* SDK - Compiler - Fixed the input argument mapping when using dsl.graph_component
Fixes https://github.com/kubeflow/pipelines/issues/3915
* Stopped relying on the argument order at all
This can make the compilation less fragile.
* SDK - Tests - Restored the ParallelFor compiler test data
Fixes https://github.com/kubeflow/pipelines/issues/4102
* Removed the pipeline-sdk-type annotations
* Fixed the test_artifact_passing_using_volume test data
* SDK - Compiler - Added support for volume-based data passing
Currently artifact passing is performed by Argo sidecar containers what download input data and upload output data to artifact repository (usually, S3-compatible blob storage like Minio).
The performance of this method is not optimal and it requires that pod disks have enough capacity to hold all artifact data.
This commit adds support for volume-based data passing.
This method involves using a single milti-write Kubernetes data volume to pass all intermediate data.
Parts of the volume are mounted to the input/output artifact directories, so when the user program reads and writes files, the files actually reside in the data volume.
This method improves the performance and reduces storage resource requirements.
The data volume must exist and support "READ_WRITE_MANY".
Limitations:
* All artifact file names must be the same (e.g. "data"). All auto-generated paths are already consistent. Avoid using any hard-coded paths.
* Passing constant values (text) as arguments for artifact inputs is not supported.
* The feature is experimental.
* Added data_passing_methods.KubernetesVolume
This class represents a configured volume-based artifact passing method.
* Added PipelineConf.data_passing_method
This property allows setting the method that will be used for intermediate data passing.
Added the compiler support for the new feature.
Example:
```python
from kfp.dsl import PipelineConf, data_passing_methods
from kubernetes.client.models import V1Volume, V1PersistentVolumeClaim
pipeline_conf = PipelineConf()
pipeline_conf.data_passing_method = data_passing_methods.KubernetesVolume(
volume=V1Volume(
name='data',
persistent_volume_claim=V1PersistentVolumeClaim('data-volume'),
),
path_prefix='artifact_data/',
)
```
* Added unit test
* Fixed bug in the unit test
Kubernetes does not validate the structures at all...
* Fixed bug in the result structure
* Fixed the test data
The class should be V1PersistentVolumeClaimVolumeSource, not V1PersistentVolumeClaimSpec.
* Fixed the test
Previously the default image was set to an old version of tensorflow image. That image is now outdated. It's also framework-specific and pretty big.
We're switching to the official python image which is small, official and framework-agnostic.
The users can easily switch to the old behavior by just specifying `base_image='tensorflow/tensorflow:1.13.2-py3'` during the component creation.
* SDK - Components - Stabilize JSON serialization by sorting keys
Otherwise serialization of the default values of the component/pipeline inputs is unstable on Python 3.5.
* Fixed the test data
* add OOB component dict and utility function
* add test
* add a transformer, which appends the component name label
* add transformer function, compiler and test
* move telemetry test
* fix none uri
* applies comments
* revert dependency on frozendict
* fixes some tests
* resolve comments
* SDK - Made outputs with original names available in ContainerOp.outputs
Previously, ContainerOp had strict requirements for the output names, so we had to convert all the names before passing them to the ContainerOp constructor. Outputs with non-pythonic names could not be accessed using their original names.
Now ContainerOp supports any output names, so we're now using the original output names.
However to support legacy pipelines, we're also adding output references with pythonic names.
* Fixed the compiler test data
* Fixed the duplicate parameter outputs in the compiled workflow
* Fixed long line
* Stabilized the output naming conflict resolution
* Fix case of missing special outputs
* SDK - Prioritize lib2to3 when stripping type annotations
It's a standard python library (although not well supported) and it doe not leave training spaces.
* Fixed compiler test data
* SDK - Annotate pods with component_ref
This preserves the information about the digest of the component and the location from which the component was loaded.
* Fixed compiler tests
* Add kfp-container-builder sa
* Allow service account to be configurable
* Fix tests
* Fix test
* Use documentation for service account to introduce compatibility with different types of installation
* updated doc
* clean up
* Update container_builder_test.py
* Update _build_image_api.py
* Update kustomization.yaml
* Add executable permission for presubmit tests mkp.sh
* SDK - Reduce python component limitations - no import errors for custom type annotations
By default, create_component_from_func copies the source code of the function and creates a component using that source code. No global imports are captured. This is problematic for the function definition, since any annotation, that uses a type that needs to be imported, will cause error. There were some special provisions for
NamedTuple, InputPath and OutputPath, but even they were brittle (for example, "typing.NamedTuple" or "components.InputPath" annotations still caused failures at runtime).
This commit fixes the issue by stripping the type annotations from function declarations.
Fixes cases that were failing before:
```python
import typing
import collections
MyFuncOutputs = typing.NamedTuple('Outputs', [('sum', int), ('product', int)])
@create_component_from_func
def my_func(
param1: CustomType, # This caused failure previously
param2: collections.OrderedDict, # This caused failure previously
) -> MyFuncOutputs: # This caused failure previously
pass
```
* Fixed the compiler tests
* Fixed crashes on print function
Code `print(line, end="")` was causing error: "lib2to3.pgen2.parse.ParseError: bad input: type=22, value='=', context=('', (2, 15))"
* Using the strip_hints library to strip the annotations
* Updating test workflow yamls
* Workaround for bug in untokenize
* Switched to the new strip_string_to_string method
* Fixed typo.
Co-Authored-By: Jiaxiao Zheng <jxzheng@google.com>
Co-authored-by: Jiaxiao Zheng <jxzheng@google.com>
* [Testing] Use gke 1.15.8 to mitigate workload identity flakiness
* Upgrade gcloud version
* Update image builder image too
* Turn on workload identity
* Update deploy-cluster.sh
* secret sample uses python3 instead
* Increase xgboost time limit
* Revert files with bad format
* Update component and pipelines to use gcloud 279.0.0
* Fix secret sample using python3
* Upgrade frontend integration test image
* Rebuild frontend integration test image
* SDK - Compiler - Fixed ParallelFor name clashes
The ParallelFor argument reference resolving was really broken.
The logic "worked" like this - of the name of the referenced output
contained the name of the loop collection source output, then it was
considered to be the reference to the loop item.
This broke lots of scenarios especially in cases where there were
multiple components with same output name (e.g. the default "Output"
output name). The logic also did not distinguish between references to
the loop collection item vs. references to the loop collection source
itself.
I've rewritten the argument resolving logic, to fix the issues.
* Argo cannot use {{item}} when withParams items are dicts
* Stabilize the loop template names
* Renamed the test case
* Replaced `_instance_to_dict(obj)` with `obj.to_dict()`
* Fixed the capitalization in _python_function_name_to_component_name
It now only changes the case of the first letter.
* Replaced the _extract_component_metadata function with _extract_component_interface
* Stopped adding newline to the component description.
* Handling None inputs and outputs
* Not including emply inputs and outputs in component spec
* Renamed the private attributes that the @pipeline decorator sets
* Changged _extract_pipeline_metadata to use _extract_component_interface
* Fixed issues based on feedback
* Script to set up workload identity for standalone deployment
* Migrate tests to run on standalone + workload identity
* Fix test script
* Switch to static GSAs for testing, because they have name length limit
* Add workload identity binding for argo
* Fix argo workload identity bindings
* Remove user-gcp-sa from tests
* Remove use_gcp_secret from xgboost sample
* Allow debugging tests locally
* Wait for policies to take effect
* Update deploy-pipeline-lite.sh
* Update deploy-pipeline-lite.sh
* [WIP] test gcloud auth list with test-runner sa
* Add namespace
* test again
* Use new image builder
* test again
* Remove debug code
* Remove usages of use_gcp_secret
* Fix unit test and tensorboard pod template
* Add debug code again to test
* Try waiting until workload identity bindings are ready
* Fix some other samples
* Fix parameterized tfx oss sample
* Add retry to image building
* Try fixing tfx oss sample
* Fix compiled tfx oss sample
* Update all google/cloud-sdk to latest
* Try fixing parameterized tfx oss sample again
* Also verify pipeline-runner ksa is working
* Fix parameterized_tfx_oss sample
* Update gcp-workload-identity-setup.sh
* Revert unneeded change
* Pin to new google/cloud-sdk
* Remove wrongly commited binaries
* SDK - Refactoring - Split the K8sHelper class
One part was only used by container builder and provided higher-level API over K8s Client.
Another was used by the compiler and did not use the kubernetes library.
* Updated the license year.
* SDK - Improve errors when ContainerOp.output is unavailable
ContainerOp.output is only available when there is only one output.
Right now, when there are multiple outputs it just holds `None` instead of the a task output reference.
In this case however it's indistinguishable from just passing None argument.
This PR gives a quick fix to make accessing the nonexistent `.output` a compile-time error.
* Fixed the implementation and added tests
* Trigger retests
* SDK - Compiler - Move volumes to templates
Argo v2.3.0+ supports per-template volume specs similiar to Kubernetes. Prior to version 2.3.0 Argo only supported workflow-level volume specs.
We had several outstanding issues caused by the need to put all volumes in the same place.
There was also the issue with input parameter reference placeholders in volume specifications which were placed outside their home templates declaring the inputs.
This change fixes those issues.
* Removed dead code line
* SDK - Moved the _container_builder from kfp.compiler to kfp.containers
This only moves the files. The imports remain the same for now.
* Simplified the imports.
* SDK - Compiler - Fix large data passing
Stop outputting parameters unless they're consumed as parameters downstream.
This prevents the situaltion when component outputs a big file, but DSL compiler instructs Argo to pick it up as parameter (parameters only hold few kilobytes of data).
As byproduct, this change fixes some minor compiler data passing bugs where some parameters were being passed around, but never consumed (happened with `ResourceOp`, `dsl.Condition` and recursion).
* Replaced ... with `raise AssertionError`
* Fixed small bug
* Removed unused variables
* Fixed names of the mark_upstream_ios_of_* functions
* Fixed detection of parameter output references
* Fixed handling of volumes
Two PRs have been merged that turned out to be slightly incompatible. This PR fixes the failing tests.
Root causes:
* The pipeline parameter default values were not properly serialized when constructing the metadata object.
* The `ParameterMeta` class did not validate the default value type, so the lack of serialization has not been caught. The `ParameterMeta` was replaced by `InputSpec` which has strict type validation.
* Previously we did not have samples with complex pipeline parameter default values (e.g. lists) that could trigger the failures. Then two samples were added that had complex default values.
* Travis does not re-run tests before merging
* Prow does not re-run Travis tests before merging
Currently, the parameter output values are not saved to storage and their values are lost as soon as garbage collector removes the workflow object.
This change makes is so the parameter output values are persisted.
* first working commit
* incrememtal commit
* in the middle of converting loop args constructor to accept pipeline param
* both cases working
* output works, passed doesn't
* about to redo compiler section
* rewrite draft done
* added withparam tests
* removed sdk/python/comp.yaml
* minor
* subvars work
* more tests
* removed unneeded artifact outputs from test yaml
* sort keys
* removed dead artifact code
* Refactor. Expose a public API to append pipeline param without interacting with dsl.Pipeline obj.
* Add unit test and fix.
* Fix docstring.
* Fix test
* Fix test
* Fix two nit problems
* Refactor
* Explicitly added mlpipeline outputs to the components that actually produce them
* Updated samples
* SDK - DSL - Stopped adding mlpipeline artifacts to every compiled template
Fixes https://github.com/kubeflow/pipelines/issues/1421
Fixes https://github.com/kubeflow/pipelines/issues/1422
* Updated the Lighweight sample
* Updated the compiler tests
* Fixed the lightweight sample
* Reverted the change to one contrib/samples/openvino
The sample will still work fine as it is now.
I'll add the change to that file as a separate PR.
* SDK - Switching python container components to Lightweight components code generator
* Fixed the tests
Had to remove the python2 test since python2 code generation is going away (python2 is near its End of Life and Kubeflow Pipelines only support python 3.5+).
* Added description for the internal add_files parameter
* Fixed typo
* Removed the `test_func_to_entrypoint` test
This was proposed by @gaoning777: `_func_to_entrypoint` is now just a reference to `_func_to_component_spec` which is extensively covered by other tests.
* SDK - Added support for raw artifact values to ContainerOp
* `ContainerOp` now gets artifact artguments from command line instead of the constructor.
* Added back input_artifact_arguments to the ContainerOp constructor.
In some scenarios it's hard to provide the artifact arguments through the `command` list when it already has resolved artifact paths.
* Exporting InputArtifactArgument from kfp.dsl
* Updated the sample
* Properly passing artifact arguments as task arguments
as opposed to default input values.
* Renamed input_artifact_arguments to artifact_arguments to reduce confusion
* Renamed InputArtifactArgument to InputArgumentPath
Also renamed input_artifact_arguments to artifact_argument_paths in the ContainerOp's constructor
* Replaced getattr with isinstance checks.
getattr is too fragile and can be broken by renames.
* Fixed the type annotations
* Unlocked the input artifact support in components
Added the test_input_path_placeholder_with_constant_argument test
* Fix bug where delete resource op should not have success_condition, failure_condition, and output parameters
* remove unnecessary whitespace
* compiler test for delete resource ops should retrieve templates from spec instead of root
* SDK - Refactoring - Serialized PipelineParam does not need type
Only the types in non-serialized PipelineParams are ever used.
* SDK - Refactoring - Serialized PipelineParam does not need value
Default values are only relevant when PipelineParam is used in the pipeline function signature and even in this case compiler captures them explicitly from the pipelineParam objects in the signature.
There is no other uses for them.
* avoid istio injector in the container builder
* find the correct namespace
* configure default ns to kubeflow if out of cluster; fix unit tests
* container build default gcs bucket
* resolve comments
* code refactor; add create_bucket_if_not_exist in containerbuilder
* support load kube config and output error, good for ai platform notebooks/local notebooks
* remove create_bucket_if_not_exist param
* SDK - Containers - Returning image name with digest
Image building functions now return image name with digest: image_repo@sha256:digest
Fixes https://github.com/kubeflow/pipelines/issues/1715
* Added comments
* Remove redundant import.
* Simplify sample_test.yaml by using withItem syntax.
* Simplify sample_test.yaml by using withItem syntax.
* Change dict to str in withItems.
* Add image pull secret sample.
* Move imagepullsecret sample from test dir to sample dir. Waiting on corresponding unit test infra refactoring.
* Update the location of imagepullsecrets so that it can serve as an example.
* Add minimal comments documenting usage.
* Remove redundant import.
* Simplify sample_test.yaml by using withItem syntax.
* Simplify sample_test.yaml by using withItem syntax.
* Change dict to str in withItems.
* Add preemptible gpu tpu sample and unittest
* Update a test utility function.
* Seperate the location of sample and gold .yaml for testing purpose.
* Added support for mulitple outputs
* Added test for multiple output
* Adding sample for multiple outputs
* func_signature now shorter form
* Added parameters tag
* Fixed func_signature mistake
* refactor component build code
* remove unnecessary import
* minor changes
* fix unit tests
* separate the container build from the component build; add support for directories in the containerbuilder
* minor fixes
* fix unit test
* fix tarball error
* revert changes
* unit test fix
* minor fix
* addressing comments
* removing the check_gcs_path function
* move namespace to the contructor of containerbuilder
* fix bugs
* refactor component build code
* remove unnecessary import
* minor changes
* fix unit tests
* fix sample test bug
* revert the change of the dependency orders
* add -u to disable python stdout buffering
* address the comments
* separate lines to look clean
* fix unit tests
* fix
* Add PipelineConf method to set ttlSecondsAfterFinished in argo workflow spec
* remove unnecessary compile test for ttl. add unit test for ttl instead.
* Configure gcp connectors in dsl
* Make configure_gcp_connector more extensible
* Add add_pod_env op handler.
* Only apply add_pod_env on ContainerOp
* Update license header
* Frontend - Show customized task display names
* Added customized name test
* Added ContainerOp.set_display_name(name) method
* Stopped writing human_name to display_name annotation for now
Reason: It's a change to existing pipelines.
* Added test for op.set_display_name
* Fix for tests that have workflows with status nodes, but without any spec or templates
* Fixed the test workflow
* Fix linter error
Error: "The key 'metadata' is not sorted alphabetically"
* add default value type checking
* add jsonschema dependency
* fix unit test error
* workaround for travis python package installation
* add back jsonschema version
* fix sample test error in type checking sample
* add jsonschema in requirements such that sphinx works fine
* SDK/Compiler - Added op and template transformers
They can be used to apply some functions (e.g. to add secrets) to all pipeline ops.
* Removed the template_transformers for now
* Moved the op_transformers to PipelineConf
* Added op_transformers test
* SDK: Create BaseOp class
* BaseOp class is the base class for any Argo Template type
* ContainerOp derives from BaseOp
* Rename dependent_names to deps
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: In preparation for the new feature ResourceOps (#801)
* Add cops attributes to Pipeline. This is a dict having all the
ContainerOps of the pipeline.
* Set some processing in _op_to_template as ContainerOp specific
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: Simplify the consumption of Volumes by ContainerOps
Add `pvolumes` argument and attribute to ContainerOp. It is a dict
having mount paths as keys and V1Volumes as values. These are added to
the pipeline and mounted by the container of the ContainerOp.
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: Add ResourceOp
* ResourceOp is the SDK's equivalent for Argo's resource template
* Add rops attribute to Pipeline: Dictionary containing ResourceOps
* Extend _op_to_template to produce the template for ResourceOps
* Use processed_op instead of op everywhere in _op_to_template()
* Add samples/resourceop/resourceop_basic.py
* Add tests/dsl/resource_op_tests.py
* Extend tests/compiler/compiler_tests.py
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: Simplify the creation of PersistentVolumeClaim instances
* Add VolumeOp: A specified ResourceOp for PVC creation
* Add samples/resourceops/volumeop_basic.py
* Add tests/dsl/volume_op_tests.py
* Extend tests/compiler/compiler_tests.py
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: Emit a V1Volume as `.volume` from dsl.VolumeOp
* Extend VolumeOp so it outputs a `.volume` attribute ready to be
consumed by the `pvolumes` argument to ContainerOp's constructor
* Update samples/resourceop/volumeop_basic.py
* Extend tests/dsl/volume_op_tests.py
* Update tests/compiler/compiler_tests.py
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: Add PipelineVolume
* PipelineVolume inherits from V1Volume and it comes with its own set of
KFP-specific dependencies. It is aligned with how PipelineParam
instances are used. I.e. consuming a PipelineVolume leads to implicit
dependencies without the user having to call the `.after()` method on
a ContainerOp.
* PipelineVolume comes with its own `.after()` method, which can be used
to append extra dependencies to the instance.
* Extend ContainerOp to handle PipelineVolume deps
* Set `.volume` attribute of VolumeOp to be a PipelineVolume instead
* Add samples/resourceops/volumeop_{parallel,dag,sequential}.py
* Fix tests/dsl/volume_op_tests.py
* Add tests/dsl/pipeline_volume_tests.py
* Extend tests/compiler/compiler_tests.py
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* SDK: Simplify the creation of VolumeSnapshot instances
* VolumeSnapshotOp: A specified ResourceOp for VolumeSnapshot creation
* Add samples/resourceops/volume_snapshotop_{sequential,rokurl}.py
* Add tests/dsl/volume_snapshotop_tests.py
* Extend tests/compiler/compiler_tests.py
NOTE: VolumeSnapshots is an Alpha feature at the time of this commit.
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* Extend UI for the ResourceOp and Volumes feature of the Compiler
* Add VolumeMounts tab/entry (Run/Pipeline view)
* Add Manifest tab/entry (Run/Pipeline view)
* Add & Extend tests
* Update tests snapshot files
Signed-off-by: Ilias Katsakioris <elikatsis@arrikto.com>
* Cleaning up the diff (before moving things back)
* Renamed op.deps back to op.dependent_names
* Moved Container, Sidecar and BaseOp classed back to _container_op.py
This way the diff is much smaller and more understandable. We can always split or refactor the file later. Refactorings should not be mixed with genuine changes.
* remove the graph component output; add support for dependency on graph component
* fix bug; adjust unit tests
* add support for explicit dependency of graph component
* adjust unit test
* add a todo
* bug fixes for unit tests
* refactor condition_param code; fix bug when the inputs task name is None; need to remove the print later
* do not pass condition param as arguments to downstream ops, remove print logs; add unit tests
* add unit test golden yaml
* fix bug
* fix the sample
Alexey Volkov
Jiaxiao Zheng
Niklas Hansson
Eterna2
Yuan (Bob) Gong
Alexey Volkov
Ning
Kevin Bache
Hamed
Zane Durante
IronPan
hongye-sun
Krassimir Valev
Ilias Katsakioris
Ning