BranchesTags

Repository files navigation

DagsHub Client


TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs

DagsHub Sign UpDiscordDagsHub on Twitter

What is DagsHub?

DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects. With DagsHub you can:

  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.

DagsHub is built firmly around open, standard formats for your project. In particular:

Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.

DagsHub Client API & CLI

This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and Direct Data Access (DDA), a component to let you stream and upload your data.

For more details on the different functions of the client, check out the docs segments:

  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine

Some functionality is supported only in Python.

To read about some of the awesome use cases for Direct Data Access, check out the relevant doc page.

Installation

pip install dagshub

Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command in your terminal:

dagshub login

Quickstart for Data Streaming

The easiest way to start using DagsHub is via the Python Hooks method. To do this:

  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data: 
    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.

🤩 Check out this colab to see an example of this Data Streaming work end to end:

Open In Colab

Next Steps

You can dive into the expanded documentation, to learn more about data streaming, data upload and experiment tracking with DagsHub

Analytics

To improve your experience, we collect analytics on client usage. If you want to disable analytics collection, set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.

Made with 🐶 by DagsHub.

About

DagsHub client libraries

dagshub.com/docs/client/

Topics

aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow

Resources

Readme
MIT license
Activity
Custom properties

Stars

103 stars

Watchers

4 watching

Forks

34 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
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
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
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
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
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
BranchesTags
Repository files navigation
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
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
DagsHub Client

TestspipLicensePython VersionDagsHub DocsDagsHub Client Docs
DagsHub Sign UpDiscordDagsHub on Twitter
What is DagsHub?
DagsHub is a platform where machine learning and data science teams can build, manage, and collaborate on their projects.
With DagsHub you can:
  1. Version code, data, and models in one place. Use the free provided DagsHub storage or connect it to your cloud storage
  2. Track Experiments using Git, DVC or MLflow, to provide a fully reproducible environment
  3. Visualize pipelines, data, and notebooks in and interactive, diff-able, and dynamic way
  4. Label your data directly on the platform using Label Studio
  5. Share your work with your team members
  6. Stream and upload your data in an intuitive and easy way, while preserving versioning and structure.
DagsHub is built firmly around open, standard formats for your project. In particular:
Therefore, you can work with DagsHub regardless of your chosen programming language or frameworks.
DagsHub Client API & CLI
This client library is meant to help you get started quickly with DagsHub. It is made up of Experiment tracking and
Direct Data Access (DDA), a component to let you stream and upload your data.
For more details on the different functions of the client, check out the docs segments:
  1. Installation & Setup
  2. Data Streaming
  3. Data Upload
  4. Experiment Tracking
    1. Autologging
  5. Data Engine
Some functionality is supported only in Python.
To read about some of the awesome use cases for Direct Data Access, check out
the relevant doc page.
Installation
pip install dagshub
Direct Data Access (DDA) functionality requires authentication, which you can easily do by running the following command
in your terminal:
dagshub login
Quickstart for Data Streaming
The easiest way to start using DagsHub is via the Python Hooks method. To do this:
  1. Your DagsHub project,
  2. Copy the following 2 lines of code into your Python code which accesses your data:

    fromdagshub.streamingimportinstall_hooksinstall_hooks()
  3. That’s it! You now have streaming access to all your project files.
🤩 Check out this colab to see an example of this Data Streaming work end to end:
Open In Colab
Next Steps
You can dive into the expanded documentation, to learn more about data streaming, data upload and
experiment tracking with DagsHub
Analytics
To improve your experience, we collect analytics on client usage. If you want to disable analytics collection,
set the DAGSHUB_DISABLE_ANALYTICS environment variable to any value.
Made with 🐶 by DagsHub.
About
DagsHub client libraries
dagshub.com/docs/client/
Topics
aidatadata-sciencedata-streamingdvchacktoberfesthacktoberfest2023kerasmachine-learningmachinelearningmlopspythonpytorchtensorflow
Resources
Readme
MIT license
Activity
Custom properties
Stars
103 stars
Watchers
4 watching
Forks
34 forks
Report repository
Releases
Packages
Used by
Contributors
Languages