Computational graph
A computational graph is a directed acyclic graph (DAG) representation of a numerical computation, where nodes represent operations (or variables) and edges represent the data, typically tensors
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Dataset API (tf.data)
The Dataset API (tf.data) is the high-performance input pipeline framework within TensorFlow for loading, transforming, and delivering data to machine learning models during training and evaluation.
Estimator (tf.estimator)
tf.estimator is a high-level TensorFlow API that encapsulates the complete lifecycle of a machine learning model, including training, evaluation, prediction, and export for serving.
Fully Sharded Data Parallel (FSDP)
Fully Sharded Data Parallel (FSDP) is a distributed training technique implemented in PyTorch that shards a model's parameters, gradients, and optimizer states across data-parallel workers, allowing models…
MindSpore
MindSpore is an open-source deep learning framework developed by Huawei that Huawei describes as "a new open source deep learning training/inference framework that could be used for mobile, edge and cloud…
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is open-source model deployment software that lets teams run trained models from any machine learning or deep learning framework on any processor (GPU, CPU, or other accelerator)…
Node (TensorFlow graph)
A node in a TensorFlow graph is a single unit of computation, such as a matrix multiplication, an addition, an activation, or a variable read, that occupies one vertex of the framework's computational graph…
PyTorch
PyTorch is an open-source software library for tensor computation and machine learning.
tf.keras
tf.keras is the high-level deep learning API built directly into the TensorFlow machine learning framework
tinygrad
tinygrad is an open-source deep learning framework written primarily in Python that aims to occupy the space between Andrej Karpathy's pedagogical micrograd and full-scale production stacks like PyTorch.
torch.compile
torch.compile is the just-in-time graph capture and compilation feature introduced in PyTorch 2.0, a release first announced at the PyTorch Conference on December 2, 2022 and shipped as a stable version on…