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Version: Python

How to install and use Python packages

This guide discusses how to install and use Python packages in Deephaven. Packages can be installed programmatically for use in a Deephaven instance or added to Deephaven Docker images to be available every time Deephaven is launched.

If you wish to do machine learning in Python, Deephaven has several pre-built Docker images for AI in Python. Refer to the Choose a Deployment section of the Quickstart guide to see what's currently available.

Once installed, a package can be imported and used like any other Python package. For an index of available Python packages and more information on them, visit the Python Package Index or check out our choosing the right Python package cheat sheet.

List all available Python packages​

Python's help function can list the packages available to Python. Run the following command from a Python session:

help("modules")

Install packages from within a Python script​

Python packages can be installed using pip from within a Python script or the Deephaven Python console. The following code block imports the Pendulum package and uses it to print the current time in Paris:

import os

os.system("pip install Pendulum")

import pendulum

now = pendulum.now("Europe/Paris")
print(now)
danger

Packages installed this way are not guaranteed to persist across restarts of the Deephaven Docker container. If the container changes at all between restarts, packages will not persist.

The subprocess module can also be used to save the output in case the installation fails.

from subprocess import Popen, PIPE

proc = Popen(["pip", "install", "nonexistent-package"], stdout=PIPE, stderr=PIPE)
stdout, stderr = proc.communicate()

print(stderr)

Install packages in a running Docker container from the command line​

Python packages can be installed using pip via docker exec.

danger

If this method is used with Deephaven Docker images, Python package installs do not persist after the Docker container exits. The package installation must be repeated each time the container is started.

Here docker exec is used to run a pip install on the running Deephaven Docker image, which is named core_server_1.

docker compose exec server pip install Pendulum

After installing the Pendulum package, we can use it within our script to print the current time in Paris.

import pendulum

now = pendulum.now("Europe/Paris")

print(now)

Permanently add packages to a custom Docker image​

To import packages and have the installations persist between sessions, you can create a custom Docker image and then use that image to start Deephaven. Let's start with the steps that are common between both.

Prerequisites​

You must acquire the necessary base images before a custom Docker image can be built. This process differs based on how you launch Deephaven:

docker compose pull

Create a custom Dockerfile​

To begin with, create a new directory. This directory should not be in a Deephaven deployment directory. You can name it whatever you'd like. For this guide, we'll name ours deephaven-custom.

mkdir deephaven-custom
cd deephaven-custom

Now, in this directory, create a file called Dockerfile. Dockerfile should use ghcr.io/deephaven/server as the base image and contain a recipe for installing the new package. When adding Pendulum, it looks like this:

FROM ghcr.io/deephaven/server
RUN pip3 install pendulum

Create a custom Docker image​

Now that we have the Dockerfile in place, we need to create the custom Docker image. To do so, run a command from the directory with Dockerfile that looks like:

docker build --tag <user>/server-<custom> .

This will create a new Docker image called <user>/server-<custom>. For this guide, we will call the image guide/server-pendulum:

docker build --tag guide/server-pendulum .

When the command finishes running, you can see the new image in your system:

docker image ls

Reference the new image​

To put it all together, we now need to reference this new image in the docker-compose file we use to launch Deephaven. The file name depends on how you build and launch Deephaven:

In the Docker Compose file, see three lines of text that look like:

services:
  server:
    image: <IMAGE_NAME>

The image used by default depends on how you build and launch Deephaven. Regardless, you need to insert your custom image name in this line. Modify the image line to use your new image:

services:
  server:
    image: guide/server-pendulum:latest

Now, when you launch Deephaven again, you can use the package!

caution

When base images are updated by rebuilding source code or redownloading pre-built images, custom images must be rebuilt to incorporate the base image changes.

Use Python packages in Deephaven​

Once installed, a package can be imported and used like any other Python package. For an index of available Python packages and more information on Python packages, visit the Python Package Index and our Choose Python packages cheat sheet.

Use packages in query strings​

Installed Python packages can be used in query strings. Let's use the Pendulum package, which we installed earlier in this guide.

import pendulum
from deephaven import time_table


def get_paris_time():
    return pendulum.now("Europe/Paris")


times_in_paris = time_table("PT5S").update(formulas=["Paris = get_paris_time()"])

deephaven.learn​

Python packages are commonly used with deephaven.learn. The deephaven.learn package provides utilities for efficient data transfer between Deephaven tables and Python data structures, particularly NumPy arrays and other data structures used by popular AI and machine learning packages.

deephaven.numpy​

The deephaven.numpy module contains functions that convert between Deephaven tables and NumPy arrays. NumPy is commonly used in Deephaven Python queries.

deephaven.pandas​

The deephaven.pandas module contains functions that convert between Deephaven tables and Pandas DataFrames. Pandas is another package that is commonly used in queries.