Jobs
Job is the simplest execution unit. You can run it in a given runtime environment on a given resource preset with given storage volumes attached. Jobs are the building blocks of your project and should be planned carefully for optimal use of the resources.
Before you start a job, you must decide:
The Docker image to use to run the job. Note that the job terminates if the Docker container fails unless a specific restart policy is used for the job.
The preset - a combination of CPU, GPU, and memory resources to use.
In complex projects, you have multiple jobs running with different preset resources that are best suitable for these specific jobs.
What are presets?
Neu.ro lets you run a job in an environment on a given preset with several parts of the storage attached. A preset here is a combination of CPU, GPU, and memory resources allocated.
You must decide and set the amount of CPU, GPU, or memory resources you want to use for a job. By default, the cpu-small preset is used. These limits ensure that you can get better resource utilization within a compute cluster.
For example, we are using the cpu-small preset in the following command as the job doesn't need a lot of processing capacity.
Sample command:
Neu.ro comes with a set of presets that are suitable for running different kinds of workloads. Some of the jobs may also require GPU resources. You can view the list of available presets using the neuro config show command.
The command lists the available presets and their configurations. For example, the cpu-small preset includes 1 CPU, 4GB of memory, and no GPU. Whereas, the gpu-k80-small includes 5 CPU, 48GB memory, and an nvidia-tesla-k80 GPU.
How do I run a job?
Each job has a unique ID. For your convenience, you can give a job a name. There can only be a single PENDING or RUNNING job with a given name.
Each job has access to its ephemeral storage (which is essentially a part of SSD on the physical machine this job runs on). This type of storage is fast but not persistent: as soon as you kill the job, the data is lost.
To make the data persistent, you can mount volumes of platform storage to the job. This type of storage is slightly slower and has some limitations. For example, running model training on data from the mounted folder is generally 10-20% slower. Also, random write operations (e.g. unzipping an archive) are very slow and are highly discouraged.
To run a job in CLI, you can use the neuro run command. This command accepts a lot of different arguments, most of which are explained in this and the following sections.
Sample commands:
Run a fast job without mounting storage:
Running a long training job with mounting storage
How can I see the list of currently running jobs?
You can use the neuro ps command to list the jobs that are currently running. You can use various options to filter the list of jobs based on status, owner, or by name. To know information about a particular job, you can use the neuro job status command.
Sample Commands:
See the list of all currently running jobs
See the list of jobs in the pending status
Can I connect to a job when it is running?
When running a job, you may sometimes want to connect to it and execute a command. You can use the neuro job exec command to connect to a running job.
Sample command:
Running a simple list command in the container hosting the job
Providing a bash terminal to the container hosting the job
A bash terminal lets you work on the container while the job is running.
What are job states?
A job is the smallest execution unit that is run until completion or until it is killed. A job goes through many states until it completes or fails. You can view a job's current state by using the neuro job status command.
Sample command:
A job can have one of the following states:
Pending: When the job is created and the resources for the job are allocated.
Pulling: When resources for the job are being accessed.
Running: When a job is being executed.
Complete: When a job is complete.
Failed: When a job fails and exits with an error code.
How do I expose the HTTP server running in a job?
A lot of applications you run on the platform have some web interface, such as Jupyter Notebooks, TensorBoard, and others. When you run a job containing such an application, you may access this web interface in your browser. For that, you need to pass a port that should be exposed via the --port option (which is 80 by default).
To open the exposed interface in the browser, there are several options:
Pass
--browseas aneuro runparameter. In this case, an OS default web browser will open up as soon as the job is running;Run
neuro job browse <NAME or ID>when the job is already running;Click on the HTTP URL for this job at the Neu.ro dashboard.
All jobs you run are hidden behind SSO by default. This means that if you share a link to the job web interface with someone, they will have to log into the platform and have granted permission to access the job (see the section below). To expose a job to everyone you need to pass --no-http-auth to neuro run. We strongly recommend avoiding this option unless you are completely sure that you want to omit the SSO security check.
Example:
This command runs a FileBrowser instance on 8085 port, exposes this port, removes SSO check, and opens the web interface in your default browser when the job is running.
How do I control the job duration?
You can control the duration of time for which jobs run using the life-span configuration parameter. You can update the life-span parameter in the [job] section of the global configuration file. The global configuration file is located in the standard neuro config path. The Neu.ro CLI uses the ~/.neuro folder by default, and the path for global config file is ~/.neuro/user.toml.
The parameter limits the default job run time, and is in string format. For example, a value of 2d3h20min would limit the job run time to 2 days, 3 hours, and 20 minutes.
Example:
You can also set this parameter for specific jobs by using the corresponding option:
It's also possible to increase a job's lifespan through bump-life-span:
This will add 2 days to the current lifespan of the <job-name> job.
How do I terminate a job?
You can terminate any job using the neuro job kill command. You must know the job name or job id to terminate a job.
Sample command:
Can I share a job with others?
Yes, neu.ro lets you share any running jobs with you teammates. You can get all details of currently running jobs using the neuro ps command. This command lists all the jobs that you own and that are shared with you.
Sample command to view all running jobs:
Before sharing a job, you must know its ID. After identifying the job you want to share, you must use the neuro share job command to share the job.
Sample command to share a job:
This shares the job363 job with mrsmariyadavydova and provides them access to manage it. You can provide the teammate the access to read, write, or manage a job. Now, your teammate can use the neuro ps command to view this job in their list of accessible jobs.
Where can I find a job's logs?
You can view the complete log for a job using the neuro job logs [job name or id] command. This command displays logs for the specified job.
The log is also displayed if you don't pass the --detach option when the job is run. The --detach option ensures that the job is not attached to logs and doesn't wait for an exit code.
Sample Command:
Can I manage jobs from the web UI?
Neuro provides an intuitive interface that lets you manage jobs. The Jobs page of the Neu.ro web interface lists all the jobs.
You can view the web interface of the job by clicking the 'three dots' icon near the job and then clicking HTTP URL.
To view the log and and other details about a job, click on the job ID.
By default, the Jobs page displays all currently running jobs. You can filter jobs by status using the corresponding drop-down list:
You can search for specific jobs using the Search field. The search functionality works with job names, IDs, and tags.
The UI also lets you kill or rerun a job by clicking KILL or RERUN in the drop-down menu accessible through the 'three dots' icon near the job.
Monitoring jobs
You can monitor various parameters of your currently running jobs such as CPU and GPU usage, network traffic, prices, etc., in the Web UI.
All of this information is accessible in the Grafana-based User Jobs Monitor feature. To access this feature, go to the Information tab and click USER JOBS MONITOR:
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