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# ask-ai
q
@ancient-wolf-19325 how do I get a file from my local minio storage into a flyte task?
j
To get a file from your local MinIO storage into a Flyte task, you can use the 
FlyteFile
type to handle file inputs. Here's a basic example: 1. Upload the File to MinIO: Ensure your file is uploaded to your MinIO storage. You can do this manually or programmatically using MinIO's client tools. 2. Reference the File in a Flyte Task: Use the 
FlyteFile
type to reference the file in your task. You can pass the S3 URI of the file stored in MinIO to the task. Here's a simple example of how you might set this up in a Flyte task:
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from flytekit import task, workflow
from flytekit.types.file import FlyteFile

@task
def process_file(input_file: FlyteFile) -> str:
    # Download the file to a local path
    input_file.download()
    with open(input_file.path, 'r') as f:
        content = f.read()
    return content

@workflow
def my_workflow(file_uri: str) -> str:
    return process_file(input_file=FlyteFile(file_uri))

# Example usage
if __name__ == "__main__":
    file_uri = "s3://my-s3-bucket/path/to/myfile.txt"
    print(my_workflow(file_uri=file_uri))
This code snippet demonstrates how to define a task that takes a 
FlyteFile
as input, downloads it, and processes it. You need to replace 
"s3://my-s3-bucket/path/to/myfile.txt"
with the actual URI of your file in MinIO. Would you like more details on this process or any specific part of it? Sources: - Flyte Data Management 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 I get File Promise(node:.file_uri.[]) does not exist when trying to run this example workflow. could you help me solve it?
j
The error message "File Promise(node:.file_uri.[]) does not exist" suggests that the file path or URI you are providing to the Flyte task is not being resolved correctly. Here are a few steps to troubleshoot and resolve this issue: 1. Check the File URI: Ensure that the URI you are passing to the workflow is correct and accessible. The URI should be in the format 
s3://<bucket-name>/<path-to-file>
and should point to a file that exists in your MinIO storage. 2. Verify MinIO Configuration: Make sure that your Flyte setup is correctly configured to access your MinIO storage. This includes setting the correct endpoint, access key, and secret key in your Flyte configuration. 3. Permissions: Ensure that the credentials used by Flyte have the necessary permissions to access the file in MinIO. This includes read permissions for the specific bucket and path. 4. Local Execution: If you are running the workflow locally, ensure that your local environment is set up to access MinIO. This might involve setting environment variables for AWS credentials or configuring a local MinIO client. 5. Debugging: Add logging or print statements in your task to output the file URI and any other relevant information. This can help you verify that the correct URI is being passed to the task. If these steps do not resolve the issue, please provide more details about your setup, such as the Flyte configuration and the exact URI you are using, so I can assist you further. Would you like more detailed guidance on any of these steps? Sources: - Flyte Data Management 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I define a flyte FlyteFile to read from my local minio bucket?
j
To define a 
FlyteFile
in Flyte that reads from your local MinIO bucket, you need to ensure that your Flyte setup is configured to access MinIO, and then use the 
FlyteFile
type to reference the file. Here's a step-by-step guide: 1. Configure Flyte to Access MinIO: Ensure that your Flyte configuration is set up to connect to your MinIO instance. This typically involves setting the endpoint, access key, and secret key in your Flyte configuration file or environment variables. 2. Use FlyteFile in Your Task: In your Flyte task, use the 
FlyteFile
type to specify the file you want to read. You will need to provide the S3 URI of the file stored in MinIO. Here's an example of how you can define a task to read a file from MinIO:
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from flytekit import task, workflow
from flytekit.types.file import FlyteFile

@task
def read_file(file: FlyteFile) -> str:
    # Download the file to a local path
    file.download()
    with open(file.path, 'r') as f:
        content = f.read()
    return content

@workflow
def my_workflow(file_uri: str) -> str:
    return read_file(file=FlyteFile(file_uri))

# Example usage
if __name__ == "__main__":
    file_uri = "s3://my-s3-bucket/path/to/myfile.txt"
    print(my_workflow(file_uri=file_uri))
3. Run the Workflow: Execute the workflow, ensuring that the 
file_uri
points to a valid file in your MinIO bucket. Make sure that the MinIO bucket and file path are correctly specified and accessible from your Flyte environment. Would you like more details on configuring Flyte to access MinIO or any other aspect of this process? Sources: - https://github.com/flyteorg/flyte/issues/5426 - https://discuss.flyte.org/t/10262872/hi-all-just-getting-my-hands-dirty-and-trying-to-wrap-my-hea/ 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I check if flyte is running locally or on a cluster?
j
To determine if Flyte is running locally or on a cluster, you can use the 
current_context
from Flyte's execution context. This context provides information about the execution environment. According to Flyte Community Discussion, you can check the 
execution_id.project
or 
execution_id.domain
in the current context. If these values evaluate to "local", it indicates that the workflow is running locally. Here's a simple example of how you might implement this check in a Flyte task:
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from flytekit import current_context, task

@task
def check_execution_environment() -> str:
    context = current_context()
    if context.execution_id.project == "local" or context.execution_id.domain == "local":
        return "Running locally"
    else:
        return "Running on a cluster"

# Example usage
if __name__ == "__main__":
    print(check_execution_environment())
Would you like more details on this approach or any specific part of it? Sources: - Flyte Community Discussion 👍 0 button 👎 1 button 💡 Hint: Mention @ancient-wolf-19325 in the thread for followups.
q
@ancient-wolf-19325 is it possible to check the --remote flag on pyflyte run inside the workflow?
j
It seems likely that checking the 
--remote
flag directly inside a Flyte workflow is not straightforward, as workflows are designed to be agnostic of their execution context. However, you can infer whether a workflow is running remotely by examining the execution context, as mentioned in the Flyte Community Discussion. The 
current_context
in Flyte provides information about the execution environment, which can help determine if the workflow is running locally or remotely. Here's an example of how you might implement this:
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from flytekit import current_context, task

@task
def check_execution_environment() -> str:
    context = current_context()
    if context.execution_id.project == "local" or context.execution_id.domain == "local":
        return "Running locally"
    else:
        return "Running remotely"

# Example usage
if __name__ == "__main__":
    print(check_execution_environment())
This approach uses the execution context to infer the environment rather than directly checking the 
--remote
flag. Would you like more details on this approach or any specific part of it? Sources: - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how can I prevent flyte from confusing data classes for named tuples?
j
To prevent Flyte from confusing data classes with named tuples, it's important to understand the distinction between these two constructs and how Flyte handles them. According to Flyte GitHub Issue #2720, Flyte uses 
NamedTuple
for multiple/named outputs, while 
dataclass
is used for defining data structures. Here are some steps to ensure Flyte correctly interprets your data classes: 1. Use 
dataclass_json
: If you are using Flytekit version < v1.11.1, make sure to decorate your data classes with 
@dataclass_json
to ensure proper serialization and deserialization. This is mentioned in the Flyte Documentation. 2. Avoid Using 
NamedTuple
for Outputs: Use 
dataclass
for defining complex data structures and avoid using 
NamedTuple
unless you specifically need to define multiple outputs with names. This helps in avoiding confusion between the two. 3. Explicitly Define Types: Ensure that all fields in your data class are explicitly typed. This helps Flyte's type system to correctly interpret the data structure. 4. Use 
Outputs
for Multiple Outputs: If you need to return multiple outputs from a task, consider using the proposed 
Outputs
construct instead of 
NamedTuple
to avoid confusion, as discussed in the GitHub Issue #2720. Would you like more details on any of these steps or further clarification? Sources: - Flyte GitHub Issue #2720 - Flyte Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I access the local cache? is there a UI option for local runs?
j
Accessing the local cache in Flyte is typically done through the command line rather than a UI, as there is no specific UI option for local cache management. According to the Flyte documentation, Flyte uses 
diskcache
to manage local caching, and the cache is stored under 
~/.flyte/local-cache/
. To interact with the local cache, you can use the 
pyflyte
command-line tool. Here are some common operations: 1. Clear the Local Cache: You can clear the local cache using the following command:
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pyflyte local-cache clear
2. Disable Local Caching: If you want to disable local caching, you can set the environment variable 
FLYTE_LOCAL_CACHE_ENABLED
to `False`:
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import os
   os.environ['FLYTE_LOCAL_CACHE_ENABLED'] = 'false'
3. Access Cache Files: You can manually inspect the cache files stored in 
~/.flyte/local-cache/
to see what has been cached. Currently, there is no UI option to view or manage the local cache directly. Management is primarily done through the command line or by manually inspecting the cache directory. Would you like more details on any of these operations or further assistance? Sources: - https://docs.flyte.org/en/latest/user_guide/development_lifecycle/caching.html#how-does-caching-work - https://github.com/flyteorg/flyte/blob/master/docs/user_guide/development_lifecycle/caching.md 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 can you use the flyte console to manage workflows running in a local Python environment?
j
No, the Flyte Console is not used to manage workflows running in a local Python environment. According to the Flyte documentation, the Flyte Console is designed to interact with workflows running on a Flyte cluster, whether local or remote. When running workflows in a local Python environment, you typically use the command line or Python scripts to manage and execute workflows. Would you like more details on how to manage workflows in a local Python environment or any other aspect of Flyte? Sources: - https://docs.flyte.org/en/latest/user_guide/getting_started_with_workflow_development/running_a_workflow_locally.html#running-a-workflow-in-a-local-flyte-cluster - https://discuss.flyte.org/t/15976518/hi-friends-my-group-is-fairly-new-to-flyte-and-we-re-working/ 👍 1 button 👎 0 button 💡 Hint: Mention @ancient-wolf-19325 in the thread for followups.
q
@ancient-wolf-19325 how does the pyflyte cli handle image change when running on remote?
j
When using the 
pyflyte
CLI to run workflows on a remote Flyte cluster, handling image changes involves specifying the container image that should be used for the tasks. According to the Flyte GitHub Issue #4904, Flyte does not automatically detect changes in the container image as part of the task structure. Therefore, if you update the image (e.g., by changing the Dockerfile or the image tag), you need to ensure that the new image is specified when running or registering the workflow. Here's how you can handle image changes: 1. Specify the Image: Use the 
--image
flag with the 
pyflyte run
or 
pyflyte register
command to specify the new image. This ensures that the updated image is used for the execution.
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pyflyte run --remote --image <new_image> path/to/workflow.py workflow_name
2. Versioning: Ensure that the image version or tag is updated to reflect changes. This helps in identifying and using the correct image version. 3. Re-register the Workflow: If the image change affects the task structure or dependencies, re-register the workflow with the updated image to ensure consistency. 4. Check for Errors: If you encounter errors related to task versioning or image changes, verify that the correct image is being used and that all dependencies are included in the image. Would you like more details on any specific part of this process? Sources: - Flyte GitHub Issue #4904 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 yes. does running --remote push images to the remote cluster?
j
Running 
pyflyte run --remote
does not automatically push images to the remote cluster. According to the Flyte documentation, you need to ensure that the Docker image is available in a container registry that the remote Flyte cluster can access. The 
--remote
flag is used to execute the workflow on a remote Flyte cluster, but it does not handle the image build or push process. To use a custom image with a remote Flyte cluster, follow these steps: 1. Build the Docker Image: Build your Docker image locally using a Dockerfile.
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docker build -t <your_image>:<tag> .
2. Push the Image to a Registry: Push the image to a container registry that the Flyte cluster can access, such as Docker Hub, Amazon ECR, or a private registry.
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docker push <your_registry>/<your_image>:<tag>
3. Specify the Image in the Command: Use the 
--image
flag with the 
pyflyte run --remote
command to specify the image.
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pyflyte run --remote --image <your_registry>/<your_image>:<tag> path/to/workflow.py workflow_name
Ensure that the Flyte cluster has access to the registry where the image is stored. Would you like more details on any of these steps? Sources: - Flyte Documentation on Customizing Dependencies 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 can you save the flyte local to a different folder, and not tmp?
j
Yes, you can change the directory where Flyte stores local execution data by setting the 
TMPDIR
environment variable. According to a Flyte Community Discussion, you can specify a different directory for temporary files by setting this environment variable before running your Flyte workflow. Here's how you can do it: 1. Set the TMPDIR Environment Variable: Before executing your Flyte workflow, set the 
TMPDIR
environment variable to the desired directory path.
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export TMPDIR=/path/to/your/directory
2. Run Your Workflow: Execute your Flyte workflow as usual. The temporary files will now be stored in the directory specified by 
TMPDIR
. This approach allows you to control where Flyte stores its temporary files during local execution. Would you like more details on this process or any other related information? Sources: - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I add logs to remote execution task?
j
To add logs to a remote execution task in Flyte, you can use the 
flytekit
logger to ensure that logs are captured and accessible through the Flyte web interface. Here's how you can set up logging for tasks running on a remote cluster: 1. Import the Logger: Use the 
flytekit
logger in your task to log messages.
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from flytekit import task, logger

   @task
   def my_task():
       logger.info("This is an info message")
       logger.warning("This is a warning message")
       logger.error("This is an error message")
2. Set the Logging Level: You can configure the logging level by setting the 
FLYTE_SDK_LOGGING_LEVEL
environment variable. This can be done in your Dockerfile or using 
ImageSpec
.
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from flytekit import ImageSpec

   ImageSpec(
       python_version="3.9.13",
       packages=["flytekit"],
       registry="localhost:30000",
       env={"FLYTE_SDK_LOGGING_LEVEL": "20"},  # Set to INFO level
   )
3. Access Logs: Once the task is executed on the remote cluster, logs will be accessible through the Flyte web interface. You can also configure Flyte to use external logging systems like CloudWatch or Stackdriver for more advanced logging capabilities. Would you like more details on configuring external logging systems or any other aspect of logging in Flyte? Sources: - Flyte GitHub Issue #4830 - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 where can I read these logs once they are written?
j
Once logs are written by a Flyte task running on a remote cluster, you can access them through the Flyte Console. According to the Flyte Console Documentation, the Flyte Console provides a web interface where you can view task logs, inputs, outputs, and metadata. Here's how you can access the logs: 1. Navigate to the Flyte Console: Open the Flyte Console in your web browser. The URL is typically in the format 
http://<flyte-console-url>/console
. 2. Locate the Workflow Execution: Find the specific workflow execution for which you want to view the logs. You can search by project, domain, or execution name. 3. View Task Logs: Click on the task within the workflow execution. This will open a panel showing task logs, inputs, outputs, and metadata. The logs section will display the logs generated by the task. If you have configured Flyte to use an external logging system like CloudWatch or Stackdriver, you can also access the logs through those platforms. Would you like more details on accessing logs through external systems or any other aspect of log management in Flyte? Sources: - Flyte Console Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 I am getting No logs found, even though the logs are configured. do you know to how to work around that?
j
If you are seeing "No logs found" in the Flyte Console despite having configured logging, there are a few steps you can take to troubleshoot and resolve the issue: 1. Verify Log Configuration: Ensure that your Flyte configuration is correctly set up to point to the logging system you are using. This includes checking the 
task_logs
configuration in your Flyte setup. Make sure the logging provider (e.g., CloudWatch, Stackdriver) is enabled and correctly configured.
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task_logs:
     plugins:
       logs:
         kubernetes-enabled: true
         cloudwatch-enabled: false
         stackdriver-enabled: false
         templates:
           - displayName: Your Log Provider
             templateUris:
               - "http://your-log-provider-url"
2. Check Permissions: Ensure that the Flyte service account has the necessary permissions to write logs to the configured logging system. This might involve setting IAM roles for AWS CloudWatch or permissions for GCP Stackdriver. 3. Inspect Task Execution: Verify that the tasks are executing successfully and that there are no errors preventing log generation. You can check the task execution status in the Flyte Console. 4. Review Log Aggregator: If you are using an external log aggregator, check directly in that system to see if logs are being received. This can help determine if the issue is with Flyte or the logging system. 5. Environment Variables: Ensure that any necessary environment variables for logging are set correctly in your task's execution environment. 6. Consult Flyte Documentation: Refer to the Flyte documentation on configuring logging links for detailed guidance on setting up logging. If these steps do not resolve the issue, please provide more details about your configuration and setup, so I can assist you further. Would you like more detailed guidance on any of these steps? Sources: - https://github.com/flyteorg/flyte/issues/4830 - https://github.com/flyteorg/flyte/issues/4829 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 using flytekit.loggers and logger.info() does not give any logs
j
If using 
flytekit.loggers
and 
logger.info()
does not produce any logs, there are a few potential reasons and solutions to consider: 1. Logging Level: Ensure that the logging level is set to capture 
INFO
level logs. By default, the logging level might be set to a higher threshold, such as 
WARNING
or 
ERROR
, which would prevent 
INFO
logs from being displayed. You can set the logging level by configuring the root logger at the start of your script:
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import logging

   logging.basicConfig(level=logging.INFO)
2. Environment Variable: Check if the 
FLYTE_SDK_LOGGING_LEVEL
environment variable is set to a level that includes 
INFO
logs. You can set this environment variable in your Dockerfile or task environment:
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import os

   os.environ['FLYTE_SDK_LOGGING_LEVEL'] = '20'  # INFO level
3. Logger Configuration: Ensure that the logger is properly configured and that there are no conflicts with other logging configurations in your code. You can explicitly configure the logger as follows:
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from flytekit import logger

   logger.setLevel(logging.INFO)
   logger.info("This is an info message")
4. Check for Errors: Verify that there are no errors in the task execution that might prevent logs from being generated or displayed. 5. Flyte Configuration: Ensure that your Flyte configuration is set up to capture and display logs. This includes checking the 
task_logs
configuration and ensuring that the logging provider is correctly configured. If these steps do not resolve the issue, please provide more details about your setup, so I can assist you further. Would you like more detailed guidance on any of these steps? Sources: - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I set credentials for FlyteFile.download() on remote run?
j
To set credentials for 
FlyteFile.download()
during a remote run, you need to ensure that the Flyte environment has access to the necessary credentials for accessing the storage backend (e.g., S3, GCS, Azure Blob Storage). Here are the steps to configure credentials for remote execution: 1. Use Environment Variables: Set the necessary environment variables for your storage backend. For example, for AWS S3, you would set 
AWS_ACCESS_KEY_ID
and 
AWS_SECRET_ACCESS_KEY
. These can be configured in your task's environment or in the Flyte deployment configuration.
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env:
     - name: AWS_ACCESS_KEY_ID
       valueFrom:
         secretKeyRef:
           name: aws-credentials
           key: access-key-id
     - name: AWS_SECRET_ACCESS_KEY
       valueFrom:
         secretKeyRef:
           name: aws-credentials
           key: secret-access-key
2. Use Flyte Secrets: Flyte supports managing secrets through its secret management system. You can define secrets in Flyte and request them in your task using the 
secret_requests
parameter.
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from flytekit import Secret, task

   @task(secret_requests=[Secret(group="aws", key="access_key"), Secret(group="aws", key="secret_key")])
   def download_file(file: FlyteFile) -> None:
       file.download()
3. Configure Flyte Deployment: Ensure that your Flyte deployment is configured to access the secret management system and that the necessary secrets are available in the environment where the task is executed. 4. Check Storage Configuration: Verify that the storage configuration in your Flyte setup is correctly pointing to the storage backend and that the credentials are correctly set up. Would you like more details on any of these steps or further assistance with configuring credentials for remote execution? Sources: - Flyte Secrets Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 I am trying to access a FlyteFile from my local minio storage using pyflyte run but I get DownloadDataError error=Failed to get data from s3://flyte/tick-data/0323.arrow
j
The error you're encountering suggests that there might be an issue with accessing the file from your local MinIO storage. Here are some steps to troubleshoot and resolve the issue: 1. Check MinIO Configuration: Ensure that your MinIO server is running and accessible. Verify that the endpoint, access key, and secret key are correctly configured in your Flyte setup. This configuration is typically found in the Flyte configuration file (
config.yaml
or similar). 2. Verify Credentials: Make sure that the credentials used by Flyte to access MinIO are correct. You can set these credentials as environment variables or in the Flyte configuration file.
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storage:
     connection:
       endpoint: http://localhost:9000
       access-key: minio
       secret-key: miniostorage
       disable-ssl: true
     type: minio
     container: "flyte"
3. Check File Path: Ensure that the file path 
s3://flyte/tick-data/0323.arrow
is correct and that the file exists in the specified bucket and path in MinIO. 4. Network Access: Verify that the network settings allow access to the MinIO server from the environment where the Flyte task is running. This might involve checking firewall rules or network policies. 5. Use Environment Variables: Set the necessary environment variables for MinIO access in your task's execution environment.
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export FLYTE_AWS_ENDPOINT="http://localhost:9000"
   export FLYTE_AWS_ACCESS_KEY_ID="minio"
   export FLYTE_AWS_SECRET_ACCESS_KEY="miniostorage"
6. Test Access: Use a tool like 
mc
(MinIO Client) or 
aws s3
CLI to test access to the file from the same environment where the Flyte task is running. This can help verify that the credentials and network access are correctly configured. Would you like more detailed guidance on any of these steps or further assistance? Sources: - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 adding the credentials to the config yaml worked when passing a file link to pyflyte run , but not when trying to create FlyteFile("s3://flyte/tick-data/0323.arrow") inside a task
j
When creating a 
FlyteFile
inside a task and accessing it from MinIO, it's important to ensure that the task's execution environment has the necessary credentials to access the MinIO storage. Here are some steps to address the issue: 1. Ensure Credentials are Available in the Task: Make sure that the credentials are available in the task's execution environment. This can be done by setting environment variables or using Flyte's secret management system. 2. Use Environment Variables in the Task: Set the necessary environment variables for MinIO access within the task. This can be done by specifying them in the task's environment configuration.
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from flytekit import task, FlyteFile

   @task(
       environment={
           "FLYTE_AWS_ENDPOINT": "http://localhost:9000",
           "FLYTE_AWS_ACCESS_KEY_ID": "minio",
           "FLYTE_AWS_SECRET_ACCESS_KEY": "miniostorage"
       }
   )
   def process_file():
       file = FlyteFile("s3://flyte/tick-data/0323.arrow")
       file.download()
       # Process the file as needed
3. Use Flyte Secrets: If you prefer not to use environment variables, you can use Flyte's secret management system to securely pass credentials to the task.
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from flytekit import Secret, task, FlyteFile

   @task(secret_requests=[Secret(group="minio", key="access_key"), Secret(group="minio", key="secret_key")])
   def process_file():
       file = FlyteFile("s3://flyte/tick-data/0323.arrow")
       file.download()
       # Process the file as needed
4. Verify Task Configuration: Ensure that the task is correctly configured to access the MinIO storage. This includes verifying that the endpoint and credentials are correctly set. 5. Check Network Access: Ensure that the task's execution environment has network access to the MinIO server. By ensuring that the task's execution environment has the necessary credentials and access, you should be able to create and use 
FlyteFile
objects within the task. Would you like more detailed guidance on any of these steps or further assistance? Sources: - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 passing FlyteFile('s3://flyte/tick-data/0323.arrow') to the workflow works, but if I try to init the FlyeFile in a task it fails with PermissionError: Forbidden. how come?
j
The issue you're encountering, where passing a 
FlyteFile
to the workflow works but initializing it within a task results in a 
PermissionError: Forbidden
, is likely related to how credentials are managed and accessed within the task's execution environment. Here are some potential reasons and solutions: 1. Credential Scope: When you pass a 
FlyteFile
to the workflow, the credentials used to access the file are likely set in the environment where the workflow is initiated. However, when you initialize a 
FlyteFile
within a task, the task's execution environment may not have the same credentials available. 2. Environment Variables: Ensure that the necessary environment variables for accessing MinIO are set within the task's execution environment. This includes 
AWS_ACCESS_KEY_ID
, 
AWS_SECRET_ACCESS_KEY
, and any other relevant variables.
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from flytekit import task, FlyteFile

   @task(
       environment={
           "AWS_ACCESS_KEY_ID": "minio",
           "AWS_SECRET_ACCESS_KEY": "miniostorage",
           "AWS_DEFAULT_REGION": "us-east-1"  # If applicable
       }
   )
   def process_file():
       file = FlyteFile("s3://flyte/tick-data/0323.arrow")
       file.download()
       # Process the file as needed
3. Flyte Secrets: Use Flyte's secret management system to securely pass credentials to the task. This ensures that the credentials are available in the task's execution environment.
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from flytekit import Secret, task, FlyteFile

   @task(secret_requests=[Secret(group="minio", key="access_key"), Secret(group="minio", key="secret_key")])
   def process_file():
       file = FlyteFile("s3://flyte/tick-data/0323.arrow")
       file.download()
       # Process the file as needed
4. Network Access: Verify that the task's execution environment has network access to the MinIO server. This might involve checking firewall rules or network policies. 5. Check Permissions: Ensure that the credentials used have the necessary permissions to access the specified bucket and file in MinIO. By ensuring that the task's execution environment has the necessary credentials and access, you should be able to initialize and use 
FlyteFile
objects within the task. Would you like more detailed guidance on any of these steps or further assistance? Sources: - Flyte Community Discussion 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 I am running this code with the same link for the input_file.
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@task
def process_arrow_file(input_file: FlyteFile, link: str) -> None:
    file = FlyteFile(link)
    file.download()
    if os.path.isfile(file.path):
        print("success! read link")
    else:
        print(f"failed to read link")
        input_file.download()
        if os.path.isfile(input_file.path):
            print("success! read link")
It fails to read the link but succeeds in reading the file. can you explain why?
j
It seems likely that the issue you're encountering, where the 
FlyteFile
initialized with a link fails to download, but the 
FlyteFile
passed as an input succeeds, is related to how Flyte handles file inputs and credentials. Here's a breakdown of why this might be happening: 1. Credential Handling: When a 
FlyteFile
is passed as an input to a task, Flyte ensures that the necessary credentials and access permissions are in place for that file. This is part of Flyte's data handling mechanism, which manages the lifecycle and access of input files. 2. Direct Initialization: When you directly initialize a 
FlyteFile
with a link inside a task, the task's execution environment must have the necessary credentials and access permissions to download the file. If these credentials are not explicitly set in the task's environment, the download will fail. 3. Environment Configuration: Ensure that the task's environment is configured with the necessary credentials to access the storage backend (e.g., MinIO, S3). This includes setting environment variables or using Flyte's secret management system. 4. Network Access: Verify that the task's execution environment has network access to the storage backend. This might involve checking firewall rules or network policies. To resolve the issue, ensure that the task's execution environment has the necessary credentials and access to download the file directly from the link. You can do this by setting environment variables or using Flyte's secret management system, as mentioned in previous responses. Would you like more detailed guidance on configuring the task environment or any other aspect of this process? Sources: - https://github.com/flyteorg/flyte/issues/6090 - https://github.com/flyteorg/flyte/issues/5426 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 whats the proper way to feed a big number of parameters to a workflow?
j
To feed a large number of parameters to a Flyte workflow efficiently, you can use structured data types such as dataclasses or dictionaries, or leverage file-based inputs like JSON or YAML. Here are some approaches: 1. Dataclasses: Use Python dataclasses to group related parameters together. This makes it easier to manage and pass complex configurations.
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from dataclasses import dataclass
   from flytekit import task, workflow

   @dataclass
   class Config:
       param1: int
       param2: str
       param3: float

   @task
   def process_config(config: Config) -> None:
       print(config)

   @workflow
   def my_workflow(config: Config) -> None:
       process_config(config=config)
2. JSON/YAML Files: Use JSON or YAML files to define parameters and pass them to the workflow. This is particularly useful for a large number of parameters or complex configurations.
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# config.yaml
   param1: 10
   param2: "example"
   param3: 3.14
You can then load this configuration in your workflow:
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import yaml
   from flytekit import task, workflow

   @task
   def load_config(file_path: str) -> dict:
       with open(file_path, 'r') as f:
           return yaml.safe_load(f)

   @workflow
   def my_workflow(config_file: str) -> None:
       config = load_config(file_path=config_file)
       # Use config as needed
3. Use Flyte's 
pyflyte run
with JSON/YAML: You can pass a JSON or YAML file directly when running the workflow using 
pyflyte run
.
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pyflyte run --remote my_workflow.py my_workflow --config-file config.yaml
These methods help manage a large number of parameters efficiently and make your workflows more maintainable. Would you like more details on any of these approaches or further assistance? Sources: - Flyte GitHub Issue #5365 - Flyte GitHub Issue #3371 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do you init a dataclass parameter using pyflyte run command?
j
To initialize a dataclass parameter using the 
pyflyte run
command, you need to pass the dataclass as a JSON string. Flyte supports serializing and deserializing dataclasses using JSON, which allows you to provide complex inputs directly from the command line. Here's how you can do it: 1. Define the Dataclass: Ensure your dataclass is defined and used in your workflow or task.
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from dataclasses import dataclass
   from flytekit import task, workflow

   @dataclass
   class Config:
       param1: int
       param2: str

   @task
   def process_config(config: Config) -> None:
       print(config)

   @workflow
   def my_workflow(config: Config) -> None:
       process_config(config=config)
2. Use 
pyflyte run
with JSON Input: When running the workflow, pass the dataclass as a JSON string using the 
--config
parameter.
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pyflyte run my_workflow.py my_workflow --config '{"param1": 10, "param2": "example"}'
This command initializes the 
Config
dataclass with the specified parameters and passes it to the workflow. Would you like more details on this process or any other aspect of using dataclasses with Flyte? Sources: - Flyte GitHub Issue #5365 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 I am trying to pass a pydantic class as a default value to workflow and it fails with Field "" of type {TYPE} in root is not serializable
j
The error you're encountering suggests that Flyte is having trouble serializing a Pydantic model when it's used as a default value in a workflow. This is a known issue with Flyte's handling of Pydantic models, as discussed in Flyte GitHub Issue #6186. Here's how you can address this issue: 1. Use Dataclasses Instead: If possible, consider using Python's 
dataclass
instead of Pydantic models for default values. Flyte has better support for dataclasses, and they can often be used interchangeably with Pydantic models for simple use cases. 2. Serialize the Pydantic Model: If you need to use a Pydantic model, you can serialize it to a JSON string and then deserialize it within the task or workflow. This approach bypasses the serialization issue.
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import json
   from pydantic import BaseModel
   from flytekit import task, workflow

   class Model(BaseModel):
       value: int

   DEFAULT_MODEL_JSON = Model(value=1).json()

   @task
   def print_model(model_json: str):
       model = Model.parse_raw(model_json)
       print(model)

   @workflow
   def print_model_workflow(model_json: str = DEFAULT_MODEL_JSON):
       print_model(model_json=model_json)
3. Check Flyte Version: Ensure you are using a version of Flyte that includes any recent fixes or improvements related to Pydantic model handling. The issue you mentioned was addressed in a Flytekit update, so upgrading to the latest version might help. Would you like more details on any of these solutions or further assistance? Sources: - Flyte GitHub Issue #6186 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 Do you have to download a flytefile to read it or can you read it directly from remote bucket?
j
To read a 
FlyteFile
directly from a remote bucket without downloading it, you can use the 
open
method provided by Flyte. This method allows you to stream the file content directly from the remote storage, which can be more efficient for certain use cases. According to the Flytekit GitHub File, you can use the 
open
method to access the file content:
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from flytekit import task
from flytekit.types.file import FlyteFile

@task
def read_file(ff: FlyteFile):
    with ff.open("r") as f:
        content = f.read()
        print(content)
This approach allows you to read the file content directly from the remote storage without explicitly downloading it to the local filesystem. Would you like more details on this method or any other aspect of working with 
FlyteFile
? Sources: - Flytekit GitHub File 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I run the same workflow over multiple parameters?
j
Sure thing! I'm looking through the available data to answer your question. Hold on a second... ⏳
q
@ancient-wolf-19325 how do I pass a list of strings to a workflow?
j
To pass a list of strings to a Flyte workflow, you can define the workflow to accept a list as an input parameter. Here's how you can do it: 1. Define the Workflow: Specify the input parameter as a list of strings using Python's type annotations.
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from typing import List
   from flytekit import task, workflow

   @task
   def process_strings(strings: List[str]) -> None:
       for s in strings:
           print(s)

   @workflow
   def my_workflow(strings: List[str]) -> None:
       process_strings(strings=strings)
2. Run the Workflow: When running the workflow, pass the list of strings as an argument. If you're using 
pyflyte run
, you can pass the list directly in the command line.
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pyflyte run my_workflow.py my_workflow --strings '["string1", "string2", "string3"]'
This setup allows you to pass a list of strings to the workflow, which can then be processed by the tasks within the workflow. Would you like more details on any specific part of this process? Sources: - https://github.com/flyteorg/flyte/pull/6048 - https://github.com/flyteorg/flytesnacks/blob/master/examples/data_types_and_io/data_types_and_io/folder.py 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 can you supply cache version dynamically to a task?
j
Yes, you can supply a cache version dynamically to a task in Flyte by using the 
with_overrides
method. This allows you to specify a different cache version at runtime without changing the task definition. Here's how you can do it: 1. Define the Task with Caching: Define your task with caching enabled and a default cache version.
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from flytekit import task, workflow

   @task(cache=True, cache_version="1.0")
   def my_task(x: int) -> int:
       return x * x
2. Override the Cache Version at Runtime: Use the 
with_overrides
method to specify a different cache version when invoking the task within a workflow.
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@workflow
   def my_workflow(x: int, dynamic_cache_version: str) -> int:
       return my_task(x=x).with_overrides(cache_version=dynamic_cache_version)
3. Run the Workflow: When running the workflow, you can pass the desired cache version dynamically.
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pyflyte run my_workflow.py my_workflow --x 5 --dynamic_cache_version "2.0"
This approach allows you to control the cache version dynamically at runtime, which can be useful for testing or when you need to invalidate the cache based on certain conditions. Would you like more details on this process or any other aspect of caching in Flyte? Sources: - Flyte GitHub Issue #6050 - Flyte Documentation on Caching 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 That piece of code you wrote out fails with AssertionError: Cannot use a promise in the cache_version Value: Promise(node:.dynamic_cache_version.[]) do you know why?
j
The error you're encountering, "AssertionError: Cannot use a promise in the cache_version," occurs because Flyte does not allow using a promise (a deferred value) as the 
cache_version
in the 
with_overrides
method. The 
cache_version
must be a concrete value at the time of task registration, not a promise that gets resolved at runtime. Here's how you can address this issue: 1. Use a Concrete Value: Ensure that the 
cache_version
is a concrete string value when you call 
with_overrides
. You cannot use a workflow input directly as a 
cache_version
if it's passed as a promise. 2. Set Cache Version Before Workflow Execution: If you need to determine the cache version dynamically, compute it before the workflow execution and pass it as a concrete value. Here's an example of how you can modify the workflow to use a concrete cache version:
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from flytekit import task, workflow

@task(cache=True, cache_version="1.0")
def my_task(x: int) -> int:
    return x * x

@workflow
def my_workflow(x: int, cache_version: str) -> int:
    # Use the cache_version directly as a string
    return my_task(x=x).with_overrides(cache_version=cache_version)
When running the workflow, ensure that you pass a concrete string for the `cache_version`:
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pyflyte run my_workflow.py my_workflow --x 5 --cache_version "2.0"
This approach ensures that the 
cache_version
is a concrete value and not a promise, which resolves the error. Would you like more details on this solution or any other aspect of using caching in Flyte? Sources: - Flyte GitHub Issue #6050 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 can you see the task cache version in the console UI?
j
Yes, you can see the task cache version in the Flyte Console UI. According to the Flyte Console Documentation, the Flyte Console provides a detailed view of task executions, including cache status and cache version information. Here's how you can view the cache version in the Flyte Console: 1. Navigate to the Flyte Console: Open the Flyte Console in your web browser. The URL is typically in the format 
http://<flyte-console-url>/console
. 2. Locate the Workflow Execution: Find the specific workflow execution for which you want to view the cache version. You can search by project, domain, or execution name. 3. View Task Details: Click on the task within the workflow execution. This will open a panel showing task details, including cache status and cache version. The cache version is part of the task metadata and is displayed alongside other execution details in the Flyte Console. Would you like more details on accessing this information or any other aspect of using the Flyte Console? Sources: - Flyte Console Documentation 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 do Deck displays get cached as well when a task using Deck is cached?
j
It seems likely that Flyte does not automatically cache Deck displays when a task is cached. According to a GitHub issue, there is a discussion about caching Flyte Decks, indicating that this feature might not be inherently supported. The issue suggests that Flyte Decks are not cached by default and that there might be a need to explicitly handle caching for Decks. If you want to ensure that Deck displays are available when a task is cached, you might need to implement a custom solution to store and retrieve Deck data separately from the task's cache. Would you like more details on potential workarounds or further assistance with handling Deck displays in Flyte? Sources: - GitHub Issue on Caching Flyte Decks 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how long does flyte keep results in the object store, and how do I configure it?
j
It seems likely that Flyte does not have a built-in mechanism to automatically delete results from the object store after a certain period. Instead, the retention of results in the object store is typically managed by the storage backend itself (e.g., S3, GCS) through lifecycle policies. 1. Configure Lifecycle Policies: To manage how long Flyte keeps results in the object store, you can configure lifecycle policies directly in your storage backend. For example, in AWS S3, you can set up a lifecycle policy to automatically delete or transition objects after a specified period. 2. Flyte Configuration: While Flyte itself does not manage the lifecycle of stored data, you can configure Flyte to use specific storage backends and ensure that the necessary permissions and configurations are in place for those backends. 3. Time-to-Live (TTL) Feature: There is a discussion about implementing a TTL feature in Flyte to specify the lifetime of Flyte-managed files, as mentioned in a GitHub issue. However, this feature may not be available yet. To configure lifecycle policies, refer to the documentation of your storage provider. For example, AWS S3 lifecycle policies can be configured through the AWS Management Console or AWS CLI. Would you like more details on configuring lifecycle policies for a specific storage backend or any other aspect of managing data retention in Flyte? Sources: - GitHub issue 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
q
@ancient-wolf-19325 how do I mange variable node demand on my kubernetes cluster for flyte?
j
To manage variable node demand on your Kubernetes cluster for Flyte, you can leverage Kubernetes' built-in features for scaling and resource management. Here are some strategies to consider: 1. Horizontal Pod Autoscaler (HPA): Use HPA to automatically scale the number of pods in a deployment based on observed CPU utilization or other select metrics. This can help manage workloads that have variable demand. - Configuration: Define an HPA resource in your Kubernetes cluster that targets the Flyte components you want to scale, such as FlytePropeller or task pods.
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apiVersion: autoscaling/v2beta2
   kind: HorizontalPodAutoscaler
   metadata:
     name: flytepropeller-hpa
   spec:
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: flytepropeller
     minReplicas: 1
     maxReplicas: 10
     metrics:
     - type: Resource
       resource:
         name: cpu
         target:
           type: Utilization
           averageUtilization: 50
2. Cluster Autoscaler: Use the Cluster Autoscaler to automatically adjust the size of your Kubernetes cluster based on the resource requests of pods. This is particularly useful for handling variable node demand. - Configuration: Ensure that your cluster is set up with the Cluster Autoscaler and that it is configured to scale up and down based on the needs of your Flyte workloads. 3. Node Affinity and Tolerations: Use node affinity and tolerations to control which nodes Flyte tasks are scheduled on. This can help optimize resource usage and ensure that tasks are scheduled on appropriate nodes. - Configuration: Define node affinity and tolerations in your Flyte task configurations to ensure they are scheduled on nodes with the necessary resources. 4. Resource Requests and Limits: Set appropriate resource requests and limits for your Flyte tasks to ensure that they have the necessary resources to run efficiently and to prevent over-provisioning. - Configuration: Define resource requests and limits in your Flyte task definitions. By using these strategies, you can effectively manage variable node demand in your Kubernetes cluster for Flyte workloads. Would you like more details on any of these strategies or further assistance with configuring your Kubernetes cluster for Flyte? Sources: - FlytePropeller Horizontal Scaling 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
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