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Make sure you’ve installed the TensorPool CLI and configured your API key.

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1. Initialize a Job Configuration

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

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2. 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",
]

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3. 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.

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4. 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>

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5. 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