target store.
ML-3008	Supports Spark using Redis as an online KV target, which caused a breaking change.
ML-3373	Supports creating a feature vector over several feature sets with different entities. (Outer joins are Tech Preview.) See Using an offline feature vector. This API will change in a future release, moving the relationship from the feature set to the feature vector.

Logging data#

ID	Description
ML-2845	Logging data using `hints`. You can now pass data into MLRun and log it using log hints, instead of the decorator. This is the initial change in MLRun to simplify wrapping usable code into MLRun without having to modify it. Future releases will continue this paradigm shift. See more details.

Projects#

ID	Description
ML-3048	When defining a new project from scratch, there is now a default `context` directory: "`./`". This is the directory from which the MLRun client runs, unless otherwise specified.

Serving graphs#

ID	Description
ML-1167	Add support for graphs that split and merge (DAG), including a list of steps for the `after` argument in the `add_step()` method. See Graph that splits and rejoins.
ML-2507	Supports configuring of consumer group name for steps following QueueSteps. See Queue (streaming).

Storey#

ID	Description
ML-2502	The event time in storey events is now taken from the `timestamp_key`. If the `timestamp_key` is not defined for the event, then the time is taken from the processing-time metadata. View in Git, and in Storey git.

UI#

Description

ML-1186

The new Projects home page provides easy and intuitive access to the full project lifecycle in three phases, with links to the relevant wizards under each phase heading: ingesting and processing data, developing and training a model, deploying and monitoring the project.

mlrun-project-homepage

UI change log in GitHub

APIs#

ID	Description
ML-3104	These APIs now only return reasons in kwargs: `log_and_raise`, `generic_error_handler`, `http_status_error_handler`.
ML-3204	New API `set_image_pull_configuration` that modifies `func.spec.image_pull_secret` and `func.spec.image_pull_policy`, instead of directly accessing these values through the spec.

Documentation#

Improvements to Set up your environment.

Infrastructure improvements#

Description

ML-2609

MLRun server is based on Python 3.9.

ML-2732

The new log collection service improves the performance and reduces heavy IO operations from the API container. The new MLRun log collector service is a gRPC server, which runs as sidecar in the mlrun-api pod (chief and worker). The service is responsible for collecting logs from run pods, writing to persisted files, and reading them on request. The new service is transparent to the end-user: there are no UI or API changes.

Breaking changes#

The behavior of ingest with aggregation changed in v1.3.0 (storey, spark, pandas engines). Now, when you ingest a "timestamp" column, it returns
<class 'pandas._libs.tslibs.timestamps.Timestamp'>.
Previously, it returned <class 'str'>
Any target data that was saved using Redis as an online target with storey engine (RedisNoSql target, introduced in 1.2.1) is not accessible after upgrading to v1.3. (Data ingested subsequent to the upgrade is unaffected.)

Deprecated and removed APIs#

Starting with v1.3.0, and continuing in subsequent releases, obsolete functions are getting removed from the code. See Deprecations and removed code.

Closed issues#

ID	Description
ML-2421	Artifacts logged via SDK with "/" in the name can now be viewed in the UI. View in Git.
ML-2534	Jobs and Workflows pages now display the tag of the executed job (as defined in the API). View in Git.
ML-2810	Fixed the Dask Worker Memory Limit Argument. View in Git.
ML-2896	`add_aggregation` over Spark fails with `AttributeError` for sqr and stdvar. View in Git.
ML-3104	Add support for project default image. View in Git.
ML-3119	Fix: MPI job run status resolution considering all workers. View in Git.
ML-3283	`project.list_models()` did not function as expected for tags and labels. The `list_artifacts` method now accept a dictionary, and docstrings were added for httpdb and for MLRunProject methods: both `list_artifacts` and `list_models`. View in Git.
ML-3286	Fix: Project page displayed an empty list after an upgrade View in Git.
ML-3316	Users with developer and data permissions can now add members to projects they created. (Previously appeared successful in the UI but users were not added). View in Git.
ML-3365 / 3349	Fix: UI Projects' metrics show N/A for all projects when ml-pipeline is down. View in Git.
ML-3378	Aggregation over a fixed-window that starts at or near the epoch now functions as expected. View in Git.
ML-3380	Documentation: added details on aggregation in windows.
ML-3389	Hyperparameters run does not present artifacts iteration when selector is not defined. View in Git.
ML-3424	Documentation: new matrix of which engines support which sources/targets. View in Git.
ML-3505	Removed the upperbound on the `google-cloud-bigquery` requirement.
ML-3575	`project.run_function()` now uses the argument `artifact_path` (previously used the project's configured `artifact_path` instead). View in Git.
ML-3403	Error on Spark ingestion with offline target without defined path (error: `NoneType` object has no attribute `startswith`). Fix: default path defined. View in Git.
ML-3446	Fix: Failed MLRun Nuclio deploy needs better error messages. View in Git.
ML-3482	Fixed model-monitoring incompatibility issue with mlrun client running v1.1.x and a server running v1.2.x. View in Git.

v1.2.3 (15 May 2023)#

Closed issues#

ID	Description
ML-3287	UI now resets the cache upon MLRun upgrades, and the Projects page displays correctly. View in Git.
ML-3801	Optimized `/projects` endpoint to work faster View in Git.
ML-3819	Reduce overly-verbose logs on the backend side. View in Git.

v1.2.2 (8 May 2023)#

Closed issues#

ID	Description
ML-3797, ML-3798	Fixed presenting and serving large-sized projects. View in Git.

v1.2.1 (8 January 2023)#

New and updated features#

Feature store#

Supports ingesting Avro-encoded Kafka records. View in Git.

Third party integrations#

Supports Confluent Kafka as a feature store data-source (Tech Preview). See Confluent Kafka data source.

Closed issues#

Fix: the Projects|Jobs|Monitor Workflows view is now accurate when filtering for > 1 hour. View in Git.
The Kubernetes Pods tab in Monitor Workflows now shows the complete pod details. View in Git.
Update the tooltips in Projects|Jobs|Schedule to explain that day 0 (for cron jobs) is Monday, and not Sunday. View in Git.
Fix UI crash when selecting All in the Tag dropdown list of the Projects|Feature Store|Feature Vectors tab. View in Git.
Fix: now updates next_run_time when skipping scheduling due to concurrent runs. View in Git.
When creating a project, the error NotImplementedError was updated to explain that MLRun does not have a DB to connect to. View in Git.
When previewing a DirArtifact in the UI, it now returns the requested directory. Previously it was returning the directory list from the root of the container. View in Git.
Load source at runtime or build time now fully supports .zip files, which were not fully supported previously.

See more#

v1.2.0 (1 December 2022)#

New and updated features#

Artifacts#

Support for artifact tagging:
- SDK: Add tag_artifacts and delete_artifacts_tags that can be used to modify existing artifacts tags and have more than one version for an artifact.
- UI: You can add and edit artifact tags in the UI.
- API: Introduce new endpoints in /projects/<project>/tags.

Auth#

Support S3 profile and assume-role when using fsspec.
Support GitHub fine grained tokens.

Documentation#

Restructured, and added new content.

Feature store#

Support Redis as an online feature set for storey engine only. (See Redis target store.)
Fully supports ingesting with pandas engine, now equivalent to ingestion with storey engine (TechPreview):
- Support DataFrame with multi-index.
- Support mlrun steps when using pandas engine: OneHotEncoder , DateExtractor, MapValue, Imputer and FeatureValidation.
Add new step: DropFeature for pandas and storey engines. (TechPreview)
Add param query for get_offline_feature for filtering the output.

Frameworks#

Add HuggingFaceModelServer to mlrun.frameworks at mlrun.frameworks.huggingface to serve HuggingFace models.

Functions#

Add function.with_annotations({"framework":"tensorflow"}) to user-created functions.
Add overwrite_build_params to project.build_function() so the user can choose whether or not to keep the build params that were used in previous function builds.
deploy_function has a new option of mock deployment that allows running the function locally.

Installation#

New option to install google-cloud requirements using mlrun[google-cloud]: when installing MLRun for integration with GCP clients, only compatible packages are installed.

Models#

The Labels in the Models > Overview tab can be edited.

Internal#

Refactor artifacts endpoints to follow the MLRun convention of /projects/<project>/artifacts/.... (The previous API will be deprecated in a future release.)
Add /api/_internal/memory-reports/ endpoints for memory related metrics to better understand the memory consumption of the API.
Improve the HTTP retry mechanism.
Support a new lightweight mechanism for KFP pods to pull the run state they triggered. Default behavior is legacy, which pulls the logs of the run to figure out the run state. The new behavior can be enabled using a feature flag configured in the API.

Breaking changes#

Feature store: Ingestion using pandas now takes the dataframe and creates indices out of the entity column (and removes it as a column in this df). This could cause breakage for existing custom steps when using a pandas engine.

Closed issues#

Support logging artifacts larger than 5GB to V3IO. View in Git.
Limit KFP to kfp~=1.8.0, <1.8.14 due to non-backwards changes done in 1.8.14 for ParallelFor, which isn’t compatible with the MLRun managed KFP server (1.8.1). View in Git.
Add artifact_path enrichment from project artifact_path. Previously, the parameter wasn't applied to project runs when defining project.artifact_path. View in Git.
Align timeouts for requests that are getting re-routed from worker to chief (for projects/background related endpoints). View in Git.
Fix legacy artifacts load when loading a project. Fixed corner cases when legacy artifacts were saved to yaml and loaded back into the system using load_project(). View in Git.
Fix artifact latest tag enrichment to happen also when user defined a specific tag. View in Git.
Fix zip source extraction during function build. View in Git.
Fix Docker compose deployment so Nuclio is configured properly with a platformConfig file that sets proper mounts and network configuration for Nuclio functions, meaning that they run in the same network as MLRun. View in Git.
Workaround for background tasks getting cancelled prematurely, due to the current FastAPI version that has a bug in the starlette package it uses. The bug caused the task to get cancelled if the client’s HTTP connection was closed before the task was done. View in Git.
Fix run fails after deploying function without defined image. View in Git.
Fix scheduled jobs failed on GKE with resource quota error. View in Git.
Can now delete a model via tag. View in Git.

See more#

v1.1.3 (28 December 2022)#

Closed issues#

The CLI supports overwriting the schedule when creating scheduling workflow. View in Git.
Slack now notifies when a project fails in load_and_run(). View in Git.
Timeout is executed properly when running a pipeline in CLI. View in Git.
Uvicorn Keep Alive Timeout (http_connection_timeout_keep_alive) is now configurable, with default=11. This maintains API-client connections. View in Git.

See more#

v1.1.2 (20 November 2022)#

New and updated features#

V3IO

v3io-py bumped to 0.5.19.
v3io-fs bumped to 0.1.15.

See more#

v1.1.1 (18 October 2022)#

New and updated features#

API#

Supports workflow scheduling.

UI#

Projects: Supports editing model labels.

See more#

v1.1.0 (6 September 2022)#

New and updated features#

API#

MLRun scalability: Workers are used to handle the connection to the MLRun database and can be increased to improve handling of high workloads against the MLRun DB. You can configure the number of workers for an MLRun service, which is applied to the service's user-created pods. The default is 2.
- v1.1.0 cannot run on top of 3.0.x.
- For Iguazio versions prior to v3.5.0, the number of workers is set to 1 by default. To change this number, contact support (helm-chart change required).
- Multi-instance is not supported for MLRun running on SQLite.
Supports pipeline scheduling.

Documentation#

Added Azure and S3 examples to Ingest features with Spark.

Feature store#

Supports S3, Azure, GCS targets when using Spark as an engine for the feature store.
Snowflake as datasource has a connector ID: iguazio_platform.
You can add a time-based filter condition when running get_offline_feature with a given vector.

Storey#

MLRun can write to parquet with flexible schema per batch for ParquetTarget: useful for inconsistent or unknown schema.

UI#

The Projects home page now has three tiles, Data, Jobs and Workflows, Deployment, that guide you through key capabilities of Iguazio, and provide quick access to common tasks.
The Projects|Jobs|Monitor Jobs tab now displays the Spark UI URL.
The information of the Drift Analysis tab is now displayed in the Model Overview.
If there is an error, the error messages are now displayed in the Projects|Jobs|Monitor jobs tab.

Workflows#

The steps in Workflows are color-coded to identify their status: blue=running; green=completed; red=error.

See more#

v1.0.6 (16 August 2022)#

Closed issues#

Import from mlrun fails with "ImportError: cannot import name dataclass_transform". Workaround for previous releases: Install pip install pydantic==1.9.2 after align_mlrun.sh.
MLRun FeatureSet was not enriching with security context when running from the UI. View in Git.
MLRun Accesskey presents as clear text in the mlrun yaml, when the mlrun function is created by feature set request from the UI. View in Git.

See more#

v1.0.5 (11 August 2022)#

Closed issues#

MLRun: remove root permissions. View in Git.
Users running a pipeline via CLI project run (watch=true) can now set the timeout (previously was 1 hour). View in Git.
MLRun: Supports pushing images to ECR. View in Git.

See more#

v1.0.4 (13 June 2022)#

New and updated features#

Bump storey to 1.0.6.
Add typing-extensions explicitly.
Add vulnerability check to CI and fix vulnerabilities.

Closed issues#

Limit Azure transitive dependency to avoid new bug. View in Git.
Fix GPU image to have new signing keys. View in Git.
Spark: Allow mounting v3io on driver but not executors. View in Git.
Tests: Send only string headers to align to new requests limitation. View in Git.

See more#

v1.0.3 (7 June 2022)#

New and updated features#

Jupyter Image: Relax artifact_path settings and add README notebook. View in Git.
Images: Fix security vulnerabilities. View in Git.

Closed issues#

API: Fix projects leader to sync enrichment to followers. View in Git.
Projects: Fixes and usability improvements for working with archives. View in Git.

See more#

v1.0.2 (19 May 2022)#

New and updated features#

Runtimes: Add java options to Spark job parameters. View in Git.
Spark: Allow setting executor and driver core parameter in Spark operator. View in Git.
API: Block unauthorized paths on files endpoints. View in Git.
Documentation: New quick start guide and updated docker install section. View in Git.

Closed issues#

Frameworks: Fix to logging the target columns in favor of model monitoring. View in Git.
Projects: Fix/support archives with project run/build/deploy methods. View in Git.
Runtimes: Fix jobs stuck in non-terminal state after node drain/pre-emption. View in Git.
Requirements: Fix ImportError on ingest to Azure. View in Git.

See more#

v1.0.0 (22 April 2022)#

New and updated features#

Feature store#

Supports snowflake as a datasource for the feature store.

Graph#

A new tab under Projects|Models named Real-time pipelines displays the real time pipeline graph, with a drill-down to view the steps and their details. [Tech Preview]

Projects#

Setting owner and members are in a dedicated Project Settings section.
The Project Monitoring report has a new tile named Consumer groups (v3io streams) that shows the total number of consumer groups, with drill-down capabilities for more details.

Resource management#

Supports preemptible nodes.
Supports configuring CPU, GPU, and memory default limits for user jobs.

UI#

Supports configuring pod priority.
Enhanced masking of sensitive data.
The dataset tab is now in the Projects main menu (was previously under the Feature store).

See more#

Open issues#

ID	Description	Workaround	Opened in
ML-2052	mlrun service default limits are not applied to the wait-container on pipeline jobs.	NA	v1.0.0
ML-2030	Need a way to move artifacts from test to production Spark.	To register artifact between different environments, e.g. dev and prod, upload your artifacts to a remote storage, e.g. S3. You can change the project artifact path using MLRun or MLRun UI. `project.artifact_path='s3:<bucket-name/..'`	v1.0.0
ML-2201	No error message is raised when an MPI job is created but pods cannot be scheduled.	NA	v1.0.0
ML-2407	Kafka ingestion service on an empty feature set returns an error.	Ingest a sample of the data manually. This creates the schema for the feature set, and then the ingestion service accepts new records.	v1.1.0
ML-2489	Cannot pickle a class inside an mlrun function.	Use cloudpickle instead of pickle.	v1.2.0
2621	Running a workflow whose project has `init_git=True`, results in Project error	Run `git config --global --add safe.directory ''` (can substitute specific directory for ).	v1.1.0
ML-3081	The Monitor Workflows page does not present logs from the correct (Nuclio) deployment.	NA	v1.2.1
ML-3294	Dask coredump during project deletion.	Before deleting a Dask project, verify that Dask was fully terminated.	v1.3.0
ML-3315	Spark ingestion does not support nested aggregations.	NA	v1.2.1
ML-3341 / 2573	The mlrun cross-runtimes build logs should use log collector.	NA	v1.2.1
ML-3386	Documentation is missing full details on the feature store sources and targets.	NA	v1.2.1
ML-3445	`project.deploy_function` operation might get stuck when running v1.3.0 demos on an Iguazio platform running v3.2.x.	Replace code: `serving_fn = mlrun.new_function("serving", image="python:3.9", kind="serving", requirements=["mlrun[complete]", "scikit-learn~=1.2.0"])` with: `function = mlrun.new_function("serving", image="python:3.9", kind="serving") function.with_commands([ "python -m pip install --upgrade pip", "pip install 'mlrun[complete]' scikit-learn==1.1.2", ])`	v1.3.0
NA	The feature store does not support schema evolution and does not have schema enforcement.	NA	v1.2.1
ML-3526	Aggregation column order is not always respected (storey engine).	NA	v1.3.0
ML-3626	The "Save and ingest" option is disabled for a scheduled feature set.	NA	v1.3.0
ML-3627	The feature store should not allows ingestion of `string` type for the timestamp key.	NA	v1.2.1
ML-3636	`get_online_feature_service` from Redis target returns truncated values.	NA	v1.3.0
ML-3646	MapValues step on Spark ingest: keys of non-string type change to string type, sometime causing failures in graph logic.	NA	v1.2.1
ML-3744	`get_offline_features` adds "Z" to the values of time_stamp column when using Spark engines.	NA	v1.3.1
ML-3867	Cannot search for project owners by first name.	NA	v1.4.0
ML-6839	Schedules have a minimum interval between two scheduled jobs. By default, a job cannot be scheduled to run more than 2 times in 10 minutes. See Scheduled jobs and workflows.	NA	v1.6.3
ML-4107	On scheduled ingestion (storey and pandas engines) from CSV source, ingests all of the source on each schedule iteration.	Use a different engine and/or source.	v1.4.0
ML-4153	When creating a passthrough feature-set in the UI, with no online target, the feature-set yaml includes a parquet offline target, which is ignored.	NA	v1.4.0
ML-4166	Project yaml file that is very large cannot be stored.	Do not embed the artifact object in the project yaml.	v1.4.0
ML-4186	on `get_offline_features` ('local'/pandas engine) with passthrough, a source parquet column of type BOOL has dtype "object" or "bool" in the response		v1.4.0
ML-4582	Custom packagers cannot be added to projects created in MLRun previous to v1.4.0.	NA	v1.5.0
ML-4655	Timestamp entities are allowed for feature store, but format is inconsistent.	NA	v1.5.0
NL-4685	When using columns with type "float" as feature set entities, they are saved inconsistently to key-value stores by different engines.	Do not use columns with type float as feature set entities.	v1.5.0
ML-4698	Parameters that are passed to a workflow are limited to 10000 chars.	NA, external Kubeflow limitation.	v1.5.0
ML-4725	ML functions show as if they are in the "Creating" status, although they were created and used.	NA	v1.4.1
ML-4740	When running function `batch_inference_v2` from the SDK, the `ingest()` function accepts 3 parameters as Data-item or other types: `dataset`, `model_path` and `model_endpoint_sample_set`. If you provided these parameters as non Data-items and later on you want to rerun this function from the UI, you need to provide these parameters as Data-item.	Prepare suitable Data-item and provide it to the batch-rerun UI.	v1.5.0
ML-4769	After deleting a project, data is still present in the Artifacts and Executions of pipelines UI.	NA	v1.4.0
ML-4881	Kubeflow pipelines parallelism parameter in dsl.ParallelFor() does not work (external dependency).	NA	v1.4.1
ML-4942	The Dask dashboard requires the relevant node ports to be open.	Your infrastructure provider must open the ports manually. If running MLRun locally or CE, make sure to port-forward the port Dask Dashboard uses to ensure it is available externally to the Kubernetes cluster.	v1.5.0
ML-4956	A function created by SDK is initially in the "initialized" state in the UI and needs to be deployed before running it.	In Edit, press Deploy	v1.5.1
ML-5573	The default value of feature-set ingest() infer_options is "all" (which includes Preview) and as a result, during ingest, preview is done as well. As a result, if a validator was configured for a feature, each violation causes two messages to be printed.	NA	v1.6.0
ML-5876	The maximum length of project name + the longest function name for `project.enable_model_monitoring` is 63 chars.	Keep the name combination at a maximum of 63 chars.	v1.6.0
ML-7159/7704	The evidently app pod memory consumption grows continuously due to use of the evidently workspace and project.	External dependency. Do not use (or only rarely use) these evidently APIs.	v1.7.0
ML-7196	The models features statistics `feature_stats` is limited to 16MB. Further limitation to 1MB when using model-monitoring over V3IO-KV will be removed in 1.8.	NA	v1.7.0
ML-7347	MM-app: Logging artifacts leaks memory due to artifact manager saving each logged artifact.	Use the same artifact key to prevent accumulation.	v1.7.0
ML-7568/7915	The SDK does not inform of invalid node selector combinations when running a function, but the pod remains stuck in the Pending state.	See Preventing and resolving conflicts.	v1.7.0
ML-7571	For executions of Dask runtimes, the UI does not show node-selectors applied to the run.	NA	v1.7.0
ML-7746	In some cases, when the pipeline is extremely large it is not displayed in the graph.	NA	v1.7.0
ML-7820	`sync_functions` should only sync the functions in the workflow, and not all of the functions within the `project.yaml`.	NA	v1.7.0
ML-7955	The Owner field is blank for artifacts that are registered in the UI.	NA	v1.7.0
ML-8064	When using notifications with `when=running` the user always gets a default notification.	NA	v1.7.0
ML-8419	When the MySQL server is unavailable, a project with non-V3IO model monitoring cannot be deleted.	Run `project.set_model_monitoring_credentials(endpoint_store_connection="v3io", stream_path="v3io", tsdb_connection="v3io", replace_creds=True)` before deleting the project.	v1.7.1

Limitations#

ID	Description	Workaround	Opened in
ML-1278	Users do not automatically have access rights to the project data of the projects they are members of.	Assign the user access permission for the project folder.	v0.8.0
ML-2014	Model deployment returns ResourceNotFoundException (Nuclio error that Service is invalid.)	Verify that all `metadata.labels` values are 63 characters or less. See the Kubernetes limitation.	v1.0.0
ML-3520	MLRun does not decompress large Kubeflow pipelines.	NA	v1.3.0
ML-3731	When trying to identify a failed step in a workflow with `mlrun.get_run_db().list_pipelines('project-name')`, the returned error is `None`.	To see the error, use `mlrun.db.get_pipelines()` instead.	v1.3.0
ML-3743	Setting AWS credentials as project secret cause a build failure on EKS configured with ECR.	When using an ECR as the external container registry, make sure that the project secrets AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY have read/write access to ECR, as described in the platform documentation	v1.3.0
ML-4386	Notifications of local runs aren't persisted.	NA	v1.5.0
ML-4767	When using mlrun-gpu image, use PyTorch versions up to and including than 2.0.1, but not higher.	You can build your own images with newer CUDA for a later release of PyTorch.	v1.5.0
ML-4907	MLRun Client does not support Win OS.	Use WSL instead.	v1.3.0
ML-5274	PySpark 3.2.x cannot always read parquet files written by pyarrow 13 or above. MLRun ingest might fail when `ingest()` is called with engine="spark" and a ParquetSource that points to parquet files that were written by pyarrow 13 or above.	Call `df.to_parquet()` with version="2.4" so that parquet files are backwards compatible.	v1.6.0
ML-5669	When using `mlrun.mlrun` image, use PyTorch versions up to and including than 2.0.1, but not higher. See MLRun runtime images	You can build your own images with newer CUDA for a later release of PyTorch.	v1.6.0
ML-5732	When using an MLRun client previous to v1.6.0, the workflow step status might show completed when it is actually aborted.	Abort the job from the SDK instead of from the UI, or upgrade the client to v1.6.0 or higher.	v1.6.0
ML-8115	Deploying a model without monitoring does not create an endpoint.	NA	v1.7.0
ML-8174	A loaded system takes a few minutes (±5) to calculate the statistics in the Projects Monitoring pane.	NA	v1.7.0

Deprecations and removed code#

In	ID	Description
v1.6.0	ML-5137	The Create/edit function pane is removed from the UI.
v1.5.0	ML-4010	Unused artifact types: BokehArtifact, ChartArtifact
v1.5.0	ML-4075	Python 3.7
v1.5.0	ML-4366	MLRun images `mlrun/ml-models` and `mlrun/ml-models-gpu`
v1.5.0	ML-3605	Model Monitoring: Most of the charts and KPIs in Grafana are now based on the data store target instead of the MLRun API. It is recommended to update the model monitoring dashboards since the old dashboards are not supported.
v1.0.0	NA	MLRun / Nuclio does not support python 3.6.

Deprecated APIs#

Will be removed	Deprecated	API	Use instead
v1.9.0	v1.7.0	Class: `mlrunn.common.schemas.RunsFormat`	`mlrun.common.formatters.RunFormat`
v1.9.0	v1.7.0	Class: `mlrunn.common.schemas.ArtifactsFormat`	`mlrun.common.formatters.ArtifactFormat`
v1.9.0	v1.7.0	Class: `mlrunn.common.schemas.ProjectsFormat`	`mlrun.common.formatters.ProjectFormat`
v1.9.0	v1.7.0	Class: `mlrunn.common.schemas.PipelinesFormat`	`mlrun.common.formatters.PipelineFormat`
v1.9.0	v1.7.0	Datastore redis:`credentials_prefix`	Datastore profiles
v1.9.0	v1.7.0	Parameter: `mlrun.runtimes.nuclio.function.RemoteRuntime.deploy` `auth_info`	NA. Was not used.
v1.9.0	v1.7.0	Parameter: `mlrun.projects.MlrunProject.list_runs` `state`	`states`
v1.9.0	v1.7.0	Parameter: `mlrun.db.httpdb.HTTPRunDB.list_runs` `state`	`states`
v1.9.0	v1.7.0	Class: `mlrun.common.runtimes.constants.RunLabels`	`RunLabels.owner` => `MlrunInternalLabels.owner` `RunLabels.v3io_user` => `MlrunInternalLabels.v3io_user`
v1.9.0	v1.7.0	Parameter: `mlrun.runtimes.base.mlrun_op` `rundb`	MLRUN_DBPATH environment variable
v1.9.0	v1.7.0	Query parameter: GET `/projects/{project}/schedules?labels="label1=val1"`	`label`, which is an array of strings
v1.9.0	v1.7.0	Query parameter: DELETE `/projects/{project}/artifacts/{key:path}?uid="some-uid"`	`object-uid`
v1.9.0	v1.7.0	Query parameter: GET `/projects/{project}/artifacts/{key:path}?uid="some-uid"`	`object-uid`
v1.9.0	v1.6.3	`FunctionSpec.clone_target_dir`	`ImageBuilder.source_code_target_dir`
v1.8.0	v1.6.0	HTTPDB: `last` parameter of `list_runs`	NA. Was not used.
v1.8.0	v1.6.0	Feature store: `get_offline_features`	`FeatureVector.get_offline_features()`
v1.8.0	v1.6.0	Feature store: `get_online_feature_service`	`FeatureVector.get_online_feature_service()`
v1.8.0	v1.6.0	Feature store: `preview`	`FeatureSet.preview()`
v1.8.0	v1.6.0	Feature store: `ingest`	`FeatureSet.ingest()`
v1.8.0	v1.6.0	Artifacts: `uid` parameter of `store_artifact`	`tree` parameter of `store_artifact` (artifact uid is generated in the backend)
v1.8.0	v1.6.0	Runtimes: `with_requirements` — `requirements` param as a requirements file	`requirements_file` param

Removed APIs#

Version	API	Use instead
v1.7.0	Function: `mlrun.utils.helpers.parse_versioned_object_uri`	`mlrun.common.helpers.parse_versioned_object_uri`
v1.7.0	`func_info`	`ast_func_info`
v1.7.0	Class: `MpiRuntimeV1Alpha1`	`MpiRuntimeV1`
v1.7.0	Feature store: `deploy_ingestion_service`	`deploy_ingestion_service_v2`
v1.7.0	`skip_deployed` parameter of `mlrun.projects.project.MlrunProject.build_image`	NA. The parameter was ignored.
v1.7.0	API endpoint GET: `/files` and `/filestat`	`/projects/{project}/filestat`
v1.7.0	`LegacyArtifact` and all legacy artifact types that inherit from it (`LegacyArtifact`, `LegacyDirArtifact`, `LegacyLinkArtifact`, `LegacyPlotArtifact`, `LegacyChartArtifact`, `LegacyTableArtifact`, `LegacyModelArtifact`, `LegacyDatasetArtifact`, `LegacyPlotlyArtifact`, `LegacyBokehArtifact`, `BokehArtifact`, `LegacyArtifact`)	`Artifact` or other artifact classes that inherit from it
v1.7.0	Parameter of mlrun.feature_store.feature_set.FeatureSet.set_targets `default_final_state`	`default_final_step`
v1.6.2	`dashboard` parameter of the RemoteRuntime `invoke`	NA. The parameter was ignored.
v1.6.0	`dashboard` parameter of `project.deploy_function`, `RemoteRuntime.deploy`, `RemoteRuntime.get_nuclio_deploy_status`, `ServingRuntime.with_secrets`	NA. The parameter was ignored.
v1.6.0	`MLRunProject.clear_context()`	NA
v1.6.0	MLRunProject object legacy parameters	metadata and spec
v1.6.0	`BaseRuntime.with_commands` and `KubejobRuntime.build_config` `verify_base_image` param	`prepare_image_for_deploy`
v1.6.0	`run_local`	`function.run(local=True)`
v1.6.0	CSVSource's `time_fields` parameter	Use `parse_dates` to parse timestamps
v1.6.0	`new_pipe_meta`	`new_pipe_metadata`
v1.6.0	ttl param from pipeline	`cleanup_ttl`
v1.6.0	objects methods from artifacts list	`to_objects`
v1.5.0	user_project- and project-related parameters of `set_environment`. (Global-related parameters are not deprecated.)	The same parameters in project-related APIs, such as `get_or_create_project`
v1.5.0	`KubeResource.gpus`	`with_limits`
v1.5.0	Dask `gpus`	`with_scheduler_limits` / `with_worker_limits`
v1.5.0	`ExecutorTypes`	`ParallelRunnerModes`
v1.5.0	Spark runtime `gpus`	`with_driver_limits` / `with_executor_limits`
v1.5.0	`mount_v3io_legacy` (mount_v3io no longer calls it)	`mount_v3io`
v1.5.0	`mount_v3io_extended`	`mount_v3io`
v1.5.0	`init_functions` in pipelines	Add the function initialization to the pipeline code instead
v1.5.0	The entire `mlrun/mlutils` library	`mlrun.framework`
v1.5.0	`run_pipeline`	`project.run`
v1.3.0	`project.functions`	`project.get_function`, `project.set_function`, `project.list_function`
v1.3.0	`project.artifacts`	`project.get_artifact`, `project.set_artifact`, `project.list_artifact`
v1.3.0	`project.func()`	`project.get_function()`
v1.3.0	`project.create_vault_secrets()`	NA
v1.3.0	`project.get_vault_secret()`	NA
v1.3.0	`MlrunProjectLegacy` class	`MlrunProject`
v1.3.0	Feature-store: usage of state in graph. For example: `add_writer_state`, and the `after_state` parameter in `_init_` methods.	`step`
v1.3.0	`mount_path` parameter in mount_v3io()	`volume_mounts`
v1.3.0	`NewTask`	`new_task()`
v1.3.0	Dask `with_limits`	`with_scheduler_limits` / `with_worker_limits`
v1.3.0	Dask `with_requests`	`with_scheduler_requests` / `with_worker_requests`

Removed CLIs#

Version	CLI	Use instead
v1.7.0	`mlrun db`	NA
v1.6.0	deploy `--dashboard (nuclio/deploy)`	No longer supported on client side. Configure using the MLRun API.
v1.6.0	project `--overwrite-schedule`	Not relevant. Running a schedule is now an operation.
v1.5.0	`--ensure-project` flag of the `mlrun project` CLI command

Change log

Contents

Change log#

v1.7.1 (2 December 2024)#

Model monitoring#

Breaking changes#

Closed issues#

v1.7.0 (1 November 2024)#

Model monitoring#

Alerts#

Projects#

Artifacts#

Runtimes#

Workflows#

Functions#

Serving graph#

Data store#

Notifications#

Breaking changes#

Feature store#

UI#

Documentation#

Closed issues#

v1.6.4 (2 July 2024)#

UI#

Closed issues#

v1.6.3 (4 June 2024)#

Workflows#

Infrastructure#

Documentation#

Breaking change#

Closed issues#

v1.6.2 (29 March 2024)#

Closed issues#

v1.6.1 (29 February 2024)#

Closed issue#

v1.6.0 (22 February 2024)#

Data store#

Feature store#

Model monitoring#

Runtimes#

UI#

Infrastructure#

Documentation#

Breaking Changes#

Closed issues#

v1.5.2 (30 November 2023)#

Closed issues#

v1.5.1 (2 November 2023)#

Closed issues#

v1.5.0 (23 October 2023)#

Data store#

Feature store#

Infrastructure#

Runtimes#

UI#

Documentation#

Breaking changes#

Deprecations#

Closed issues#

v1.4.1 (8 August 2023)#

Closed issues#

v1.4.0 (23 July 2023)#

New and updated features#

Functions#

Notifications#

Projects#

Serving#

UI#

Documentation#

New documentation pages#

Breaking changes#

Deprecations and future deprecations#

Closed issues#

v1.3.4 (23 August 2023)#

Closed issues#

v1.3.3 (7 Jun 2023)#

Closed issues#

v1.3.2 (4 Jun 2023)#

Closed issues#