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One Piece Staff Official Twitter Team Posted for Greek Olympian!

After the pose, the Greek Olympian took that resembled one signature pose of the fictional character Monkey D. Luffy, the team behind the creation of the One Piece manga, posted the following:

Congratulations Miltiadi!


Rough notes on Ray

Just a bunch of snippets that we used to deploy a local Ray cluster. We couldn’t get the second node to connect to the cluster even though no error was given.

From https://www.anaconda.com/products/individual-b
 wget https://repo.anaconda.com/archive/Anaconda3-2021.05-Linux-x86_64.sh
 bash ./Anaconda3-2021.05-Linux-x86_64.sh
 source ~/anaconda3/bin/activate
 conda create --name ray.3.7 python=3.7.10;
 conda activate ray.3.7;
 conda install --name ray.3.7 pip;
 pip install ray==1.1.0
 pip install gym pandas torch ray ray[default] ray[rllib] ray[serve] ray[tune];

 ON WORKERS:
 From https://docs.docker.com/engine/install/ubuntu/
 sudo apt-get remove docker docker-engine docker.io containerd runc;
 sudo apt-get update;
 sudo apt-get install apt-transport-https ca-certificates curl gnupg lsb-release;
 curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
 echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
 sudo apt-get update
 sudo apt-get install docker-ce docker-ce-cli containerd.io
 sudo addgroup --system docker
 sudo adduser $USER docker
 newgrp docker
 sudo systemctl restart docker

 Uninstall Docker Engine
 Uninstall the Docker Engine, CLI, and Containerd packages:
 sudo apt-get purge docker-ce docker-ce-cli containerd.io
 Images, containers, volumes, or customized configuration files on your host are not automatically removed. To delete all images, containers, and volumes:
 sudo rm -rf /var/lib/docker
  sudo rm -rf /var/lib/containerd
 You must delete any edited configuration files manually.


 2021-06-09 03:56:34,453    WARNING services.py:1740 -- WARNING: The object store is using /tmp instead of /dev/shm because /dev/shm has only 7963275264 bytes available. This will harm performance! You may be able to free up space by deleting files in /dev/shm. If you are inside a Docker container, you can increase /dev/shm size by passing '--shm-size=10.24gb' to 'docker run' (or add it to the run_options list in a Ray cluster config). Make sure to set this to more than 30% of available RAM.

 ON MASTER:
 ray up office.yaml
 ray up -vvvvvv office.yaml
 ray exec office.yaml 'ray status'

The yaml file that we used was the following

# A unique identifier for the head node and workers of this cluster.
cluster_name: default

# This executes all commands on all nodes in the docker container,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled. Assumes Docker is installed.
docker:
    image: "rayproject/ray-ml:1.1.0" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
    # image: rayproject/ray:latest-gpu   # use this one if you don't need ML dependencies, it's faster to pull
    container_name: "ray_container"
    # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
    # if no cached version is present.
    pull_before_run: True
    run_options: ["--shm-size=10.24gb"]  # Extra options to pass into "docker run"

provider:
    type: local
    head_ip: 192.168.1.14
    ## head_ip: YOUR_HEAD_NODE_HOSTNAME
    # You may need to supply a public ip for the head node if you need
    # to run `ray up` from outside of the Ray cluster's network
    # (e.g. the cluster is in an AWS VPC and you're starting ray from your laptop)
    # This is useful when debugging the local node provider with cloud VMs.
    # external_head_ip: YOUR_HEAD_PUBLIC_IP
    worker_ips: [192.168.1.70]
    ## worker_ips: [WORKER_NODE_1_HOSTNAME, WORKER_NODE_2_HOSTNAME, ... ]
    # Optional when running automatic cluster management on prem. If you use a coordinator server,
    # then you can launch multiple autoscaling clusters on the same set of machines, and the coordinator
    # will assign individual nodes to clusters as needed.
    #    coordinator_address: "<host>:<port>"
    # cache_stopped_nodes: False

# How Ray will authenticate with newly launched nodes.
auth:
    ssh_user: tux
    #ssh_user: YOUR_USERNAME
    # Optional if an ssh private key is necessary to ssh to the cluster.
    ssh_private_key: ~/.ssh/id_rsa

# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == max_workers == len(worker_ips).
min_workers: 20

# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == max_workers == len(worker_ips).
max_workers: 20
# The default behavior for manually managed clusters is
# min_workers == max_workers == len(worker_ips),
# meaning that Ray is started on all available nodes of the cluster.
# For automatically managed clusters, max_workers is required and min_workers defaults to 0.

initial_workers: 20

# The autoscaler will scale up the cluster faster with higher upscaling speed.
# E.g., if the task requires adding more nodes then autoscaler will gradually
# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.
# This number should be > 0.
upscaling_speed: 1.0

idle_timeout_minutes: 5

# Files or directories to copy to the head and worker nodes. The format is a
# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
file_mounts: {
#    "/path1/on/remote/machine": "/path1/on/local/machine",
#    "/path2/on/remote/machine": "/path2/on/local/machine",
}

# Files or directories to copy from the head node to the worker nodes. The format is a
# list of paths. The same path on the head node will be copied to the worker node.
# This behavior is a subset of the file_mounts behavior. In the vast majority of cases
# you should just use file_mounts. Only use this if you know what you're doing!
cluster_synced_files: []

# Whether changes to directories in file_mounts or cluster_synced_files in the head node
# should sync to the worker node continuously
file_mounts_sync_continuously: False

# Patterns for files to exclude when running rsync up or rsync down
rsync_exclude:
    - "**/.git"
    - "**/.git/**"

# Pattern files to use for filtering out files when running rsync up or rsync down. The file is searched for
# in the source directory and recursively through all subdirectories. For example, if .gitignore is provided
# as a value, the behavior will match git's behavior for finding and using .gitignore files.
rsync_filter:
    - ".gitignore"

# List of commands that will be run before `setup_commands`. If docker is
# enabled, these commands will run outside the container and before docker
# is setup.
initialization_commands: []

# List of shell commands to run to set up each nodes.
setup_commands: []
    # Note: if you're developing Ray, you probably want to create a Docker image that
    # has your Ray repo pre-cloned. Then, you can replace the pip installs
    # below with a git checkout <your_sha> (and possibly a recompile).
    # To run the nightly version of ray (as opposed to the latest), either use a rayproject docker image
    # that has the "nightly" (e.g. "rayproject/ray-ml:nightly-gpu") or uncomment the following line:
    # - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-2.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"

# Custom commands that will be run on the head node after common setup.
head_setup_commands: []

# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands: []

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
    - ray stop
    - ulimit -c unlimited && ray start --head --port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml

# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
    - ray stop
    - ray start --address=$RAY_HEAD_IP:6379

Cloudflare: Error 522 – Connection timed out 2

Recently, we’ve been noticing weird behavior on sites that are proxied behind Cloudflare servers. While visiting a site from one internet connection, we are constantly getting a 522 error. If at that moment, we switch our internet connection on that machine and visit the same page, it works as expected.

We are sure that the servers we have access to did not run out of resources, nor does the firewall block Cloudflare IPs. All firewalls are properly configured to accept connections from the official Cloudflare IPs that were retrieved from here.

We will investigate it further, hopefully we will figure it out.


Cold-brew iceberg coffee

This guide will show you how to make delicious cold-brew coffee at home. Iced coffee will never taste watery or bitter again.

You can drink decent iced coffee all year round, but if you detest how watered down or bitter most iced coffees end up, you only have one choice: cold-brew coffee. Every time, this process creates an icy and smooth cup.

Cold-brew coffee doesn’t take a high level of expertise or special equipment to produce, and there are numerous examples of how to go about doing so on the internet. You only need a big container and a strainer to prepare the coffee.

Steps for making cold-brew coffee

The following shows how it works: Grind the coffee coarsely, and you can perform this job yourself in your own home or at the place where you buy your beans. Pour the grounds into a container, then add water. Cover and let sit for 12 hours or overnight.

When this happens, the coffee absorbs into the water at a prolonged rate, which results in a solid, concentrated brew. The next morning, strain the liquid, and you’re good to go.

Steps for making cold-brew iceberg coffee

This section takes it a bit further, which actually makes the coffee process even longer! Once the above procedure is done, pour the iced coffee into ice cube molds and freeze until solid, about 5 hours.

Then, finally, after so many hours of waiting for a cup of coffee… pour some fresh cold-brew coffee in a glass and add lots of cold-brew coffee ice cubes to it!

Enjoy!

Alternative steps for making cold-brew iceberg coffee

Instead of pouring the iced coffee into ice cube molds, pour coffee into a glass and fill it up to half. Then put that glass into the freezer and let it freeze solid.

The next day, pour fresh cold-brew coffee in that glass and give it a few minutes for the ice to get unstuck from the bottom of the glass! You will be drinking icy coffee all day long! (This actually works by pouring filter coffee or other warm coffee instead of cold-brew, and it gets cold really fast! There might be a chance for the glass to break, never happened to us, but still… you never know.).