Workloads / examples

Bring the work.
Take home the result.

Fine-tuning, evaluations and batch inference. Your code does the work; Symbioza handles the cloud execution within your spending limit.

YOUR CODE. YOUR DATA. YOUR LIMIT.

Start with a workload you own. These are planning prompts, not ready-made containers or benchmark results. Bring working code and accessible data; your agent helps prepare the request.

01 /

Fine-tune your model.

Bring a training script, a compatible container and a dataset you can use. Get the resulting weights and metrics back.

Training code · model access · dataset

WHAT YOU ASK YOUR CODE TO SAVEmodel weights + training metrics

Your container must include your code. Resuming training requires checkpoint save-and-resume support in your command.

Example prompt · adapt to your job

Estimate my fine-tuning job with Symbioza. Use my container, training command and dataset. Ask me for anything missing, including the total spending limit. Save the weights and metrics to /workspace/artifacts/. Show me the estimate. Do not submit yet.

ESTIMATE FIRST

02 /

Put a model to the test.

Run your evaluation harness against a defined dataset. Save a machine-readable report so you can compare the results.

Evaluation harness · model · test data

WHAT YOU ASK YOUR CODE TO SAVEmetrics.json + evaluation report

Record model, dataset and code versions for repeatable comparisons. Hardware-sensitive comparisons need additional validation.

Example prompt · adapt to your job

Estimate my model evaluation with Symbioza. Use my evaluation container, command and test data. Ask for missing requirements and my total spending limit. Write metrics.json to /workspace/artifacts/. Show me the estimate. Do not submit yet.

ESTIMATE FIRST

03 /

Work through the whole dataset.

Run batch inference using your own model and code. Point the job at the input data and collect the processed output.

Inference code · model · input data

WHAT YOU ASK YOUR CODE TO SAVEpredictions.csv + processing summary

Size disk space for input, extracted data and outputs. Use dataset links that stay valid long enough for staging.

Example prompt · adapt to your job

Estimate batch inference with Symbioza using my container, model and dataset URL. Ask for missing requirements and my total spending limit. Write predictions.csv and a summary to /workspace/artifacts/. Show me the estimate. Do not submit yet.

ESTIMATE FIRST

Copy a prompt to start a conversation with your agent.

Before you hand it off

Give the run a clear finish.

Know what success looks like before compute starts.

Put the check in your command.

Validate the output, return a meaningful exit code and save the files you need under /workspace/artifacts/.

Check the outcome in three parts.

Read the run status, file delivery status and billing status separately. A completed command alone does not confirm delivery or a final charge.

The next step is a price.

Connect your agent and ask for an estimate. An estimate is free and books no compute.

Get your first estimate

Bring the work.Take home the result.

Fine-tune your model.

Put a model to the test.

Work through the whole dataset.

Give the run a clear finish.

Put the check in your command.

Check the outcome in three parts.

The next step is a price.

Bring the work.
Take home the result.