Tools for applied deep learning, now open source
An open-source, self-hosted platform for developing applied machine learning and deep learning projects. Atlas’ SDK and CLI allow you and your team to schedule, run, track and reproduce DL jobs concurrently and share GPU & CPU resources.
How it works:
1. Import Seamlessly
Merge Atlas easily into any Python code. The lightweight SDK is flexible to use with any Python frameworks.
2. Run Experiments
Atlas’ scheduler manages resources and runs experiments from the terminal or SDK.
3. Track Results
Atlas' GUI automatically records everything about your experiments, making reproducibility a cinch.
Atlas’s built-in scheduler automates, queues, and executes 1000s of experiments concurrently, using any IDE or Python framework.
Experiment management & tracking
Tag experiments and easily track hyperparameters, metrics, and artifacts such as images, GIFs, and audio clips in a web-based dashboard.
Send your jobs to TensorBoard with one click using Atlas. Ramp up data processing time using NVIDIA RAPIDS with Atlas’ custom worker image functionality.
Powered by the ML community worldwide
As of June 2020, Atlas is maintained exclusively by the ML community worldwide. To contribute to Atlas as an open-source tool, check out the Github for it here.