To get the details needed to set up an SSHCluster with Dask, you will need the following information from each of the systems you wish to include in your cluster:

1. Usernames and Hostnames (addresses):

   • Each system should have an SSH server running.
   • Determine the username and hostname (or IP address) for each system. You may already have this if have SSH access. For most Unix systems, you can use who for your username and hostname for the machine name.

2. SSH Connectivity (connect_options):

   • Ensure that SSH keys are set for password-less login between the machine where you're setting up the SSHCluster and the remote systems.
   • For purposes, it's recommended to work with known hosts and not skip known hosts check.

3. GPU Device IDs (worker_options):

• On each system that has a GPU, you can determine available GPUs and their IDs by using the command nvidia-smi if NVIDIA drivers are installed.
  • The output will list the GPUs where CUDA_VISIBLE_DEVICES environment variable can be set correspondingly to indicate which GPUs the Dask workers should use.

For a secure setup, the recommended way is to have proper SSH keys set up and not to ignore the known hosts check unless you understand the security implications. Also, check with your systems administrator or IT department for the correct usernames, hostnames, and policies regarding SSH access and GPU use.

To connect a Dask client running in a Docker container to an SSHCluster, you will need to ensure that the Docker container is properly set up with the necessary network access and SSH configurations. Here are the steps:

1. Docker Networking:

   • Ensure that the Docker container has network access to the SSH ports of the target machines. This may involve configuring Docker network settings or opening ports on firewalls.

2. SSH Configuration:

   • Install SSH client inside the Docker container if it's not already present.
   • Mount the necessary SSH private key inside the container so that it can use it to connect to the other machines. This can be done by using a bind mount -v /path/on/host/.ssh:/root/.ssh.
   • Alternatively, you can use ssh-copy-id from your host to copy the SSH key to the target machines.
   • Ensure SSH known hosts are properly handled. The known_hosts file can also be mounted into the container or you can set connect_options to ignore known hosts, but this latter method is not recommended for production environments due to security implications.

3. Dask Configuration:

   • Configure the SSHCluster in your Docker container with the correct addresses and options as you've already outlined. If you have mounted the SSH private key and known_hosts file correctly, you should not need to change the connect_options.

Next, run your Dask client code inside the Docker container. Your Dask client will attempt to connect to remote machines using the SSH information provided. Make sure that the remote machines have the necessary Python environment and Dask worker setup to accept connections from your Dockerized Dask client.

Here is an example of how you might execute a Dask script inside a Docker container, while mounting the .ssh directory:

docker run -it --network=host -v /path/on/host/.ssh:/root/.ssh my-dask-container

Remember to replace /path/on/host/.ssh with the actual path to your SSH directory and my-dask-container with the name of your Docker image.

Lastly, for any Dask distributed environment, ensure that all nodes (including the Docker host for SSHCluster) have compatible versions of Python, Dask, and other dependent libraries to prevent compatibility issues.

Certainly! Below are the general steps to set up and run a Dask client in a Docker container, which connects to Dask SSHCluster. This assumes you have Docker installed, and you have a basic understanding of Docker usage.

Step 1: Create a Dockerfile for your Dask container.

# Use an official Python runtime as a parent image
FROM python:3.8

# the working directory in the container
WORKDIR /usr/src/app

# Install any packages specified in requirements.txt
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy the directory contents into the container at /usr/src/app
COPY . .

# Make port 878 available to the world outside this container
EXPOSE 878

# Run app.py when the container launches
CMD ["python", "./app.py"]

** 2:** Create a requirements.txt file that lists the packages that need to be installed.

dask[distributed]
paramiko  # typically needed for SSH connections in Python

Step 3: Write your Dask application, let's call it .py.

from dask.distributed import Client, SSHCluster

addresses = ['user@machine1', 'user@machine2']
cluster = SSHCluster    addresses,
    connect_options={"known_hosts": None},
    worker_options={"env": {"CUDA_VISIBLE_DEVICES": "0"}}
)
client = Client(cluster)

# Exampleask operation
def slow_increment(x):
    from time import sleep
    sleep(1)
    return + 1

futures = client.map(slow_increment, range(10))
result = client.g(futures)
print(result)

Step 4: Build your Docker image.

Step 5: Run Docker container, mounting the .ssh directory and ensuring proper networking.

docker run -it --network=host -v ~/.ssh:/root/.ssh myaskimage

Ensure the following before executing the steps:

• Your .ssh directory contains the necessary private key and the known_hosts file.
• The SSH servers on machine and machine2 have the corresponding public key and are configured to accept SSH connections. The Docker host, machine1, and machine2 have necessary firewall rules (if any firewalls are used) to allow SSH traffic. All machines have the port (usually 8786 for Dask scheduler and 8787 for Dask dashboard) open if wish to access the Dask dashboard from your browser.
• The user specified in the addresses has the necessary permissions to spawn Dask workers on machine1 and machine2.
• CUDA and any other dependent software are installed on machine1 and machine2, and CUDA_VISIBLE is set correctly depending on the available GPUs.

After executing docker run, your container will start and the app.py script will execute, connecting to your SSHCluster. Remember that this is a basic example and for production deployments, one should consider security aspects, proper error, configuration management, and use of container orchestration tools like Kubernetes, Docker Swarm, etc.

Sure! addresses is a list of SSH connection addresses that the SSHCluster will use to connect to the remote systems in the SSHCluster. Each address should be in the format of username@host, where username is the username to use for the SSH connection and host is the hostname or IP address of the remote system.

To find these details, you can follow these steps:

1. Identify the Hostnames/IP addresses of the systems you want to include in your SSHCluster.
2. Determine the usernames for each system that will be used for SSH connections.
3. Check if there are any security policies or restrictions on using SSH connections.
4. If you are using Docker, check the hosts file in your Docker container to see if the remote systems' hostnames are already configured.
5. If you are using a cloud provider, check the cloud provider's documentation or console to see if the remote systems' hostnames are already configured.
6. If you are connecting to a remote system through a proxy server, check the proxy server's documentation to see if the remote systems' hostnames are already configured.
7. You can use the ssh-find-hosts command to automatically discover the hostnames of remote systems that have an SSH server running. This command works by attempting to connect to the remote system using the default SSH port (22) and then displaying the hostname of the remote system that accepted the connection.

Once you have the addresses list, you can use it to initialize the SSHCluster in your Dask code. The SSHCluster constructor takes a list of SSHAddress objects as input, where each SSHAddress object represents a single SSH connection address.

Here is an example of how you can use the ssh-find-hosts command to automatically discover the hostnames of remote systems:

import ssh

# Use the ssh-find-hosts command to discover the hostnames of remote systems
ssh_hosts = ssh.find_hosts('some-proxy-server')

# Create an SSHCluster with the discovered hostnames
cluster = ssh.SSHCluster(ssh_hosts)

I hope this helps! Let me know if you have any further questions.

Certainly! Below is a sample README file in markdown format, providing the details and steps for setting up a Dask SSHCluster in a Docker container.

# Setting up Dask SSHCluster in Docker

## Introduction

This guide provides steps to set up and run a Dask client in a Docker container, which connects to a Dask SSHCluster. The SSHCluster connects to remote systems to distribute the Dask computations. This guide assumes familiarity with Docker usage and the necessary SSH setup.

## Steps

### Step 1: Create a Dockerfile

Create a `Dockerfile` for your Dask container. Below is an example `Dockerfile` that sets up the Dask environment.

```Dockerfile
# Use an official Python runtime as a parent image
FROM python:3.8

# the working directory in the container
WORKDIR /usr/src/app

# Install any packages specified in requirements.txt
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy the directory contents into the container at /usr/src/app
COPY . .

# Make port 878 available to the world outside this container
EXPOSE 878

# Run app.py when the container launches
CMD ["python", "./app.py"]

Step 2: Create requirements.txt

Create a requirements.txt file that lists the required packages, including dask[distributed] and paramiko for SSH connections in Python.

dask[distributed]
paramiko

Step 3: Write Dask Application

Write your Dask application, e.g., app.py, where addresses is a list of SSH connection addresses for the SSHCluster.

from dask.distributed import Client, SSHCluster

addresses = ['user@machine1', 'user@machine2']
cluster = SSHCluster(
    addresses,
    connect_options={"known_hosts": None},
    worker_options={"env": {"CUDA_VISIBLE_DEVICES": "0"}}
)
client = Client(cluster)

# Dask operations
# ...

# Example Dask operation
def slow_increment(x):
    from time import sleep
    sleep(1)
    return x + 1

futures = client.map(slow_increment, range(10))
result = client.gather(futures)
print(result)

Step 4: Build Docker Image

Build your Docker image using the following command in the terminal.

docker build -t mydaskimage .

Step 5: Run Docker Container

Run the Docker container, ensuring the necessary mounting and networking for SSH connectivity.

docker run -it --network=host -v ~/.ssh:/root/.ssh myaskimage

Pre-execution Checks

Before executing the above steps, ensure:

• Your .ssh directory contains the necessary private key and the known_hosts file.
• The SSH servers on machine1 and machine2 have the corresponding public key and are configured to accept SSH connections.
• Docker host, machine1, and machine2 have necessary firewall rules (if any) to allow SSH traffic.
• All machines have the Dask scheduler and dashboard ports open if access is required.
• The specified user in the addresses has the necessary permissions to spawn Dask workers on the remote machines.
• CUDA and related software are installed on the remote machines, and CUDA_VISIBLE is set correctly depending on available GPUs.

After executing docker run, your container will start, and the app.py script will execute, connecting to your SSHCluster. Additional considerations should be made for production deployments, including security aspects, error handling, configuration management, and orchestration with tools like Kubernetes or Docker Swarm.

Additional Information

For additional details and considerations, please consult the Dask and Docker documentation, as well as your system administrator or IT department for SSH access and GPU use policies.

I hope this format is helpful! Let me know if you need further assistance.