Neptune vs Trax

Trax vs Neptune: What are the differences?

Trax: Your path to advanced deep learning (By Google). 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.; Neptune: The most lightweight experiment tracking tool for machine learning. It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

Trax and Neptune belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Trax are:

• Advanced deep learning
• Actively used and maintained in the Google Brain team
• Runs without any changes on CPUs, GPUs and TPUs

On the other hand, Neptune provides the following key features:

• Experiment tracking
• Experiment versioning
• Experiment comparison