ashemag

import os
from dotenv import load_dotenv
import requests
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
from datetime import date
from collections import defaultdict
import modal

load_dotenv()

ashemag

# specify base image 
FROM tensorflow/tensorflow:latest-py3

# create directory to organize files in and enter dir 
WORKDIR /app

COPY requirements.txt .
RUN pip3 install -r requirements.txt
RUN pip3 install --upgrade protobuf
COPY . .

ashemag

name: Download/Process Nifty Data

inputs:
  - {name: experiment_name, type: String, description: 'Experiment name'}
  - {name: data_filename, type: String, description: 'Name of data in GCP'}

outputs: 
  - {name: result, type: string}

ashemag

import numpy as np
import tensorflow as tf
import os
import argparse
from build.gcp_utils import download_from_gcp, write_to_gcp

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"

def process_data(filename):
    print("Downloading from gcp...", flush=True)

ashemag

import kfp


def configure_task(task, cache=True):
    task.container.add_resource_request("memory", "50Gi")
    task.container.add_resource_limit("memory", "50Gi")
    task.container.add_resource_request("cpu", "250m")

    if not cache:
        task.execution_options.caching_strategy.max_cache_staleness = "P0D"