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Jobs are ideal for running experiments on models and code that already works. If you’re starting a new project, using a cluster for interactive development is recommended instead.
Make sure you’ve installed the TensorPool CLI and configured your API key.

Initialize a Job

Create a job configuration file in your project directory:
tp job init
This creates a tp.config.toml file that defines your training job.

Configure Your Job

Edit the generated tp.config.toml file to specify your training commands and GPU requirements:
commands = [
    "pip install -r requirements.txt",
    "python train.py --epochs 100",
]

instance_type = "1xH100"

outputs = [
    "checkpoints/",
    "model.pth",
    "results.json",
]

ignore = [
    ".venv",
    "venv/",
    "__pycache__/",
    ".git",
    "*.pyc",
]

Submit Your Job

Push your job to TensorPool:
tp job push tp.config.toml
Your code will be uploaded and executed on the specified GPU instance. You’ll receive a job ID to track progress.

Monitor Your Job

Stream real-time logs from your running job:
tp job listen <job_id>
Check job status and details:
tp job info <job_id>

Download Results

Once your job completes, pull the output files:
tp job pull <job_id>
This downloads all files specified in the outputs section of your configuration.

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Next Steps