Collaborative Development

As you already know, the core concepts in are jobs, storage, and environments. You can share a job, a path on storage, or an image on the platform registry with your teammates, granting them permission to read, update, or even remove this entity.

We recommend keeping the project code in the Git repository. In this case, each teammate has a local copy of the repository and may run jobs independently. To set up your project, please follow these steps.

Initiate the project

First, you need to create a new project from the project template. Run neuro project init and answer several simple questions.

Initiate a git repository and push this project to Git

Then, you need to put this new project into the git repository. Follow the instructions for the Git hosting of your choice (for example, here are the instructions for GitHub).

Organize your data

You have several options for storing your project data in a shared place.

Platform storage

You can upload data to your platform storage using neuro cp command in CLI or FileBrowser in Web UI. In this case, you need to share the data with your teammates explicitly.

For example, this is how you can upload the data at the cifar-10 folder in your storage and share it with Alice:

neuro cp -r local-folder-with-data storage:cifar-10
neuro share storage:cifar-10 alice manage

After that, you need to update the DATA_DIR_STORAGE variable in the project Makefile to keep the full URI of your data. This step allows your teammates to use this data folder in their project copies as well (here neuro-public is the name of our default cluster, and bob is your platform user name):


After that, your data becomes available in the /data folder in the local file system of the jobs you and your teammates run.


You can use AWS or GCP buckets to keep the data outside the platform. In this case, you need to add your access tokens in the project config folder according to AWS and GCP guides. Note that Git does not track these tokens, so your teammates also have to put their tokens in their local project copies .

Public resources

Your data may also be available at some public resource that doesn’t require any authentication. In this case, you may either put a copy of data on the platform storage (see above) or download the data to the job container’s local file system on every run (if the data size is relatively small).

Set up the project and run jobs

Now all your teammates can clone the project and start working on it through their local copies. Here are some steps every teammate should make independently.

  • To set up the working environment, run make setup (this is a necessary step to perform every time you update pip dependencies in requirements.txt or system requirements in apt.txt).

  • To run a Jupyter Notebooks session, run make jupyter. Notebooks are saved in the <project>/notebooks folder on your platform storage. To download them in the local copy of the project, run make download-notebooks.

  • To run training from source code, run make train with a corresponding Python command, for example:

make train TRAIN_CMD="python ./"

You may get more information about the project functionality in the file in your project folder.

Share running jobs

Any job you run on the platform you can share with your teammates. To get a list of running jobs that are available to you (i.e., yours and the ones shared with you), run neuro ps.

There are two properties of each job, which are essential for sharing: ID and name. The ID is a unique identifier, while the name may repeat for different job runs. To get the job ID, you may take a look at the job list (neuro ps) or check out a particular job status (neuro status my-cool-job).

For example, to share a jupyter-awesome-project job with ID job-fb835ab1-5285-4360-8ee1-880a8ebf824c with Alice (where awesome-project is a short name of your project), run:

neuro share job:job-fb835ab1-5285-4360-8ee1-880a8ebf824c alice read

This command allows Alice to access this job via its ID or its full URI, which consists of a cluster name, an owner user name, and the job name or ID: job://neuro-public/bob/jupyter-awesome-project:

# read the logs
neuro logs job://neuro-public/bob/jupyter-awesome-project
neuro logs job-fb835ab1-5285-4360-8ee1-880a8ebf824c
# run the interactive bash session:
neuro exec job://neuro-public/bob/jupyter-awesome-project bash
neuro exec job-fb835ab1-5285-4360-8ee1-880a8ebf824c bash
# open web interface in the default web browser:
neuro browse job://neuro-public/bob/jupyter-awesome-project
neuro browse job-fb835ab1-5285-4360-8ee1-880a8ebf824c

Also, Alice gets access to this job in her Web UI and can monitor the logs or open the web interface right there.

Please note that if someone gets the write access to your Jupyter Notebooks job, they can modify the notebooks on your platform storage. Therefore, to update those notebooks in the git repository, you have to download them, commit, and push.

There is also a shortcut for sharing all your jobs (past, current, and future ones alike) with your teammates:

neuro share job: alice read

Share Docker images

Our project contains a base environment we recommend using for most projects. This environment is based on deepo. It contains recent versions of the most popular ML/DL libraries (including Tensorflow 2.0 and PyTorch 1.4). When you run make setup, additional dependencies you state in requirements.txt and apt.txt are installed in that environment, which is then saved in your platform Docker registry. In this case, there is no need to share the images between the teammates, as they build similar images from the same code base.

In rare cases, though, you may want to use a specific image as a base one. If that image is public, all you need to do is to update the BASE_ENV variable in the project Makefile:


If the image is not public, you need to make available to your teammates:

# upload to your registry:
neuro image push project-specific-docker-image
# share with your teammates:
neuro share image:project-specific-docker-image alice read
# update the Makefile with the full URI of your image:

Please note that some functionality may be missing in the custom base images. In particular, you may need to log into AWS and GCP manually from within your jobs.