FAQ
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Last updated
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You can upload your datasets to the Platform using Neuro CLI. Neuro CLI supports basic file system operations for copying and moving files to and from the platform storage.
From your terminal or command prompt, change to the directory containing your dataset, and run:
The URI storage:data/ indicates that the destination is the platform. In a similar fashion,
downloads dataset to your current directory locally.
You can access your dataset from within a container by giving --volume storage:data/:/var/storage/data/:rw to neuro run as a parameter when starting a new job.
If your aim is to download or upload data from external system, be it AWS S3 bucket, or Azure Blob Storage, checkout our package usage.
To work with your dataset from within a container, to troubleshoot a model, or to get shell access to a GPU instance, you can execute a command shell within a running job in interactive mode.
To do so, copy the job id of a running job (you can run neuro ps to see the list), and run:
For example,
This command starts bash within the running job and connects your terminal to it.
Assuming you have a local Docker image named helloworld built on your local machine, you can push it to the Neuro Platform by running:
After that, you can start the job by running:
To kill all jobs that are currently running on your behalf, run the following command:
For example,
Sometimes you want to execute two or three commands in a job without having to connect to it. For example, you may want to change the working directory and to run training. To achieve this, you need to wrap your commands in ”bash -c ‘<commands>’” call, like this:
There are two ways to get the output of your running job:
Run it without the --detach option.
Connect to a running job output with neuro log <JOB>, where JOB is either the id or the name of your job.
In some cases, Python caches the output of the scripts, so that you won’t get any output until the job finishes. To overcome this problem, provide -u option to python, like this: