patil-suraj
/ bottom_sheet_fix.dart
Created
May 4, 2019 05:47
— forked from crimsonsuv/bottom_sheet_fix.dart
Flutter Modal bottom sheet whith input fix and full screen sheet
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| //Flutter Modal Bottom Sheet | |
| //Modified by Suvadeep Das | |
| //Based on https://gist.github.com/andrelsmoraes/9e4af0133bff8960c1feeb0ead7fd749 | |
| import 'dart:async'; | |
| import 'package:flutter/material.dart'; | |
| import 'package:meta/meta.dart'; | |
| const Duration _kBottomSheetDuration = const Duration(milliseconds: 200); |
patil-suraj
/ yahoo_finance.py
Created
June 12, 2019 06:07
— forked from scrapehero/yahoo_finance.py
Python 2 code to extract stock market data from Yahoo Finance
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| from lxml import html | |
| import requests | |
| from exceptions import ValueError | |
| from time import sleep | |
| import json | |
| import argparse | |
| from collections import OrderedDict | |
| from time import sleep | |
| def parse(ticker): |
patil-suraj
/ update_swift.sh
Created
October 4, 2019 15:44
— forked from sgugger/update_swift.sh
Updating S4TF
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| #!/usr/bin/env bash | |
| pushd ~/swift/ | |
| rm -rf usr | |
| popd | |
| pushd ~/download/ | |
| rm swift-tensorflow-DEVELOPMENT-cuda10.0-cudnn7-ubuntu18.04.tar.gz | |
| wget https://storage.googleapis.com/s4tf-kokoro-artifact-testing/latest/swift-tensorflow-DEVELOPMENT-cuda10.0-cudnn7-ubuntu18.04.tar.gz | |
| tar -xf swift-tensorflow-DEVELOPMENT-cuda10.0-cudnn7-ubuntu18.04.tar.gz | |
| mv usr/ ~/swift/ | |
| mv ~/swift/usr/lib/python3.6 ~/swift/usr/lib/python3.7 |
patil-suraj
/ pipeline_parallel.py
Created
October 2, 2024 11:32
— forked from 3outeille/pipeline_parallel.py
Self contained example of how pipeline parallel works (AFAB and 1F1B) in 200 LOC
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| #VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py | |
| import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F | |
| from torch.optim import AdamW | |
| from torch.utils.data import DataLoader, DistributedSampler | |
| from datasets import load_dataset | |
| from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer | |
| STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1" | |
| def set_all_seed(seed): |