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#-------------------------------------------------------------
# types
#-------------------------------------------------------------
type C = concept type C
    proc name(x: C, msg: string): string

type AnyC = object
    name: proc(msg: string): string # doesn't contain C

type A = object

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# client-task-pool.py

from aiohttp import ClientSession, TCPConnector
import asyncio
import sys
from pypeln.task import TaskPool

limit = 1000

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# client-async-sem.py

from aiohttp import ClientSession, TCPConnector
import asyncio
import sys

limit = 1000

async def fetch(url, session):
    async with session.get(url) as response:

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# timed.sh

ulimit -n 3000 # increase to avoid "out of file descriptors" error

python server.py &

sleep 1 # Wait for server to start

/usr/bin/time --format "Memory usage: %MKB\tTime: %e seconds\tCPU usage: %P" "$@"

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from aiohttp import ClientSession, TCPConnector
import asyncio
import sys
import pypeln as pl


limit = 1000
urls = ("http://localhost:8080/{}".format(i) for i in range(int(sys.argv[1])))

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MyModule:
  b: list
    - Initializer
    - Parameter((5, 13), float32)
  a: dict
    mlps: list
      - MLP:
          linear1: Linear
            w: Parameter((2, 3), float32)
            b: Parameter((3,), float32)

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from aiohttp import ClientSession
from pypeln import io
import asyncio
import sys

async def fetch(url, session):
    async with session.get(url) as response:
        return await response.read()

async def main():

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rsync --exclude-from=.gitignore -avz -e ssh . $1

while inotifywait -r -e modify,create,delete . ; do
    rsync --exclude-from=.gitignore -avz --delete -e ssh . $1
done 

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# PARAMETERS
MODEL = "ResNet50"
OUTPUT_DIRECTORY = "models/resnet50"
EPOCHS = 90
BATCH_SIZE = 6
IMAGE_SIZE = 224  # image size in pixels
DATASET = "imagenet2012:5.1.*"  # TFDS dataset name and version
DTYPE = "float16"  # float16 for mixed_precision or float32 for normal mode
LEARNING_RATE = 0.1 * BATCH_SIZE / 256.0
MOMENTUM = 0.9

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model = elegy.Model(
    module=MixtureModel(k=k), 
    loss=MixtureNLL(), 
    optimizer=optax.adam(3e-4),
)

model.summary(X_train[:batch_size], depth=1)

model.fit(
    x=X_train,