# Metaflow library for SAP AI Core
The `sap-ai-core-metaflow` package is a [Metaflow plugin](https://metaflow.org) that generates [Argo Workflows](https://argoproj.github.io/argo-workflows).
It adds Argo Workflows capabilities using Metaflow's `@kubernetes` decorator.
## Table of Contents
* [Plugin Installation](#plugin-installation)
* [Install and configure Argo Workflows](#install-and-configure-argo-workflows)
* [Configure Metaflow](#configure-metaflow)
* [Run the Argo WorkflowTemplate](#run-the-argo-workflowtemplate)
* [Metaflow-Argo Decorators](#metaflow-argo-decorators)
* [Command-line Parameters and Environment Variables](#command-line-parameters-and-environment-variables)
* [Other Supported Features](#other-supported-features)
* [Use with SAP AICore](#use-with-sap-aicore)
## Plugin Installation
```sh
pip install sap-ai-core-metaflow
```
## Install and configure Argo Workflows
With the Metaflow-Argo plugin you can execute flows in a K8S cluster.
> Note: you can also install a local K8S cluster using docker desktop (Mac, Windows) or K3D, minikube (Linux).
* Install [Argo Workflows](https://argoproj.github.io/argo-workflows/installation/) in the K8S cluster.
* Optionally expose [argo-server](https://argoproj.github.io/argo-workflows/argo-server/) to access ArgoWorkflows UI.
## Configure Metaflow
For a quick-start follow the steps below.\
(Find complete documentation here: [Configuring Metaflow](https://outerbounds.com/docs/configure-metaflow).)
## The Metaflow _config_ File
An S3 bucket can be configured to store training data and Metaflow code packages.
* Create the config file: `~/.metaflowconfig/config.json` containing your S3 _bucket_ and _prefix_:
``` json
{
"METAFLOW_DEFAULT_DATASTORE": "s3",
"METAFLOW_DATASTORE_SYSROOT_S3": "s3://<bucket>/<prefix>"
}
```
In addition, your local [aws cli](https://aws.amazon.com/cli/) has to be configured for the same bucket.
## Create the K8S Secret
This secret allows an Argo Workflow to access the S3 bucket when running in the cluster:
```bash
kubectl create secret generic default-object-store-secret --from-literal=AWS_ACCESS_KEY_ID=XXX --from-literal=AWS_SECRET_ACCESS_KEY=YYY
```
## Run the Argo WorkflowTemplate
Here we use [helloworld.py](https://github.com/Netflix/metaflow/blob/master/metaflow/tutorials/00-helloworld/helloworld.py) from the [Metaflow Tutorials](https://docs.metaflow.org/getting-started/tutorials).
### Deploy the Argo WorkflowTemplate
```bash
python helloworld.py --with kubernetes:secrets=default-object-store-secret argo create
```
You should see the following output:
```bash
Metaflow 2.5.0 executing HelloFlow for user:<xyz>
Validating your flow...
The graph looks good!
Running pylint...
Pylint is happy!
Deploying helloflow to Argo Workflow Templates...
WorkflowTemplate helloflow is pushed to Argo Workflows successfully.
```
### Trigger the Execution of the WorkflowTemplate
```bash
python helloworld.py argo trigger
```
You should see a `run-id` after a successful start of a workflow:
```bash
Metaflow 2.5.0 executing HelloFlow for user:<xyz>
Validating your flow...
The graph looks good!
Running pylint...
Pylint is happy!
Workflow helloflow is triggered on Argo Workflows (run-id helloflow-92vbn).
```
### Check the Execution Status
```bash
python helloworld.py argo list-runs
```
The flow will display the state `Running` for some time and finally the flow shows
`Succeeded`:
```bash
Metaflow 2.5.0 executing HelloFlow for user:<xyz>
Validating your flow...
The graph looks good!
Running pylint...
Pylint is happy!
helloflow-92vbn startedAt:'2022-06-15T14:00:27Z' Succeeded
```
## Metaflow-Argo Decorators
In addition to general Metaflow decorators, i.e. `@kubernetes`,
`@resources`, `@environment`, etc. the Metaflow-Argo plugin supports
additional Argo-specific customization for steps which support
input_artifacts ,output_artifacts, labels and shared_memory.
## The `@argo` Decorator on Step Level
A Metaflow _step_ can override the default values specified on the
flow level or add extra configurations.
### Labels
Labels are attached as metadata to the pod executing a _step_:
``` python
...
@argo(labels = {'plan': 'basic'})
@step
def training():
...
```
The _step_ labels override the flow labels.
### Input Artifacts
The artifacts configuration is inserted into the generated
WorkflowTemplate as-is.
``` python
@argo(input_artifacts=[{
'name': 'input_dataset',
'path': '/tmp/train.csv',
's3': {
'endpoint': 's3.amazonaws.com',
'bucket': 'my-bucket-name',
'key': 'path/in/bucket',
'accessKeySecret': {
'name': 'my-s3-credentials',
'key': 'accessKey'
},
'secretKeySecret': {
'name': 'my-s3-credentials',
'key': 'secretKey'
}
}}])
@step
def start(self):
ds = pd.read_csv('/tmp/train.csv')
```
If the [Default Artifact Repository](https://argoproj.github.io/argo-workflows/key-only-artifacts) is configured, the credentials can be ommited:
``` python
@argo(input_artifacts=[{
'name': 'input_dataset',
'path': '/tmp/spam.csv',
's3': {
'key': 'path/in/bucket'
}
}])
```
### Output Artifacts
> Prerequisite: The [Default Artifact Repository](https://argoproj.github.io/argo-workflows/configure-artifact-repository/#configure-the-default-artifact-repository) is configured
If the configuration below is added to a _step_, Argo will upload the output artifact to the specified S3 bucket.
``` python
@argo(output_artifacts=[{
'name': 'model',
'globalName': 'model',
'path': '/tmp/model',
'archive': {
'none': {}
}}])
@step
def train_model(self):
...
with open('/tmp/model', 'wb') as f:
pickle.dump(self.model, f)
```
### Configure additional Disk Space using Persistent Volume Claim (PVC)
If the pod's disk space is not sufficient, one can mount extra storage using a [Persistent Volume Claim](https://kubernetes.io/docs/concepts/storage/persistent-volumes/).
The example below creates a pvc in WorkflowTemplate with a default size of 50GiB:
``` python
@argo(mount_pvc="/mnt/data")
@step
def start(self):
# use /mnt/data for huge files
```
To set the size for the `PVC`, add a value for the command-line parameter `volumen-claim` in MiB:
```bash
python helloflow.py argo create --volume-claim=100000
```
### Shared Memory Allocation
For adding more `shared memory` in MiB use the following configuration:
``` python
@argo(shared_memory=100)
@step
def train_model(self):
...
```
## Command-line Parameters and Environment Variables
The Metaflow-Argo plugin allows overriding default `flow` values. This done through environment variables or command-line parameters. Command-line parameters naturally have precedence over `envvars`.
## Command-line Parameters
### List Command-line Parameters
```bash
Usage: helloworld.py argo create [OPTIONS]
Deploy a new version of this workflow to Argo Workflow Templates.
Options:
--image-pull-secret TEXT Docker image pull secret.
--label TEXT Label to attach to the workflow template.
--annotation TEXT Annotation to attach to the workflow template.
--volume-claim INTEGER Size of the volumeClaim in MiB
--k8s-namespace TEXT Deploy into the specified kubernetes namespace.
--embedded Don't download code package into step
containers.Docker images should have a flow and
all dependencies embedded.
--max-workers INTEGER Maximum number of parallel pods. [default: 100]
--workflow-timeout INTEGER Workflow timeout in seconds.
--only-json Only print out JSON sent to Argo. Do not deploy
anything.
--help Show this message and exit.
```
### Set the Flow Name
The Argo WorkflowTemplate name is taken by default from class name of the the Metaflow script. If you want to change the flow name, use:
```bash
python helloworld.py argo --name=hello create
```
### `@kubernetes` Decorator Command Line Parameter
The Metaflow command-line parameter `--with=kubernetes` allows for setting configurations when using a kubernetes cluster:
* `secrets` to access object store, multiple secrets can be passed separated by `,`
``` python
python helloworld.py --with=kubernetes:secrets=default-object-store-secret,s3-secret argo create
```
* `image` to specify a docker image
``` python
python helloworld.py --with=kubernetes:secrets=default-object-store-secret,image=demisto/sklearn:1.0.0.29944 argo create
```
* `disk` is used to reserve ephemeral storage in MiB for the node.
``` python
python helloworld.py --with=kubernetes:secrets=default-object-store-secret,disk=1024 argo create
```
### Embedded code package
The flag `--embedded` generates steps' commands without a code package download; expects a code package to be already *embedded*
into a docker image.
## Environment Variables
### Prefix for the Flow Name
To add a prefix to the flow name set the environment variable `ARGO_WORKFLOW_PREFIX`.
### Default K8S Namespace
The kubernetes namespace to store templates and execute workflows can be
set with envvar `METAFLOW_KUBERNETES_NAMESPACE`.
### Default Environment Variables for Command-line Parameters
Metaflow provides envvars for the correspondent command-line parameters by setting this prefix: `METAFLOW_ARGO_CREATE_` e.g.:
```bash
export METAFLOW_ARGO_CREATE_MAX_WORKERS=4
```
## Other Supported Features
The Metaflow-Argo plugin supports also many Metaflow features.
## `@kubernetes` Decorator
The Metaflow-Argo plugin makes use of the specifications defined in
`@kubernetes` decorator to configure container resources.
``` python
@kubernetes(image='tensorflow/tensorflow:2.6.0', cpu=8, memory=60000)
@step
def training():
...
```
## `@resources` Decorator
Full support for [Requesting resources with \@resources
decorator](https://docs.metaflow.org/metaflow/scaling#requesting-resources-with-resources-decorator).
If similar resources are specified in both `@kubernetes` and
`@resources`, the maximum is taken.
``` python
@kubernetes(image='tensorflow/tensorflow:2.4.2-gpu',
node_selector='gpu=nvidia-tesla-k80')
@resources(memory=60000, cpu=2)
@step
def training():
...
```
## `@environment` Decorator
In addition to `env` and `envFrom` envvars can be specified in the decorator:
``` python
@environment(vars={'var':os.getenv('var_1')})
@step
def start(self):
print(os.getenv('var'))
```
## Display Logs for a Step
Metaflow stores stdout and stderr of every step/task and provides a
convenient way to access them, passing run-id and step-name:
```bash
python 00-helloworld/helloworld.py logs helloflow-bdmmw/start
```
Optionally specify a task-id, useful when have many instances of the
same step, e.g. when use foreach:
```bash
python 00-helloworld/helloworld.py logs helloflow-bdmmw/hello/helloflow-bdmmw-3938807173
```
## Resume flows
See [How to debug failed
flows](https://docs.metaflow.org/metaflow/debugging#how-to-debug-failed-flows)
for details.
```bash
python 00-helloworld/helloworld.py resume --origin-run-id=helloflow-bdmmw
```
## Schedule flows
Metaflow\'s
[\@schedule](https://docs.metaflow.org/going-to-production-with-metaflow/scheduling-metaflow-flows#scheduling-a-flow)
decorator is fully supported. E.g. a workflow template with the
`@schedule(hourly=True)` will automatically trigger workflow execution
on every hour start.
## Automatically rerun failed steps
`@retry` decorator helps to deal with transient failures. See [Retrying
Tasks with the retry
Decorator](https://docs.metaflow.org/metaflow/failures#retrying-tasks-with-the-retry-decorator).
## Handle failures programmatically
What if a default behavior to stop a whole flow if a particular step
fails is too limiting? With a help of `@catch` decorator, it\'s possible
to handle step\'s failure in a next step. [Catching Exceptions with the
catch
Decorator](https://docs.metaflow.org/metaflow/failures#catching-exceptions-with-the-catch-decorator).
## Limit the Step Execution Time
Use `@timeout` decorator to guard yourself from accidental resources
consumption. Read [Timing out with the timeout
Decorator](https://docs.metaflow.org/metaflow/failures#timing-out-with-the-timeout-decorator)
for more details.
## Use with SAP AICore
SAP AI Core does not allow access to its K8s cluster or argo-server. Workflow Templates are generated in json/yaml format and synced to K8s via the registered Git repo. The Workflows can then be triggered via `SAP AI API`.
The mandatory information for SAP AI Core is provided through labels and annotations.
```bash
python helloworld.py --with=kubernetes:secrets=default-object-store-secret,image=docker.io/<my-repo>/helloworld argo create
--image-pull-secret=<docker-secret>
--label="scenarios.ai.sap.com/id:ml_usecase_name"
--label="ai.sap.com/version:0.1.0"
--annotation="scenarios.ai.sap.com/name:Hello-World"
--annotation=""executables.ai.sap.com/name":"helloworld"
--only-json > hello-world.json
```
[Here](https://blogs.sap.com/2022/04/20/train-your-model-in-sap-ai-core-using-the-metaflow-argo-plugin/) you can find a step-by-step example for SAP AI Core.
Raw data
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"description": "# Metaflow library for SAP AI Core\n\nThe `sap-ai-core-metaflow` package is a [Metaflow plugin](https://metaflow.org) that generates [Argo Workflows](https://argoproj.github.io/argo-workflows).\n\nIt adds Argo Workflows capabilities using Metaflow's `@kubernetes` decorator.\n\n## Table of Contents\n\n* [Plugin Installation](#plugin-installation)\n* [Install and configure Argo Workflows](#install-and-configure-argo-workflows)\n* [Configure Metaflow](#configure-metaflow)\n* [Run the Argo WorkflowTemplate](#run-the-argo-workflowtemplate)\n* [Metaflow-Argo Decorators](#metaflow-argo-decorators)\n* [Command-line Parameters and Environment Variables](#command-line-parameters-and-environment-variables)\n* [Other Supported Features](#other-supported-features)\n* [Use with SAP AICore](#use-with-sap-aicore)\n\n## Plugin Installation\n\n```sh\npip install sap-ai-core-metaflow\n```\n\n## Install and configure Argo Workflows\n\nWith the Metaflow-Argo plugin you can execute flows in a K8S cluster.\n> Note: you can also install a local K8S cluster using docker desktop (Mac, Windows) or K3D, minikube (Linux).\n\n* Install [Argo Workflows](https://argoproj.github.io/argo-workflows/installation/) in the K8S cluster.\n* Optionally expose [argo-server](https://argoproj.github.io/argo-workflows/argo-server/) to access ArgoWorkflows UI.\n\n## Configure Metaflow\n\nFor a quick-start follow the steps below.\\\n(Find complete documentation here: [Configuring Metaflow](https://outerbounds.com/docs/configure-metaflow).)\n\n## The Metaflow _config_ File\n\nAn S3 bucket can be configured to store training data and Metaflow code packages.\n\n* Create the config file: `~/.metaflowconfig/config.json` containing your S3 _bucket_ and _prefix_:\n\n``` json\n{\n \"METAFLOW_DEFAULT_DATASTORE\": \"s3\",\n \"METAFLOW_DATASTORE_SYSROOT_S3\": \"s3://<bucket>/<prefix>\"\n}\n```\n\nIn addition, your local [aws cli](https://aws.amazon.com/cli/) has to be configured for the same bucket.\n\n## Create the K8S Secret\n\nThis secret allows an Argo Workflow to access the S3 bucket when running in the cluster:\n\n```bash\n kubectl create secret generic default-object-store-secret --from-literal=AWS_ACCESS_KEY_ID=XXX --from-literal=AWS_SECRET_ACCESS_KEY=YYY\n```\n\n## Run the Argo WorkflowTemplate\n\nHere we use [helloworld.py](https://github.com/Netflix/metaflow/blob/master/metaflow/tutorials/00-helloworld/helloworld.py) from the [Metaflow Tutorials](https://docs.metaflow.org/getting-started/tutorials).\n\n### Deploy the Argo WorkflowTemplate\n\n```bash\n python helloworld.py --with kubernetes:secrets=default-object-store-secret argo create\n```\n\nYou should see the following output:\n\n```bash\n Metaflow 2.5.0 executing HelloFlow for user:<xyz>\n Validating your flow...\n The graph looks good!\n Running pylint...\n Pylint is happy!\n Deploying helloflow to Argo Workflow Templates...\n WorkflowTemplate helloflow is pushed to Argo Workflows successfully.\n```\n\n### Trigger the Execution of the WorkflowTemplate\n\n```bash\n python helloworld.py argo trigger\n```\n\nYou should see a `run-id` after a successful start of a workflow:\n\n```bash\n Metaflow 2.5.0 executing HelloFlow for user:<xyz>\n Validating your flow...\n The graph looks good!\n Running pylint...\n Pylint is happy!\n Workflow helloflow is triggered on Argo Workflows (run-id helloflow-92vbn).\n```\n\n### Check the Execution Status\n\n```bash\n python helloworld.py argo list-runs\n```\n\nThe flow will display the state `Running` for some time and finally the flow shows\n`Succeeded`:\n\n```bash\n Metaflow 2.5.0 executing HelloFlow for user:<xyz>\n Validating your flow...\n The graph looks good!\n Running pylint...\n Pylint is happy!\n helloflow-92vbn startedAt:'2022-06-15T14:00:27Z' Succeeded\n```\n\n## Metaflow-Argo Decorators\n\nIn addition to general Metaflow decorators, i.e. `@kubernetes`,\n`@resources`, `@environment`, etc. the Metaflow-Argo plugin supports\nadditional Argo-specific customization for steps which support\ninput_artifacts ,output_artifacts, labels and shared_memory.\n\n## The `@argo` Decorator on Step Level\n\nA Metaflow _step_ can override the default values specified on the\nflow level or add extra configurations.\n\n### Labels\n\nLabels are attached as metadata to the pod executing a _step_:\n\n``` python\n...\n\n @argo(labels = {'plan': 'basic'})\n @step\n def training():\n ...\n```\n\nThe _step_ labels override the flow labels.\n\n### Input Artifacts\n\nThe artifacts configuration is inserted into the generated\nWorkflowTemplate as-is.\n\n``` python\n@argo(input_artifacts=[{\n 'name': 'input_dataset',\n 'path': '/tmp/train.csv',\n 's3': {\n 'endpoint': 's3.amazonaws.com',\n 'bucket': 'my-bucket-name',\n 'key': 'path/in/bucket',\n 'accessKeySecret': {\n 'name': 'my-s3-credentials',\n 'key': 'accessKey'\n },\n 'secretKeySecret': {\n 'name': 'my-s3-credentials',\n 'key': 'secretKey'\n }\n }}])\n@step\ndef start(self):\n ds = pd.read_csv('/tmp/train.csv')\n```\n\nIf the [Default Artifact Repository](https://argoproj.github.io/argo-workflows/key-only-artifacts) is configured, the credentials can be ommited:\n\n``` python\n@argo(input_artifacts=[{\n 'name': 'input_dataset',\n 'path': '/tmp/spam.csv',\n 's3': {\n 'key': 'path/in/bucket'\n }\n }])\n```\n\n### Output Artifacts\n\n> Prerequisite: The [Default Artifact Repository](https://argoproj.github.io/argo-workflows/configure-artifact-repository/#configure-the-default-artifact-repository) is configured\n\nIf the configuration below is added to a _step_, Argo will upload the output artifact to the specified S3 bucket.\n\n``` python\n@argo(output_artifacts=[{\n 'name': 'model',\n 'globalName': 'model',\n 'path': '/tmp/model',\n 'archive': {\n 'none': {}\n }}])\n@step\ndef train_model(self):\n ...\n with open('/tmp/model', 'wb') as f:\n pickle.dump(self.model, f)\n```\n\n### Configure additional Disk Space using Persistent Volume Claim (PVC)\n\nIf the pod's disk space is not sufficient, one can mount extra storage using a [Persistent Volume Claim](https://kubernetes.io/docs/concepts/storage/persistent-volumes/).\n\nThe example below creates a pvc in WorkflowTemplate with a default size of 50GiB:\n\n``` python\n@argo(mount_pvc=\"/mnt/data\")\n@step\ndef start(self):\n # use /mnt/data for huge files\n```\n\nTo set the size for the `PVC`, add a value for the command-line parameter `volumen-claim` in MiB:\n\n```bash\n python helloflow.py argo create --volume-claim=100000\n```\n\n### Shared Memory Allocation\n\nFor adding more `shared memory` in MiB use the following configuration:\n\n``` python\n@argo(shared_memory=100)\n@step\ndef train_model(self):\n ...\n```\n\n## Command-line Parameters and Environment Variables\n\nThe Metaflow-Argo plugin allows overriding default `flow` values. This done through environment variables or command-line parameters. Command-line parameters naturally have precedence over `envvars`.\n\n## Command-line Parameters\n\n### List Command-line Parameters\n\n```bash\nUsage: helloworld.py argo create [OPTIONS]\n\n Deploy a new version of this workflow to Argo Workflow Templates.\n\nOptions:\n --image-pull-secret TEXT Docker image pull secret.\n --label TEXT Label to attach to the workflow template.\n --annotation TEXT Annotation to attach to the workflow template.\n --volume-claim INTEGER Size of the volumeClaim in MiB\n --k8s-namespace TEXT Deploy into the specified kubernetes namespace.\n --embedded Don't download code package into step\n containers.Docker images should have a flow and\n all dependencies embedded.\n\n --max-workers INTEGER Maximum number of parallel pods. [default: 100]\n --workflow-timeout INTEGER Workflow timeout in seconds.\n --only-json Only print out JSON sent to Argo. Do not deploy\n anything.\n\n --help Show this message and exit. \n```\n\n### Set the Flow Name\n\nThe Argo WorkflowTemplate name is taken by default from class name of the the Metaflow script. If you want to change the flow name, use:\n\n```bash\n python helloworld.py argo --name=hello create\n```\n\n### `@kubernetes` Decorator Command Line Parameter\n\nThe Metaflow command-line parameter `--with=kubernetes` allows for setting configurations when using a kubernetes cluster:\n\n* `secrets` to access object store, multiple secrets can be passed separated by `,`\n\n``` python\n python helloworld.py --with=kubernetes:secrets=default-object-store-secret,s3-secret argo create\n```\n\n* `image` to specify a docker image\n\n``` python\n python helloworld.py --with=kubernetes:secrets=default-object-store-secret,image=demisto/sklearn:1.0.0.29944 argo create\n```\n\n* `disk` is used to reserve ephemeral storage in MiB for the node.\n\n``` python\n python helloworld.py --with=kubernetes:secrets=default-object-store-secret,disk=1024 argo create\n```\n\n### Embedded code package\n\nThe flag `--embedded` generates steps' commands without a code package download; expects a code\tpackage\tto be already *embedded*\ninto a docker image.\n\n## Environment Variables\n\n### Prefix for the Flow Name\n\nTo add a prefix to the flow name set the environment variable `ARGO_WORKFLOW_PREFIX`.\n\n### Default K8S Namespace\n\nThe kubernetes namespace to store templates and execute workflows can be\nset with envvar `METAFLOW_KUBERNETES_NAMESPACE`.\n\n### Default Environment Variables for Command-line Parameters\n\nMetaflow provides envvars for the correspondent command-line parameters by setting this prefix: `METAFLOW_ARGO_CREATE_` e.g.:\n\n```bash\n export METAFLOW_ARGO_CREATE_MAX_WORKERS=4\n```\n\n## Other Supported Features\n\nThe Metaflow-Argo plugin supports also many Metaflow features.\n\n## `@kubernetes` Decorator\n\nThe Metaflow-Argo plugin makes use of the specifications defined in\n`@kubernetes` decorator to configure container resources.\n\n``` python\n\n @kubernetes(image='tensorflow/tensorflow:2.6.0', cpu=8, memory=60000)\n @step\n def training():\n ...\n```\n\n## `@resources` Decorator\n\nFull support for [Requesting resources with \\@resources\ndecorator](https://docs.metaflow.org/metaflow/scaling#requesting-resources-with-resources-decorator).\nIf similar resources are specified in both `@kubernetes` and\n`@resources`, the maximum is taken.\n\n``` python\n\n @kubernetes(image='tensorflow/tensorflow:2.4.2-gpu',\n node_selector='gpu=nvidia-tesla-k80')\n @resources(memory=60000, cpu=2)\n @step\n def training():\n ...\n```\n\n## `@environment` Decorator\n\nIn addition to `env` and `envFrom` envvars can be specified in the decorator:\n\n``` python\n@environment(vars={'var':os.getenv('var_1')})\n@step\ndef start(self):\n print(os.getenv('var'))\n```\n\n## Display Logs for a Step\n\nMetaflow stores stdout and stderr of every step/task and provides a\nconvenient way to access them, passing run-id and step-name:\n\n```bash\n python 00-helloworld/helloworld.py logs helloflow-bdmmw/start\n```\n\nOptionally specify a task-id, useful when have many instances of the\nsame step, e.g. when use foreach:\n\n```bash\n python 00-helloworld/helloworld.py logs helloflow-bdmmw/hello/helloflow-bdmmw-3938807173\n```\n\n## Resume flows\n\nSee [How to debug failed\nflows](https://docs.metaflow.org/metaflow/debugging#how-to-debug-failed-flows)\nfor details.\n\n```bash\n python 00-helloworld/helloworld.py resume --origin-run-id=helloflow-bdmmw\n```\n\n## Schedule flows\n\nMetaflow\\'s\n[\\@schedule](https://docs.metaflow.org/going-to-production-with-metaflow/scheduling-metaflow-flows#scheduling-a-flow)\ndecorator is fully supported. E.g. a workflow template with the\n`@schedule(hourly=True)` will automatically trigger workflow execution\non every hour start.\n\n## Automatically rerun failed steps\n\n`@retry` decorator helps to deal with transient failures. See [Retrying\nTasks with the retry\nDecorator](https://docs.metaflow.org/metaflow/failures#retrying-tasks-with-the-retry-decorator).\n\n## Handle failures programmatically\n\nWhat if a default behavior to stop a whole flow if a particular step\nfails is too limiting? With a help of `@catch` decorator, it\\'s possible\nto handle step\\'s failure in a next step. [Catching Exceptions with the\ncatch\nDecorator](https://docs.metaflow.org/metaflow/failures#catching-exceptions-with-the-catch-decorator).\n\n## Limit the Step Execution Time\n\nUse `@timeout` decorator to guard yourself from accidental resources\nconsumption. Read [Timing out with the timeout\nDecorator](https://docs.metaflow.org/metaflow/failures#timing-out-with-the-timeout-decorator)\nfor more details.\n\n## Use with SAP AICore\n\nSAP AI Core does not allow access to its K8s cluster or argo-server. Workflow Templates are generated in json/yaml format and synced to K8s via the registered Git repo. The Workflows can then be triggered via `SAP AI API`.\n\nThe mandatory information for SAP AI Core is provided through labels and annotations.\n\n```bash\npython helloworld.py --with=kubernetes:secrets=default-object-store-secret,image=docker.io/<my-repo>/helloworld argo create\n --image-pull-secret=<docker-secret>\n --label=\"scenarios.ai.sap.com/id:ml_usecase_name\"\n --label=\"ai.sap.com/version:0.1.0\"\n --annotation=\"scenarios.ai.sap.com/name:Hello-World\"\n --annotation=\"\"executables.ai.sap.com/name\":\"helloworld\"\n --only-json > hello-world.json\n```\n\n[Here](https://blogs.sap.com/2022/04/20/train-your-model-in-sap-ai-core-using-the-metaflow-argo-plugin/) you can find a step-by-step example for SAP AI Core.\n\n\n",
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