| import numpy as np | |
| import jax | |
| import matplotlib.pyplot as plt | |
| import jax.numpy as jnp | |
| def inverse_square_root_schedule( | |
| max_lr: float, | |
| warmup_steps: int, | |
| ): | |
| def schedule(count): |
| import pickle | |
| import numpy as np | |
| from tqdm import tqdm | |
| import os | |
| # Create a large numpy array and save it to a pickle file | |
| # a = np.random.random((1000, 1000)) | |
| a = np.random.random((40000,10000)) | |
| with open('large_array.pkl', 'wb') as f: | |
| pickle.dump(a, f) |
| sudo ncdu / | |
| sudo docker container ls -a --format '{{.ID}}:{{.Names}}' | xargs -I {} sh -c 'echo "Container {} ($(echo {} | cut -d: -f2)): $(sudo docker inspect --format "{{.GraphDriver.Data.MergedDir}}" $(echo {} | cut -d: -f1) | sed "s/\/merged$//") (Size: $(sudo du -sh $(sudo docker inspect --format "{{.GraphDriver.Data.MergedDir}}" $(echo {} | cut -d: -f1) | sed "s/\/merged$//") | cut -f1))"' | |
| sudo docker image ls --format '{{.ID}}:{{.Repository}}:{{.Tag}}' | xargs -I {} sh -c 'echo "Image {} ($(echo {} | cut -d: -f2):$(echo {} | cut -d: -f3)): $(sudo docker inspect --format "{{.GraphDriver.Data.UpperDir}}" $(echo {} | cut -d: -f1) | sed "s/\/diff$//") (Size: $(sudo du -sh $(sudo docker inspect --format "{{.GraphDriver.Data.UpperDir}}" $(echo {} | cut -d: -f1) | sed "s/\/diff$//") | cut -f1))"' |
| import more_itertools | |
| import itertools | |
| """ | |
| >>> create_named_matrix(2,3,5) | |
| array([[['0,0,0', '0,0,1', '0,0,2', '0,0,3', '0,0,4'], | |
| ['0,1,0', '0,1,1', '0,1,2', '0,1,3', '0,1,4'], | |
| ['0,2,0', '0,2,1', '0,2,2', '0,2,3', '0,2,4']], | |
| [['1,0,0', '1,0,1', '1,0,2', '1,0,3', '1,0,4'], | |
| ['1,1,0', '1,1,1', '1,1,2', '1,1,3', '1,1,4'], |
| namespace functions { | |
| // Execute a block of Python code. This can be used to perform calculations, process data, or any other task that can be accomplished through Python code. | |
| type python = (_: { | |
| code: string, | |
| }) => any; | |
| // Create images from a text-only prompt. This tool is used to generate visual content based on detailed descriptions provided in text form. | |
| type dalle = (_: { | |
| size?: "1792x1024" | "1024x1024" | "1024x1792", |
| from more_itertools import windowed, repeat_last | |
| import numpy as np | |
| from more_itertools import grouper | |
| input_ids = np.arange(65) | |
| # input_ids = np.arange(4097) | |
| width = 16 | |
| stride = 4 |
| sudo apt-get update -y | |
| # Add Docker's official GPG key: | |
| sudo apt-get purge -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-ce-rootless-extras | |
| sudo rm -rf /var/lib/docker | |
| sudo rm -rf /var/lib/containerd | |
| sudo apt-get update | |
| sudo apt-get install -y ca-certificates curl gnupg | |
| sudo install -m 0755 -d /etc/apt/keyrings | |
| curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg |
| openapi: 3.1.0 | |
| info: | |
| contact: | |
| name: StackQL Studios | |
| url: https://github.com/stackql/google-discovery-to-openapi | |
| email: info@stackql.io | |
| title: Cloud TPU API | |
| description: TPU API provides customers with access to Google TPU technology. | |
| version: v2 | |
| x-discovery-doc-revision: '20231026' |
| <html> | |
| <head><meta charset="utf-8" /></head> | |
| <body> | |
| <div> <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script> | |
| <script type="text/javascript">/** | |
| * plotly.js v2.24.1 | |
| * Copyright 2012-2023, Plotly, Inc. | |
| * All rights reserved. | |
| * Licensed under the MIT license | |
| */ |
Your task: Combine multiple texts into one detailed document. Include every piece of information from each source. The goal is to avoid repetition while being thorough and exhaustive.
Essential steps:
Warnings: