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Storage and Persistence

The target environments of isolated functions have access to a directory /data. The /data directory is persistent and user-specific. This directory allows you to store and retrieve data across function invocations, enabling you to persist data between executions.

Accessing the /data directory is simple, as it is automatically mounted in the function's environment. You can read from and write to the /data directory just as you would with any other directory in your file system.

Here are two examples to demonstrate the use of the /data directory:

Example 1: Storing User Preferences

Let's say you have a function that generates personalized recommendations for users based on their preferences. The function can store the preferences in the /data directory so that they persist across invocations. The code would look something like this:

def generate_recommendations(user_id, preferences):
preferences_file = f"/data/user_{user_id}_preferences.txt"
with open(preferences_file, "w") as f:
# Generate recommendations based on the stored preferences
# ...

Example 2: Persisting Model Weights

In machine learning, you may want to persist the model weights so that you don't have to download them every time you need to make a prediction. The /data directory provides an easy way to persist the model weights between function invocations.

Here's an example of how you might use the /data directory to store model weights in a deep learning scenario:

import os
import tensorflow as tf
from fal_serverless import isolated

def train_and_predict(data, model_weights_file='/data/model_weights.h5'):
model = create_model()
if os.path.exists(model_weights_file):
return model.predict(data)

def create_model():
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
return model

In this example, the function train_and_predict first checks if the model weights file model_weights.h5 exists in the /data directory. If it does, the function loads the weights into the model. If not, the function trains the model and saves the weights to the /data directory. This way, on subsequent invocations, the function can simply load the weights from the /data directory, which will be much faster than retraining the model from scratch.

sync_dir Function

The sync_dir function allows you to easily upload local directories to the persistent /data directory. Here's an example of how to use the sync_dir function:

from fal_serverless import sync_dir, isolated

# Upload a local directory to the persistent /data directory
sync_dir("path/to/local/dir", "remote_dir")

# An isolated function to list the contents of the uploaded directory
def test():
import os
os.system("ls /data/sync/remote_dir")

# Execute the test function
test() # prints contents of the uploaded directory

In this example, the local directory specified by path/to/local/dir is uploaded to /data/sync/remote_dir in the fal-serverless environment.