From 2a9f0a10c5afc682a2110973e192b8541b50dd8a Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Wed, 18 Oct 2023 17:45:19 +0300 Subject: [PATCH 01/23] Added main structure to add c2d main flow --- .gitbook/assets/c2d/c2d_detailed_flow.png | Bin 0 -> 194085 bytes SUMMARY.md | 1 + developers/aquarius/README.md | 2 - developers/compute-to-data/README.md | 2 +- developers/compute-to-data/compute-job.md | 9 + developers/compute-to-data/compute-to-data.md | 32 --- .../user-defined-parameters.md | 186 ------------------ 7 files changed, 11 insertions(+), 221 deletions(-) create mode 100644 .gitbook/assets/c2d/c2d_detailed_flow.png create mode 100644 developers/compute-to-data/compute-job.md delete mode 100644 developers/compute-to-data/compute-to-data.md delete mode 100644 developers/compute-to-data/user-defined-parameters.md diff --git a/.gitbook/assets/c2d/c2d_detailed_flow.png b/.gitbook/assets/c2d/c2d_detailed_flow.png new file mode 100644 index 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^0n=TF3QS9=_7p%ti|gtCi%2FGrQ; zf`5SHKO!KFtAlb~_|VOL>^~(v)^cy+jkW?u`J84xFIMzk%w>yqem=w;P4{C}Bj~u` zCPa*wAMS#lUS7-SlH>mG^wewz2Zrtbt*TTuj}e^C(0dKU=Ym3~_jaHQ^l^nxnP`ad zkgpe7W-!!L;F?kEV&M|LRfd|TQcfFPUwb>|zUCBm%EPd1c&JC-{4B>=8e39SxyM#k z_f7mooF>beiJwG^d0qvN6zbQ+1BK(;%sDSq_IxjNO9*mhk`eb*iC``Wb)h(U26b>+ z4UIKD0#DFiCO#F1!J%IJ9S<|2>aP!>4aUt5PnYzof%mr_XKM7DgLmz)`%n$jvr|9$ z^tJosQkY0$TCWY*J|f8&bWy6t>h~)jTs^Qjc*`j)YSu9rIE0B~TQmGm-T+u0|1(gR z5JML*i~1c&orU=8l`I>8*>~h&#-wkdyKrV>`S3PKVl|XoCX4xL53&YqlPxAGT>li4 zpxU&`?N`4*FkQu(K2WnV!|4U9!>`0%AEXjCW~d-_`ibs+i#kV%yN36n&);daa@HNG-7m&7*CF8(0#^b(>wEN@je7|%SD&58{kpaEiEKe2 z2&(a9smI*@cS9*@7|sM$yB@7SnhQVGd zf5d)UF>Rodud&=h3ub*yvk>_v5GO$f;IQYjD+-TExPMyi_ER+ce>Wo?Bj6uc+nldG z_-5Dgd2lLRR03Ol4)v{2F8%-aO9>8?Q=tT>xHaql-mCnd^$PeRNrrHik|vWpe)#|S f$EX+AOt&B4y6$|}Z(+592L33@smWGKn+5+5E*_-U literal 0 HcmV?d00001 diff --git a/SUMMARY.md b/SUMMARY.md index 63aea7a1..68aa821b 100644 --- a/SUMMARY.md +++ b/SUMMARY.md @@ -92,6 +92,7 @@ - [Compute to data](developers/compute-to-data/README.md) - [Architecture](developers/compute-to-data/compute-to-data-architecture.md) - [Datasets & Algorithms](developers/compute-to-data/compute-to-data-datasets-algorithms.md) + - [Jobs](developers/compute-to-data/compute-job.md) - [Writing Algorithms](developers/compute-to-data/compute-to-data-algorithms.md) - [Compute Options](developers/compute-to-data/compute-options.md) - [Aquarius](developers/aquarius/README.md) diff --git a/developers/aquarius/README.md b/developers/aquarius/README.md index 9691b788..748d545e 100644 --- a/developers/aquarius/README.md +++ b/developers/aquarius/README.md @@ -8,8 +8,6 @@ The core job of Aquarius is to continually look out for new metadata being creat Aquarius has its own interface (API) that allows you to easily query this metadata. With Aquarius, you don't need to do the time-consuming task of scanning the data chains yourself. It offers you a convenient shortcut to the information you need. It's ideal for when you need a search feature within your dApp. - -

Aquarius high level overview

### What does Aquarius do? diff --git a/developers/compute-to-data/README.md b/developers/compute-to-data/README.md index e2d1e526..fd18c1b1 100644 --- a/developers/compute-to-data/README.md +++ b/developers/compute-to-data/README.md @@ -15,7 +15,7 @@ Private data holds immense value as it can significantly enhance research and bu Private data has the potential to drive groundbreaking discoveries in science and technology, with increased data improving the predictive accuracy of modern AI models. Due to its scarcity and the challenges associated with accessing it, private data is often regarded as the most valuable. By utilizing private data through Compute-to-Data, significant rewards can be reaped, leading to transformative advancements and innovative breakthroughs. {% hint style="info" %} -The Ocean Protocol provides a compute environment that you can access at the following address: [https://stagev4.c2d.oceanprotocol.com/](https://stagev4.c2d.oceanprotocol.com/). Feel free to explore and utilize this platform for your needs. +The Ocean Protocol provides a compute environment that you can access at the following [address](https://stagev4.c2d.oceanprotocol.com/). Feel free to explore and utilize this platform for your needs. {% endhint %} We suggest reading these guides to get an understanding of how compute-to-data works: diff --git a/developers/compute-to-data/compute-job.md b/developers/compute-to-data/compute-job.md new file mode 100644 index 00000000..e8190a89 --- /dev/null +++ b/developers/compute-to-data/compute-job.md @@ -0,0 +1,9 @@ +--- +title: Compute Jobs +section: developers +description: Specification of compute options for assets in Ocean Protocol. +--- + +

Compute detailed flow diagram

+ + diff --git a/developers/compute-to-data/compute-to-data.md b/developers/compute-to-data/compute-to-data.md deleted file mode 100644 index d3bfff50..00000000 --- a/developers/compute-to-data/compute-to-data.md +++ /dev/null @@ -1,32 +0,0 @@ ---- -title: Compute-to-Data -slug: /concepts/compute-to-data/ -section: concepts -description: Providing access to data in a privacy-preserving fashion ---- - -# Compute-to-Data - -### Quick Start - -* [Compute-to-Data example](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/c2d-flow.md) - -### Motivation - -The most basic scenario for a Publisher is to provide access to the datasets they own or manage. However, a Publisher may offer a service to execute some computation on top of their data. This has some benefits: - -* The data **never** leaves the Publisher enclave. -* It's not necessary to move the data; the algorithm is sent to the data. -* Having only one copy of the data and not moving it makes it easier to be compliant with data protection regulations. - -[This page](https://oceanprotocol.com/technology/compute-to-data) elaborates on the benefits. - -### Further Reading - -* [Compute-to-Data architecture](compute-to-data-architecture.md) -* [Tutorial: Writing Algorithms](compute-to-data-algorithms.md) -* [Tutorial: Set Up a Compute-to-Data Environment](../../infrastructure/compute-to-data-minikube.md) -* [Use Compute-to-Data in Ocean Market](https://blog.oceanprotocol.com/compute-to-data-is-now-available-in-ocean-market-58868be52ef7) -* [Build ML models via Ocean Market or Python](https://medium.com/ravenprotocol/machine-learning-series-using-logistic-regression-for-classification-in-oceans-compute-to-data-18df49b6b165) -* [Compute-to-Data Python Quickstart](https://github.com/oceanprotocol/ocean.py/blob/main/READMEs/c2d-flow.md) -* [(Old) Compute-to-Data specs](https://github.com/oceanprotocol-archive/OEPs/tree/master/12) (OEP12) diff --git a/developers/compute-to-data/user-defined-parameters.md b/developers/compute-to-data/user-defined-parameters.md deleted file mode 100644 index 4ae86f47..00000000 --- a/developers/compute-to-data/user-defined-parameters.md +++ /dev/null @@ -1,186 +0,0 @@ ---- -description: >- - Learn how to define and use custom parameters while downloading assets or - using dataset in Compute-to-data environment ---- - -# User defined parameters - -### Overview - -Ocean Protocol allows dataset buyers to provide custom parameters that can be used to fetch the downloaded data in a specific format, download a different type of data or pass some additional input to the algorithms in the Compute-to-Data job. - -There 2 are types of parameters that asset publishers can support: - -- User defined parameters -- Algorithm custom parameters - -### Publish a dataset that uses custom parameters - -The dataset publisher can support these parameters to allow filtering or querying of the published dataset. The additional parameters that facilitate this are called `User defined parameters`. The Provider combines the original asset URL and the entered parameter values into a new URL and then streams the response from the modified URL back to the buyer. - -#### Use case for user defined parameters - -For example, if the publisher has published an URL `https://example.com` which serves large size historical weather data from all over the world, the publisher could allow buyers to filter the data based on location, type of data, etc. It is possible to do this using user defined parameters. - -Suppose the publisher defines the following 2 parameters: - -- `location`: A string indicating region code -- `type`: A string indicating the type of weather data. It can be temperature/humidity/pressure. - -Suppose the buyer wants to download the temperature data in the region code `XYZ`. While downloading the data, the buyer enters the desired parameter values using ocean.py. - -The provider will decrypt the URL from the DDO published on-chain, construct a new URL with the additional parameters and finally stream data to the buyer. - -Internally, the new URL will be of the format `https://example.com/?location=XYZ&type=temperature`. The server hosting the data has to read these parameters and serve the appropriate data. - -The following steps will specify how the publisher can support additional parameters. - -#### Step 1: Create a service - -The below python script exposes a REST endpoint that takes two parameters, namely: `location` and `type`. Let's assume that the dataset publisher hosts the service at domain `example.com` along with HTTPS support. The publisher must ensure that the URL is accessible to Provider. - -The code snippet is only for demo purposes and not for production use. - -```python -from flask import Flask, request -def get_data(data_type: str, location: str): - ''' - Add some business logic here to get - the required data with given parameters - ''' - return {} -@app.route('/', methods=['GET']) -def serve_content(): - args = request.args - data_type = args.get('type') - location = args.get('location') - result = get_data(data_type, location) - return result -``` - -#### Step 2: Publish dataset asset with compute service - -The publisher now must provide the file URL as `https://example.org` while publishing the asset, as shown in the below image. - -![Compute to data parameters](../../.gitbook/assets/c2d/compute-to-data-parameters-publish-dataset.png) - -For a complete tutorial on publishing asset using Ocean Marketplace read [our guide on publishing with Ocean Market](../../user-guides//publish-data-nfts.md). - -### Publish an algorithm that uses custom parameters - -#### Use case for algorithm custom parameters - -For example, if the algorithm publisher has published an URL `https://example.org` which serves python script to analyze historical weather data published in the previous section. If the algorithm publisher wants buyers to specify the number of iterations the algorithm must perform over the data, it is possible to do so using algorithm custom parameters. - -Suppose the algorithm publisher defines a parameter called `iterations` and expects the buyer to give this input before running the algorithm in a Compute-to-Data environment. The buyer can enter the desired parameter value using ocean.py or ocean.js. - -The provider passes the entered parameters and saves them in a specific path in the Compute-to-Data environment. The algorithm can later read this value and perform required computations. - -The following steps will specify how the algorithm publisher can support additional algorithm custom parameters. - -#### Step 1: Create an algorithm - -The code snippet is only for demo purposes and not for production use. - -```python -def run_algorithm(i: int): - pass -def read_algorithm_custom_input(): - parameters_file = os.path.join(os.sep, "data", "inputs", "algoCustomData.json") - with open(parameters_file, "r") as file: - return json.load(file) -algorithm_inputs = read_algorithm_custom_input() -iterations = algorithm_inputs["iterations"] -for i in range(iterations): - # Run some machine learning algorithm - print(f"Running iteration {i}") - result = run_algorithm(i) -output_dir = os.path.join(os.sep, "data", "outputs") -with open(os.path.join(output_dir, "result"), "w") as f: - f.write(result) -``` - -#### Step 2: Publish algorithm asset - -The publisher now must provide the file URL as `https://example.org` while publishing the algorithm asset, as shown in the below image. - -![Publish algorithm asset](../../.gitbook/assets/c2d/compute-to-data-parameters-publish-algorithm.png) - -For a complete tutorial on publishing asset using Ocean Marketplace read [our guide on publishing with Ocean Market](../../user-guides/publish-data-nfts.md). - -### Starting compute job with custom parameters - -In this example, the buyer wants to run the algorithm with certain parameters on a selected dataset. The code snippet below shows how the buyer can start the compute job with custom parameter values. Before embarking on this tutorial you should familiarize yourself with how to: - -- Search for a dataset using [Ocean market](https://market.oceanprotocol.com/) or [Aquarius](../aquarius/README.md) -- [Allow an algorithm to run on the dataset](https://github.com/oceanprotocol/ocean.py/blob/6eb068df338abc7376430cc5ba7fe2d381508328/READMEs/c2d-flow.md#5-alice-allows-the-algorithm-for-c2d-for-that-data-asset) -- Buy datatokens using [Ocean market](https://market.oceanprotocol.com/) or [ocean.py](https://github.com/oceanprotocol/ocean.py) -- [Set up ocean.py](../ocean.py/install.md) - -{% tabs %} -{% tab title="Python" %} - -
# Import dependencies
-from config import web3_wallet, ocean, config, web3_wallet
-from ocean_lib.models.compute_input import ComputeInput
-# Replace theses variables with the appropriate did values
-dataset_did = "did:op:<>"
-algorithm_did = "did:op:<>"
-
-# Define algorithm input
-algorithm_input = {
-        "iterations": 1000
-}
-
-# Define dataset parameters
-dataset_input = {
-    "type": "temperature",
-    "location": "XYZ"
-}
-
-# Resolve assets using Aquarius
-aquarius = Aquarius.get_instance(config.metadata_cache_uri)
-DATA_asset = aquarius.wait_for_asset(dataset_did)
-ALGO_asset = aquarius.wait_for_asset(algorithm_did)
-
-compute_service = DATA_asset.services[0]
-algo_service = ALGO_asset.services[0]
-free_c2d_env = ocean.compute.get_free_c2d_environment(compute_service.service_endpoint)
-    
-DATA_compute_input = ComputeInput(DATA_asset, compute_service, userdata=dataset_input)
-ALGO_compute_input = ComputeInput(ALGO_asset, algo_service)
-
-# Pay for the compute job
-datasets, algorithm = ocean.assets.pay_for_compute_service(
-    datasets=[DATA_compute_input],
-    algorithm_data=ALGO_compute_input,
-    consume_market_order_fee_address=web3_wallet.address,
-    wallet=web3_wallet,
-    compute_environment=free_c2d_env["id"],
-    valid_until=int((datetime.utcnow() + timedelta(days=1)).timestamp()),
-    consumer_address=free_c2d_env["consumerAddress"],
-)
-
-assert datasets, "pay for dataset unsuccessful"
-assert algorithm, "pay for algorithm unsuccessful"
-
-# Start compute job
-job_id = ocean.compute.start(
-    consumer_wallet=web3_wallet,
-    dataset=datasets[0],
-    compute_environment=free_c2d_env["id"],
-    algorithm=algorithm,
-    algorithm_algocustomdata=algorithm_input
-)
-# Printing the job id here. Use this job_id to retrive the result of the compute job. 
-print("job_id", job_id)
- -Execute script - -```bash -python start_compute.py -``` - -{% endtab %} -{% endtabs %} From a3d05219e95ec880cf27019829a3205a816339b7 Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Thu, 19 Oct 2023 15:07:12 +0300 Subject: [PATCH 02/23] Added C2D workflow details --- SUMMARY.md | 2 +- developers/compute-to-data/compute-job.md | 9 --- .../compute-to-data/compute-workflow.md | 62 +++++++++++++++++++ 3 files changed, 63 insertions(+), 10 deletions(-) delete mode 100644 developers/compute-to-data/compute-job.md create mode 100644 developers/compute-to-data/compute-workflow.md diff --git a/SUMMARY.md b/SUMMARY.md index 4fe8f8a6..c8c98034 100644 --- a/SUMMARY.md +++ b/SUMMARY.md @@ -92,7 +92,7 @@ - [Compute to data](developers/compute-to-data/README.md) - [Architecture](developers/compute-to-data/compute-to-data-architecture.md) - [Datasets & Algorithms](developers/compute-to-data/compute-to-data-datasets-algorithms.md) - - [Jobs](developers/compute-to-data/compute-job.md) + - [Jobs](developers/compute-to-data/compute-workflow.md) - [Writing Algorithms](developers/compute-to-data/compute-to-data-algorithms.md) - [Compute Options](developers/compute-to-data/compute-options.md) - [Aquarius](developers/aquarius/README.md) diff --git a/developers/compute-to-data/compute-job.md b/developers/compute-to-data/compute-job.md deleted file mode 100644 index e8190a89..00000000 --- a/developers/compute-to-data/compute-job.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -title: Compute Jobs -section: developers -description: Specification of compute options for assets in Ocean Protocol. ---- - -

Compute detailed flow diagram

- - diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md new file mode 100644 index 00000000..217c98a9 --- /dev/null +++ b/developers/compute-to-data/compute-workflow.md @@ -0,0 +1,62 @@ +--- +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! 🖼️✨ + +

Compute detailed flow diagram

From 983d733d2ec0135ece15ef2c183119cf876bf9f2 Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Thu, 19 Oct 2023 15:09:17 +0300 Subject: [PATCH 03/23] no message --- SUMMARY.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SUMMARY.md b/SUMMARY.md index c8c98034..7ef97042 100644 --- a/SUMMARY.md +++ b/SUMMARY.md @@ -92,7 +92,7 @@ - [Compute to data](developers/compute-to-data/README.md) - [Architecture](developers/compute-to-data/compute-to-data-architecture.md) - [Datasets & Algorithms](developers/compute-to-data/compute-to-data-datasets-algorithms.md) - - [Jobs](developers/compute-to-data/compute-workflow.md) + - [Workflow](developers/compute-to-data/compute-workflow.md) - [Writing Algorithms](developers/compute-to-data/compute-to-data-algorithms.md) - [Compute Options](developers/compute-to-data/compute-options.md) - [Aquarius](developers/aquarius/README.md) From 6fe97ffc87883741e4a4531e4472ed065b515893 Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Thu, 19 Oct 2023 15:14:07 +0300 Subject: [PATCH 04/23] Updated image and the order of text/image --- developers/compute-to-data/compute-workflow.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 217c98a9..c224f1a2 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -6,7 +6,11 @@ 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! 🌊 +For visual clarity, here's an image of the workflow in action! 🖼️✨ + +

Compute detailed flow diagram

+ +Below, we'll outline each step in detail 📝 ## Starting a C2D Job 1. The consumer initiates a compute-to-data job by selecting the desired data asset and algorithm. @@ -56,7 +60,3 @@ Ready for the deep dive? Let's get started! 🌊 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! 🖼️✨ - -

Compute detailed flow diagram

From 8ef6b2a434862f8bd58fa12d7e2df5e248fbae9b Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Thu, 19 Oct 2023 15:17:45 +0300 Subject: [PATCH 05/23] updated intro --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index c224f1a2..452b8d54 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -4,7 +4,7 @@ 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. +🚀 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! 🖼️✨ From c998fa5344d432c2087579ab35751b8d5300e839 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:21:24 +0300 Subject: [PATCH 06/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Alex Coseru --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 452b8d54..ab5861ac 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -33,7 +33,7 @@ Below, we'll outline each step in detail 📝 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. +15. The operator engine launches the algorithm pod on the Kubernetes cluster, with volume containing data asset(s) and algorithm mounted. 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. From 68c7cda42ec015fa222e4d377b343cadce360e08 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:22:02 +0300 Subject: [PATCH 07/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Bogdan Fazakas --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index ab5861ac..427f4135 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -15,7 +15,7 @@ Below, we'll outline each step in detail 📝 ## 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. +3. If the orders for data asset, algorithm and compute environment fees 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. From 509a8b883b2640d3c14f4952c3a414c4b60eb1f2 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:22:48 +0300 Subject: [PATCH 08/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Bogdan Fazakas --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 427f4135..eb0e8fda 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -13,7 +13,7 @@ For visual clarity, here's an image of the workflow in action! 🖼️✨ Below, we'll outline each step in detail 📝 ## Starting a C2D Job -1. The consumer initiates a compute-to-data job by selecting the desired data asset and algorithm. +The consumer selects a preferred environment from the provider's list and initiates a compute-to-data job by choosing a data asset-algorithm pair along with the preferred compute environment. 2. The provider checks the orders on the blockchain. 3. If the orders for data asset, algorithm and compute environment fees are valid, the provider can commence the compute flow. 4. The provider informs the consumer of the job number's successful creation. From f3055e2d8c6d1b0ebf25ce0478db9e920bdfd171 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:23:33 +0300 Subject: [PATCH 09/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Alex Coseru --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index eb0e8fda..90386334 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -24,7 +24,7 @@ The consumer selects a preferred environment from the provider's list and initia 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. +10. After volume creation and allocation, the operator engine starts "pod-configuration" as a new pod in the cluster. ## Loading Assets and Algorithms 11. The pod configuration requires the data asset and algorithm, prompting a request to the provider for retrieval. From ce69cbe8ece2caf0295254da48f23ec87685f9d4 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:24:09 +0300 Subject: [PATCH 10/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Alex Coseru --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 90386334..391fa897 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -27,7 +27,7 @@ The consumer selects a preferred environment from the provider's list and initia 10. After volume creation and allocation, the operator engine starts "pod-configuration" as a new pod in the cluster. ## Loading Assets and Algorithms -11. The pod configuration requires the data asset and algorithm, prompting a request to the provider for retrieval. +11. Pod-configuration requests the necessary data asset(s) and algorithm from their respective providers. 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. From deb7415136b9ad63ba0316ee3743841fcc66d8c2 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:24:52 +0300 Subject: [PATCH 11/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Alex Coseru --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 391fa897..78c6b239 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -36,7 +36,7 @@ The consumer selects a preferred environment from the provider's list and initia 15. The operator engine launches the algorithm pod on the Kubernetes cluster, with volume containing data asset(s) and algorithm mounted. 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. +18. Now that the results are available, the operator engine starts "pod-publishing". 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. From a68c9a7c0772e8cf647998ab5e84f6f54ad5fa13 Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:25:56 +0300 Subject: [PATCH 12/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: Alex Coseru --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 78c6b239..e7874b7f 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -35,7 +35,7 @@ The consumer selects a preferred environment from the provider's list and initia ## Running the Algorithm on Data Asset(s) 15. The operator engine launches the algorithm pod on the Kubernetes cluster, with volume containing data asset(s) and algorithm mounted. 16. Kubernetes runs the algorithm pod. -17. When the algorithm completes processing the dataset, the operator engine receives confirmation. +17. The Operator engine monitors the algorithm, stopping it if it exceeds the specified time limit based on the chosen environment. 18. Now that the results are available, the operator engine starts "pod-publishing". 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. From f10b2d5179a63fdfc7e1b3a0ef13aef90ec6155a Mon Sep 17 00:00:00 2001 From: Ana <84312885+AnaLoznianu@users.noreply.github.com> Date: Wed, 25 Oct 2023 18:27:11 +0300 Subject: [PATCH 13/23] Update developers/compute-to-data/compute-workflow.md Co-authored-by: MBadea17 <89251478+MBadea17@users.noreply.github.com> --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index e7874b7f..bee85c27 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -17,7 +17,7 @@ The consumer selects a preferred environment from the provider's list and initia 2. The provider checks the orders on the blockchain. 3. If the orders for data asset, algorithm and compute environment fees 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. +5. With the job ID and confirmation of the orders, the provider starts the job by calling the operator service. 6. 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^0n=TF3QS9=_7p%ti|gtCi%2FGrQ; zf`5SHKO!KFtAlb~_|VOL>^~(v)^cy+jkW?u`J84xFIMzk%w>yqem=w;P4{C}Bj~u` zCPa*wAMS#lUS7-SlH>mG^wewz2Zrtbt*TTuj}e^C(0dKU=Ym3~_jaHQ^l^nxnP`ad zkgpe7W-!!L;F?kEV&M|LRfd|TQcfFPUwb>|zUCBm%EPd1c&JC-{4B>=8e39SxyM#k z_f7mooF>beiJwG^d0qvN6zbQ+1BK(;%sDSq_IxjNO9*mhk`eb*iC``Wb)h(U26b>+ z4UIKD0#DFiCO#F1!J%IJ9S<|2>aP!>4aUt5PnYzof%mr_XKM7DgLmz)`%n$jvr|9$ z^tJosQkY0$TCWY*J|f8&bWy6t>h~)jTs^Qjc*`j)YSu9rIE0B~TQmGm-T+u0|1(gR z5JML*i~1c&orU=8l`I>8*>~h&#-wkdyKrV>`S3PKVl|XoCX4xL53&YqlPxAGT>li4 zpxU&`?N`4*FkQu(K2WnV!|4U9!>`0%AEXjCW~d-_`ibs+i#kV%yN36n&);daa@HNG-7m&7*CF8(0#^b(>wEN@je7|%SD&58{kpaEiEKe2 z2&(a9smI*@cS9*@7|sM$yB@7SnhQVGd zf5d)UF>Rodud&=h3ub*yvk>_v5GO$f;IQYjD+-TExPMyi_ER+ce>Wo?Bj6uc+nldG z_-5Dgd2lLRR03Ol4)v{2F8%-aO9>8?Q=tT>xHaql-mCnd^$PeRNrrHik|vWpe)#|S f$EX+AOt&B4y6$|}Z(+592L33@smWGKn+5+5E*_-U diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index bee85c27..c50ba27b 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -18,45 +18,47 @@ The consumer selects a preferred environment from the provider's list and initia 3. If the orders for data asset, algorithm and compute environment fees 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 provider starts the job by calling the operator service. -6. The operator service communicates with the operator engine to initiate a new job. +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 commence 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 starts "pod-configuration" as a new pod in the cluster. +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 Assets and Algorithms -11. Pod-configuration requests the necessary data asset(s) and algorithm from their respective providers. -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. +13. Pod-configuration requests the necessary data asset(s) and algorithm from their respective providers. +14. The files are downloaded by the pod configuration via provider. +15. The pod configuration writes the assets in the job volume. +16. 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, with volume containing data asset(s) and algorithm mounted. -16. Kubernetes runs the algorithm pod. -17. The Operator engine monitors the algorithm, stopping it if it exceeds the specified time limit based on the chosen environment. -18. Now that the results are available, the operator engine starts "pod-publishing". -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. +17. The operator engine launches the algorithm pod on the Kubernetes cluster, with volume containing data asset(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 -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. +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 -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. +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 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. +31. Equipped with job details, the asset 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 asset consumer. From 21ca2e87994e30ca8d1aaa61b5633605e3b632fa Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Wed, 1 Nov 2023 14:16:16 +0200 Subject: [PATCH 15/23] Fixed list --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index c50ba27b..99c728b8 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -13,7 +13,7 @@ For visual clarity, here's an image of the workflow in action! 🖼️✨ Below, we'll outline each step in detail 📝 ## Starting a C2D Job -The consumer selects a preferred environment from the provider's list and initiates a compute-to-data job by choosing a data asset-algorithm pair along with the preferred compute environment. +1. The consumer selects a preferred environment from the provider's list and initiates a compute-to-data job by choosing a data asset-algorithm pair along with the preferred compute environment. 2. The provider checks the orders on the blockchain. 3. If the orders for data asset, algorithm and compute environment fees are valid, the provider can commence the compute flow. 4. The provider informs the consumer of the job number's successful creation. From f2c513d9560004f1ba576dee2a6455a86974817a Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Wed, 1 Nov 2023 14:23:52 +0200 Subject: [PATCH 16/23] Steps adjustments --- developers/compute-to-data/compute-workflow.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 99c728b8..af140581 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -13,24 +13,24 @@ For visual clarity, here's an image of the workflow in action! 🖼️✨ 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 data asset-algorithm pair along with the preferred compute environment. +1. The consumer selects a preferred environment from the provider's list and initiates a compute-to-data job by choosing a data asset-algorithm pair. 2. The provider checks the orders on the blockchain. -3. If the orders for data asset, algorithm and compute environment fees are valid, the provider can commence the compute flow. -4. The provider informs the consumer of the job number's successful creation. +3. If the orders for data asset, algorithm and compute environment fees are valid, the provider can start the compute flow. +4. The provider informs the consumer of the job id's 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 commence 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 +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 Assets and Algorithms 13. Pod-configuration requests the necessary data asset(s) and algorithm from their respective providers. -14. The files are downloaded by the pod configuration via provider. +14. The files are downloaded by the pod configuration via the provider. 15. The pod configuration writes the assets in the job volume. 16. The pod configuration informs the operator engine that it's ready to start the job. From 1aad928d597483968790dec99acc24576f12c12f Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Mon, 6 Nov 2023 17:34:13 +0200 Subject: [PATCH 17/23] Update the data asset(s) label to dataset --- .../compute-to-data/compute-workflow.md | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index af140581..8b8f7eb8 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -13,9 +13,9 @@ For visual clarity, here's an image of the workflow in action! 🖼️✨ 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 data asset-algorithm pair. +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 data asset, algorithm and compute environment fees are valid, the provider can start the compute flow. +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 id's 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. @@ -28,14 +28,14 @@ Below, we'll outline each step in detail 📝 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 Assets and Algorithms -13. Pod-configuration requests the necessary data asset(s) and algorithm from their respective providers. +## 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 assets in the job volume. +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 Data Asset(s) -17. The operator engine launches the algorithm pod on the Kubernetes cluster, with volume containing data asset(s) and algorithm mounted. +## 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". @@ -52,13 +52,13 @@ Below, we'll outline each step in detail 📝 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 asset consumer. +30. With the job details, the provider can share them with the dataset consumer. ## Retrieving Job Results -31. Equipped with job details, the asset consumer can retrieve the results from the recently executed job. +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 asset consumer. +36. The provider then delivers the results to the dataset consumer. From f0ee84d3a624e1ea08dedd6d4dc4863a6673ba18 Mon Sep 17 00:00:00 2001 From: Ana Loznianu Date: Mon, 6 Nov 2023 19:21:23 +0200 Subject: [PATCH 18/23] Updated apostrophe --- developers/compute-to-data/compute-workflow.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/developers/compute-to-data/compute-workflow.md b/developers/compute-to-data/compute-workflow.md index 8b8f7eb8..d97dcf67 100644 --- a/developers/compute-to-data/compute-workflow.md +++ b/developers/compute-to-data/compute-workflow.md @@ -16,7 +16,7 @@ Below, we'll outline each step in detail 📝 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 id's successful creation. +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. 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z24hXxQ6oir7o+L}J1Tw)LvC9<@xyBgi%`aV0ib5{ag-Nn@_p{F+#VkXB1byQ^|$Hr zJQ=^KFY`%CvePVaSxBD8&BP_&^);yWg5-ph7*=3kyx zc}Ds?HUUAvWCH*BUmor}o6DmvZvb4Vy^U|~<)#5K1xFI!kvHf?x{9)((I7~F`3L+^bSX~rk+GW}CFp3fs z^5t^lN8ko^3y0&tZ)NuzJj~;0K}ug-X63^wl!jgZ*xsB!vd6r+1PCFDJNm?Fzh4zJw;N_CLKy(%m)Af002ovPDHLkV1m?0Z^Zxr literal 0 HcmV?d00001 diff --git a/predictoor/README.md b/predictoor/README.md index 15e204a2..1a9ec22e 100644 --- a/predictoor/README.md +++ b/predictoor/README.md @@ -1,6 +1,6 @@ --- description: Tomorrow belongs to those who can hear it coming. — David Bowie -cover: ../.gitbook/assets/cover/predictoor_banner.jpg +cover: ../.gitbook/assets/cover/predictoor_banner.png coverY: 0 --- From 21d458be2b44cbd9ecec4f86cdc2d74853294b7b Mon Sep 17 00:00:00 2001 From: Trent McConaghy Date: Tue, 7 Nov 2023 09:17:51 +0100 Subject: [PATCH 20/23] Tweak apys.md - remove unwanted line --- data-farming/apys.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/data-farming/apys.md b/data-farming/apys.md index 426a3113..f54f78f1 100644 --- a/data-farming/apys.md +++ b/data-farming/apys.md @@ -7,8 +7,6 @@ description: >-

    Multiply, and multiply again. And again.

    -**Contents:** - **This page** is about APYs. There's also [estimates of APY vs time](apys-plot.md) and [a guide to estimating APYs](apys-guide.md). # About APYs From b0ef6b286fcd8beb9a9d6cb923967a92cbc739a0 Mon Sep 17 00:00:00 2001 From: Trent McConaghy Date: Tue, 7 Nov 2023 09:19:32 +0100 Subject: [PATCH 21/23] Tweak apys.md - small bug fix --- data-farming/apys.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-farming/apys.md b/data-farming/apys.md index f54f78f1..fe241ca0 100644 --- a/data-farming/apys.md +++ b/data-farming/apys.md @@ -33,5 +33,5 @@ We can express yield as a percentage simply by multiplying by 100 (%). For examp The yield for one week is **Weekly Percentage Yield**, or **WPY**. For example, if you started with 1000 OCEAN and earned 5 OCEAN from DF, in one week, then your WPY = 0.005 = 0.5%. -The yield for one year is **Annual Percentage Yield**, or **APY**. APY can be estimated from WPYs. Assuming no compounding, `APY = WPY*52`. We drill deeper below. +The yield for one year is **Annual Percentage Yield**, or **APY**. APY can be estimated from WPYs. Assuming no compounding, `APY = WPY*52`. We drill deeper in the [guide to estimate APYs](apys/apys-guide.md). From 1e6837ba5933c5d4a13f6faaf5afc900f37048ab Mon Sep 17 00:00:00 2001 From: Trent McConaghy Date: Tue, 7 Nov 2023 09:20:16 +0100 Subject: [PATCH 22/23] Fix previous commit --- data-farming/apys.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-farming/apys.md b/data-farming/apys.md index fe241ca0..bfdbf3bb 100644 --- a/data-farming/apys.md +++ b/data-farming/apys.md @@ -33,5 +33,5 @@ We can express yield as a percentage simply by multiplying by 100 (%). For examp The yield for one week is **Weekly Percentage Yield**, or **WPY**. For example, if you started with 1000 OCEAN and earned 5 OCEAN from DF, in one week, then your WPY = 0.005 = 0.5%. -The yield for one year is **Annual Percentage Yield**, or **APY**. APY can be estimated from WPYs. Assuming no compounding, `APY = WPY*52`. We drill deeper in the [guide to estimate APYs](apys/apys-guide.md). +The yield for one year is **Annual Percentage Yield**, or **APY**. APY can be estimated from WPYs. Assuming no compounding, `APY = WPY*52`. We drill deeper in the [guide to estimate APYs](apys-guide.md). From 98120ffde348ec1ade0d520beabd95b317217c73 Mon Sep 17 00:00:00 2001 From: Trent McConaghy Date: Tue, 7 Nov 2023 09:30:44 +0100 Subject: [PATCH 23/23] Tweak pdr-intro.md: link to Oasis Sapphire --- predictoor/pdr-intro.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/predictoor/pdr-intro.md b/predictoor/pdr-intro.md index f21ecfac..512a18f2 100644 --- a/predictoor/pdr-intro.md +++ b/predictoor/pdr-intro.md @@ -13,7 +13,7 @@ description: >- **“Trader” agents buy aggregate predictions,** then use them to take action like buying or selling. The more accurate the predictions, the more easily they make \$, the longer they stick around to keep buying prediction feeds from trading profits. -**Predictoor is built on the Ocean Protocol stack**, including contracts for tokenized data and middleware to cache metadata. To keep predictions private unless paid for, Predictoor uses Oasis Sapphire privacy-preserving EVM chain. +**Predictoor is built on the Ocean Protocol stack**, including contracts for tokenized data and middleware to cache metadata. To keep predictions private unless paid for, Predictoor uses **[Oasis Sapphire](https://oasisprotocol.org/sapphire)** privacy-preserving EVM chain. **The initial dapp is live at [predictoor.ai](https://predictoor.ai).** It’s for up/down predictions of BTC, ETH, and other tokens’ prices. The dapp helps users build a mental model of Predictoor behavior. Predictoors and traders’ main workflow is to do run predicting / trading bots with the help of the Py SDK. We have seeded Predictoor with bots that have AI/ML models of accuracy comfortably above 50% — a precondition to make \$ trading. @@ -53,4 +53,4 @@ The contract then makes agg_predval visible to its subscribers. The predicted va - **Actions by Traders.** Typically, traders exit their position immediately, exactly 5 minutes since they got the 5-minute-ahead prediction and acted*. If the prediction feed was accurate enough and trading fees & slippage weren't too high, then the trader makes money on average. -- **Actions by Trueval agent; predictoors get paid.** The trueval agent is a process that grabs price feeds from e.g. Binance and submits it to chain, inside the smart contract*. The "submit" transaction also takes the opportunity to calculate each Predictoor's reward or slashing, and update their OCEAN holdings in the contract accordingly. (Funds aren't sent per se, they're made available via ERC20 "approve", for the predictoor to transfer at some later point). Predictoor 3 got his OCEAN slashed because he was wrong; predictoors 1 and 2 receive that as income, in addition to receiving income from prediction feed sales to traders. Predictoors can claim their balance anytime. \ No newline at end of file +- **Actions by Trueval agent; predictoors get paid.** The trueval agent is a process that grabs price feeds from e.g. Binance and submits it to chain, inside the smart contract*. The "submit" transaction also takes the opportunity to calculate each Predictoor's reward or slashing, and update their OCEAN holdings in the contract accordingly. (Funds aren't sent per se, they're made available via ERC20 "approve", for the predictoor to transfer at some later point). Predictoor 3 got his OCEAN slashed because he was wrong; predictoors 1 and 2 receive that as income, in addition to receiving income from prediction feed sales to traders. Predictoors can claim their balance anytime.

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