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 docker

This repository contains the source files for rapidsai Docker images

Table of Contents

Image Types

There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:

TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes

At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.

Image Locations

RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:

Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly

Extending Images

Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.

Custom Token Example

For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.

Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:

FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.

This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.

Building Images

The Dockerfiles can be built from the root project directory with the following command:

docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/

If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.

You can override these defaults by specifying Docker build arguments:

docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/

The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:

docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/

Contributing

Please see CONTRIBUTING.md for details on how to contribute to this repo.

About

Dockerfile templates for creating RAPIDS Docker Images

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Readme
Apache-2.0 license

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
 docker
This repository contains the source files for rapidsai Docker images
Table of Contents
Image Types
There are currently three different types of Docker images, which follow the same conventions provided by the NVIDIA CUDA Docker images, and allow users to use the RAPIDS images as a drop-in replacements for their CUDA images. Each type is supported on a combination of OS, Python version, and CUDA version which produces a variety of available image types. The different types are described below:
TypeDescriptionTarget Audience
baseExtends the corresponding CUDA image to add conda and the RAPIDS conda packages in a rapids conda environmentUsers that do not need examples or the need to modify and/or build RAPIDS sources
runtimeExtends the base image to add the RAPIDS Jupyter notebooks, all dependencies of the notebooks installed to the rapids conda environment, and runs a Jupyter server as the default Docker ENTRYPOINTUsers interested in exploring the example notebooks
develExtends the corresponding CUDA image to add the full RAPIDS build and test toolchain (gcc, build tools, etc.) to the system and/or rapids environment as well as the notebooks, their dependencies, and runs a Jupyter server as the default Docker ENTRYPOINTUsers that are doing active development on RAPIDS and need to build and test their changes
At a high-level, the differences between base, runtime, and devel is the way RAPIDS is installed. base and runtime are identical in how RAPIDS is installed, with the only difference between them is that runtime has (many) more 3rd-party packages installed to support the notebooks. devel is completely different in that RAPIDS is built from source in the container and installed into the rapids environment using an install command. Because of these differences, we often refer to the images as base & runtime and devel.
Image Locations
RAPIDS releases both stable and nightly images in the following repositories. stable releases match our conda stable version releases. While our nightly releases are generated every night from the latest WIP development branch. Below is a table of their repositories and tag lists:
Typestable Repositorynightly Repository
baserapidsai/rapidsairapidsai/rapidsai-nightly
runtimerapidsai/rapidsairapidsai/rapidsai-nightly
develrapidsai/rapidsai-devrapidsai/rapidsai-dev-nightly
Extending Images
Like any Docker image, the RAPIDS images can be extended to suit the needs of individual teams. Whether it is to add custom libraries, change security settings, or other customizations; using FROM and our RAPIDS images allows users to customize the container, but easily update to the latest versions with a new docker build.
Custom Token Example
For example, the runtime and devel images use an empty token for securing the Jupyter notebook server. While this is a fast easy solution for dev and exploratory environments, those in production environments may need more security.
Using the following short Dockerfile users can leverage the existing RAPIDS images and build a custom secure image:
FROM rapidsai/rapidsai-nightly:cuda10.2-runtime-ubuntu18.04-py3.7
RUN sed -i "s/NotebookApp.token=''/NotebookApp.token='secure-token-here'/g" /opt/docker/bin/entrypoint_source

Once built, the resulting image will be secured with the new token.
This example can be repurposed by replacing the sed command with other commands for custom libraries or settings.
Building Images
The Dockerfiles can be built from the root project directory with the following command:
docker build -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
If no build arguments are specified, the image will be built with the OS specified in the file name (i.e. ubuntu18.04 or centos7) and the default Python and CUDA versions specified in settings.yaml.
You can override these defaults by specifying Docker build arguments:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 -f generated-dockerfiles/centos7-base.Dockerfile context/
The Ubuntu images can take an additional LINUX_VER build argument to use other supported Ubuntu versions:
docker build --build-arg CUDA_VER=10.2 --build-arg PYTHON_VER=3.7 --build-arg LINUX_VER=ubuntu16.04 -f generated-dockerfiles/ubuntu18.04-base.Dockerfile context/
Contributing
Please see CONTRIBUTING.md for details on how to contribute to this repo.
About
Dockerfile templates for creating RAPIDS Docker Images
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages