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DataSphere Jobs

Written by
Updated at October 24, 2024

In DataSphere, you can run jobs, i.e., computations on DataSphere VMs outside JupyterLab, remotely. These can be Python scripts and projects, Bash scripts, and executable binary files compiled for Linux x86_64.

Jobs are created and run in projects. However, they do not depend on project notebooks and running VMs.

To use DataSphere Jobs, authenticate as a user account.

Before running a job, set up a Python virtual environment, install DataSphere CLI using the pip install datasphere command, and prepare a configuration file describing all the parameters for running the job. You can also install and configure the Yandex Cloud CLI to use it for authentication in Yandex Cloud.

You can also work with jobs in Visual Studio Code using the DataSphere Jobs Toolkit extension.

You can find jobs in the DataSphere Jobs tab of a project. Their progress and results will be available under Run history.

Tip

For long-running jobs, we recommend saving intermediate results in an S3 object storage.

Storing job data

DataSphere Jobs stores input data cache, environments, logs, and job execution results. You can reuse the data and share it across jobs in a single project. This means repeated runs of the same job will not load input data into DataSphere each time but will reuse the data loaded on the first run.

The size of stored data is limited. For more information about DataSphere limits, see Quotas and limits in DataSphere.

The default job data lifetime is 14 days. You can change this value using DataSphere CLI:

datasphere project job set-data-ttl --id <job_ID> --days <lifetime_in_days>

Job configuration file

When creating a job, specify its parameters in the config.yaml file, such as the configuration of computing resources to run the job on and required files with input data. Depending on the settings specified in the configuration file, DataSphere analyzes the job, identifies dependencies, deploys the environment on the VM, and runs the job code. Job execution results are saved in the DataSphere project files listed in the job configuration.

# Job name
name: simple-python-script
# Job description
desc: Program description

# Entry point parameters to run computations
cmd: >  # Multi-line YAML string
  python3 src/main.py
    --params ${PARAMS}
    --features ${<connector_ID>}/features.tsv
    --validate ${CIFAR}/val.json
    --normalizer ${DS_PROJECT_HOME}/misc/norm.bin
    --model ${MODEL}
    --epochs 5

# Files with input data
inputs:
  - misc/logging.yaml  # File path relative to the job run directory on the local computer.
  - /usr/share/params.json: # Absolute path to the file on the local computer is saved to the PARAMS variable.
      var: PARAMS

# Files with results.
outputs:
  - data/model.bin: MODEL  # Relative path to the file is saved to the MODEL variable.
  - other/metrics.png  # Relative path to the file.

# Resources required to run a job must be available in the project.
s3-mounts: # S3 connectors.
  - <connector_ID>   # S3 connector ID.
                           # Connector name is not set, therefore, the connector can be accessed by its ID.
datasets:
  - <dataset_ID>:  # ID of the dataset available in the project.
      var: CIFAR   # CIFAR: Variable for accessing the dataset.

# Environment parameters.
env:
  vars:  # Environment variables.
    - DEVICE_COUNT: 8    # You can specify the value of the environment variable explicitly.
    - PYTHONBUFFERED     # If the parameter is not specified, its value will be taken from the current environment.
  docker: <Docker_image_ID>  # Docker image available in the DataSphere project.
  # You can also specify a Docker image in the image registry.
  # docker:
  #   image: <path_to_image_in_registry>:<tag>  # For example, <cr.yandex/crtabcdef12345678900/myenv:0.1>.
                                              # For Docker Hub, you only need to specify `<name>:<tag>`, e.g., `ubuntu:focal`.
  #   username: <login>
  #   password: 
  #     secret-id: PASSWORD  # DataSphere secret name.

  # Environment dependency build method.
  python: auto # Full environment build automation.

  # python: # Environment parameters are set manually. If no parameters are set, their values will be automatically taken from the current environment.
  #   type: manual
  #   version: 3.10.5  # Python version.
  #   requirements-file: requirements.txt  # File with environment parameters.

# Flags for running jobs.
flags:
  - attach-project-disk # Mount project storage.

# Computing resource configurations to run a job.
cloud-instance-types:
  - g2.1 # Priority configuration.
  - g1.1 # Second priority configuration.

# Extended working directory configuration.
working-storage:
  type: SSD    # Disk type in use. Optional, the default type is SSD. Possible values: SSD
  size: 150Gb  # Working directory size from 100 GB to 10 TB.

# Configuring graceful shutdown.
graceful-shutdown:
  signal: SIGTERM  # Signal to be sent to the job process on pressing Ctrl + C (cancel); SIGTERM by default.
                   # Possible values: SIGTERM, SIGINT, SIGHUP, SIGUSR1, SIGUSR2
  timeout: 15s     # Timeout after which the job process gets SIGKILL if it has not finished yet.

The config.yaml file has multiple sections.

  1. The name and description sections specify the job name and description. The job name must be unique within the project. A description is optional and helps find the required job in the project.

  2. The cmd section specifies the job entry point. Specify how to run your code and set all the run parameters to be provided to the executable file or script. You can use variables and IDs that will be specified in the sections below to set paths to the required files.

  3. The input section specifies files with input data and other information you need to provide to run the job from your local computer. You can set a file path relative to the job directory or save the relative or absolute file path to a variable to use it in other sections of the configuration file.

  4. The output section specifies files to save the computation results to. Once the job is executed, these files will appear on your PC. The file paths are specified in the same way as in input.

  5. DataSphere resources used in jobs are specified in the s3-mounts (S3 connectors) and datasets(datasets) sections. To use an S3 connector or a dataset in a job, specify the ID of an available project resource and, optionally, define a variable for it. If no variable is set, resources in the cmd section can be accessed by their ID.

    You can also use project storage in your job. To do this, set the attach-project-disk flag in the flags section. The project storage will be mounted to the VM the job is running on as an external disk for data reads. The storage path will be available in the DS_PROJECT_HOME environment variable.

  6. The env section defines environment parameters for running the job: the VM environment build method, environment variables, and, optionally, a Docker image built in DataSphere or stored in a different image registry. You can specify the registry authorization credentials if required.

    There are two ways to set up the environment for your Python projects:

    • Allow DataSphere to automatically identify all required dependencies, analyze the current environment on your local computer, and build and migrate the environment. To do this, specify python: auto in the env section.
    • You can specify the Python interpreter version and the relevant libraries yourself either directly in the configuration file or in a separate requirements.txt file. If you explicitly set at least one parameter, the missing ones will be fetched from the current environment automatically.

    Warning

    If you set up the environment in auto mode or specify only some of the dependencies, run your job from the Python virtual environment with all the current packages and local modules installed. This will enable the datasphere library to automatically identify the job runtime environment parameters.

    You can set environment variables under vars in the env section. Project secrets will also be added to the environment variables when running the job.

    Note

    By default, DataSphere uses the conda package manager with pre-installed Python 3.10 to run jobs. To reduce environment migration time, use the same Python version for your jobs.

  7. The cloud-instance-types section defines the valid computing resource configuration types the job can run on. Configurations are specified in order of priority, i.e., if resources are available, the job will use the first configuration to run. If there are no VMs with the first configuration available, the job will try to run on the second one, then the third, and so on.

    If you only have a single configuration, you may also use the old cloud-instance-type field, e.g., cloud-instance-type: g1.1; however, it is preferable to use the new one.

  8. The working-storage section specifies the extended working directory parameters. By default, the working directory is created on the system disk. The directory's size is not guaranteed and is usually about 20 GB. If you need more space to complete the job, you can specify this explicitly. The extended working directory can range in size from 100 GB to 10 TB.

    You pay for the extended working directory specified in the working-storage section according to the data storage pricing policy.

  9. The graceful-shutdown section defines the graceful shutdown parameters. If the section is not specified, then the SIGKILL signal is sent to the job when the user presses Ctrl + C. In this section, you can redefine the signal itself as well as the wait time for graceful shutdown.

See also

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