pcuenca

#!/bin/sh
# 
# Download and install iOS provisioning profiles
# Requires https://github.com/nomad/cupertino
#
# Usage
# - Login to your account once:
# ios login
# - Configure TEAM and PROFILE (instructions below)
# - Run update_provisioning_profile.sh at anytime, usually after adding/removing devices to the profile

pcuenca

//
//  MakeContiguousKernel.metal
//
//  Created by Pedro Cuenca on 20220307.
//  Copyright © 2022 LateNiteSoft S.L. All rights reserved.
//

#include <metal_stdlib>
using namespace metal;

pcuenca

pcuenca

Amazon EC2:

• Create a Dedicated Host. Wait for it to become "Available".
• Select the host. Click "Actions" and select "Launch instance(s) onto host".
• Create the instance (Ventura on arm). Attach a secondary drive to be used as hub cache. I chose 1.5 TB.
• Connect to the instance when it's ready

Mac VM:

• Enable vnc access

  sudo defaults write /var/db/launchd.db/com.apple.launchd/overrides.plist com.apple.screensharing -dict Disabled -bool false\n

pcuenca

using Newtonsoft.Json.Linq;
using System.Dynamic;
using System.Text.RegularExpressions;


public class CamelToSnakeDynamicWrapper : DynamicObject
{
    private readonly JObject _jObject;

    public static string CamelToSnake(string camelCase)

pcuenca

import torch
import torch.utils.benchmark as benchmark
import argparse
from diffusers import DiffusionPipeline, LCMScheduler

PROMPT = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
LORA_ID = "lcm-sd/lcm-sdxl-lora-huber-final"

pcuenca

def upload_to_hub(path: str, name: str, hf_path: str):
    import os
    from huggingface_hub import HfApi, logging, ModelCard

    repo_id = f"mlx-community/{name}"

    card = ModelCard.load(hf_path)
    card.data.tags = ["mlx"] if card.data.tags is None else card.data.tags + ["mlx"]
    card.text = f"""
# {name}

pcuenca

curl -s -X GET "https://huggingface.co/api/models?author=mlx-community" | jq ".[].id"

pcuenca

import argparse
import numpy as np
import torch
import torch.nn as nn
import coremltools as ct
from transformers import AutoTokenizer, AutoModelForCausalLM

# When using float16, all predicted logits are 0. To be debugged.
compute_precision = ct.precision.FLOAT32
compute_units = ct.ComputeUnit.CPU_ONLY

pcuenca