Core concepts


A Project is a collection of your code repostitories, workspaces and jobs that you can share and collaborate on with your teammates. Your projects can also be forked by team members or made public so that others in the Onepanel community can view and fork them.


Workspaces are full Linux computing environments that come pre-installed with all the tools you need to explore data and build and experiment with your models. They include Python, JupyterLab, TensorFlow, PyTorch and other well known deep learning libraries and tools, as well as full terminal access. You can optionally install custom packages and dependencies into your workspaces.

A single workspace can be paused and resumed at any time or be upgraded to multiple GPUs and downgraded with ease.


Jobs allow you to execute your code in parallel, on multiple dedicated machines which are fully managed by Onepanel. With jobs, you can try different hyper-parameters, code and datasets and then compare the results and metrics for each job and continue iterating on the best results.

While a job is running, you can view running logs, system metrics and training metrics. You also have full TensorBoard and terminal access to each running job.

Once a job completes, a snapshot of logs, code, commands, datasets, environments and output is automatically saved. This allows you to share your results with others and reproduce/re-run the same experiments at a later time.


With Onepanel Datasets, you can search or create version controlled datasets which you can then mount into a job or workspace. It is ideal to separate your data from your code from your data so you can collaborate with others and try different code on the same underlying dataset.


Onepanel Environments are CPU/GPU optimized and are pre-configured with all tools you need to build deep learning models, annotate your images and much more. They can be further customized with additional packages and dependencies.