torchstudio

KO | EN

gitlite — search

#python #react #typescript #android #rust #deep-learning #macos #nodejs #java #windows #awesome #kubernetes #vue #mcp

(★ 392)

IDE for PyTorch and its ecosystem

torchstudio Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

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](https://raw.githubusercontent.com/TorchStudio/torchstudio/main/architecture.svg)

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)

torchstudio

torchstudio Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

torchstudio

torchstudio Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README