PyTorch development services
Accelerate deep learning projects by building neural networks using PyTorch
The ML library developed by the Facebook AI Research lab (FAIR) is an open-source solution used in a growing range of applications. The PyTorch ecosystem is accomplished by serial libraries like Torchvision (for computer vision), Torchtext (for natural language processing), or even Torchaudio (for sound processing).
The packages provide ready-made models and popular datasets, complementing the whole ecosystem. Much of PyTorch's strength results from the open-source character, as it's a sum of countless contributions of machine learning developers and researchers worldwide. Growing as the community behind it grows, PyTorch is nearly unrestricted in building DL/ML solutions.
The TorchScript ties up the unified research to production framework. Transforming PyTorch modules into a production-friendly form with TorchScript enables faster execution of models, becoming independent of Python runtime and increasing performance.
It is designed to work smoothly with the Python ecosystem and can be used with popular Python packages.
PyTorch supports methodology for mixed-precision training, combining single-precision and half-precision formats.
Libtorch library core
It is written mostly in C++ to achieve higher performance.
GPUs enable 50x or greater speed-ups in comparison to CPU calculations.
Distributed Data Parallelism
The feature enables running models across multiple machines to scale projects.
Fast & easy execution
PyTorch strives to make writing and using models as easy and productive as possible.
High speed of development
It provides a strong and constantly growing ecosystem.
Adoption of PyTorch by e.g. Microsoft and OpenAI assures its further development.
There’s a possibility to set PyTorch on a vast amount of cloud-based environments from renowned providers.
The network’s behavior can be modified at runtime in a dynamic manner, improving efficient model optimization. This allows for great flexibility and facilitates the implementation of a lot of new architectures.
Representing the neural networks, modules are fundamental to PyTorch. Modules are individual operations, representing the building blocks of a neural network, in the DL domain called layers. Modules are easy to transform, allowing fast construction of any model.
PyTorch uses special data structures called Tensors to store and operate on multidimensional number arrays. They are similar to NumPy arrays but can be operated on GPUs which significantly speeds up the calculations.
Automatic differentiation evaluates functions' derivatives in neural networks. PyTorch contains the Autograd package, providing this functionality to automate processes and create computational graphs with nodes corresponding to mathematical operations.
PyTorch’s relationship with Python results in the possibility to use debugging tools of the latter. PyTorch offers the option of viewing any variable in the debugger or simply printing its state.
Convenient access to data
The possibility to load practically any type of data – the user can easily use pre-loaded datasets as well as own data using custom DataLoaders.
Instead of breaking the open door each time commonly used models are needed, researchers can freely adapt existing pre-trained neural networks.
A reasonable learning curve and intuitive API make PyTorch a solution easy to adopt among your Python developers team. Fast to introduce in place of similar solutions – an all built-in “plug and play” with minimum configuration.
Reduced development time
Thanks to the close relations with Python and a similar syntax, PyTorch supports productivity. Equipped with a simple interface and API, the environment runs smoothly on Windows and Linux. Less hassle, more work done.
The community of PyTorch users is known as a friendly and helpful environment for developers and researchers. The forum behind it is full of tips on using various architectures, making it the right place for your team to search for answers and support.
Neural networks can be used in object detection, tracking, and image segmentation. The Torchvision package, a part of the PyTorch ecosystem, provides necessary functionalities. The library consists of pre-trained models, popular datasets, and common image transformations for computer vision.
Tesla and Uber adopt PyTorch in building neural networks, changing the automotive industry. The revolution is possible thanks to multitasking models that collect loads of traffic data. Engineers integrate neural networks to run efficiently in cars, providing real-time reactions in all scenarios.
Solutions built using PyTorch support various industries. For one, smart machines let farmers get rid of weeds, reduce costs and pesticide usage. In such cases, PyTorch is the base of a computer vision and machine learning system, recognizing crops from weeds and targeting the latter for spraying.
Generative Adversarial Network (GAN) models are trained with two opposing neural networks, one generating new data samples, and the other recognizing real examples. These models are capable of generating images, performing text-to-image translations, or even reconstructing videos from photos.
Training machines to identify and understand human languages enables e.g. gathering information from documents. In automatic text classification and translation, the Torchtext package is used, thanks to pre-built, generic loaders for popular datasets and text resources, including vocabulary objects.
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