When you start a project in Saturn Cloud, you may require special libraries or customizations for your work. The way to ensure that these all get installed when the machine is spun up is by using images. An image will contain the instructions that explain all the libraries and tools you want installed before you start work.
When starting out with Saturn Cloud, most people will use one of our standard images, which provide most data science packages you will need. However, if (for example) your company has a designated Docker image, you can use that instead!
Select a Default Image
You can choose from the Saturn Cloud default image selection when you create a custom project. This is the same way you will select a custom image, if you choose to create one below.
To create a custom project, select “Create Project” from the “Projects” section of the menu.
In this form, you will see a section called “Workspace Settings”. One of the selectors in this section is “Images”. Click that selector, and you’ll be given a list of all the images available to you.
Choose your image, and continue creating the project as usual.
If you don’t see the image you want, please let us know at firstname.lastname@example.org and we’ll help you!
Build a Custom Image
To build your own image, select “Images” from the “Tools” section of the menu.
From here, you’ll see the blue “New Image” button at the top right of the screen. Click this, and you’ll be taken to a form.
In the first half of the form, you will choose the source of your Docker image. If your image is hosted somewhere else, and you have a link to it, select “External” and the form’s contents will change to look like this. Otherwise, leave this box as “Saturn”, even if you are going to be filling in your own image specifications.
External: Add an Image Path
If you’re adding an image that already exists in your personal or business repository, just fill in the correct Image URI that leads to your image. If you don’t know what this is, let us know or consult with your image hosting system.
Internal: List Specifications
If your image is not externally created, you can just fill in the parameters you want for the image, including the names of the libraries and packages.
- Source: A “Saturn” image is one that Saturn builds for you via configuration files specified below. External is any existing image you want to add to the system. Saturn is configured to pull from the ECR (Elastic Container Registry) associated with your AWS account, and can also pull from any public repository. External images must conform to a certain specification to fully function in Saturn.
- Share with: Admin users can elect to share the image with any user in Saturn (including your whole company)
- Image URI: the name of the image you wish to load (if External), or build.
The below options are only available for Internal (“Saturn”) image building.
- Copy configuration from: If you’ve already built an image, this drop down lets you load that configuration, so that you can modify it, and do a new build.
- Build Data: Saturn knows how to build images using a few configuration file types. More information is available from repo2docker.
- Conda environment: Things you would add to an environment.yml file
- pip environment: Things you would add to a requirements.txt
- apt get: a list of apt get packages
- postBuild: a bash script that is executed in your Docker container after everything else has been run
Replicate a Conda Environment
One powerful configuration option is to use a custom conda environment. To do this, in the
Build Data section, paste in the YAML describing an environment. If you have an existing conda environment you use locally, you can run
conda env export –no-builds to generate this YAML file.
When you build an image from scratch like this, it generates a brand new python environment. These are not modifications of Saturn’s default images, so you must include all the packages you’re going to want to use.
Dask Distributed Library
Then, select the version of the Dask
distributed library you want your image to contain. If you’re not sure, or just want the most current version, you can visit the PyPi page or the official changelog to see what the current version is.
CPU or GPU
Finally, you need to indicate whether your image is customized for CPU or GPU. If you have not requested libraries specially designed for GPU computation, then CPU is usually the right choice.
Click “Add” when you have entered all the information, and your image will be built. Note: This may take some time, as all your packages and dependencies need to be built to ensure it will work!
After the image build is complete, your image is ready to use in creating a new project.
See Your Image
If you find that later you want to refer back to the specifications of an image you built in Saturn Cloud, you can visit the images page and click the names of each. Custom images don’t currently show their full contents.
Advanced: GPU Images
Saturn supports instances with T4 GPUs (g4dn), as well as instances with V100 GPUs (including p3.16xlarge which has 8 V100 GPUs). These instances must be paired with GPU images, which means the images contain libraries that run on GPU hardware. Otherwise there will be no CUDA drivers available, and you won’t be able to use the GPU card. Saturn offers a default
saturn-gpu image, which includes common GPU utilities for machine learning including RAPIDS, PyTorch and Tensorflow/Keras.
If you are building your own custom image, you’ll want to take this into account.
Tensorflow has GPU specific versions. In
pip, call for
tensorflow-gpu. In conda, look through the list to find a GPU build.
$ conda search tensorflow ... #> tensorflow 2.2.0 eigen_py36h84d285f_0 pkgs/main #> tensorflow 2.2.0 eigen_py37h1b16bb3_0 pkgs/main #> tensorflow 2.2.0 gpu_py37h1a511ff_0 pkgs/main #> tensorflow 2.2.0 gpu_py38hb782248_0 pkgs/main #> tensorflow 2.2.0 mkl_py36h5a57954_0 pkgs/main
Then in the image specifications you can ask for:
dependencies: - tensorflow=2.2.0=gpu_py37h1a511ff_0
Similarly, PyTorch has GPU versions. Conda is the easiest way to access these. Look for “cuda” in the name, as this indicates GPU support.
$ conda search pytorch ... #> pytorch 1.5.1 py3.7_cpu_0 pytorch #> pytorch 1.5.1 py3.7_cuda10.1.243_cudnn7.6.3_0 pytorch #> pytorch 1.5.1 py3.7_cuda10.2.89_cudnn7.6.5_0 pytorch #> pytorch 1.5.1 py3.7_cuda9.2.148_cudnn7.6.3_0 pytorch #> pytorch 1.5.1 py3.8_cpu_0 pytorch #> pytorch 1.5.1 py3.8_cuda10.1.243_cudnn7.6.3_0 pytorch #> pytorch 1.5.1 py3.8_cuda10.2.89_cudnn7.6.5_0 pytorch
You have to consider the CUDA version and the Python version here. This example shows selecting CUDA 10.1.
channels: - pytorch - defaults dependencies: - pytorch=1.5.1=py3.7_cuda10.1.243_cudnn7.6.3_0
Like PyTorch, for RAPIDS you need to specify the CUDA version. You can get this from the
rapidsai conda channel.
channels: - rapidsai - defaults dependencies: - rapids=0.14.1=cuda10.1_py37_0
Need help, or have more questions? Contact us at:
- On Intercom, using the icon at the bottom right corner of the screen