@Glime I’m using LaunchPlan.get_or_create() to define launchplans for a workflow. Is there a way to mark them so that when I run

pyflyte register

I can have one launchplan create in the production domain, and another create in the staging domain?

To create launch plans for different domains (e.g., production and staging) using

LaunchPlan.get_or_create()

, you can utilize environment variables to dynamically set the domain during the launch plan creation process. This approach allows you to specify different domains based on your deployment environment, making it possible to have separate launch plans for production and staging. Here's a conceptual approach based on the information provided in the context: 1. ​*Use Environment Variables*​: Define environment variables in your CI/CD pipeline or local environment to distinguish between production and staging. For example, you could have an environment variable named

WORKFLOW_DOMAIN

that can be set to either

production

or

staging

. 2. ​*Conditional Launch Plan Creation*​: In your Python code where you define the launch plans using

LaunchPlan.get_or_create()

, use the

WORKFLOW_DOMAIN

environment variable to conditionally set the domain for each launch plan. You can create a conditional logic block that checks the value of

WORKFLOW_DOMAIN

and creates the appropriate launch plan for the specified domain. 3. ​*Register Launch Plans*​: When you run

pyflyte register

, it will register the launch plans based on the current setting of the

WORKFLOW_DOMAIN

environment variable. You can control which domain's launch plan gets registered by setting this environment variable accordingly before running the register command. Here is a simplified example to illustrate this approach:

import os
from flytekit import LaunchPlan, CronSchedule
from my_workflows import my_workflow

# Check the environment variable to determine the domain
domain = os.getenv("WORKFLOW_DOMAIN")

if domain == "production":
    # Create and register the launch plan for production
    prod_launch_plan = LaunchPlan.get_or_create(
        name="my_workflow_prod_launch_plan",
        workflow=my_workflow,
        # Additional configurations specific to production
    )
elif domain == "staging":
    # Create and register the launch plan for staging
    staging_launch_plan = LaunchPlan.get_or_create(
        name="my_workflow_staging_launch_plan",
        workflow=my_workflow,
        # Additional configurations specific to staging
    )

Before running

pyflyte register

, ensure the

WORKFLOW_DOMAIN

environment variable is set to the desired domain:

export WORKFLOW_DOMAIN=production  # or staging
pyflyte register --pkgs my_package ...

This approach allows you to dynamically create and register launch plans for different domains by simply changing an environment variable. --- This is an AI-generated response, take its recommendations with caution. Please help us improve the model accuracy by giving this response a thumbs up/thumbs down reaction. Sources https://discuss.flyte.org/t/1011/hi-team-i-am-a-ds-from-gojek-currently-working-on-using-flyt/ https://docs.flyte.org/en/latest/api/flytekit/design/authoring.html/ 👍 button 👎 button

@Glime is there a way I could add a wandb link that corresponds to a task either under the Logs section or in a flyte deck or something? What would be recommended there?

To add a Weights & Biases (wandb) link that corresponds to a task in Flyte, you have two primary options based on the context provided: 1. ​*Using Dynamic Log Links*​: You can configure dynamic log links in your Flyte deployment to automatically generate and display links to external resources like wandb in the Flyte console's Logs section. This approach involves configuring your Flyte deployment to recognize and render these links based on task execution metadata. To achieve this, you would update your Flyte deployment configuration to include a dynamic log link for wandb. You would use templating to construct the wandb URL, which can include various placeholders for task execution metadata (e.g.,

.taskConfig.port

,

.podName

). This method requires that the necessary information (like the wandb project or run ID) is available in the task's configuration or metadata. Example configuration snippet:

plugins:
      logs:
        dynamic-log-links-enabled: true
        dynamic-log-links:
          wandb: <https://wandb.ai/{project}/{run_id}>

In your task definition, you would specify that you want to use this dynamic log link. The specific placeholders (

{project}

,

{run_id}

) would need to be replaced with the actual path to your wandb project and run ID, which might be dynamically determined based on the task execution. 2. ​*Using Flyte Decks*​: Another approach is to use Flyte Decks to create a more interactive and customized visualization or dashboard that includes your wandb links among other information. This approach is more flexible and allows for a richer presentation of information but requires more development effort. You would use the Flytekit's

Deck

and

Html

objects to create custom visualizations or dashboards that can be embedded within the Flyte console. Example code snippet for creating a simple deck with an HTML widget containing a wandb link:

from flytekit import task, workflow
    from flytekit.deck import Deck, Html

    @task
    def my_task() -> str:
        # Your task logic here
        wandb_run_url = "<https://wandb.ai/{project}/{run_id}>"
        deck = Deck(title="My Wandb Run")
        deck += Html(f"<a href='{wandb_run_url}'>Wandb Run Link</a>")
        deck.flush()
        return wandb_run_url

    @workflow
    def my_workflow():
        my_task()

This code creates a deck with a single HTML widget containing a link to a wandb run. The

deck.flush()

method sends the deck to the Flyte console where it can be viewed. Both methods have their use cases. If you simply want to link to external resources like wandb runs, configuring dynamic log links is straightforward and integrates well with the existing Logs section of the Flyte console. If you need more customization or want to present additional information alongside the wandb links, using Flyte Decks provides a powerful way to create rich, interactive dashboards. Sources https://github.com/flyteorg/flyte/issues/4773/ https://github.com/flyteorg/flyte/issues/4063/ https://github.com/flyteorg/flyte/issues/4830/ 👍 button 👎 button

@Glime can you show a more in depth example of how to do option 1 with log links? For example, show how to use .podName, and how I could get the pod name from inside the task, so that I could set th wandb run ID to be the pod name

To implement option 1 with log links, specifically for integrating Weights & Biases (wandb) and using the pod name as the wandb run ID, you'll need to follow a few steps. This involves configuring dynamic log links in Flyte and ensuring your task can access and use the pod name as the wandb run ID. Here's a more in-depth example: Step 1: Configure Dynamic Log Links in Flyte First, you need to configure your Flyte deployment to support dynamic log links. This configuration allows Flyte to dynamically generate log links based on execution metadata, such as the pod name. You would typically do this in your Flyte deployment configuration (e.g., in a ConfigMap if you're using Kubernetes). Example configuration snippet to add to your Flyte deployment configuration:

plugins:   logs:     dynamic-log-links-enabled: true     dynamic-log-links:       wandb: <https://wandb.ai/{project}/{run_id}>

In this configuration,

{run_id}

would be dynamically replaced with the actual run ID you specify when launching the task. You can customize the URL structure based on your wandb project setup. Step 2: Use Pod Name as wandb Run ID in Your Task To use the pod name as the wandb run ID, your task needs to access the pod name from its environment. Kubernetes injects certain environment variables into pods, but the pod name is not directly available as an environment variable by default. However, you can use the Downward API to expose the pod name as an environment variable. Here's how you can modify a Flyte task to access the pod name and use it as the wandb run ID:

from flytekit import task, workflow import os import wandb  # Function to initialize wandb def init_wandb(run_id):     wandb.init(project="your_project_name", name=run_id)  @task def my_task():     # Access the pod name from the environment variable     pod_name = os.getenv("POD_NAME")          # Initialize wandb with the pod name as the run ID     init_wandb(run_id=pod_name)          # Your task logic here     print("Running task with wandb run ID:", pod_name)  @workflow def my_workflow():     my_task()  # Modify your Pod spec to include the POD_NAME environment variable using the Downward API pod_spec = {     "containers": [         {             "name": "mycontainer",             "env": [                 {                     "name": "POD_NAME",                     "valueFrom": {                         "fieldRef": {                             "fieldPath": "metadata.name"                         }                     }                 }             ]         }     ] }

In this example,

init_wandb

is a function that initializes wandb with a specific run ID. The

my_task

function retrieves the pod name from the

POD_NAME

environment variable and passes it to

init_wandb

as the run ID. You need to ensure that your pod specification includes the

POD_NAME

environment variable, which is set using the Downward API to expose the pod's metadata.name (i.e., the pod name). Step 3: Register Your Task and Workflow After defining your task and workflow, and ensuring your Flyte deployment is configured for dynamic log links, you can register your tasks and workflows with Flyte. When executing the task, Flyte will use the configured dynamic log link template, replacing

{run_id}

with the pod name, allowing you to access the corresponding wandb run directly from the Flyte console's log links. This approach integrates wandb with Flyte by using dynamic log links and the Kubernetes Downward API, enabling seamless tracking and visualization of your machine learning experiments and their logs.

@Glime is there a way to get more verbose TypeError logs, like a stack trace, when I run

pyflyte register

? I’m just getting

Failed with Unknown Exception <class 'TypeError'> Reason: str expected, not int

and I have no idea what variable its referring to

To get more verbose logs, including stack traces, when running

pyflyte register

, you can increase the logging level of the Flytekit to debug or set specific environment variables to enable more detailed logging. However, based on the provided context, there isn't a direct mention of a built-in flag or command specifically for

pyflyte register

to increase verbosity directly related to TypeError exceptions. That said, a general approach to increase verbosity for debugging purposes with Flytekit (the Python SDK for Flyte) involves setting the environment variable

FLYTE_SDK_LOGGING_LEVEL

to a more verbose level before running your command. For debugging, you can set it to

DEBUG

to get more detailed logs, which might include the stack trace or more context around errors like the

TypeError

you're encountering. Here's how you can do it in a Unix-like shell:

export FLYTE_SDK_LOGGING_LEVEL=DEBUG pyflyte register ...

This command sets the logging level to

DEBUG

(which is more verbose than the default) and then runs the

pyflyte register

command. With this setting, Flytekit logs more detailed information about its operations, which could help identify the source of the

TypeError

. Additionally, reviewing the Flytekit source code or documentation might provide more insights into enabling verbose error logging or handling specific exceptions like

TypeError

more gracefully. If the issue persists, consider reaching out to the Flyte community through their GitHub issues or Discuss forum for more targeted support. Remember, the effectiveness of this approach depends on how Flytekit and your code handle logging. If the error is deep within library code or triggered by specific conditions not logged at the DEBUG level, you might need to investigate further or add custom logging around the suspected source of the error in your code.

@Glime I tried the code you gave:

deck = Deck(title="My Wandb Run")
        deck += Html(f"<a href='{wandb_run_url}'>Wandb Run Link</a>")
        deck.flush()

But the Flyte Deck button never shows up. Is there something other than flush that I need to do to get it to show up, especially if this is the first deck of this task? I found this in the docs:

flytekit.current_context().default_deck.append(
        MarkdownRenderer().to_html(f"[Wandb run link]({link_url})")
    )

Does that work if I don’t have a deck yet, or is creating the deck the way to go?

To ensure that the Flyte Deck button shows up and your custom deck is properly displayed, it's important to understand how decks work in Flyte and how they are rendered. Based on the context provided and the Flyte documentation, here are some insights and steps you can follow: 1. Ensure Deck Rendering is Enabled: First, make sure that deck rendering is enabled for your task. This is done by setting

disable_deck=False

in the

@task

decorator. This step is crucial because, by default, deck rendering might be disabled to optimize performance. Example:

from flytekit import task      @task(disable_deck=False)     def my_task():         ...

2. Using the Default Deck: If you don't have a deck yet or if it's the first deck of the task, you can use the

flytekit.current_context().default_deck

to append your content. This approach works well because every task in Flyte has a default deck associated with it, which can be used to render custom HTML content, including links to external resources like Weights & Biases (wandb). Example:

from flytekit import current_context     from flytekit.deck.renderer import MarkdownRenderer      link_url = "<https://wandb.ai/your_project/your_run>"     current_context().default_deck.append(         MarkdownRenderer().to_html(f"[Wandb run link]({link_url})")     )

3. Creating and Flushing a New Deck: If you prefer to create a new deck (for example, to group certain types of information together), you can instantiate a

Deck

object, add content to it, and ensure it's appended to the Flyte context's decks. The

flush()

method you mentioned is used to send the deck to the Flyte console, but it's crucial that the deck is also recognized by the Flyte context. Example:

from flytekit import Deck, Html, current_context      deck = Deck(title="My Wandb Run")     wandb_run_url = "<https://wandb.ai/your_project/your_run>"     deck += Html(f"<a href='{wandb_run_url}'>Wandb Run Link</a>")      # Append the new deck to the current context's decks     current_context().user_space_params.decks.append(deck)

4. Check for Any Additional Configuration Requirements: Depending on your Flyte deployment or the specific version of Flytekit you're using, there might be additional steps or configurations required to enable deck rendering. It's a good idea to consult the Flyte documentation or the Flyte community for any recent updates or tips. In summary, if it's the first deck of the task or you don't have a deck yet, using

flytekit.current_context().default_deck

is a straightforward way to go. Ensure that deck rendering is enabled for your task, and consider creating a new deck if you need to group specific types of information together. SOURCES https://docs.flyte.org/en/latest/_modules/flytekit/deck/deck.html, https://github.com/flyteorg/flyte/issues/4463, https://docs.flyte.org/projects/cookbook/en/latest/getting_started/visualizing_task_input_and_output.html

@Glime The deck doesn’t show up until the task has completed, is there a way for the deck to show up as soon as I create it in the code, so that I can have the wandb to follow along (e.g. for a long training run)

Based on the discussions and information available from the Flyte community and documentation, as of the current state, Flyte does not support rendering or updating decks in real-time during task execution. Decks are only persisted and become visible in the Flyte console after the task has completed. This behavior is consistent across various discussions in the Flyte community: 1. Real-time Deck Updates: The idea of having a deck appear in the Flyte console before the task has finished and potentially updating it several times as training progresses was discussed. The consensus was that this feature is not supported today. The decks are assembled and made visible only after the task execution is complete. 2. Dynamic Deck Rendering: There was interest in rendering Flyte decks during the execution of a task, suggesting that it would be a valuable feature. However, the current implementation only persists the deck after the task completes. This means that any updates to the deck during task execution are not reflected in real-time in the Flyte console. 3. Deck Persistence on Task Failure: The possibility of having a FlyteDeck show up even when a task fails was also discussed. This would require persisting the Flyte deck when a task fails, which is not supported as of the latest discussions. However, there was openness to contributions that could enable this functionality. 4. Documentation and Examples: The Flyte documentation provides examples of how to visualize task input and output using decks. These examples illustrate the use of decks for completed tasks but do not cover real-time updates during task execution. In summary, as per the current capabilities and discussions within the Flyte community, decks in Flyte are designed to be visible only after a task has completed. This means that for long-running tasks, such as training models, where you might want to monitor progress in real-time through Weights & Biases (wandb), you would not be able to use Flyte decks for real-time monitoring. The deck will only be available after the task has finished executing. For real-time monitoring of long-running tasks, you would need to rely on external tools like wandb directly, accessing them outside the Flyte console until such a feature is supported by Flyte in the future. SOURCES https://discuss.flyte.org/t/11984347/is-there-a-way-in-a-task-to-make-a-deck-appear-in-the-flyte-, https://docs.flyte.org/projects/cookbook/en/latest/getting_started/visualizing_task_input_and_output.html

@Glime how can I authenticate inside of a CI/CD environment, so that I can use Remote to run Launchplans? I’m trying with

FLYTE_CREDENTIALS_CLIENT_ID

and

FLYTE_CREDENTIALS_CLIENT_SECRET

, but I’m getting the following error right now

Underlying Exception: Status Code (401) received from IDP: {"error":"invalid_client","error_description":"Client authentication failed (e.g., unknown client, no client authentication included, or unsupported authentication method)."}

To authenticate within a CI/CD environment for running LaunchPlans with Flyte, especially when facing issues with

FLYTE_CREDENTIALS_CLIENT_ID

and

FLYTE_CREDENTIALS_CLIENT_SECRET

leading to a

401

error, it's crucial to ensure that the client ID and client secret are correctly configured and recognized by Flyte. The error you're encountering indicates a problem with client authentication, which could be due to incorrect credentials or misconfiguration. Here are steps to troubleshoot and resolve the authentication issue: 1. Verify Credentials: Ensure that the

FLYTE_CREDENTIALS_CLIENT_ID

and

FLYTE_CREDENTIALS_CLIENT_SECRET

you are using are correct and have been obtained from your identity provider (IdP). These credentials must match what's registered in your Flyte deployment for the CI/CD system. 2. Use Environment Variable Redirection: If your CI system does not allow setting custom environment variables directly, you can use environment variable redirection. For example, if your CI system sets the client secret under a different variable name, you can redirect it as follows:

export FLYTE_CREDENTIALS_CLIENT_SECRET_FROM_ENV_VAR=OTHER_ENV_VARIABLE

This tells Flyte to look for the client secret in

OTHER_ENV_VARIABLE

instead of

FLYTE_CREDENTIALS_CLIENT_SECRET

. 3. File-Based Secret Configuration: If your CI system supports mounting secrets as files, you can use the

FLYTE_CREDENTIALS_CLIENT_SECRET_FROM_FILE

environment variable to specify the path to the file containing the client secret:

export FLYTE_CREDENTIALS_CLIENT_SECRET_FROM_FILE=/path/to/client/secret/file

4. Check for Misconfiguration in Helm Chart or Flyte Deployment: If you're deploying Flyte using Helm, ensure that the client ID and secret are correctly configured in your Helm values or directly in your Kubernetes secrets. Misconfiguration here could lead to the

invalid_client

error. 5. Review Flyte and Flytectl Configuration: Ensure that your

flyteadmin

configuration (for Flyte) and

config.yaml

(for Flytectl) are correctly set up for client credentials flow. This includes setting the correct

authType

and ensuring that the endpoint and client credentials are correctly specified. 6. Consult Flyte Documentation and Community: Review the Flyte authentication setup documentation for more details on configuring authentication, especially in CI/CD environments. If the issue persists, consider reaching out to the Flyte community on GitHub or Discuss for more specific guidance. Given the error message you're seeing, the issue likely lies in how the client ID and secret are being passed or recognized by Flyte. Double-checking these configurations against the documentation and ensuring that your CI system is correctly passing these values should help resolve the authentication issue. SOURCES • https://docs.flyte.org/en/latest/deployment/configuration/auth_setup.html • https://discuss.flyte.org/t/8579436/hi-community-i-tried-to-integrate-self-hosted-gitlab-as-an-e