erikbern

import sys

import modal

stub = modal.Stub(
    image=modal.Image.debian_slim().pip_install(["datasets", "torch", "transformers"])
)


class Predictor:

erikbern

if __name__ == "__main__":
    secret = modal.Secret({"FOO": os.environ["FOO"]})
else:
    secret = modal.Secret.from_name("baz")

erikbern

import io
import sys

import modal

stub = modal.Stub(
    image=modal.Image.debian_slim()
    .apt_install(["git"])
    .pip_install(
        [

erikbern

import json
import subprocess
import sys
import tempfile

import modal

stub = modal.Stub()
stub.sv = modal.SharedVolume().persist("valhalla")
image = modal.DockerhubImage("valhalla/valhalla:run-latest", setup_commands=["apt-get update", "apt-get install -y python3-pip"])

erikbern

# Note: this assumes you have a notebook locally named my_notebook.ipynb

import modal

stub = modal.Stub(
    image=modal.DebianSlim().pip_install(["papermill", "ipykernel"]),
    mounts=[modal.Mount(local_file="my_notebook.ipynb", remote_dir="/root")],
)

@stub.function

erikbern

import io
import modal

stub = modal.Stub(image=modal.DebianSlim().pip_install(["prophet"]))

@stub.function
def run():
    import pandas as pd
    from prophet import Prophet
    from matplotlib import pyplot

erikbern

# Let's say you have a method `map(f, inputs)` that performs some operation on inputs
# Let's also sayu that this function returns a generator.
# A user might pass a function f that has some side effect, e.g. `def f(x): print(x)`
# This might lead to confusing results if the output is never consumed:
# `map(f, inputs)` -> this does nothing, can be confusing to the user
# `list(map(f, inputs))` -> this executes `f` on all inputs
# To remedy the situation, we can provide a helpful warning to the user if the generator
# is never consumed.

### Helper code:

erikbern

from matplotlib import pyplot
import random
import time

pyplot.style.use("ggplot")
now = time.time()

def generate_user(censor=now):
    # Pick some point in time the user was created
    t_created = t = now - random.random() * 1e7

erikbern

async def intercept_coro(coro, interceptor):
    # This roughly corresponds to https://gist.github.com/erikbern/ad7615d22b700e8dbbafd8e4d2f335e1
    # The underlying idea is that we can execute a coroutine ourselves and use it to intercept
    # any awaitable object. This lets the coroutine await arbitrary awaitable objects, not just
    # asyncio futures. See how this is used in object.load.
    value_to_send = None
    while True:
        try:
            awaitable = coro.send(value_to_send)
            assert inspect.isawaitable(awaitable)

erikbern

# First, let's create an awaitable object.
# In this case it's a very dumb container of integers.
# Any time a coroutine runs `await Thing(n)` it just resolves to n
# However, we could resolve it to something completely different if we wanted to

class Thing:
    def __init__(self, n):
        self._n = n

    def __await__(self):