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Collaborative Development

Introduction
As you already know, the core concepts in Neu.ro are jobs, storage, and environments.
You can share a job, a path on the 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 a Git repository. In this case, each teammate will have a local copy of the repository and may run jobs independently. To set up your project, please follow these steps.
Initiating a new Neu.ro project
First, you will need to create a new project from the Neuro project template. 
To do this, install the cookiecutter package and initialize cookiecutter-neuro-project:
$ pipx install cookiecutter
$ cookiecutter gh:neuro-inc/cookiecutter-neuro-project --checkout release
The latter command will prompt you to enter some information about the project and then create it based on your responses.
Pushing the project to a Git repository
Then, you need to put this new project into a Git repository. Just follow the instructions for the Git hosting of your choice (for example, here are the instructions for GitHub).
Organizing your data
You have a few options for storing your project data in a shared space.
Platform storage
You can upload data to your platform storage both through the CLI and through the Web UI. 
CLI
Web UI
To upload data through the CLI, use the neuro cp command. For example:
$ neuro cp -r <local-folder-with-data> storage:cifar-10
This will upload data from your local folder to the cifar-10 folder on your platform storage.
To upload data through the Web UI, you will first need to open the Filebrowser. To do this, click RUN A JOB in the Storage browser widget:
After that, click RUN in the new window:

Once the Filebrowser is opened, navigate to the folder you want to upload your data to and click the Upload icon:
Then, select the files or folder you want to upload:
After you have your files uploaded to the platform storage, you can share them with your teammates. Sharing is implemented differently in the CLI and the Web UI.
CLI
Web UI
You can give permanent access to folders and files through the CLI with the help of the neuro share command. 
$ neuro share storage:cifar-10 alice manage
This will share the cifar-10 storage folder with Alice and give her manage-level access to it (this means she will be able to read, change, and delete files in this folder).
After that, you need to update the data/remote: value in the project's .neuro/live.yaml file to keep the full URI of your data. This allows your teammates to use this data folder in their copies of the project (here, default is the name of our default cluster, and bob is your username on the platform):
  data:
    remote: storage://default/bob/cifar-10
    mount: /project/data
    local: data
After that, your data becomes available in the /data folder in the local file system of the jobs you and your teammates work with.
Sharing folders and files through the Filebrowser gives temporary access to them through the Web UI to any user with a link to them.
Select the files and/or folders you want to share in the Filebrowser and click the Share icon:
You can then create permanent and temporary access links for the selected items. To create a permanent link, click Get Permanent Link:
To create a temporary link, specify the required time period and click the Create icon:
Buckets
You can use AWS or GCP buckets to store the data outside the Neu.ro platform. In this case, you need to add your access tokens to the project's config folder according to AWS and GCP guides. Note that Git doesn't track these tokens, so your teammates also have to put their tokens in their local copies of the project .
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 this data to 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).
Setting up the project and running jobs
Now all your teammates can clone the project and start working on it in their local copies. Here are some steps every teammate should follow independently.
To set up the working environment, run neuro-flow build myimage (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 neuro-flow run jupyter. Notebooks are saved in the <project>/notebooks folder on your platform storage. To download them to the local copy of the project, run neuro-flow download notebooks.
To run training from source code, update .neuro/live.yaml for your train job and run neuro-flow run train. For example:
jobs:
    train:
    ...
    bash: |
        python $[[ volumes.code.mount ]]/train.py
You can get more information about the Neu.ro project's functionality in the HELP.md file in your project folder.
Sharing running jobs 
You can share any job you run on the platform with your teammates. 
To do that, you will need to know the ID or the name of the job you want to share. 
The ID is a job's unique identifier, while the name may repeat for different job runs.
Viewing job IDs and names
You can view the IDs and names of currently running jobs available to you both in the CLI and the Web UI.
CLI
Web UI
To view the list of currently running jobs, run neuro ps. 
You can also check a particular job's status neuro status <my-cool-job>.
You can view the IDs and names of all currently running jobs in the left part of the Jobs section. Make sure the job filter is set to Running.
Clicking the job ID will open the Job Details window.
Sharing jobs
CLI
Web UI
To share the jupyter-awesome-project job with an ID of job-fb835ab1-5285-4360-8ee1-880a8ebf824c with Alice (where awesome-project is your project's slug), run:
$ neuro share job:job-fb835ab1-5285-4360-8ee1-880a8ebf824c alice read
You can also share jobs using their names:
$ neuro share job:jupyter-awesome-project alice read
However, keep in mind that different runs of the same job can have the same name.
To share a job, click Share in the drop-down list to its right:
Next, enter the name of the user you want to share the job with and the access level. When this is done, click SHARE:
This allows Alice to access this job either via its ID or its full URI. The URI consists of a cluster name, the owner's user name, and the job's name or ID: job://default/bob/jupyter-awesome-project.
# read the logs
neuro logs job://default/bob/jupyter-awesome-project
neuro logs job-fb835ab1-5285-4360-8ee1-880a8ebf824c   
​
# run the interactive bash session:
neuro exec job://default/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://default/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 job's logs or work with it there.
Please note that, if someone gets write-level 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.
You can instantly share a new job by adding --share <username> when running it.
There is also a shortcut for sharing all your jobs (past, current, and future ones alike) with your teammates in the CLI:
$ neuro share job: alice read
Sharing 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 neuro-flow build myimage, additional dependencies you state in requirements.txt and apt.txt are installed in that environment, which is then saved on the platform's Docker registry. In this case, there is no need to share the images with teammates, as they build similar images from the same code base.
In rare cases, though, you may want to use a different image as a base. If that image is public, all you need to do is to update the images/myimage/ref variable in the project's.neuro/live.yamlfile:
images:
  myimage:
    ref: ufoym/deepo
If the image is not public, you need to make it 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 .neuro/live.yaml file with the full URI of your image:
images:
  myimage:
    ref: image://default/bob/project-specific-docker-image
Please note that some functionality may be missing in custom Docker images. In particular, you may need to log into AWS and GCP manually from within your jobs. 
Running Your Code
FAQ
Last modified 1yr ago