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+---
+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. 💫 Get ready to uncover the intricacies of C2D and how it utilizes various actors and components for a blend of privacy and efficiency.
+
+Ready for the deep dive? Let's get started! 🌊
+
+## Starting a C2D Job
+1. The consumer initiates a compute-to-data job by selecting the desired data asset and algorithm.
+2. The provider checks the orders on the blockchain.
+3. If the orders are valid, the provider can commence the compute flow.
+4. The provider informs the consumer of the job number's successful creation.
+5. With the job ID and confirmation of the orders, the consumer starts the job by calling the operator service.
+6. The operator service communicates with the operator engine to initiate a new job.
+
+## Creating the K8 Cluster and Allocating Job Volumes
+7. As a new job begins, volumes are created on the Kubernetes cluster, a task handled by the operator engine.
+8. The cluster creates and allocates volumes for the job using the job volumes
+9. The volumes are created and allocated to the pod
+10. After volume creation and allocation, the operator engine initiates the `run script` on the pod configuration.
+
+## Loading Assets and Algorithms
+11. The pod configuration requires the data asset and algorithm, prompting a request to the provider for retrieval.
+12. The provider uploads assets to the allocated job volume.
+13. Upon completion of file uploads, the provider notifies the pod configuration that the assets are ready for the job.
+14. The pod configuration informs the operator engine that it's ready to start the job.
+
+## Running the Algorithm on Data Asset(s)
+15. The operator engine launches the algorithm pod on the Kubernetes cluster.
+16. Kubernetes runs the algorithm pod.
+17. When the algorithm completes processing the dataset, the operator engine receives confirmation.
+18. Now that the results are available, the operator engine runs the script on the pod publishing component.
+19. The pod publishing uploads the results, logs, and admin logs to the output volume.
+20. 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
+21. The operator engine deletes the K8 volumes.
+22. The Kubernetes cluster removes all used volumes.
+23. Once volumes are deleted, the operator engine finalizes the job.
+24. The operator engine informs the operator service that the job is completed, and the results are now accessible.
+
+## Retrieving Job Details
+25. The consumer retrieves job details by calling the provider's `get job details`.
+26. The provider communicates with the operator service to fetch job details.
+27. The operator service returns the job details to the provider.
+28. With the job details, the provider can share them with the asset consumer.
+
+## Retrieving Job Results
+29. Equipped with job details, the asset consumer can retrieve the results from the recently executed job.
+30. The provider engages the operator engine to access the job results.
+31. As the operator service lacks access to this information, it uses the output volume to fetch the results.
+32. The output volume provides the stored job results to the operator service.
+33. The operator service shares the results with the provider.
+34. The provider then delivers the results to the asset consumer.
+
+
+For visual clarity, here's an image of the workflow in action! 🖼️✨
+
+
