Skip to content

Latest commit

 

History

History
163 lines (105 loc) · 7.3 KB

miner.md

File metadata and controls

163 lines (105 loc) · 7.3 KB
Raw

Miner

Miners train locally and periodically publish their best model to hugging face and commit the metadata for that model to the Bittensor chain.

Miners can only have one model associated with them on the chain for evaluation by validators at a time. The list of allowed model types by block can be found in constants/init.py. Other relevant constraints are also listed in that file.

The communication between a miner and a validator happens asynchronously chain and therefore Miners do not need to be running continuously. Validators will use whichever metadata was most recently published by the miner to know which model to download from hugging face.

System Requirements

Miners will need enough disk space to store their model as they work on. Max size of model is defined in constants/init.py. It is recommended to have at least 50 GB of disk space.

Getting started

Prerequisites

  1. Get a Hugging Face Account:

Miner and validators use hugging face in order to share model state information. Miners will be uploading to hugging face and therefore must attain a account from hugging face along with a user access token which can be found by following the instructions here.

Make sure that any repo you create for uploading is public so that the validators can download from it for evaluation.

  1. Clone the repo
git clone https://github.com/macrocosm-os/pretraining.git

  1. Setup your python virtual environment or Conda environment.

  2. Install the requirements. From your virtual environment, run

cd pretraining
python -m pip install -e .

Note: flash-attn may not have their dependencies set up correctly. If you run into issues try installing those requirements separately first:

pip install packaging
pip install wheel
pip install torch

  1. Make sure you've created a Wallet and registered a hotkey.

  2. (Optional) Run a Subtensor instance:

Your node will run better if you are connecting to a local Bittensor chain entrypoint node rather than using Opentensor's. We recommend running a local node as follows and passing the --subtensor.network local flag to your running miners/validators. To install and run a local subtensor node follow the commands below with Docker and Docker-Compose previously installed.

git clone https://github.com/opentensor/subtensor.git
cd subtensor
docker compose up --detach

Running the Miner

The mining script uploads a model to hugging face which will be evaluated by validators.

See Validator Psuedocode for more information on how they the evaluation occurs.

Env File

The Miner requires a .env file with your hugging face access token in order to upload models.

Create a .env file in the pretraining directory and add the following to it:

HF_ACCESS_TOKEN="YOUR_HF_ACCESS_TOKEN"

Starting the Miner

To start your miner the most basic command is

python neurons/miner.py --wallet.name coldkey --wallet.hotkey hotkey --hf_repo_id my-username/my-project --avg_loss_upload_threshold YOUR_THRESHOLD

  • --wallet.name: should be the name of the coldkey that contains the hotkey your miner is registered with.

  • --wallet.hotkey: should be the name of the hotkey that your miner is registered with.

  • --hf_repo_id: should be the namespace/model_name that matches the hugging face repo you want to upload to. Must be public so that the validators can download from it.

  • --avg_loss_upload_threshold: should be the minimum average loss before you want your miner to upload the model.

Flags

The Miner offers some flags to customize properties, such as how to train the model and which hugging face repo to upload to.

You can view the full set of flags by running

python ./neurons/miner.py -h

Some flags you may find useful:

  • --offline: when set you can run the miner without being registered and it will not attempt to upload the model.

  • --wandb_entity + --wandb_project: when both flags are set the miner will log its training to the provided wandb project.

  • --device: by default the miner will use your gpu but you can specify with this flag if you have multiple.

Training from pre-existing models

  • --load_best: when set you will download and train the model from the current best miner on the network.
  • --load_uid: when passing a uid you will download and train the model from the matching miner on the network.
  • --load_model_dir: the path to a local model directory [saved via Hugging Face API].
  • --load_model: the path to a safetensors file [not necessarily saved from Hugging Face API].
  • --upload_b16: if the model should be uploaded with bfloat16.

Manually uploading a model

In some cases you may have failed to upload a model or wish to upload a model without further training.

Due to rate limiting by the Bittensor chain you may only upload a model every 20 minutes.

You can manually upload with the following command:

python scripts/upload_model.py --load_model_dir <path to model> --competition_id 0 --hf_repo_id my-username/my-project --wallet.name coldkey --wallet.hotkey hotkey

Note: We recommend keeping your hugging face repo private until after you have committed your metadata to the chain. This ensures other miners are unable to upload your model as their own until a later block. Adding the --update_repo_visibility flag will also automatically attempt to update the hugging face repo visibility to public after committing to the chain.

Note: If you are not sure about the competition ID, you can add the --list_competitions flag to get a list of all competitions. You can also check out competition IDs in competitions/data.py.

Running a custom Miner

The list of allowed model types by block can be found in constants/init.py

In that file are also the constraints per block for

  1. The details of every competition.
  2. All the model constraints (sequence length, allowed model types, dataset, etc)

The pretain/mining.py file has several methods that you may find useful. Example below.

import pretrain as pt
import bittensor as bt
from transformers import PreTrainedModel

# Load a model from another miner.
model: PreTrainedModel = await pt.mining.load_remote_model(uid=123, download_dir="mydir")

# Save the model to local file.
pt.mining.save(model, "model-foo/")

# Load the model from disk.
pt.mining.load_local_model("model-foo/", **kwargs)

# Publish the model for validator evaluation.
wallet = bt.wallet()
await pt.mining.push(model, repo="jdoe/my-repo", wallet=wallet, competition_id=1)

# Get the URL to the best model for a given competition
best_uid = pt.graph.best_uid(competition_id=1)
print(await pt.mining.get_repo(best_uid))