Taoverse Package
This is a package containing various python modules for use in bittensor subnets.
Currently it is primarily supporting the Macrocosmos SN 9 Pretraining and SN 37 Finetuning subnets but much of the code is generally useful and all are welcome to leverage it for their own work as well.
Find the latest published package here: https://pypi.org/project/taoverse/
Following is an overview of each module and some of the bigger pieces of code they include.
Metagraph
The metagraph module contains code relating to metagraph operations.
-
The
MetagraphSyncerallows for easily refreshing a specified metagraph at a specified cadence. It will operate in asynchronously on the asyncio event loop and will notify any registered listeners after each sync. -
The
MinerIteratorprovides a thread safe infinite iterator that safely handles adding new uids.
Model
The model module contains code relating to large language models.
-
The
ModelTrackerwill track model metadata and evaluation history on a per hotkey basis. It is designed to keep state across restarts and has methods to save and load state. -
The
ModelUpdaterreads metadata from the chain, validates it, and checks if it is an update to a previous tracked hotkey. -
The model.competition module supports grouping models into distinct competitions.
- The
CompetitionTrackerhelps track weights at a per competition and per subnet level. Like theModelTrackerit is designed to keep state across restarts. - The
EpislonFuncallows for computing epsilon with a custom function on a per competition basis. Both aFixedEpsilonand aLinearDecayimplementation are included.
- The
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The model.storage module supports storing and retrieving models and metadata.
- For models there are included implementations for Hugging Face and the local disk.
- For metadata there is an included implementation using the Bittensor chain.
Utilities
The utilities module contains code found to be generically useful in the context of subnets.
- The
PerfMonitoris a context manager that tracks the performance of a block of code by taking several samples - In
utils.pythere are some helpers torun_in_threadorrun_in_subprocess.- Running metagraph operations in a separate thread with a ttl can help avoid getting stuck on those actions.
- Running model evaluation within a sub process can help ensure that the model is completely removed from the gpu afterwards.
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Release files for taoverse 2.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| taoverse-2.0.2.tar.gz | 55.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| taoverse-2.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 116.3 kB
Release files / taoverse-2.0.2.tar.gz
| Download URL | taoverse-2.0.2.tar.gz |
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| Size | 55.7 kB |
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