How To Run A Python Job#

Introduction#

All members of a project in Hopsworks can launch the following types of applications through a project's Jobs service:

• Python
• Apache Spark

Launching a job of any type is very similar process, what mostly differs between job types is the various configuration parameters each job type comes with. Hopsworks support scheduling jobs to run on a regular basis, e.g backfilling a Feature Group by running your feature engineering pipeline nightly. Scheduling can be done both through the UI and the python API, checkout our Scheduling guide.

UI#

Step 1: Jobs overview#

The image below shows the Jobs overview page in Hopsworks and is accessed by clicking Jobs in the sidebar.

Step 2: Create new job dialog#

Click New Job and the following dialog will appear.

Step 3: Set the job type#

By default, the dialog will create a Spark job. To instead configure a Python job, select PYTHON.

Step 4: Set the script#

Next step is to select the python script to run. You can either select From project, if the file was previously uploaded to Hopsworks, or Upload new file which lets you select a file from your local filesystem as demonstrated below. By default, the job name is the same as the file name, but you can customize it as shown.

Step 5 (optional): Set the Python script arguments#

In the job settings, you can specify arguments for your Python script. Remember to handle the arguments inside your Python script.

Step 6 (optional): Additional configuration#

It is possible to also set following configuration settings for a PYTHON job.

• Environment: The python environment to use
• Container memory: The amount of memory in MB to be allocated to the Python script
• Container cores: The number of cores to be allocated for the Python script
• Additional files: List of files that will be locally accessible in the working directory of the application. Only recommended to use if project datasets are not mounted under /hopsfs. You can always modify the arguments in the job settings.

Step 7: Execute the job#

Now click the Run button to start the execution of the job. You will be redirected to the Executions page where you can see the list of all executions.

Once the execution is finished, click on Logs to see the logs for the execution.

Code#

Step 1: Upload the Python script#

This snippet assumes the python script is in the current working directory and named script.py.

It will upload the python script to the Resources dataset in your project.

import hopsworks

project = hopsworks.login()

dataset_api = project.get_dataset_api()

uploaded_file_path = dataset_api.upload("script.py", "Resources")

Step 2: Create Python job#

In this snippet we get the JobsApi object to get the default job configuration for a PYTHON job, set the python script and override the environment to run in, and finally create the Job object.

jobs_api = project.get_job_api()

py_job_config = jobs_api.get_configuration("PYTHON")

# Set the application file
py_job_config['appPath'] = uploaded_file_path

# Override the python job environment
py_job_config['environmentName'] = "python-feature-pipeline"

job = jobs_api.create_job("py_job", py_job_config)

Step 3: Execute the job#

In this snippet we execute the job synchronously, that is wait until it reaches a terminal state, and then download and print the logs.

# Run the job
execution = job.run(await_termination=True)

# Download logs
out, err = execution.download_logs()

f_out = open(out, "r")
print(f_out.read())

f_err = open(err, "r")
print(f_err.read())

Configuration#

The following table describes the JSON payload returned by jobs_api.get_configuration("PYTHON")

Accessing project data#

Absolute paths#

The project datasets are mounted under /hopsfs, so you can access data.csv from the Resources dataset using /hopsfs/Resources/data.csv in your script.

Relative paths#

The script's working directory is the folder it is located in. For example, if it is located in the Resources dataset, and you have a file named data.csv in that dataset, you simply access it using data.csv. Also, if you write a local file, for example output.txt, it will be saved in the Resources dataset.

API Reference#

Jobs

Executions