py-llama predictor
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agent-arti/readme.md
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agent-arti/readme.md
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ToDo: copy arti code here
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agent-pyter/ccxt.py
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agent-pyter/ccxt.py
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# # https://github.com/ccxt/ccxt/tree/master/examples/py
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# # ! pip install ccxt
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# # //cjs
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# # var ccxt = require ('ccxt')
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# # console.log (ccxt.exchanges) // print all available exchanges
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# # py
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# import ccxt
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# #print(ccxt.exchanges)
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# #import ccxt.async_support as ccxt
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# # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# # import ccxt
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# # hitbtc = ccxt.hitbtc({'verbose': True})
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# # bitmex = ccxt.bitmex()
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# # huobipro = ccxt.huobipro()
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# # exmo = ccxt.exmo({
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# # 'apiKey': 'YOUR_PUBLIC_API_KEY',
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# # 'secret': 'YOUR_SECRET_PRIVATE_KEY',
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# # })
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# # kraken = ccxt.kraken({
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# # 'apiKey': 'YOUR_PUBLIC_API_KEY',
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# # 'secret': 'YOUR_SECRET_PRIVATE_KEY',
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# # })
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# # exchange_id = 'binance'
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# # exchange_class = getattr(ccxt, exchange_id)
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# # exchange = exchange_class({
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# # 'apiKey': 'YOUR_API_KEY',
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# # 'secret': 'YOUR_SECRET',
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# # })
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# # hitbtc_markets = hitbtc.load_markets()
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# # print(hitbtc.id, hitbtc_markets)
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# # print(bitmex.id, bitmex.load_markets())
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# # print(huobipro.id, huobipro.load_markets())
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# # print(hitbtc.fetch_order_book(hitbtc.symbols[0]))
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# # print(bitmex.fetch_ticker('BTC/USD'))
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# # print(huobipro.fetch_trades('LTC/USDT'))
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# # print(exmo.fetch_balance())
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# # # sell one ฿ for market price and receive $ right now
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# # print(exmo.id, exmo.create_market_sell_order('BTC/USD', 1))
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# # # limit buy BTC/EUR, you pay €2500 and receive ฿1 when the order is closed
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# # print(exmo.id, exmo.create_limit_buy_order('BTC/EUR', 1, 2500.00))
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# # # pass/redefine custom exchange-specific order params: type, amount, price, flags, etc...
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# # kraken.create_market_buy_order('BTC/USD', 1, {'trading_agreement': 'agree'})
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# # # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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# # # -*- coding: utf-8 -*-
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# # import os
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# # import sys
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# # from pprint import pprint
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# # root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# # sys.path.append(root + '/python')
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# import ccxt # noqa: E402
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# # -----------------------------------------------------------------------------
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# # print('CCXT Version:', ccxt.__version__)
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# # -----------------------------------------------------------------------------
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# exchange = ccxt.coinbase({
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# 'apiKey': 'tk2ShLJCmByejn78',
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# 'secret': 'UcJfI5HzQmkEjclCeHFSfG8hnNYxaESv',
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# # 'verbose': True, # for debug output
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# })
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# symbol = 'BTC/USDT'
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# timeframe = '1m'
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# since = None
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# limit = None # not used by coinbase
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# try:
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# # Max 300 Candles
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# candles = exchange.fetch_ohlcv(symbol, timeframe, since, limit)
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# pprint(candles)
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# except Exception as err:
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# print(err)
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agent-pyter/lag-llama
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agent-pyter/lag-llama
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Subproject commit 948665530fcda634df9d7df0bee5e19b87785eb9
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agent-pyter/lag-llama.ipynb
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agent-pyter/lag-llama.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "04e8bf3c-845f-49bb-9e9c-992d6b8948f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# https://colab.research.google.com/drive/1XxrLW9VGPlZDw3efTvUi0hQimgJOwQG6?usp=sharing#scrollTo=gyH5Xq9eSvzq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "37f96736-8654-4852-a144-fd75df22aaf7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Cloning into 'lag-llama'...\n",
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"remote: Enumerating objects: 124, done.\u001b[K\n",
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"remote: Counting objects: 100% (69/69), done.\u001b[K\n",
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"remote: Compressing objects: 100% (43/43), done.\u001b[K\n",
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"remote: Total 124 (delta 39), reused 47 (delta 26), pack-reused 55\u001b[K\n",
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"Receiving objects: 100% (124/124), 190.17 KiB | 2.29 MiB/s, done.\n",
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"Resolving deltas: 100% (49/49), done.\n"
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]
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}
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],
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"source": [
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"!git clone https://github.com/time-series-foundation-models/lag-llama/"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "f5fac8fa-5ac8-4330-97e0-8a2f4237ba0f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/workspace/repos/git.d-popov.com/ai-kevin/agent-pyter/lag-llama/lag-llama\n"
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]
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}
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],
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"source": [
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"cd ./lag-llama"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "968625c9-00fd-4037-b97c-33dfc4758491",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: gluonts[torch] in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (0.14.4)\n",
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"Requirement already satisfied: torch>=2.0.0 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (2.2.1)\n",
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"Requirement already satisfied: pytorch-lightning<2.2,>=2.0 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from gluonts[torch]->-r requirements.txt (line 1)) (2.1.4)\n",
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"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from torch>=2.0.0->-r requirements.txt (line 3)) (8.9.2.26)\n",
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"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from torch>=2.0.0->-r requirements.txt (line 3)) (11.0.2.54)\n",
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"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from torch>=2.0.0->-r requirements.txt (line 3)) (10.3.2.106)\n",
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"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from torch>=2.0.0->-r requirements.txt (line 3)) (11.4.5.107)\n",
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"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from torch>=2.0.0->-r requirements.txt (line 3)) (2.19.3)\n",
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"Requirement already satisfied: wcwidth in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from prompt-toolkit<4.0.0,>=3.0.1->InquirerPy==0.3.4->huggingface_hub[cli]->-r requirements.txt (line 7)) (0.2.13)\n",
|
||||
"Requirement already satisfied: aiosignal>=1.1.2 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->torch>=2.0.0->-r requirements.txt (line 3)) (1.3.1)\n",
|
||||
"Requirement already satisfied: attrs>=17.3.0 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->torch>=2.0.0->-r requirements.txt (line 3)) (23.2.0)\n",
|
||||
"Requirement already satisfied: frozenlist>=1.1.1 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->torch>=2.0.0->-r requirements.txt (line 3)) (1.4.1)\n",
|
||||
"Requirement already satisfied: multidict<7.0,>=4.5 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->torch>=2.0.0->-r requirements.txt (line 3)) (6.0.5)\n",
|
||||
"Requirement already satisfied: yarl<2.0,>=1.0 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec->torch>=2.0.0->-r requirements.txt (line 3)) (1.9.4)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip install -r requirements.txt #--quiet # this could take some time # ignore the errors displayed by colab"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "8f10c802-4ffa-40f7-bd62-14ff13fae03c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Requirement already satisfied: requests in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (2.31.0)\n",
|
||||
"Requirement already satisfied: charset-normalizer<4,>=2 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from requests) (3.3.2)\n",
|
||||
"Requirement already satisfied: idna<4,>=2.5 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from requests) (3.4)\n",
|
||||
"Requirement already satisfied: urllib3<3,>=1.21.1 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from requests) (2.0.7)\n",
|
||||
"Requirement already satisfied: certifi>=2017.4.17 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from requests) (2023.7.22)\n",
|
||||
"Requirement already satisfied: matplotlib in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (3.8.3)\n",
|
||||
"Requirement already satisfied: contourpy>=1.0.1 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (1.2.0)\n",
|
||||
"Requirement already satisfied: cycler>=0.10 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (0.12.1)\n",
|
||||
"Requirement already satisfied: fonttools>=4.22.0 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (4.49.0)\n",
|
||||
"Requirement already satisfied: kiwisolver>=1.3.1 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (1.4.5)\n",
|
||||
"Requirement already satisfied: numpy<2,>=1.21 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (1.23.5)\n",
|
||||
"Requirement already satisfied: packaging>=20.0 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (23.2)\n",
|
||||
"Requirement already satisfied: pillow>=8 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (10.0.1)\n",
|
||||
"Requirement already satisfied: pyparsing>=2.3.1 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (3.1.2)\n",
|
||||
"Requirement already satisfied: python-dateutil>=2.7 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from matplotlib) (2.9.0)\n",
|
||||
"Requirement already satisfied: six>=1.5 in /config/miniconda3/envs/pygame/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip install --upgrade requests\n",
|
||||
"!pip install matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "0a64aa15-1477-44bc-b772-a9342a5640c8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Consider using `hf_transfer` for faster downloads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.\n",
|
||||
"./lag-llama.ckpt\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!huggingface-cli download time-series-foundation-models/Lag-Llama lag-llama.ckpt --local-dir ./"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "a328c513-558f-45ca-b900-b669c4ef33ed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from itertools import islice\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import matplotlib.dates as mdates\n",
|
||||
"\n",
|
||||
"import torch\n",
|
||||
"from gluonts.evaluation import make_evaluation_predictions, Evaluator\n",
|
||||
"from gluonts.dataset.repository.datasets import get_dataset\n",
|
||||
"\n",
|
||||
"from gluonts.dataset.pandas import PandasDataset\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from lag_llama.gluon.estimator import LagLlamaEstimator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"id": "f098efb9-490c-46b7-9ea3-bea1f2871fa5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_lag_llama_predictions(dataset, prediction_length, num_samples=100):\n",
|
||||
" ckpt = torch.load(\"lag-llama.ckpt\", map_location=torch.device('cuda:0')) # Uses GPU since in this Colab we use a GPU.\n",
|
||||
" estimator_args = ckpt[\"hyper_parameters\"][\"model_kwargs\"]\n",
|
||||
"\n",
|
||||
" estimator = LagLlamaEstimator(\n",
|
||||
" ckpt_path=\"lag-llama.ckpt\",\n",
|
||||
" prediction_length=prediction_length,\n",
|
||||
" context_length=32, # Should not be changed; this is what the released Lag-Llama model was trained with\n",
|
||||
"\n",
|
||||
" # estimator args\n",
|
||||
" input_size=estimator_args[\"input_size\"],\n",
|
||||
" n_layer=estimator_args[\"n_layer\"],\n",
|
||||
" n_embd_per_head=estimator_args[\"n_embd_per_head\"],\n",
|
||||
" n_head=estimator_args[\"n_head\"],\n",
|
||||
" scaling=estimator_args[\"scaling\"],\n",
|
||||
" time_feat=estimator_args[\"time_feat\"],\n",
|
||||
"\n",
|
||||
" batch_size=1,\n",
|
||||
" num_parallel_samples=100\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" lightning_module = estimator.create_lightning_module()\n",
|
||||
" transformation = estimator.create_transformation()\n",
|
||||
" predictor = estimator.create_predictor(transformation, lightning_module)\n",
|
||||
"\n",
|
||||
" forecast_it, ts_it = make_evaluation_predictions(\n",
|
||||
" dataset=dataset,\n",
|
||||
" predictor=predictor,\n",
|
||||
" num_samples=num_samples\n",
|
||||
" )\n",
|
||||
" forecasts = list(forecast_it)\n",
|
||||
" tss = list(ts_it)\n",
|
||||
"\n",
|
||||
" return forecasts, tss"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "e7e6dd60-7c0c-483f-86d4-b2ba7c4104d3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from gluonts.dataset.pandas import PandasDataset\n",
|
||||
"\n",
|
||||
"url = (\n",
|
||||
" \"https://gist.githubusercontent.com/rsnirwan/a8b424085c9f44ef2598da74ce43e7a3/raw/b6fdef21fe1f654787fa0493846c546b7f9c4df2/ts_long.csv\"\n",
|
||||
")\n",
|
||||
"df = pd.read_csv(url, index_col=0, parse_dates=True)\n",
|
||||
"# Set numerical columns as float32\n",
|
||||
"for col in df.columns:\n",
|
||||
" # Check if column is not of string type\n",
|
||||
" if df[col].dtype != 'object' and pd.api.types.is_string_dtype(df[col]) == False:\n",
|
||||
" df[col] = df[col].astype('float32')\n",
|
||||
"\n",
|
||||
"# Create the Pandas\n",
|
||||
"dataset = PandasDataset.from_long_dataframe(df, target=\"target\", item_id=\"item_id\")\n",
|
||||
"\n",
|
||||
"backtest_dataset = dataset\n",
|
||||
"prediction_length = 24 # Define your prediction length. We use 24 here since the data is of hourly frequency\n",
|
||||
"num_samples = 100 # number of samples sampled from the probability distribution for each timestep\n",
|
||||
"forecasts, tss = get_lag_llama_predictions(backtest_dataset, prediction_length, num_samples)\n",
|
||||
"len(forecasts)\n",
|
||||
"forecasts[0].samples.shape\n",
|
||||
"plt.figure(figsize=(20, 15))\n",
|
||||
"date_formater = mdates.DateFormatter('%b, %d')\n",
|
||||
"plt.rcParams.update({'font.size': 15})\n",
|
||||
"\n",
|
||||
"# Iterate through the first 9 series, and plot the predicted samples\n",
|
||||
"for idx, (forecast, ts) in islice(enumerate(zip(forecasts, tss)), 9):\n",
|
||||
" ax = plt.subplot(3, 3, idx+1)\n",
|
||||
"\n",
|
||||
" plt.plot(ts[-4 * prediction_length:].to_timestamp(), label=\"target\", )\n",
|
||||
" forecast.plot( color='g')\n",
|
||||
" plt.xticks(rotation=60)\n",
|
||||
" ax.xaxis.set_major_formatter(date_formater)\n",
|
||||
" ax.set_title(forecast.item_id)\n",
|
||||
"\n",
|
||||
"plt.gcf().tight_layout()\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "74dc9a03-435e-40a5-bbda-4ddac9f6cfb9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Set numerical columns as float32\n",
|
||||
"for col in df.columns:\n",
|
||||
" # Check if column is not of string type\n",
|
||||
" if df[col].dtype != 'object' and pd.api.types.is_string_dtype(df[col]) == False:\n",
|
||||
" df[col] = df[col].astype('float32')\n",
|
||||
"\n",
|
||||
"# Create the Pandas\n",
|
||||
"dataset = PandasDataset.from_long_dataframe(df, target=\"target\", item_id=\"item_id\")\n",
|
||||
"\n",
|
||||
"backtest_dataset = dataset\n",
|
||||
"prediction_length = 24 # Define your prediction length. We use 24 here since the data is of hourly frequency\n",
|
||||
"num_samples = 100 # number of samples sampled from the probability distribution for each timestep\n",
|
||||
"forecasts, tss = get_lag_llama_predictions(backtest_dataset, prediction_length, num_samples)\n",
|
||||
"len(forecasts)\n",
|
||||
"forecasts[0].samples.shape\n",
|
||||
"plt.figure(figsize=(20, 15))\n",
|
||||
"date_formater = mdates.DateFormatter('%b, %d')\n",
|
||||
"plt.rcParams.update({'font.size': 15})\n",
|
||||
"\n",
|
||||
"# Iterate through the first 9 series, and plot the predicted samples\n",
|
||||
"for idx, (forecast, ts) in islice(enumerate(zip(forecasts, tss)), 9):\n",
|
||||
" ax = plt.subplot(3, 3, idx+1)\n",
|
||||
"\n",
|
||||
" plt.plot(ts[-4 * prediction_length:].to_timestamp(), label=\"target\", )\n",
|
||||
" forecast.plot( color='g')\n",
|
||||
" plt.xticks(rotation=60)\n",
|
||||
" ax.xaxis.set_major_formatter(date_formater)\n",
|
||||
" ax.set_title(forecast.item_id)\n",
|
||||
"\n",
|
||||
"plt.gcf().tight_layout()\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b8be08b6-0cfd-45b5-ac23-142e9f388049",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
1
agent-pyter/notes.md
Normal file
1
agent-pyter/notes.md
Normal file
@ -0,0 +1 @@
|
||||
https://github.com/ccxt/ccxt/tree/master/examples/py/
|
||||
47
doc/oi-notes.md
Normal file
47
doc/oi-notes.md
Normal file
@ -0,0 +1,47 @@
|
||||
|
||||
|
||||
|
||||
interpreter --api_base http://192.168.0.11:11434/v1/
|
||||
|
||||
interpreter --model "gpt-3.5-turbo" # mistral
|
||||
interpreter --model "mistral" --api_base http://192.168.0.11:11434/v1/
|
||||
|
||||
Mac/Linux: 'export OPENAI_API_KEY=your-key-here',
|
||||
Windows: 'setx OPENAI_API_KEY your-key-here' then restart terminal.
|
||||
interpreter --local
|
||||
|
||||
interpreter --api_base http://192.168.0.11:11434/v1 --api_key "" --model openai/local
|
||||
|
||||
|
||||
|
||||
interpreter --api_base http://192.168.0.137:1234/v1 --api_key "" --model openai/local
|
||||
192.168.0.137
|
||||
|
||||
|
||||
# Load a model, start the server, and run this example in your terminal
|
||||
# Choose between streaming and non-streaming mode by setting the "stream" field
|
||||
|
||||
curl http://192.168.0.11:11434/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"messages": [
|
||||
{ "role": "system", "content": "Always answer in rhymes." },
|
||||
{ "role": "user", "content": "Introduce yourself." }
|
||||
],
|
||||
"temperature": 0.7,
|
||||
"max_tokens": -1,
|
||||
"stream": false
|
||||
}'
|
||||
|
||||
|
||||
curl http://192.168.0.137:1234/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"messages": [
|
||||
{ "role": "system", "content": "Always answer in rhymes." },
|
||||
{ "role": "user", "content": "Introduce yourself." }
|
||||
],
|
||||
"temperature": 0.7,
|
||||
"max_tokens": -1,
|
||||
"stream": false
|
||||
}'
|
||||
16
translate-nllb/nllb.py
Normal file
16
translate-nllb/nllb.py
Normal file
@ -0,0 +1,16 @@
|
||||
# conda activate transformers
|
||||
# conda install pip
|
||||
# pip install https://github.com/huggingface/transformers/archive/nllb.zip -q
|
||||
# pip install transformers
|
||||
|
||||
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
|
||||
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
|
||||
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
|
||||
|
||||
translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang="tam_Taml", tgt_lang='eng_Latn', max_length = 400)
|
||||
translator("திஸ் ஐஸ் எ வெரி குட் மாடல் ")
|
||||
|
||||
# --------------------------------------------
|
||||
https://colab.research.google.com/drive/1o9r0QbEQZ1tn4eBVv-wajAtgwi6Lp-ZJ?usp=sharing#scrollTo=q49lJjXd4Jct
|
||||
https://colab.research.google.com/drive/1QEF0U9AaBeQdjLw7SyRty2ENAD4Qiiil#scrollTo=TFm232e77QRT
|
||||
Loading…
x
Reference in New Issue
Block a user