Luis Capelo luiscape

## install_packages.sh
#
#  Original solution via StackOverflow:
#    http://stackoverflow.com/questions/35802939/install-only-available-packages-using-conda-install-yes-file-requirements-t
#

#
#  Install via `conda` directly.
#  This will fail to install all
#  dependencies. If one fails,
#  all dependencies will fail to install.

## sd_torch_2_0.py
# ## Basic setup
from __future__ import annotations

import io
import os
import time
from pathlib import Path

import modal

## mounting_hf_model.py
import io
import time
import modal
import os
import torch
import diffusers

from pathlib import Path

stub = modal.Stub("local-model-mount-test")

## call_inside_modal.py
"""
Script to run batch inference using HuggingFace's zero-shot text-classification model on Modal.

Based on the tutorial: https://modal.com/docs/guide/ex/batch_inference_using_huggingface

Goal: filter a large Hugging Face dataset for food-related images (based on the text caption).
"""
import io

import modal

## batch_inference_modal.py
"""
Script to run batch inference using HuggingFace's zero-shot text-classification model on Modal.

Based on the tutorial: https://modal.com/docs/guide/ex/batch_inference_using_huggingface

Goal: filter a large Hugging Face dataset for food-related images (based on the text caption).
"""
import io

import modal

## stable_diffusion_cli.py
import io
import os
import time
from pathlib import Path

import modal
import typer

stub = modal.Stub("stable-diffusion-cli")
app = typer.Typer()

## diffusers-rs-on-modal.py
# Runs diffusers-rs on Modal.
import os
import time
import modal
import subprocess

CARGO_PATH:str = "/root/.cargo/bin/cargo"


def _convert_clip():

## download_stable_diffusion_models.py
"""This script downloads all neural networks used in the HuggingFace `diffuser`'s
`StableDiffusionPipeline` pipeline. This also downloads the parameters from the scheduler
`EulerAncestralDiscreteScheduler` because that is a fast and effective scheduler.

This requires the env var `HUGGINGFACE_TOKEN` to be populated with a HuggingFace
access token.

The default cache location is: /vol/cache. This can be changed by populating
the environment variable `CACHE_PATH`.

## jupyter_notebook_theme.sh
#!/bin/bash

#
#  Instally the theme library.
#
pip install jupyterthemes

#
# Configure to use preferred theme.
#

## plot_facets.py
#
#  From Seaborn's official documentation:
#
#    https://seaborn.pydata.org/generated/seaborn.FacetGrid.html
#
>>> import pandas as pd
>>> df = pd.DataFrame(
...     data=np.random.randn(90, 4),
...     columns=pd.Series(list("ABCD"), name="walk"),
...     index=pd.date_range("2015-01-01", "2015-03-31",
	#
	# Original solution via StackOverflow:
	# http://stackoverflow.com/questions/35802939/install-only-available-packages-using-conda-install-yes-file-requirements-t
	#

	#
	# Install via `conda` directly.
	# This will fail to install all
	# dependencies. If one fails,
	# all dependencies will fail to install.
	# ## Basic setup
	from __future__ import annotations

	import io
	import os
	import time
	from pathlib import Path

	import modal
	"""
	Script to run batch inference using HuggingFace's zero-shot text-classification model on Modal.

	Based on the tutorial: https://modal.com/docs/guide/ex/batch_inference_using_huggingface

	Goal: filter a large Hugging Face dataset for food-related images (based on the text caption).
	"""
	import io

	import modal
	# Runs diffusers-rs on Modal.
	import os
	import time
	import modal
	import subprocess

	CARGO_PATH:str = "/root/.cargo/bin/cargo"


	def _convert_clip():
	"""This script downloads all neural networks used in the HuggingFace `diffuser`'s
	`StableDiffusionPipeline` pipeline. This also downloads the parameters from the scheduler
	`EulerAncestralDiscreteScheduler` because that is a fast and effective scheduler.

	This requires the env var `HUGGINGFACE_TOKEN` to be populated with a HuggingFace
	access token.

	The default cache location is: /vol/cache. This can be changed by populating
	the environment variable `CACHE_PATH`.
	#!/bin/bash

	#
	# Instally the theme library.
	#
	pip install jupyterthemes

	#
	# Configure to use preferred theme.
	#
	#
	# From Seaborn's official documentation:
	#
	# https://seaborn.pydata.org/generated/seaborn.FacetGrid.html
	#
	>>> import pandas as pd
	>>> df = pd.DataFrame(
	... data=np.random.randn(90, 4),
	... columns=pd.Series(list("ABCD"), name="walk"),
	... index=pd.date_range("2015-01-01", "2015-03-31",