What is Trax?
It helps you understand and explore advanced deep learning. It is actively used and maintained in the Google Brain team.
You can use It either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It includes a number of deep learning models (ResNet, Transformer, RNNs, ...) and has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. It runs without any changes on CPUs, GPUs and TPUs.
Trax is a tool in the Machine Learning Tools category of a tech stack.
Trax is an open source tool with 8K GitHub stars and 811 GitHub forks. Here’s a link to Trax's open source repository on GitHub
Who uses Trax?
Developers
8 developers on StackShare have stated that they use Trax.
Trax's Features
- Advanced deep learning
- Actively used and maintained in the Google Brain team
- Runs without any changes on CPUs, GPUs and TPUs
Trax Alternatives & Comparisons
What are some alternatives to Trax?
Trailblazer
Trailblazer is a thin layer on top of Rails. It gently enforces encapsulation, an intuitive code structure and gives you an object-oriented architecture. In a nutshell: Trailblazer makes you write logicless models that purely act as data objects, don't contain callbacks, nested attributes, validations or domain logic. It removes bulky controllers and strong_parameters by supplying additional layers to hold that code and completely replaces helpers.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
