From 0f6212524d5f11d0b57a116f70b06dc52107791f Mon Sep 17 00:00:00 2001 From: Stan Hatko Date: Fri, 9 Dec 2022 14:44:38 -0500 Subject: [PATCH 1/3] document removal of Kubeflow pipelines from AAW, and future replacement by Argo workflows --- docs/en/3-Pipelines/Kubeflow-Pipelines.md | 853 +--------------------- 1 file changed, 2 insertions(+), 851 deletions(-) diff --git a/docs/en/3-Pipelines/Kubeflow-Pipelines.md b/docs/en/3-Pipelines/Kubeflow-Pipelines.md index dab6fcf81..32fc9a8ef 100644 --- a/docs/en/3-Pipelines/Kubeflow-Pipelines.md +++ b/docs/en/3-Pipelines/Kubeflow-Pipelines.md @@ -1,854 +1,5 @@ # Overview -[Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/) -is a platform for building machine learning workflows for deployment in a -Kubernetes environment. It enables authoring _pipelines_ that encapsulate -analytical workflows (transforming data, training models, building visuals, -etc.). These _pipelines_ can be shared, reused, and scheduled, and are built to -run on compute provided via Kubernetes. Here is an example of a pipeline with -many `sample` steps feeding into a single `average` step. This image comes from -the Kubeflow Pipelines UI. +[Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/) are in the process of being removed from AAW. -![A Kubeflow Pipeline](../images/kf-pipeline_with_result.png) - -In the context of the Advanced Analytics Workspace, Kubeflow Pipelines are -interacted with through: - -- The [Kubeflow UI](../1-Experiments/Kubeflow.md), where from the Pipelines menu - you can upload pipelines, view the pipelines you have and their results, etc. -- The Kubeflow Pipelines python - [SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/), accessible - through the - [Jupyter Notebook Servers](../1-Experiments/Kubeflow.md#create-a-server), - where you can define your components and pipelines, submit them to run now, or - even save them for later. - - -??? example "More examples in the notebooks" - More comprehensive pipeline examples specifically made for this platform are - available on [GitHub](https://github.com/StatCan/aaw-contrib-jupyter-notebooks) (and in - every Notebook Server at `/jupyter-notebooks`). You can also check out - [public sources](https://github.com/kubeflow/pipelines/tree/master/samples). - -See -[the official Kubeflow docs](https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/) -for a more detailed explanation of Kubeflow Pipelines. - -## What are pipelines and how do they work? - -A -[_pipeline_](https://www.kubeflow.org/docs/pipelines/overview/concepts/pipeline/) -in Kubeflow Pipelines consists of one or more -[_pipeline components_](https://www.kubeflow.org/docs/pipelines/overview/concepts/component/) -chained together to form a workflow. The _components_ are like functions, -describing the individual steps in your workflow (such as pulling columns from a -data store, transforming data, or training a model). The _pipeline_ is the logic -that glues _components_ together, such as: - -1. Run Component-A -2. Pass the output from Component-A to Component-B and Component-C -3. ... - -In the above image, the logic would be running many `sample` steps followed by a -single `average` step. - -At their core, each _component_ has: - -- A standalone application, packaged as a - [Docker image](https://docs.docker.com/get-started/), for doing the actual - work. The code in the Docker image could be a shell script, Python script, or - anything else you can run from a Linux terminal, and generally will have a - command line interface for data exchange (accessible through `docker run`) -- A YAML file that describes how Kubeflow Pipelines runs this code (what Docker - image should be run, what command line arguments does it accept, what output - does it generate) - -Each _component_ should be **single purpose**, **modular**, and **reusable**. - -# Setup - -## Define and run your first pipeline using the Python SDK - -While _pipelines_ and _components_ are defined in Kubeflow Pipelines by YAML -files that use Docker images, that does not mean we have to work directly with -either YAML files or Docker images. The -[Kubeflow Pipelines SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/) -provides a way for us to define our _pipeline_ and _components_ directly in -Python code, where the SDK then translates our Python code to YAML files for us. - -For our first example, let's define a -[simple pipeline](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/kfp-basics/average_with_docker_components.ipynb) -using only the Python SDK. The purpose of this section is to give a high level -view of _component_ and _pipeline_ authoring, not a deep dive. More detailed -looks into defining your own _components_, passing data between _components_, -and returning data from your _pipeline_ are explained in more detail in further -sections. - -The demo pipeline we define will do the following: - -1. Accept five numbers as arguments -2. Average of the first three numbers -3. Average of the last two numbers -4. Average of the results of (2) and (3) - -To do this, we will first define our _component_. Our `average` _component_ will -call a Docker image that does the following: - -- Accepts one or more numbers as command line arguments -- Returns the average of these numbers by writing them to an output file in the - container (by default, to `out.txt`) - -This Docker image is already built for us and stored in our container registry -here: `k8scc01covidacr.azurecr.io/kfp-components/average:v1`. Don't worry if you -don't know Docker - since the image is built already, we only have to tell -Kubeflow Pipelines where it is. - - -??? info "Full details of the `average` component's Docker image are in - [GitHub](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/tree/master/kfp-basics/containers/average)" - This image effectively runs the following code (slightly cleaned up for - brevity). By making `average.py` accept an arbitrary set of numbers as - inputs, we can use the same `average` _component_ for all steps in our - _pipeline_: - - import argparse - - def parse_args(): - parser = argparse.ArgumentParser(description="Returns the average of one or " - "more numbers as a JSON file") - parser.add_argument("numbers", type=float, nargs="+", help="One or more numbers") - parser.add_argument("--output_file", type=str, default="out.txt", help="Filename " - "to write output number to") - return parser.parse_args() - - if __name__ == '__main__': - args = parse_args() - numbers = args.numbers - output_file = args.output_file - - print(f"Averaging numbers: {numbers}") - avg = sum(numbers) / len(numbers) - print(f"Result = {avg}") - - print(f"Writing output to {output_file}") - with open(output_file, 'w') as fout: - fout.write(str(avg)) - - print("Done") - -To make our `average` image into a Kubeflow Pipelines _component_, we make a -`kfp.dsl.ContainerOp` in Python that defines how Kubeflow Pipelines interacts -with our container, specifying: - -- The Docker image location to use -- How to pass arguments to the running container -- What outputs to expect from the container - -We could use `ContainerOp` directly, but since we'll use `average` a few times -we instead create a factory function we can reuse: - -```python -from kfp import dsl - -def average_op(*numbers): - """ - Factory for average ContainerOps - - Accepts an arbitrary number of input numbers, returning a ContainerOp that - passes those numbers to the underlying Docker image for averaging - - Returns output collected from ./out.txt from inside the container - - """ - # Input validation - if len(numbers) < 1: - raise ValueError("Must specify at least one number to take the average of") - - return dsl.ContainerOp( - name="average", # What will show up on the pipeline viewer - image="k8scc01covidacr.azurecr.io/kfp-components/average:v1", # The image that KFP runs to do the work - arguments=numbers, # Passes each number as a separate command line argument - # Note that these arguments get serialized to strings - file_outputs={'data': './out.txt'}, # Expect an output file called out.txt to be generated - # KFP can read this file and bring it back automatically - ) -``` - -To define our pipeline, we create a Python function decorated by the -`@dsl.pipeline` decorator. We invoke our `average_op` factory to use our average -container. We pass each `average` some inputs, and even use their outputs by -accessing `avg_*.output`. - -```python -@dsl.pipeline( - name="my pipeline's name" -) -def my_pipeline(a, b, c, d, e): - """ - Averaging pipeline which accepts five numbers and does some averaging - operations on them - """ - # Compute averages for two groups - avg_1 = average_op(a, b, c) - avg_2 = average_op(d, e) - - # Use the results from _1 and _2 to compute an overall average - average_result_overall = average_op(avg_1.output, avg_2.output) -``` - -Finally, while we've defined our pipeline in Python, Kubeflow Pipelines itself -needs everything defined as a YAML file. This final step uses the Kubeflow -Pipelines Python SDK to translate our pipeline function into a YAML file that -describes exactly how Kubeflow Pipelines can interact with our component. Unzip -it and take a look for yourself! - -```python -from kfp import compiler -pipeline_yaml = 'pipeline.yaml.zip' -compiler.Compiler().compile( - my_pipeline, - pipeline_yaml -) -print(f"Exported pipeline definition to {pipeline_yaml}") -``` - - -??? warning "Kubeflow Pipelines is a lazy beast" - It is useful to keep in mind what computation is happening when you run this - python code versus what happens when you submit the pipeline to Kubeflow - Pipelines. Although it seems like everything is happening in the moment, try - adding `print(avg_1.output)` to the above pipeline and see what happens when - you compile your pipeline. The Python SDK we're using is for _authoring_ - pipelines, not for running them, so results from components will never be - available when you run this Python code. The is discussed more below in - *Understanding what computation occurs when*. - -To actually run our pipeline, we define an experiment: - -```python -experiment_name = "averaging-pipeline" - -import kfp -client = kfp.Client() -exp = client.create_experiment(name=experiment_name) - -pl_params = { - 'a': 5, - 'b': 5, - 'c': 8, - 'd': 10, - 'e': 18, -} -``` - -And then run an instance of our pipeline with the arguments we want: - -```python -import time - -run = client.run_pipeline( - exp.id, # Run inside the above experiment - experiment_name + '-' + time.strftime("%Y%m%d-%H%M%S"), # Give our job a name with a timestamp so its unique - pipeline_yaml, # Pass the .yaml.zip we created above. This defines the pipeline - params=pl_params # Pass our parameters we want to run the pipeline with -) -``` - -This can all be seen in the -[Kubeflow Pipelines UI](../1-Experiments/Kubeflow.md): - -![Example of an Experiment in Kubeflow Pipelines UI, highlighting how to choose runs within the experiment](../images/kfp_experiment.png) - -![Example of a Run in Kubeflow Pipelines UI, highlighting Inputs and Outputs](../images/kfp_run.png) - -Later when we want to reuse the pipeline, we can pass different arguments and do -it all again. - - -!!! info "We create our experiment, upload our pipeline, and run from Python in - this example, but we could also do all this through the Kubeflow Pipelines - UI above. - -## Understanding what computation occurs when - -The above example uses Python code to define: - -- The interface between Kubeflow Pipelines and our Docker containers doing the - work (by defining `ContainerOp`'s) -- The logic of our pipeline (by defining `my_pipeline`). - -But when we run `compiler.Compiler().compile()` and `client.run_pipeline()`, -what actually happens? - -It is important to remember that everything we run in Python here is specifying -the _pipeline_ and its _components_ in order to write YAML definitions, it is -**not** doing the work of the pipeline. When running -`compiler.Compiler().compile()`, we are not running our pipeline in the typical -sense. Instead, KFP **uses** `my_pipeline` to build a YAML version of it. When -we `compile`, the KFP SDK is passing placeholder arguments to `my_pipeline` and -tracing where they (and any other runtime data) go, such as any output a -_component_ produces. When `compile` encounters a `ContainerOp`, nothing runs -now - instead it takes note that a container will be there in future and -remembers what data it will consume/generate. This can be seen by modifying and -recompiling our pipeline: - -```python -@dsl.pipeline( - name="my pipeline's name" -) -def my_pipeline(a, b, c, d, e): - """ - Averaging pipeline which accepts five numbers and does some averaging operations on them - """ - # NEW CODE - x = 1 + 1 - print(f"The value of x is {x}") - print(f"The value of a is {a}") - - # Compute averages for two groups - avg_1 = average_op(a, b, c) - avg_2 = average_op(d, e) - - # NEW CODE - print(f"The value of avg_1.output is {avg_1.output}") - - # Use the results from _1 and _2 to compute an overall average - average_result_overall = average_op(avg_1.output, avg_2.output) -``` - -And when we `compile`, we see print statements: - -``` -The value of x is 2 -The value of a is {{pipelineparam:op=;name=a}} -The value of avg_1.output is {{pipelineparam:op=averge;name=data}} -``` - -In the first print statement everything is normal. In the second and third print -statements, however, we see string placeholders rather than actual output. So -while `compile` does "execute" `my_pipeline`, the KFP-specific parts of the code -don't actually generate results. This can also be seen in the YAML file that -`compile` generates, for example looking at the portion defining our -`average_result_overall` component: - -```yaml -- name: average-3 - container: - args: - [ - "{{inputs.parameters.average-data}}", - "{{inputs.parameters.average-2-data}}", - ] - image: k8scc01covidacr.azurecr.io/kfp-components/average:v1 - inputs: - parameters: - - { name: average-2-data } - - { name: average-data } - outputs: - artifacts: - - { name: average-3-data, path: ./out.txt } - metadata: - labels: { pipelines.kubeflow.org/pipeline-sdk-type: kfp } -``` - -In this YAML we see the input parameters passed are placeholders for data from -previous components rather than their actual value. This is because while KFP -knows **a** result from `average-data` and `average-2-data` will be passed to -average, but the **value** of that result is not available until the pipeline is -actually run. - - -??? info "Component naming within the YAML file" - Because we made an `average_op` factory function with `name='average'` above, - our YAML file has component names that automatically increment to avoid - recreating the same name twice. We could have been fancier with our - factory functions to more directly control our names, giving an argument - like `name='average_first_input_args'`, or could even have explicitly - defined the name in our pipeline by using - `avg_1 = average_op(a, b, c).set_display_name("Average 1")`. - -As one more example, let's try two more pipelines. One has a for loop inside -which prints "Woohoo!" a fixed number of times. whereas the other does the same -but loops `n` times (where `n` is a pipeline parameter): - - -!!! info "Pipeline parameters are described more below, but they work like - parameters for functions. Pipelines can accept data (numbers, string URL's - to large files in MinIO, etc.) as arguments, allowing a single generic - pipeline to work in many situations." - -```python -@dsl.pipeline( - name="my pipeline's name" -) -def another_pipeline(): - """ - Prints to the screen 10 times - """ - for i in range(10): - print("Woohoo!") - - # And just so we've got some component going too... - avg = average_op(n) - -compiler.Compiler().compile( - another_pipeline, - "another.yaml.zip" -) -``` - -```python -@dsl.pipeline( - name="my pipeline's name" -) -def another_another_pipeline(n): - """ - Prints to the screen n times - """ - for i in range(n): - print("Woohoo!") - - # And just so we've got some component going too... - avg = average_op(n) - -compiler.Compiler().compile( - another_another_pipeline, - "another.yaml.zip" -) -``` - -The first works as you'd expect, but the second raises the exception: - -`TypeError: 'PipelineParam' object cannot be interpreted as an integer` - -Why? Because when authoring the pipeline `n` is a placeholder and has no value. -KFP cannot define a pipeline from this because it does not know how many times -to loop. We'd hit similar problems if using `if` statements. There are some ways -around this, but they're left to the reader to explore through the -[Kubeflow Pipelines docs](https://www.kubeflow.org/docs/components/pipelines/overview/). - -Why does pipeline authoring behave this way? Because _pipelines_ (and -_components_) are meant to be reusable definitions of logic that are defined in -static YAML files, with all dynamic decision making done inside components. This -can make them a little awkward to define, but also helps them be more reusable. - -# Once you've got the basics ... - -## Data Exchange - -### Passing data into, within, and from a pipeline - -In the first example above, we pass: - -- Numbers into our pipeline -- Numbers between components within our pipeline -- A number back to the user at the end - -But as discussed above, pipeline arguments and component results are just -placeholder objects – so how does KFP know our values are numeric? The answer -is: it doesn't. In fact, it didn't even treat them as numbers above. Instead it -treated them as strings. It is just that our pipeline components worked just as -well with `"5"` as they would have with `5`. - -A safe default assumption is that all data exchange happens as a string. When we -passed `a, b, ...` into the pipeline, those numbers were implicitly stringified -because they eventually become command line arguments for our Docker container. -When we read the result of `avg_1` from its `out.txt`, that result was read as a -string. By calling `average_op(avg_1.output, avg_2.output)`, we ask KFP to pass -the **string** output from `avg_1` and `avg_2` to a new `average_op`. It just so -happened that, since `average_op` passes each string as a command line argument -to our Docker image, it didn't really matter they were strings. - -You can still use non-string data types, but you need to pass them as serialized -versions. So if we wanted our `avg_1` component to return both the numbers -passed to it and the average returned as a dictionary, for example: - -```python -{ - 'numbers': [5, 5, 8], - 'result': 6.0, -} -``` - -We could modify our `average.py` in the Docker image write our dictionary of -numbers and result to `out.txt` as JSON. But then when we pass the result to -make `average_result_overall`, that component needs to deserialize the above -JSON and pull the data from it that it needs. And because these results are not -available when authoring the pipeline, something like this does not work: - -```python -def my_pipeline(a, b, c, d, e): - """ - Averaging pipeline which accepts five numbers and does some averaging operations on them - """ - # Compute averages for two groups - avg_1 = average_op_that_returns_json(a, b, c) - avg_2 = average_op_that_returns_json(d, e) - - # THIS DOES NOT WORK! - import json - avg_1_result = json.loads(avg_1.output)['result'] - avg_2_result = json.loads(avg_2.output)['result'] - - # Use the results from _1 and _2 to compute an overall average - average_result_overall = average_op(avg_1.output, avg_2.output) -``` - -At `compile` time, `avg_1.output` is just a placeholder and can't be treated -like the JSON it will eventually become. To do something like this, we need to -interpret the JSON string within a container. - -### Passing Secrets - -Pipelines often need sensitive information (passwords, API keys, etc.) to -operate. To keep these secure, these cannot be passed to a Kubeflow Pipeline -using a pipeline argument, environment variable, or as a hard coded value in -python code, as they will be exposed as plain text for others to read. - -To address this issue, we use -[Kubernetes secrets](https://kubernetes.io/docs/concepts/configuration/secret/) -as a way to securely store and pass sensitive information. Each **secret** is a -key-value store containing some number of key-value pairs. The secrets can be -passed _by reference_ to the pipeline. - - -??? note "Secrets are only accessible within their own namespace" - Secrets can only be accessed within the namespace they are created in. - If you need to use a secret in another namespace, you need to add it there - manually. - -#### Create a key-value store - -The example below creates a key-value store called **elastic-credentials** which -contains two key-value pairs: - -``` -"username": "USERNAME", -"password": "PASSWORD" -``` - -```bash -kubectl create secret generic elastic-credentials \ - --from-literal=username="YOUR_USERNAME" \ - --from-literal=password="YOUR_PASSWORD" -``` - -#### Get an existing key-value store - -```bash -# The secrets will be base64 encoded. -kubectl get secret elastic-credentials -``` - -#### Mounting Kubernetes Secrets to Environment Variables in Container Operations - -Once the secrets are defined in the project namespace, you can mount specific -secrets as environment variables in your container using the Kubeflow SDK. - -**Example** - -This example is based off of a -[snippet from the Python KFP source code](https://github.com/kubeflow/pipelines/blob/0795597562e076437a21745e524b5c960b1edb68/sdk/python/kfp/aws.py#L33-L57). - -This example shows how (1) an Elasticsearch username(2) an Elasticsearch -password, and (3) a GitLab deploy token are passed to the container operation as -environment variables. - -```python -# Names of k8s secret key-value stores -ES_CREDENTIALS_STORE = "elastic-credentials" -GITLAB_CREDENTIALS_STORE = "gitlab-credentials" -# k8s secrets key names -ES_USER_KEY = "username" -ES_PASSWORD_KEY = "password" -GITLAB_DEPLOY_TOKEN_KEY = 'token' -# Names of environment variables that secrets should be mounted to in the -# container -ES_USER_ENV = "ES_USER" -ES_PASS_ENV = "ES_PASS" -GITLAB_DEPLOY_TOKEN_ENV = "GITLAB_DEPLOY_TOKEN" -# ... -container_operation = dsl.ContainerOp( - name='some-op', - image='some-image', -).add_env_variable( - k8s_client.V1EnvVar( - name=ES_USER_ENV, - value_from=k8s_client.V1EnvVarSource( - secret_key_ref=k8s_client.V1SecretKeySelector( - name=ES_CREDENTIALS_STORE, - key=ES_USER_KEY - ) - ) - ) -) \ - .add_env_variable( - k8s_client.V1EnvVar( - name=ES_PASS_ENV, - value_from=k8s_client.V1EnvVarSource( - secret_key_ref=k8s_client.V1SecretKeySelector( - name=ES_CREDENTIALS_STORE, - key=ES_PASSWORD_KEY - ) - ) - ) - ) \ - .add_env_variable( - k8s_client.V1EnvVar( - name=GITLAB_DEPLOY_TOKEN_ENV, - value_from=k8s_client.V1EnvVarSource( - secret_key_ref=k8s_client.V1SecretKeySelector( - name=GITLAB_CREDENTIALS_STORE, - key=GITLAB_DEPLOY_TOKEN_KEY - ) - ) - ) - ) -``` - -### Parameterizing pipelines - -Whenever possible, create pipelines in a generic way: define parameters that -might change as pipeline inputs instead of writing values directly in your -Python code. For example, if you want a pipeline to process data from -`minimal-tenant/john-smith/data1.csv`, don't hard code that path - instead, -accept it as a pipeline parameter. This way you can call the same pipeline -repeatedly by passing it the data location as an argument. You can -[see this approach in our example notebooks](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/mapreduce-pipeline/Compute-Pi-with-reusable-components-and-minio.ipynb), -where we accept MinIO credentials and the location to store our results as -pipeline parameters. - -## Passing complex/large data to/from a pipeline - -Although small data can often be stringified, passing by string is not suitable -for complex data (large parquet files, images, etc.). It is common to use blob -storage (for example: [MinIO](../5-Storage/MinIO.md)) or other outside storage -methods to persist data between components or even for later use. A typical -pattern would be: - -- Upload large/complex input data to blob storage (e.g. training data, a saved - model, etc.) -- Pass the location of this data into the pipeline as parameters, and make your - pipeline/components fetch the data as required -- For each component in a pipeline, specify where they place outputs in the same - way -- For each component also `return` the path where it has stored its data (in - this case, the string we passed it in the above bullet). This feels redundant, - but it is a common pattern that lets you chain operations together - -Here is a schematic example of this pattern: - -```python -def my_blobby_pipeline(path_to_numbers_1, path_to_numbers_2, path_for_output): - """ - Averaging pipeline which accepts two groups of numbers and does some averaging operations on them - """ - # Compute averages for two groups - avg_1 = average_op_that_takes_path_to_blob(path_to_numbers=path_to_numbers_1, - output_location=path_for_output + "/avg_1" - ) - avg_2 = average_op_that_takes_path_to_blob(numbers=path_to_numbers_2, - output_location=path_for_output + "/avg_2" - ) - - # Note that this assumes the average_op can take multiple paths to numbers. You could also have an - # aggregation component that combines avg_1 and avg_2 into a single file of numbers - - paths_to_numbers = [ - avg_1.output, - avg_2.output - ] - average_result_overall = average_op(path_to_numbers=paths_to_numbers, - output_location=path_for_output + "/average_result_overall" - ) -``` - -Within this platform, the primary method for persisting large files is through -MinIO as described in our [_Storage_ documentation](../index.md#storage). -Examples of this are also described in our -[example notebooks](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/tree/master/self-serve-storage) -(also found in `jupyter-notebooks/self-serve-storage/` on any notebook server). - -## Typical development patterns - -### End-to-end pipeline development - -A typical pattern for building pipelines in Kubeflow Pipelines is: - -1. Define components for each of your tasks -2. Compose your components in a `@dsl.pipeline` decorated function -3. `compile()` your pipeline, upload your YAML files, and run - -This pattern lets you define portable components that can be individually tested -before combining them into a full pipeline. Depending on the type and complexity -of task, there are different methods for building the components. - -## Methods for authoring components - -Fundamentally, every component in Kubeflow Pipelines runs a container. Kubeflow -Pipelines offers several methods to define these components with different -levels of flexibility and complexity. - -#### User-defined container components - -You can define tasks through custom Docker images. The design pattern for this -is: - -- Define (update) code for your task and commit to Git -- Build an image from your task (through manual command or CI pipeline) -- Test running this Docker image locally (and iterate if needed) -- Push the image to a container registry (usually Docker hub, but it will be - Azure Container Registry in our case on the Advanced Analytics Workspace) -- Update the Kubeflow Pipeline to point to the new image (via `dsl.ContainerOp` - like above) and test the pipeline - -This lets you run anything you can put into a Docker image as a task in Kubeflow -Pipelines. You can manage and test your images and have complete control over -how they run and what dependencies use. The `docker run` interface for each -container becomes the API that Kubeflow Pipelines `dsl.ContainerOp` interacts -with – running the containers is effectively like running them locally using a -terminal. Anything you can make into a container with that interface can be run -in Kubeflow Pipelines. - - -!!! danger "...however, for security reasons the platform currently does not - allow users to build/run custom Docker images. This is planned for the - future, but in interim see _Lightweight components_ for a way to develop - pipelines without custom images" - -#### Lightweight Python components - -While full custom containers offer great flexibility, sometimes they're heavier -than needed. The Kubeflow Pipelines SDK also allows for -[Lightweight Python Components](https://www.kubeflow.org/docs/pipelines/sdk/lightweight-python-components/), -which are components that can be built straight from Python without building new -container images for each change. These components are great for fast iteration -during development, as well as for simple tasks that can be written and managed -easily. - -This is an example of a lightweight pipeline with a single component that -concatenates strings: - -```python -import kfp -from kfp import dsl -from kfp.components import func_to_container_op - -def concat_string(a, b) -> str: - return f"({a} | {b})" - -concat_string_component = func_to_container_op(concat_string, - base_image="python:3.8.3-buster" - ) -@dsl.pipeline( - name="My lightweight pipeline", -) -def pipeline(str1, str2, str3): - # Note that we use the concat_string_component, not the - # original concat_string() function - concat_result_1 = concat_string_component(str1, str2) - - # By using cancat_result_1's output, we define the dependency of - # concat_result_2 on concat_result_1 - concat_result_2 = concat_string_component(concat_result_1.output, str3) -``` - -We see that our `concat_string` component is defined directly in Python rather -than from a Docker image. In the end, our function still runs in a container, -but we don't have to built it ourselves: `func_to_container_op()` runs our -Python code inside the provided base image (`python:3.8.3-buster`). This lets -use avoid building every time we change our code. The base image can be anything -accessible by Kubeflow, which includes all images in the Azure Container -Registry and any -[whitelisted images](https://github.com/StatCan/gatekeeper-policies/blob/master/general/container-allowed-images/constraint.yaml) -from Docker hub. - - -??? danger "Lightweight components have a number of advantages but also some drawbacks" - See [this description](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/kfp-basics/demo_kfp_lightweight_components.ipynb) - of their basic characteristics, as well as - [this example](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/mapreduce-pipeline/Compute-Pi-with-lightweight-components-and-minio.ipynb) - which uses them in a more complex pipeline - - -??? info "A convenient base image to use is the the image your notebook server is running" - By using the same image as your notebook server, you ensure Kubeflow - Pipelines has the same packages available to it as the notebook where you - do your analysis. This can help avoid errors from importing packages - specific to your environment. You can find that link from the notebook - server page as shown below, but make sure you prepend the registry URL (so - the below image would have - `base_image=k8scc01covidacr.azurecr.io/machine-learning-notebook-cpu:562fa4a2899eeb9ae345c51c2491447ec31a87d7` - ). Note that while using a fully featured base image for iteration is fine, - it's good practice to keep production pipelines lean and only supply the - necessary software. That way you reduce the startup time for each step in - your pipeline. - ![A Kubeflow Pipeline](../images/kubeflow_pipelines_notebook_server_image.png) - -#### Defining components directly in YAML - -Components can be defined directly with a YAML file, where the designer can run -terminal commands from a given Docker image. This can be a great way to make -non-Python pipeline components from existing containers. As with all components, -we can pass both arguments and data files into/out of the component. For -example: - -```yaml -name: Concat Strings -inputs: - - { - name: Input text 1, - type: String, - description: "Some text to echo into a terminal and tee to a file", - } - - { - name: Input text 2, - type: String, - description: "Some text to echo into a terminal and tee to a file", - } -outputs: - - { name: Output filename, type: String } -implementation: - container: - image: bash:5 - command: - - bash - - -ex - - -c - - | - echo "$0 | $1" | tee $2 - - { inputValue: Input text 1 } - - { inputValue: Input text 2 } - - { outputPath: Output filename } -``` - -This example concatenates two strings like our lightweight example above. We -then define a component in python from this YAML: - -```python -from kfp.components import load_component_from_file -echo_and_tee = load_component_from_file('path/to/echo_and_tee.yaml') - -@dsl.pipeline -def my_pipeline(): - echo_and_tee_task_1 = echo_and_tee("My text to echo") - - # A second use that consumes the return of the first one - echo_and_tee_task_2 = echo_and_tee(echo_and_tee_task_1.output) -``` - -See -[this example](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/mapreduce-pipeline/Compute-Pi-with-reusable-components-and-minio.ipynb) -for more details on using existing components. - -#### Reusing existing components - -Similar to well abstracted functions, well abstracted components can reduce the -amount of code you have to write for any given project. For example, rather than -teaching your machine learning `train_model` component to also save the -resulting model to MinIO, you can instead have `train_model` return the model -and then Kubeflow Pipelines can pass the model to a reusable `copy_to_minio` -component. This reuse pattern applies to components defined through any means -(containers, lightweight, or YAML). -[Take a look at our example notebook](https://github.com/StatCan/aaw-contrib-jupyter-notebooks/blob/master/mapreduce-pipeline/Compute-Pi-with-reusable-components-and-minio.ipynb), -which reuses provided components for simple file IO tasks. +They will be replaced by Argo Workflows, which will be implemented and documented soon. 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