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# runllm-flyte-trial
a
New RunLLM response generated: >>>*# Question* Hello there! 👋 Question on handling files/directories on s3/gcs for performance. In all the examples I've seen about reading files from a remote directory, we define workflows as multiple tasks. To my understanding, generally the first one yields a FlyteDirectory, and later tasks work on the content that was downloaded locally (tmp/...). For instance, when reading from a bucket containing two txt files (
foo.txt
and 
bar.txt
):
Copy code
import os
from flytekit import task, workflow, Resources
from flytekit.types.file import FlyteFile
from flytekit.types.directory import FlyteDirectory
from typing import Tuple

@workflow
def wf(dirpath: str) -> str:

    # These two below works, as get_dir downloads the directory content to the local disk.
    flytedir = get_dir(dirpath=dirpath)
    foo, bar = do_stuff_on_dir(flytedir=flytedir)

    hello = say_hello(foo=foo, bar=bar)

    return hello

@task()
def get_dir(dirpath: str) -> FlyteDirectory:
    fd = FlyteDirectory(path=dirpath)
    return fd

@task()
def do_stuff_on_dir(flytedir: FlyteDirectory) -> Tuple[str, str]:
    texts = []

    for fname in os.listdir(flytedir):
        filepath = os.path.join(flytedir, fname)

        with open(filepath, 'r') as f:
            content = f.read()
        texts.append(content)

    # Do stuff
    texts = [texts[1], texts[0]]

    return texts

@task(requests=Resources(cpu="1", mem="1000Mi", ephemeral_storage="2000Mi"), limits=Resources(cpu="2", mem="1000Mi", ephemeral_storage="3000Mi"))
def say_hello(foo: str, bar: str) -> str:
    return f"Hello, {foo} and {bar}!"
However when run on a cluster (deployment on a single cluster) this comes with a k8s overhead for spawning the temporary pods for each task. To speed up the workflow I was thinking about reducing the overhead by aggregating tasks, reading files from the bucket and doing stuff on them in a single task. Is there a preferred way to do it, effectively getting to the same local tmp directory structure before returning a FlyteDirectory as a task output? For instance something like the following chunk works in sandbox but not on my single cluster deployment (exploiting 
FlyteDirectory.listdir(flytedir)
)
Copy code
import os
from flytekit import task, workflow, Resources
from flytekit.types.file import FlyteFile
from flytekit.types.directory import FlyteDirectory
from typing import Tuple


@workflow
def wf(dirpath: str) -> str:

    foo, bar = get_dir_and_do_stuff(dirpath=dirpath)

    hello = say_hello(foo=foo, bar=bar)

    return hello

@task()
def get_dir_and_do_stuff(dirpath: str) -> Tuple[str, str]:
    
    flytedir = FlyteDirectory(path=dirpath)

    texts = []
    # for fname in os.listdir(flytedir): # This works only locally (not even sandbox)
    for fname in flytedir.listdir(flytedir):  # This works on sandbox but not on cluster

        filepath = os.path.join(flytedir, fname)
        infile = FlyteFile(path=filepath)
>>```
with open(infile, 'r') as f: content = f.read() texts.append(content) # Do stuff # ... return texts @task() def say_hello(foo: str, bar: str) -> str: return f"Hello, {foo} and {bar}!"```
Copy code
I'd like to avoid the alternative of having a single task which instead of exploiting FlyteDirectories just uses the GS client to read from a bucket.

Any other tip for optimizing workflows for speed?

Thanks!
*# Answer*
To optimize workflows for speed and reduce Kubernetes overhead, you can aggregate tasks and handle file operations within a single task. However, there are some nuances to consider when dealing with FlyteDirectory and FlyteFile types, especially in different environments (local, sandbox, cluster).

### Key Points:
1. **FlyteDirectory Handling**: Flyte handles FlyteDirectory by downloading the contents to a local path. However, the behavior can differ between local, sandbox, and cluster environments.
2. **FlyteFile and FlyteDirectory Methods**: Methods like `listdir` and `download` can help manage files within these types.

### Aggregating Tasks:
You can combine the operations of downloading a directory and processing its contents within a single task. This approach reduces the overhead of spawning multiple pods.

### Example:
Here's an optimized version of your workflow that aggregates the tasks:
import os from flytekit import task, workflow, Resources from flytekit.types.directory import FlyteDirectory from typing import Tuple @workflow def wf(dirpath: str) -> str: foo, bar = get_dir_and_do_stuff(dirpath=dirpath) hello = say_hello(foo=foo, bar=bar) return hello @task() def get_dir_and_do_stuff(dirpath: str) -> Tuple[str, str]: flytedir = FlyteDirectory(path=dirpath) # Ensure the directory is downloaded flytedir.download() texts = [] for fname in os.listdir(flytedir.path): filepath = os.path.join(flytedir.path, fname) with open(filepath, 'r') as f: content = f.read() texts.append(content) # Do stuff texts = [texts[1], texts[0]] # Example operation return texts @task(requests=Resources(cpu="1", mem="1000Mi", ephemeral_storage="2000Mi"), limits=Resources(cpu="2", mem="1000Mi", ephemeral_storage="3000Mi")) def say_hello(foo: str, bar: str) -> str: return f"Hello, {foo} and {bar}!" ``` ### Explanation: 1. Single Task for Directory and Processing: The 
get_dir_and_do_stuff
task handles both downloading the directory and processing its contents. 2. Downloading Directory: The 
download
method ensures that the directory contents are available locally. 3. Processing Files: Using 
os.listdir
and 
open
to read file contents. ### Notes: - FlyteDirectory.download(): This method ensures that the directory contents are downloaded to the local path. This is crucial for ensuring consistency across different environments.
>>- Local Path Handling: When using 
os.listdir
, make sure to use the 
path
attribute of FlyteDirectory to access the local directory.
References: - FlyteDirectory Documentation - Flyte Issues on GitHub Would you like more details on any specific part of this approach? Sources: - FlyteDirectory Documentation - Flyte Issues on GitHub Slack Link https://flyte-org.slack.com/archives/CP2HDHKE1/p1719839357.902199 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.
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