---
title: Compute Workflow
section: developers
description: Understanding the Compute-to-Data (C2D) Workflow
---

🚀  Now that we've introduced the key actors and provided an overview of the process, it's time to delve into the nitty-gritty of the compute workflow. We'll dissect each step, examining the inner workings of Compute-to-Data (C2D). From data selection to secure computations, we'll leave no stone unturned in this exploration. 

For visual clarity, here's an image of the workflow in action! 🖼️✨

<figure><img src="../../.gitbook/assets/c2d/c2d_detailed_flow.png" alt=""><figcaption><p>Compute detailed flow diagram</p></figcaption></figure>

Below, we'll outline each step in detail 📝

## Starting a C2D Job
1. The consumer selects a preferred environment from the provider's list and initiates a compute-to-data job by choosing a dataset-algorithm pair.
2. The provider checks the orders on the blockchain.
3. If the orders for dataset, algorithm and compute environment fees are valid, the provider can start the compute flow.
4. The provider informs the consumer of the job's id successful creation.
5. With the job ID and confirmation of the orders, the provider starts the job by calling the operator service.
6. The operator service adds the new job in its local jobs queue.
7. It's the operator engine's responsibility to periodically check the operator service for the list of pending jobs. If there are available resources for a new job, the operator engine requests the job list from the operator service to decide whether to initiate a new job.
8. The operator service provides the list of jobs, and the operator engine is then prepared to start a new job.

## Creating the K8 Cluster and Allocating Job Volumes
9. As a new job begins, volumes are created on the Kubernetes cluster, a task handled by the operator engine.
10. The cluster creates and allocates volumes for the job using the job volumes.
11. The volumes are created and allocated to the pod.
12. After volume creation and allocation, the operator engine starts "pod-configuration" as a new pod in the cluster.

## Loading Datasets and Algorithms
13. Pod-configuration requests the necessary dataset(s) and algorithm from their respective providers.
14. The files are downloaded by the pod configuration via the provider.
15. The pod configuration writes the datasets in the job volume.
16. The pod configuration informs the operator engine that it's ready to start the job.

## Running the Algorithm on Dataset(s)
17. The operator engine launches the algorithm pod on the Kubernetes cluster, with volume containing dataset(s) and algorithm mounted.
18. Kubernetes runs the algorithm pod.
19. The Operator engine monitors the algorithm, stopping it if it exceeds the specified time limit based on the chosen environment.
20. Now that the results are available, the operator engine starts "pod-publishing".
21. The pod publishing uploads the results, logs, and admin logs to the output volume.
22. Upon successful upload, the operator engine receives notification from the pod publishing, allowing it to clean up the job volumes.

## Cleaning Up Volumes and Allocated Space
23. The operator engine deletes the K8 volumes.
24. The Kubernetes cluster removes all used volumes.
25. Once volumes are deleted, the operator engine finalizes the job.
26. The operator engine informs the operator service that the job is completed, and the results are now accessible.

## Retrieving Job Details
27. The consumer retrieves job details by calling the provider's `get job details`.
28. The provider communicates with the operator service to fetch job details.
29. The operator service returns the job details to the provider.
30. With the job details, the provider can share them with the dataset consumer.

## Retrieving Job Results
31. Equipped with job details, the dataset consumer can retrieve the results from the recently executed job.
32. The provider engages the operator engine to access the job results.
33. As the operator service lacks access to this information, it uses the output volume to fetch the results.
34. The output volume provides the stored job results to the operator service.
35. The operator service shares the results with the provider.
36. The provider then delivers the results to the dataset consumer.