Takashi Nagata nagataka

AWS

aws login コマンドを初めて使ってみたが、これであればアクセスキーを作らなくていいので良さそうだなと思った。 https://dev.classmethod.jp/articles/aws-cli-aws-login/

SAMを試す (論文メモはこちら - nagataka/Read-a-Paper#55) SAM3を使うにはHugging Faceのレポジトリ上からアクセスリクエストが必要そうなので、一旦SAM2で実験。

たまに色々と忘れるので便利な資料をメモ

The repository is organized as follows:

src : Contains the source codes for all .... The source code is written in Python and it takes advantage of Numpy and Matplotlib. In order to run a simulation you have to use the file run_xxxx.py.
tools: In this folder you can find some tools for.... With yyy.py you can reproduce the figures found in ().
data: Here are saved all the results once you run a simulation.
params: Here you can find all the configuration files containing all the parameters (for each experiments).

	def construct_index(docs):
	term_dict = {}
	postings = {}
	for id, doc in docs.items():

	terms = list(set(doc.lower().split()))
	for term in terms:
	term_count = term_dict.get(term, 0)
	term_posting = postings.get(term, None)

	# OpenAI gym custom environment mimicking Blocking Maze
	# See Sutton and Barto "Reinforcement Learning an Introduction"
	# Example 8.2: Blocking Maze
	from enum import Enum
	import sys
	import copy

	import gym
	from gym import error, spaces, utils
	from gym.utils import seeding

	{
	"python.formatting.provider": "black",
	"python.linting.pylintEnabled": false,
	"python.linting.flake8Enabled": true,
	"python.linting.flake8Args": [
	"--ignore=E501,W503"
	],
	"python.sortImports.args": [
	"-m 3"
	],

	import random
	import numpy as np
	np.random.seed(0)

	def kerri(p, b):
	"""https://en.wikipedia.org/wiki/Kelly_criterion
	"""
	return (p*(b+1)-1 )/b

	N = 300

	import gym
	import ray
	from ray.rllib.agents.ppo import PPOTrainer, DEFAULT_CONFIG

	import pprint as pp

	#tune.run(PPOTrainer, config={"env": "Breakout-v0", "use_pytorch": True})
	ray.init(num_gpus=1, ignore_reinit_error=True, log_to_driver=False)

	# https://github.com/ray-project/ray/blob/master/rllib/agents/ppo/ppo.py#L15

	#!/bin/bash

	set -eu

	### Incoming WebHooks URL
	WEBHOOKURL="https://hooks.slack.com/services/FILL_YOUR_WEBHOOKURL"

	### channel
	CHANNEL=${CHANNEL:-"#notifications"}