- Download WSL2 Kernel
- run
wsl --set-default-version 2in windows command line, so that all future WSL machine will use WSL2.
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| /* | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free D. | |
| This file is the complete algorithm, | |
| Everything else is just efficiency. | |
| @karpathy | |
| Translation by @DannyArends | |
| */ | |
| import std.algorithm : countUntil, fold, joiner, map, maxElement, min, sort, sum, uniq; |
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| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
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| from langchain_openai import ChatOpenAI | |
| from langchain_core.messages import HumanMessage, SystemMessage, BaseMessage | |
| from langchain_core.outputs import LLMResult | |
| from langchain_core.callbacks import BaseCallbackHandler | |
| from typing import Any, Dict | |
| from pathlib import Path | |
| from datetime import datetime | |
| class LoggingHandler(BaseCallbackHandler): |
WARNING: Article moved to separate repo to allow users contributions: https://github.com/raysan5/custom_game_engines
A couple of weeks ago I played (and finished) A Plague Tale, a game by Asobo Studio. I was really captivated by the game, not only by the beautiful graphics but also by the story and the locations in the game. I decided to investigate a bit about the game tech and I was surprised to see it was developed with a custom engine by a relatively small studio. I know there are some companies using custom engines but it's very difficult to find a detailed market study with that kind of information curated and updated. So this article.
Nowadays lots of companies choose engines like [Unreal](https:
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| def log_run(gridsearch: sklearn.GridSearchCV, experiment_name: str, model_name: str, run_index: int, conda_env, tags={}): | |
| """Logging of cross validation results to mlflow tracking server | |
| Args: | |
| experiment_name (str): experiment name | |
| model_name (str): Name of the model | |
| run_index (int): Index of the run (in Gridsearch) | |
| conda_env (str): A dictionary that describes the conda environment (MLFlow Format) | |
| tags (dict): Dictionary of extra data and tags (usually features) |
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| # This Crystal source file is a multiple threaded implementation to perform an | |
| # extremely fast Segmented Sieve of Zakiya (SSoZ) to find Twin Primes <= N | |
| # for the 64-bit range 0..18,446,744,073,709,551,615 (2**64 - 1) | |
| # Compile as: $ crystal build --release -Dpreview_mt --mcpu native twinprimes_ssoz.cr | |
| # To reduce binary size do: $ strip twinprimes_ssoz | |
| # Thread workers default to 4, set to system max for optimum performance. | |
| # Single val: $ CRYSTAL_WORKERS=8 ./twinprimes_ssoz val1 | |
| # Range vals: $ CRYSTAL_WORKERS=8 ./twinprimes_ssoz val1 val2 | |
| # val1 and val2 can be entered as either: 123456789 or 123_456_789 |
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| #include <opencv2/opencv.hpp> | |
| #include <pcl/common/common_headers.h> | |
| #include <pcl/io/pcd_io.h> | |
| #include <pcl/point_types.h> | |
| #include <pcl/point_cloud.h> | |
| #include <pcl/visualization/pcl_visualizer.h> | |
| #include <Eigen/Core> | |
| #include <Eigen/LU> |
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| #! /usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| """This module's docstring summary line. | |
| This is a multi-line docstring. Paragraphs are separated with blank lines. | |
| Lines conform to 79-column limit. | |
| Module and packages names should be short, lower_case_with_underscores. | |
| Notice that this in not PEP8-cheatsheet.py |
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