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TorchStudio Package

The torchstudio package contains 2 levels of scripts:

  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)

TorchStudio Architecture

Modules

Additional modules scripts can be added to the following torchstudio subfolders:

  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)

These modules are exposed in the application as follow:

  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers

Root Scripts

The root scripts are management routines interfacing the modules and other processing tasks with the main application.

  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)

About

IDE for PyTorch and its ecosystem

torchstudio.ai

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392 stars

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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" + '

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TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
Resources
Readme
MIT license
Activity
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Stars
392 stars
Watchers
5 watching
Forks
29 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('^' + ".*" + '

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LICENSE
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architecture.graphml
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Repository files navigation
TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
Resources
Readme
MIT license
Activity
Custom properties
Stars
392 stars
Watchers
5 watching
Forks
29 forks
Report repository
Releases
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('^' + ".*" + '

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torchstudio
LICENSE
LICENSE
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README.md
architecture.graphml
architecture.graphml
architecture.svg
architecture.svg
Repository files navigation
TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
Resources
Readme
MIT license
Activity
Custom properties
Stars
392 stars
Watchers
5 watching
Forks
29 forks
Report repository
Releases
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" + '

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32 Commits
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NameName
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torchstudio
LICENSE
LICENSE
README.md
README.md
architecture.graphml
architecture.graphml
architecture.svg
architecture.svg
Repository files navigation
TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
Resources
Readme
MIT license
Activity
Custom properties
Stars
392 stars
Watchers
5 watching
Forks
29 forks
Report repository
Releases
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('^' + ".*" + '

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32 Commits
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torchstudio
torchstudio
LICENSE
LICENSE
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README.md
architecture.graphml
architecture.graphml
architecture.svg
architecture.svg
Repository files navigation
TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
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392 stars
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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('^' + ".*" + '

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architecture.svg
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TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
Resources
Readme
MIT license
Activity
Custom properties
Stars
392 stars
Watchers
5 watching
Forks
29 forks
Report repository
Releases
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
Latest commit
History
32 Commits
Folders and files
NameName
Last commit message
Last commit date
torchstudio
torchstudio
LICENSE
LICENSE
README.md
README.md
architecture.graphml
architecture.graphml
architecture.svg
architecture.svg
Repository files navigation
TorchStudio Package
The torchstudio package contains 2 levels of scripts:
  • Modules (files in the subfolders of the torchstudio package folder)
  • Root (files in the torchstudio package folder)
TorchStudio Architecture
Modules
Additional modules scripts can be added to the following torchstudio subfolders:
  • analyzers: analyze a dataset, fill a weights list if relevant, and generate a PIL image report (classes inheriting from torchstudio.modules.Analyzer)
  • datasets: define a tensors dataset (classes inheriting from torch.utils.data.Dataset)
  • loss: calculate loss between inference and target (classes inheriting from torch.nn.Modules._Loss)
  • metrics: calculate evaluation metric between inference and target (classes inheriting from torchstudio.modules.Metric or torchmetrics.metric.Metric or - catalyst.metrics._metric.IMetric or ignite.metrics.metric.Metric (with some adaptation to update()))
  • models: define a neural network model (classes inheriting from torch.nn.Module)
  • optim: optimize model's weights (classes inheriting from torch.optim.Optimizer)
  • renderers: render a numpy tensor into a PIL image (classes inheriting from torchstudio.modules.Renderer)
  • schedulers: adjust optimizer learning rate (classes inheriting from torch.optim._LRScheduler)
These modules are exposed in the application as follow:
  • The Dataset tab expose the modules from the following folders: datasets, analyzers, renderers
  • The Model tabs expose the modules from the following folders: models, loss, metrics, optim, schedulers
Root Scripts
The root scripts are management routines interfacing the modules and other processing tasks with the main application.
  • datasetload.py: handles dataset tensors transfer (started locally or remotely when clicking the Load button in the Dataset tab)
  • datasetanalyze.py: handles dataset tensors analysis (started locally or remotely when clicking the Analyze button in the Dataset tab)
  • graphdraw.py: draw model graph into svg (started locally by the graph display in the Model tabs)
  • metricsplot.py: plot training metrics into an image (started locally by the metrics display in the Model and Dashboard tabs)
  • modelbuild.py: build, package and graph a model (started locally when clicking the Build button in the Model tabs)
  • modeltrain.py: handles the model training and inference (started locally or remotely when clicking the Train button in the Model tabs)
  • modules.py: definition for base classes used by modules in sub folders
  • parametersplot.py: plot parameters into an image (started locally by the parameters display in the Dashboard tab)
  • pythoncheck.py: check python satisfies the requirements (started locally when launching the application)
  • pythoninstall.py: install the necessary python conda packages (started locally when setting up the application)
  • pythonparse.py: parse modules and code chunks (started locally when launching the application)
  • sshtunnel.py: ssh tunnel to execute scripts remotely as if they were local (started locally to launch remote scripts)
  • tcpcodec.py: tcp socket communication functions (used locally and remotely by the other root scripts)
  • tensorrender.py: handles the rendering of tensors into images (started locally by the tensor displays in the Dataset and Model tabs)
About
IDE for PyTorch and its ecosystem
torchstudio.ai
Resources
Readme
MIT license
Activity
Custom properties
Stars
392 stars
Watchers
5 watching
Forks
29 forks
Report repository
Releases
Contributors
Languages