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torchstudio
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IDE for PyTorch and its ecosystem
Download README (.md)
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README.md
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torchstudio
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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)  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)
