liangfu

""" Example script for defining and using custom models in AutoGluon Tabular """

from autogluon.core.utils import infer_problem_type
from autogluon.tabular import TabularDataset, TabularPredictor
from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
from autogluon.core.data import LabelCleaner
from autogluon.core.models import AbstractModel

from skl2onnx import convert_sklearn, get_model_alias
from skl2onnx.common._registration import get_shape_calculator, get_converter

liangfu

# Benchmark inference time of pretrained AutoGluon Tabular models
"""
set -e
DEV_BRANCH=reset-thread-2
git checkout master
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py --sample=1
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py
git checkout $DEV_BRANCH
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py --sample=1
/opt/conda/envs/py38/bin/python3 example_dev_tabular.py

liangfu

#!/bin/sh

ProgName=$(basename $0)
instance_ids=i-05806fb95d65fffff
region=us-west-2

sub_help(){
    echo "Usage: $ProgName <subcommand> [options]\n"
    echo "Subcommands:"
    echo "    start     Start mldev instances"

liangfu

apt update
apt install -y emacs-nox aptitude screen xcscope-el gdb nano || echo 0 && echo "Success"
python3 -m pip install git-remote-codecommit opennmt-tf==1.25.1

cat << EOF > ~/.gdbinit
set print thread-events off
define hook-run
  catch throw
end
define hook-quit

liangfu

import numpy as np
import time
import math

def burn_cpu():
    a = np.random.rand(1024, 1024)
    b = np.random.rand(1024, 1024)
    tic = time.time()
    c = np.dot(a, b)
    toc = time.time()

liangfu

import boto3, json

question = """
How are you today?
"""

def main():
    session = boto3.Session()
    bedrock = session.client(service_name='bedrock-runtime', region_name="us-west-2")

liangfu

from typing import Any, Dict, List, Optional, Tuple, Type

import torch
import torch_xla.core.xla_model as xm
import torch_xla.experimental.custom_kernel  # Required to register custom ops.

class PallasAttentionBackend:
    @torch.compile(backend="openxla")
    @staticmethod
    def copy_blocks(

liangfu

import torch
import torch_xla.core.xla_model as xm

@torch.compile(backend="openxla")
def toy_example(a, b):
    x = a / (torch.abs(a) + 1)
    if b.sum() < 0:
        b = b * -1
    return x * b

liangfu

import time
import torch
import torch_xla.core.xla_model as xm

N = 128
n_iters = 100

def main():
    device = xm.xla_device()
    src = torch.arange(1, 2*N+1).reshape((2, N)).to(device=device)

liangfu

import torch
import os
import depyf
import torch_xla.core.xla_model as xm

os.environ["NEURON_CC_FLAGS"]= " --model-type=transformer -O1 "
os.environ["NEURON_COMPILE_CACHE_URL"] = os.path.join(os.getcwd(), "_compile_cache")

@torch.compiler.allow_in_graph
def write_to_kv_cache(