gau-nernst

import torch
from diffusers import FluxPipeline
from torch import nn


class ModelOffloaderV2:
    def __init__(self, model: nn.Module, record_stream: bool = False):
        # move model to pinned memory. keep a model copy in CPU pinned memory.
        for p in model.parameters():
            p.data = p.data.cpu().pin_memory()

simonster

# Copyright 2022 Google LLC.
# SPDX-License-Identifier: Apache-2.0
# Author: Maithra Raghu <[email protected]>


def compute_distance_matrix(patch_size, num_patches, length):
  """Helper function to compute distance matrix."""

  distance_matrix = np.zeros((num_patches, num_patches))

CallmeMehdi

TF-Hub text embedding modules for underrepresented languages

Mentors:

• Morgan Roff
• Sayak Paul
• jaeyounkim

This is a summary of my GSoC 2021 project. In this project, I tried to produce text embedding modules trained on underrepresented languages like Arabic and Swahili and publish them on tfhub.dev.

sararob

# Copyright 2021 Google LLC.
# SPDX-License-Identifier: Apache-2.0
import kfp
import json
import time
from google.cloud import bigquery
from google.cloud.exceptions import NotFound
from kfp.v2.google.client import AIPlatformClient

client = bigquery.Client()

aravindsrinivas

import functools
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.python.tpu import tpu_function


BATCH_NORM_DECAY = 0.9
BATCH_NORM_EPSILON = 1e-5

ariG23498

borisdayma

To be posted in: https://forums.fast.ai/c/fastai-users/fastai-v2/

Title: Proposed workflow to compare & monitor models using WandbCallback

Content:

Hi,

I’ve been working on WandbCallback for the past few months (with a lot of help from @sgugger) and I'm very excited to show how it works!

fclesio

def get_classification_report(y_test, y_pred):
    '''Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''
    from sklearn import metrics
    report = metrics.classification_report(y_test, y_pred, output_dict=True)
    df_classification_report = pd.DataFrame(report).transpose()
    df_classification_report = df_classification_report.sort_values(by=['f1-score'], ascending=False)
    return df_classification_report

SamuelMarks

Google Cloud configuration

First: install the CLI program for your distribution: https://cloud.google.com/sdk/install

Parameters

Modify accordingly:

export REGION='us-central1'
export ZONE='us-central1-f'
export PROJECT_NAME='proj'

YashasSamaga

DISCLAIMER

This gist is unofficial. It was created for personal use but have kept it public in case it would be of use to others. This document is not updated regularly and may not reflect the current status of the CUDA backend.