Running jobs in DataSphere Jobs

In DataSphere Jobs, you can remotely run jobs, i.e., Python and bash scripts and executable binary files, on a Yandex DataSphere VM.

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

Before running a job, install and configure the Yandex Cloud CLI to use it for authentication in Yandex Cloud. You should also install the datasphere library in your Python environment using the pip install datasphere command.

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

When you run a job, the datasphere library analyzes the environment, collects code dependencies, and can provide them to DataSphere for deploying the environment on a cloud VM. To avoid unnecessary system dependencies that can affect a job's performance, we recommend using a virtual environment, such as venv or conda.

Create a jobCreate a job

1. Install the datasphere library:

   pip install datasphere
   

2. Prepare a script or an executable binary file.

3. Prepare a file with input data.

4. Configure the job settings. In the config.yaml file, specify the resources for running your job and its runtime configuration:

   name: <job_name>
   desc: <job_description>
   cmd: >
       python3 <executable_file> --data ${DATA} --result ${OUTPUT}
   env:
     python: auto
   inputs:
     - <inputs>: DATA
   outputs:
     - <outputs>: OUTPUT
   cloud-instance-types:
     - <computing_resource_configuration>
     - <computing_resource_configuration>
     - <computing_resource_configuration>
   

   Where:

   • name: Job name.
   • desc: Job description.
   • cmd: Script file and variables for inputs and outputs.
   • env: Environment parameters. The python: auto value indicates that the code and pip dependencies should be provided to DataSphere.
   • inputs: File with input data. You can change the DATA variable name.
   • outputs: File with results. You can change the OUTPUT variable name.
   • cloud-instance-types: List of valid computing resource configurations to run the job, sorted by priority.

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

5. Open the command-line shell in the directory with the files you prepared and run your job:

   datasphere project job execute -p <project_ID> -c config.yaml
   

   To copy the project ID, select the project on the DataSphere homepage and click ID .

You can track the job execution progress on the project page in the DataSphere interface.

ExampleExample

Let's take an example of training a classification model on the MNIST dataset with sample handwritten digits. Model training in DataSphere is run remotely and a file with the trained model is returned as a result. You can find other job run examples in the repository on GitHub.

1. Install the tensorflow library:

   pip install tensorflow==2.12.0
   

2. Prepare a script in a file named example.py:

   import argparse
   import json
   import os
   import shutil
   import tensorflow as tf
   
   parser = argparse.ArgumentParser(prog='example')
   parser.add_argument('-i', '--input', required=True, help='Input file')
   parser.add_argument('-m', '--model', required=True, help='Output file')
   
   def make_archive(source, destination):
       base = os.path.basename(destination)
       name = base.split(".")[0]
       fmt = base.split(".")[1]
       shutil.make_archive(name, fmt, source)
   
   def main(epoch_count, model_file):
       print("TensorFlow version: ", tf.__version__)
       print("")
       print(os.system("nvidia-smi"))
       print("")
   
       print("Load MNIST dataset...")
       mnist = tf.keras.datasets.mnist
       (x_train, y_train), (x_test, y_test) = mnist.load_data()
       x_train, x_test = x_train / 255.0, x_test / 255.0
   
       print("Build Sequential model...")
       model = tf.keras.models.Sequential([
         tf.keras.layers.Flatten(input_shape=(28, 28)),
         tf.keras.layers.Dense(128, activation="relu"),
         tf.keras.layers.Dropout(0.2),
         tf.keras.layers.Dense(10)
       ])
   
       #predictions = model(x_train[:1]).numpy()
       #tf.nn.softmax(predictions).numpy()
   
       loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
       #loss_fn(y_train[:1], predictions).numpy()
   
       print("Compile model...")
       model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
   
       print("Fit...")
       model.fit(x_train, y_train, epochs=epoch_count)
   
       print("Evaluate...")
       model.evaluate(x_test,  y_test, verbose=2)
   
       print(f"Save model to '{model_file}'")
       tf.keras.models.save_model(model, "model", save_format="tf")
       make_archive("model", model_file)
   
       print("Done")
   
   
   if __name__ == "__main__":
       args = parser.parse_args()
   
       epoch_count = 5
   
       with open(args.input) as f:
           data = json.load(f)
           epoch_count = int(data["epoch_count"])
   
       main(epoch_count, args.model)
   

3. Prepare the input.json file with input data:

   {
       "epoch_count" : 3
   }
   

4. Create the config.yaml file with job parameters:

   name: simple-tf-script
   desc: Simple TF script
   cmd: python3 example.py --input ${INPUT} --model ${MODEL}
   env:
     python: auto
   inputs:
     - input.json: INPUT
   outputs:
     - model.zip: MODEL
   cloud-instance-types:
     - g1.1
   

5. Run the job:

   datasphere project job execute -p <project_ID> -c config.yaml
   

   To copy the project ID, select the project on the DataSphere homepage and click ID .

The model will be saved to the model.zip archive in the job folder.

See alsoSee also

• DataSphere Jobs
• GitHub repository with job run examples