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<@ULVA73B9P> Hey! I would like to make a prefect t...
# ask-marvin
v
@Marvin Hey! I would like to make a prefect task that would run a machine learning model. The problem is that this model uses batches, so it is output several results at a time. For example, it can take 5 images as input and output 5 predictions. I want to cache these predictions using prefect. How to better do so? Just run special task on every output (aka start 5 tasks just to cache results) or there is a better solution?
m
Caching predictions from a machine learning model in Prefect can indeed be a bit tricky, especially when dealing with batch outputs like in your case. It sounds like you're considering launching a separate task for each prediction purely for caching purposes, but there might be a more efficient approach. Before diving deeper, could you confirm which version of Prefect you're using? Prefect 2.x and 3.x have some differences in handling tasks and caching, and knowing the version will help provide more accurate advice. In general, you might consider: 1. Chunking and Aggregated Storage: Instead of creating individual tasks for each prediction, you could store the batch predictions collectively in a way that allows easy retrieval. This could be a single cache operation storing an entire batch's results if the storage plugin allows for it. 2. Custom Caching Solutions: Prefect allows for custom caching logic which might enable you to design a solution where you cache the entire batch's predictions as a single entity rather than individual tasks. Let's explore these options more specifically once the version is clarified, ensuring we leverage the best capabilities Prefect offers in your setup.
v
@Marvin I am using prefect 2.20. So let's say I would like to use prefect for aws. I run my model and receive 5 results. Ideally I would load into aws s3 5 files: res1.txt, res2.txt and so on. How to do so in one task? I also would like to let know prefect that these files are already computed, so I won't start pipeline again
m
To effectively use Prefect for caching multiple outputs in one task and storing them on AWS S3, here's a concise strategy based on Prefect 2.20 capabilities: 1. Install and Configure Prefect AWS: Ensure you have the 
prefect-aws
package installed and configured. This allows you to easily integrate with AWS services, including S3. You can do this by running:
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shell
   pip install 'prefect[aws]<3'
   prefect block register -m prefect_aws
2. Use S3 as a Storage Block: Define an S3 storage block where your task results will be stored. This is done by creating a block in Prefect and referencing it in your tasks:
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python
   from prefect import task, Flow
   from prefect_aws.s3 import S3Bucket

   my_s3_block = S3Bucket.load("my_s3_block_name")

   @task(result_storage=my_s3_block)
   def write_predictions_to_s3(predictions):
       import boto3

       s3_client = boto3.client('s3', region_name="your-region")
       bucket_name = "your-bucket-name"
       for i, prediction in enumerate(predictions):
           filename = f"res{i+1}.txt"
           s3_client.put_object(Bucket=bucket_name, Key=filename, Body=prediction)
       return "Files uploaded to S3"

   with Flow("Upload ML Predictions") as flow:
       predictions = ["prediction1", "prediction2", "prediction3", "prediction4", "prediction5"]
       write_predictions_to_s3(predictions)
3. Caching Strategy: To avoid re-running the task if the predictions are already computed and stored, you can implement caching logic using tags or Prefect's cache mechanisms, like 
cache_key
and `cache_expiration`:
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python
   @task(result_storage=my_s3_block, cache_key_fn=lambda *args: hash(tuple(args)), cache_expiration=3600)
   def write_predictions_to_s3(predictions):
       # Your implementation
For more insights and examples of AWS integrations, check these resources: - Prefect AWS Integration Guide - Using Prefect with Big Data on Cloud Providers By consolidating results into one task and storing them collectively with defined caching, you'll streamline efficiency while leveraging Prefect's powerful workflow orchestration. If you have any further questions or need more detailed assistance, feel free to ask!
v
@Marvin Could you please reply the same question, but not for aws, but for example for google cloud storage?
m
To use Prefect for caching task output and storing it on Google Cloud Storage (GCS) with Prefect 2.20, here's a streamlined approach: 1. Install and Configure Prefect GCP: Ensure that you have the 
prefect-gcp
package installed and properly configured. This package facilitates interaction with Google Cloud services.
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shell
   pip install 'prefect[gcp]<3'
   prefect block register -m prefect_gcp
2. Use Google Cloud Storage as a Storage Block: Create a Google Cloud Storage (GCS) block for task results. Define this storage block in your Prefect setup and reference it in your task:
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python
   from prefect import task, Flow
   from prefect_gcp.cloud_storage import GcsBucket

   my_gcs_block = GcsBucket.load("my_gcs_block_name")

   @task(result_storage=my_gcs_block)
   def write_predictions_to_gcs(predictions):
       from google.cloud import storage

       client = storage.Client()
       bucket = client.bucket('your-bucket-name')
       
       for i, prediction in enumerate(predictions):
           blob = bucket.blob(f'res{i+1}.txt')
           blob.upload_from_string(prediction)
       return "Files uploaded to GCS"

   with Flow("Upload ML Predictions") as flow:
       predictions = ["prediction1", "prediction2", "prediction3", "prediction4", "prediction5"]
       write_predictions_to_gcs(predictions)
3. Implement Caching: You can control caching using Prefect's cache features like 
cache_key
and 
cache_expiration
, allowing you to skip execution if outputs already exist:
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python
   @task(result_storage=my_gcs_block, cache_key_fn=lambda *args: hash(tuple(args)), cache_expiration=timedelta(days=1))
   def write_predictions_to_gcs(predictions):
       # Your code implementation
For more detailed guides on using Google Cloud with Prefect, take a look at: - Prefect GCP Integration Guide - General tips on big data and cloud provider integrations: Prefect's Guide on Big Data This setup ensures your Prefect tasks efficiently handle multiple outputs and caching while leveraging the power of Google Cloud Storage to store predictions. If there's anything further you'd like help with, just let me know!
v
@Marvin Perfect. Is it possible to keep the version of my task? For example let's say I have changed the code, so I would like to recompute results for everything that I cached. How to add this into prefect?
m
In Prefect 2.20, if you want to invalidate the cache and recompute results when you've made changes to your task's code, you can use a versioning scheme combined with Prefect's caching features. Here's a straightforward plan: 1. Task Versioning through Cache Key: You can incorporate a version number directly into the cache key function. This way, whenever you update your task code, you can manually change the version number, which will effectively change the cache key and force a cache refresh. 2. Using 
refresh_cache
: You can set the 
refresh_cache
option to 
True
to force a cache refresh. However, to selectively refresh based on your own criteria (like version changes), you should focus on cache key manipulation. Here's how you might implement this:
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python
from prefect import task

def versioned_cache_key(context, parameters):
    # Include a version number in the cache key
    version = "1.0.0"  # Increment this with code changes
    return f"{version}-{context.task_run.flow_run_id}-{parameters}"

@task(cache_key_fn=versioned_cache_key, refresh_cache=False)
def my_task(data):
    # Your task code here
    return perform_expensive_computation(data)

# Increment `version` in `versioned_cache_key` whenever you change task logic
By introducing a version number, you ensure that any update in the code logically results in a different cache key, prompting a recomputation of tasks when needed. For more technical details, you might find these Prefect documentation sections informative: - Task Concepts and Caching Feel free to ask more questions if needed! Even the greatest robots need updates now and then, just like your tasks!
v
@Marvin Okay let's say I am trying to run cache upon a pytorch dataloader. Are there any example on that? Should I invoke batch on every item in dataloader?
m
When working with a PyTorch DataLoader and Prefect, you can leverage batch processing within a single task to efficiently handle caching and execution. Here's a suggested approach to process and cache your data leveraging Prefect: ### Approach 1. Batch Processing: It's often more efficient to handle batches directly within a single task rather than spawning multiple tasks for each item in a batch from a DataLoader. 2. Caching Batch Results: Implement caching at the batch level by assigning a unique cache key for each batch. This minimizes the need to recompute results if the batch hasn't changed. ### Example Code Below is an example of how you might structure your Prefect task with a PyTorch DataLoader:
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python
from prefect import task, Flow
from torch.utils.data import DataLoader
import torch

def my_model(batch):
    # Simulate model processing
    return batch * 2

@task
def process_batch(batch):
    # Processing a single batch
    results = my_model(batch)
    # Here's where you'd handle caching logic, possibly storing results somewhere persistent
    return results

@task
def dataloader_to_batches(dataloader):
    batches = []
    for batch in dataloader:
        batches.append(batch)
    return batches

data = [torch.tensor([i]) for i in range(10)]  # Example data
dataloader = DataLoader(data, batch_size=2)

with Flow("PyTorch DataLoader with Prefect") as flow:
    batches = dataloader_to_batches(dataloader)
    for batch in batches:
        process_batch(batch)

flow.run()
### Key Points - Batching: Process and cache entire batches to reduce computational overhead. - Unique Cache Keys: Use distinct cache keys for each batch based on its contents or an associated identifier. - Prefect Dask: Consider using Prefect Dask to parallelize batch processing, especially beneficial if dealing with large data or computationally intensive operations. This setup balances efficient execution with caching, avoiding unnecessary recomputation for unchanged data. Let me know if further complexities arise or if you'd like more specific tasks addressed!
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