
# Dataset Card for Openvalidators dataset

## Dataset Description

- **Repository:** https://github.com/opentensor/validators
- **Homepage:** https://bittensor.com/

### Dataset Summary

The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the [OpenValidators](https://github.com/opentensor/validators) project in [W&B](https://wandb.ai/opentensor-dev/openvalidators/table). It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing.


### How to use

The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale.

The OpenValidators dataset gives you the granularity of extracting data by ************run_id************, by ************************************OpenValidators version************************************ and by ******************************************************************multiple OpenValidators versions.****************************************************************** The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.

**Downloading by run id**

For example, to download the data for a specific run, simply specify the corresponding ********************************************OpenValidators version******************************************** and the ************************wandb run id************************ in the format `version/raw_data/run_id.parquet`:

```python
from datasets import load_dataset

version = '1.0.4' # OpenValidators version
run_id = '0plco3n0' # WandB run id
run_id_dataset = load_dataset('opentensor/openvalidators-test', data_files=f'{version}/raw_data/{run_id}.parquet')
```

_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._

**Downloading by OpenValidators version**

One can also leverage the `datasets` library to download all the runs within a determined ****************************OpenValidators**************************** version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific ****************************OpenValidators**************************** version state.

```python
from datasets import load_dataset

version = '1.0.4' # Openvalidators version
version_dataset = load_dataset('opentensor/openvalidators-test', data_files=f'{version}/raw_data/*')
```

**Downloading by multiple OpenValidators version**

Utilizing the `datasets` library, users can efficiently download runs from multiple **OpenValidators** versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.

```python
from datasets import load_dataset

versions = ['1.0.0', '1.0.1', '1.0.2', '1.0.4'] # Desired versions for extraction
data_files = [f'{version}/raw_data/*' for version in versions] # Set data files directories
dataset = load_dataset('opentensor/openvalidators-test', data_files={ 'test': data_files })
```

**Analyzing metadata**

All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.

```python
import pandas as pd

version = '1.0.4' # OpenValidators version for metadata analysis
df = pd.read_csv(f'hf://datasets/opentensor/openvalidators-test/{version}/metadata.csv')
```

## Dataset Structure

### Data Instances

**versioned raw_data**

The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at `version/raw_data` where each file is a wandb run.

**metadata**

This dataset defines the current state of the wandb data ingestion by **run id**.

### Data Fields

**Raw data**

The versioned raw_data collected from W&B follows the following schema:

- `_runtime`:  (float64) Runtime of the event
- `_step`: (int64) Step of the event
- `_timestamp`: (float64) Timestamp of the event
- `answer_completions`: (list(string)) Completions of the answer_prompt
- `answer_prompt`: (string) Prompt used to generate the answer
- `answer_rewards`: (list(float64)) Rewards of the answer responses
- `answer_times`: (list(float64)) Elapsed time of answer responses
- `answer_uids`: (list(int32)) UIDs of nodes that answered the answer_prompt
- `base_prompt`: (string) Bootstrap prompt
- `best_answer`: (string) Best answer response
- `best_followup`: (string) Best followup response
- `block`: (float64) Subtensor current block
- `followup_completions`: (list(string)) Completions of the base_prompt
- `followup_rewards`: (list(float64)) Rewards of the followup responses
- `followup_times`: (list(float64)) Ellapsed time of followup responses
- `followup_uids`: (list(int64)) UIDs of nodes that answered the base_prompt
- `gating_loss`: (float64) Gating model loss
- `gating_scorings`: (list(float64)) Gating model scores
- `moving_averaged_scores`: (list(float64)) Moving averaged scores at the time of the event
- `set_weights`: (list(list(float64))) Processed weights of nodes by uid
- `step_length`: (float64) Time difference from beginning of forward call to event logging

**Metadata**

- `run_id`: (string) Wandb Run Id
- `completed`: (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- `downloaded`: (boolean) Flag indicating if the run_id data has been downloaded
- `last_checkpoint`: (string) Last checkpoint of the run_id
- `hotkey`: (string) Hotkey associated with the run_id
- `openvalidators_version`: (string) Version of OpenValidators associated with the run_id
- `problematic`: (boolean) Flag indicating if the run_id data had problems to be ingested
- `problematic_reason`: (string) Reason for the run_id being problematic (Exception message)
- `wandb_json_config`: (string) JSON configuration associated with the run_id in Wandb
- `wandb_run_name`: (string) Name of the Wandb run
- `wandb_user_info`: (string) Username information associated with the Wandb run
- `wandb_tags`: (list) List of tags associated with the Wandb run
- `wandb_createdAt`: (string) Timestamp of the run creation in Wandb


## Dataset Creation

### Curation Rationale

This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network. 
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining.

### Source Data

#### Initial Data Collection and Normalization

The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a `metadata.csv` file is included to manage the collection state, while the raw data of each run is saved in the `.parquet` format with the file name corresponding to the run ID (e.g., `run_id.parquet`). Please note that the code for this data collection process will be released for transparency and reproducibility.

#### Who are the source language producers?

The language producers for this dataset are all the openvalidators  that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at https://wandb.ai/opentensor-dev/openvalidators/table.

### Licensing Information
The dataset is licensed under the [MIT License](https://github.com/opentensor/validators/blob/main/LICENSE)


### Supported Tasks and Leaderboards

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]
size_categories:1M<n<10M license:mit region:us
{"license": "mit", "size_categories": ["1M<n<10M"], "viewer": false}
TAGS
#size_categories-1M<n<10M #license-mit #region-us 


# Dataset Card for Openvalidators dataset

## Dataset Description

- Repository: URL
- Homepage: URL

### Dataset Summary

The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the OpenValidators project in W&B. It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing.


### How to use

The 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale.

The OpenValidators dataset gives you the granularity of extracting data by run_id, by OpenValidators version and by multiple OpenValidators versions. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function.

Downloading by run id

For example, to download the data for a specific run, simply specify the corresponding OpenValidators version and the wandb run id in the format 'version/raw_data/run_id.parquet':



_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._

Downloading by OpenValidators version

One can also leverage the 'datasets' library to download all the runs within a determined OpenValidators version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific OpenValidators version state.



Downloading by multiple OpenValidators version

Utilizing the 'datasets' library, users can efficiently download runs from multiple OpenValidators versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.



Analyzing metadata

All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.



## Dataset Structure

### Data Instances

versioned raw_data

The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at 'version/raw_data' where each file is a wandb run.

metadata

This dataset defines the current state of the wandb data ingestion by run id.

### Data Fields

Raw data

The versioned raw_data collected from W&B follows the following schema:

- '_runtime':  (float64) Runtime of the event
- '_step': (int64) Step of the event
- '_timestamp': (float64) Timestamp of the event
- 'answer_completions': (list(string)) Completions of the answer_prompt
- 'answer_prompt': (string) Prompt used to generate the answer
- 'answer_rewards': (list(float64)) Rewards of the answer responses
- 'answer_times': (list(float64)) Elapsed time of answer responses
- 'answer_uids': (list(int32)) UIDs of nodes that answered the answer_prompt
- 'base_prompt': (string) Bootstrap prompt
- 'best_answer': (string) Best answer response
- 'best_followup': (string) Best followup response
- 'block': (float64) Subtensor current block
- 'followup_completions': (list(string)) Completions of the base_prompt
- 'followup_rewards': (list(float64)) Rewards of the followup responses
- 'followup_times': (list(float64)) Ellapsed time of followup responses
- 'followup_uids': (list(int64)) UIDs of nodes that answered the base_prompt
- 'gating_loss': (float64) Gating model loss
- 'gating_scorings': (list(float64)) Gating model scores
- 'moving_averaged_scores': (list(float64)) Moving averaged scores at the time of the event
- 'set_weights': (list(list(float64))) Processed weights of nodes by uid
- 'step_length': (float64) Time difference from beginning of forward call to event logging

Metadata

- 'run_id': (string) Wandb Run Id
- 'completed': (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- 'downloaded': (boolean) Flag indicating if the run_id data has been downloaded
- 'last_checkpoint': (string) Last checkpoint of the run_id
- 'hotkey': (string) Hotkey associated with the run_id
- 'openvalidators_version': (string) Version of OpenValidators associated with the run_id
- 'problematic': (boolean) Flag indicating if the run_id data had problems to be ingested
- 'problematic_reason': (string) Reason for the run_id being problematic (Exception message)
- 'wandb_json_config': (string) JSON configuration associated with the run_id in Wandb
- 'wandb_run_name': (string) Name of the Wandb run
- 'wandb_user_info': (string) Username information associated with the Wandb run
- 'wandb_tags': (list) List of tags associated with the Wandb run
- 'wandb_createdAt': (string) Timestamp of the run creation in Wandb


## Dataset Creation

### Curation Rationale

This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network. 
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining.

### Source Data

#### Initial Data Collection and Normalization

The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a 'URL' file is included to manage the collection state, while the raw data of each run is saved in the '.parquet' format with the file name corresponding to the run ID (e.g., 'run_id.parquet'). Please note that the code for this data collection process will be released for transparency and reproducibility.

#### Who are the source language producers?

The language producers for this dataset are all the openvalidators  that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at URL

### Licensing Information
The dataset is licensed under the MIT License


### Supported Tasks and Leaderboards







### Contributions


# Dataset Card for Openvalidators dataset ## Dataset Description

- Repository: URL
- Homepage: URL ### Dataset Summary

The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the OpenValidators project in W&B. It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing. ### How to use

The 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale.

The OpenValidators dataset gives you the granularity of extracting data by run_id, by OpenValidators version and by multiple OpenValidators versions. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function.

Downloading by run id

For example, to download the data for a specific run, simply specify the corresponding OpenValidators version and the wandb run id in the format 'version/raw_data/run_id.parquet':



_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._

Downloading by OpenValidators version

One can also leverage the 'datasets' library to download all the runs within a determined OpenValidators version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific OpenValidators version state.



Downloading by multiple OpenValidators version

Utilizing the 'datasets' library, users can efficiently download runs from multiple OpenValidators versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.



Analyzing metadata

All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state. ## Dataset Structure ### Data Instances

versioned raw_data

The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at 'version/raw_data' where each file is a wandb run.

metadata

This dataset defines the current state of the wandb data ingestion by run id. ### Data Fields

Raw data

The versioned raw_data collected from W&B follows the following schema:

- '_runtime':  (float64) Runtime of the event
- '_step': (int64) Step of the event
- '_timestamp': (float64) Timestamp of the event
- 'answer_completions': (list(string)) Completions of the answer_prompt
- 'answer_prompt': (string) Prompt used to generate the answer
- 'answer_rewards': (list(float64)) Rewards of the answer responses
- 'answer_times': (list(float64)) Elapsed time of answer responses
- 'answer_uids': (list(int32)) UIDs of nodes that answered the answer_prompt
- 'base_prompt': (string) Bootstrap prompt
- 'best_answer': (string) Best answer response
- 'best_followup': (string) Best followup response
- 'block': (float64) Subtensor current block
- 'followup_completions': (list(string)) Completions of the base_prompt
- 'followup_rewards': (list(float64)) Rewards of the followup responses
- 'followup_times': (list(float64)) Ellapsed time of followup responses
- 'followup_uids': (list(int64)) UIDs of nodes that answered the base_prompt
- 'gating_loss': (float64) Gating model loss
- 'gating_scorings': (list(float64)) Gating model scores
- 'moving_averaged_scores': (list(float64)) Moving averaged scores at the time of the event
- 'set_weights': (list(list(float64))) Processed weights of nodes by uid
- 'step_length': (float64) Time difference from beginning of forward call to event logging

Metadata

- 'run_id': (string) Wandb Run Id
- 'completed': (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- 'downloaded': (boolean) Flag indicating if the run_id data has been downloaded
- 'last_checkpoint': (string) Last checkpoint of the run_id
- 'hotkey': (string) Hotkey associated with the run_id
- 'openvalidators_version': (string) Version of OpenValidators associated with the run_id
- 'problematic': (boolean) Flag indicating if the run_id data had problems to be ingested
- 'problematic_reason': (string) Reason for the run_id being problematic (Exception message)
- 'wandb_json_config': (string) JSON configuration associated with the run_id in Wandb
- 'wandb_run_name': (string) Name of the Wandb run
- 'wandb_user_info': (string) Username information associated with the Wandb run
- 'wandb_tags': (list) List of tags associated with the Wandb run
- 'wandb_createdAt': (string) Timestamp of the run creation in Wandb ## Dataset Creation ### Curation Rationale

This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network. 
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining. ### Source Data #### Initial Data Collection and Normalization

The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a 'URL' file is included to manage the collection state, while the raw data of each run is saved in the '.parquet' format with the file name corresponding to the run ID (e.g., 'run_id.parquet'). Please note that the code for this data collection process will be released for transparency and reproducibility. #### Who are the source language producers?

The language producers for this dataset are all the openvalidators  that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at URL ### Licensing Information
The dataset is licensed under the MIT License ### Supported Tasks and Leaderboards ### Contributions
TAGS
#size_categories-1M<n<10M #license-mit #region-us 
 # Dataset Card for Openvalidators dataset ## Dataset Description

- Repository: URL
- Homepage: URL ### Dataset Summary

The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the OpenValidators project in W&B. It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing. ### How to use

The 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale.

The OpenValidators dataset gives you the granularity of extracting data by run_id, by OpenValidators version and by multiple OpenValidators versions. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function.

Downloading by run id

For example, to download the data for a specific run, simply specify the corresponding OpenValidators version and the wandb run id in the format 'version/raw_data/run_id.parquet':



_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._

Downloading by OpenValidators version

One can also leverage the 'datasets' library to download all the runs within a determined OpenValidators version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific OpenValidators version state.



Downloading by multiple OpenValidators version

Utilizing the 'datasets' library, users can efficiently download runs from multiple OpenValidators versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.



Analyzing metadata

All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state. ## Dataset Structure ### Data Instances

versioned raw_data

The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at 'version/raw_data' where each file is a wandb run.

metadata

This dataset defines the current state of the wandb data ingestion by run id. ### Data Fields

Raw data

The versioned raw_data collected from W&B follows the following schema:

- '_runtime':  (float64) Runtime of the event
- '_step': (int64) Step of the event
- '_timestamp': (float64) Timestamp of the event
- 'answer_completions': (list(string)) Completions of the answer_prompt
- 'answer_prompt': (string) Prompt used to generate the answer
- 'answer_rewards': (list(float64)) Rewards of the answer responses
- 'answer_times': (list(float64)) Elapsed time of answer responses
- 'answer_uids': (list(int32)) UIDs of nodes that answered the answer_prompt
- 'base_prompt': (string) Bootstrap prompt
- 'best_answer': (string) Best answer response
- 'best_followup': (string) Best followup response
- 'block': (float64) Subtensor current block
- 'followup_completions': (list(string)) Completions of the base_prompt
- 'followup_rewards': (list(float64)) Rewards of the followup responses
- 'followup_times': (list(float64)) Ellapsed time of followup responses
- 'followup_uids': (list(int64)) UIDs of nodes that answered the base_prompt
- 'gating_loss': (float64) Gating model loss
- 'gating_scorings': (list(float64)) Gating model scores
- 'moving_averaged_scores': (list(float64)) Moving averaged scores at the time of the event
- 'set_weights': (list(list(float64))) Processed weights of nodes by uid
- 'step_length': (float64) Time difference from beginning of forward call to event logging

Metadata

- 'run_id': (string) Wandb Run Id
- 'completed': (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- 'downloaded': (boolean) Flag indicating if the run_id data has been downloaded
- 'last_checkpoint': (string) Last checkpoint of the run_id
- 'hotkey': (string) Hotkey associated with the run_id
- 'openvalidators_version': (string) Version of OpenValidators associated with the run_id
- 'problematic': (boolean) Flag indicating if the run_id data had problems to be ingested
- 'problematic_reason': (string) Reason for the run_id being problematic (Exception message)
- 'wandb_json_config': (string) JSON configuration associated with the run_id in Wandb
- 'wandb_run_name': (string) Name of the Wandb run
- 'wandb_user_info': (string) Username information associated with the Wandb run
- 'wandb_tags': (list) List of tags associated with the Wandb run
- 'wandb_createdAt': (string) Timestamp of the run creation in Wandb ## Dataset Creation ### Curation Rationale

This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network. 
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining. ### Source Data #### Initial Data Collection and Normalization

The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a 'URL' file is included to manage the collection state, while the raw data of each run is saved in the '.parquet' format with the file name corresponding to the run ID (e.g., 'run_id.parquet'). Please note that the code for this data collection process will be released for transparency and reproducibility. #### Who are the source language producers?

The language producers for this dataset are all the openvalidators  that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at URL ### Licensing Information
The dataset is licensed under the MIT License ### Supported Tasks and Leaderboards ### Contributions
passage: TAGS
#size_categories-1M<n<10M #license-mit #region-us 
# Dataset Card for Openvalidators dataset## Dataset Description

- Repository: URL
- Homepage: URL### Dataset Summary

The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated by the OpenValidators project in W&B. It contains hundreds of thousands of records and serves researchers, data scientists, and miners in the Bittensor network. The dataset provides information on network performance, node behaviors, and wandb run details. Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis. Miners can use the generated data to fine-tune their models and enhance their incentives in the network. The dataset's continuous updates support collaboration and innovation in decentralized computing. passage: ### How to use

The 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale.

The OpenValidators dataset gives you the granularity of extracting data by run_id, by OpenValidators version and by multiple OpenValidators versions. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function.

Downloading by run id

For example, to download the data for a specific run, simply specify the corresponding OpenValidators version and the wandb run id in the format 'version/raw_data/run_id.parquet':



_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._

Downloading by OpenValidators version

One can also leverage the 'datasets' library to download all the runs within a determined OpenValidators version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific OpenValidators version state.



Downloading by multiple OpenValidators version

Utilizing the 'datasets' library, users can efficiently download runs from multiple OpenValidators versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.



Analyzing metadata

All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.## Dataset Structure### Data Instances

versioned raw_data

The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at 'version/raw_data' where each file is a wandb run.

metadata

This dataset defines the current state of the wandb data ingestion by run id.