The AEA ML (machine learning) skills demonstrate an interaction between two AEAs trading data.

There are two types of AEAs:

  • The ml_data_provider which sells training data.
  • The ml_model_trainer which trains a model

Discussion

The scope of the specific demo is to demonstrate how to create a simple AEA with integration of machine learning, and the usage of the AEA framework. The ml_data_provider AEA will provide some sample data and will deliver to the client upon payment. Once the client gets the data, it will train the model. The process can be found in the tasks.py file. This demo does not utilize a smart contract. As a result, we interact with a ledger only to complete a transaction.

Since the AEA framework enables us to use third-party libraries hosted on PyPI we can directly reference the external dependencies. The aea install command will install each dependency that the specific AEA needs and is listed in the skill's YAML file.

Communication

This diagram shows the communication between the two AEAs.

sequenceDiagram participant ml_model_trainer participant ml_data_provider participant Search participant Ledger activate ml_model_trainer activate ml_data_provider activate Search activate Ledger ml_data_provider->>Search: register_service ml_model_trainer->>Search: search Search-->>ml_model_trainer: list_of_agents ml_model_trainer->>ml_data_provider: call_for_terms ml_data_provider->>ml_model_trainer: terms ml_model_trainer->>Ledger: request_transaction ml_model_trainer->>ml_data_provider: accept (incl transaction_hash) ml_data_provider->>Ledger: check_transaction_status ml_data_provider->>ml_model_trainer: data loop train ml_model_trainer->>ml_model_trainer: tran_model end deactivate ml_model_trainer deactivate ml_data_provider deactivate Search deactivate Ledger

Preparation instructions

Dependencies

Follow the Preliminaries and Installation sections from the AEA quick start.

Launch an OEF search and communication node

In a separate terminal, launch a local OEF search and communication node.

python scripts/oef/launch.py -c ./scripts/oef/launch_config.json

Keep it running for the following demo.

Demo instructions

Create data provider AEA

First, fetch the data provider AEA:

aea fetch fetchai/ml_data_provider:0.6.0
cd ml_data_provider
aea install
Alternatively, create from scratch.

The following steps create the data provider from scratch: 

aea create ml_data_provider
cd ml_data_provider
aea add connection fetchai/oef:0.5.0
aea add connection fetchai/ledger:0.1.0
aea add skill fetchai/ml_data_provider:0.5.0
aea config set agent.default_connection fetchai/oef:0.5.0
aea install
In `ml_data_provider/aea-config.yaml` replace `ledger_apis: {}` with the following based on the network you want to connect. To connect to Fetchai: 
ledger_apis:
  fetchai:
    network: testnet
and add 
default_routing:
  fetchai/ledger_api:0.1.0: fetchai/ledger:0.1.0

Create model trainer AEA

Then, fetch the model trainer AEA:

aea fetch fetchai/ml_model_trainer:0.6.0
cd ml_model_trainer
aea install
Alternatively, create from scratch.

The following steps create the model trainer from scratch: 

aea create ml_model_trainer
cd ml_model_trainer
aea add connection fetchai/oef:0.5.0
aea add connection fetchai/ledger:0.1.0
aea add skill fetchai/ml_train:0.5.0
aea config set agent.default_connection fetchai/oef:0.5.0
aea install
In `ml_model_trainer/aea-config.yaml` replace `ledger_apis: {}` with the following based on the network you want to connect. To connect to Fetchai: 
ledger_apis:
  fetchai:
    network: testnet
and add 
default_routing:
  fetchai/ledger_api:0.1.0: fetchai/ledger:0.1.0

Generate wealth for the model trainer AEA

The model trainer needs to have some wealth to purchase the training data.

First, create the private key for the model trainer AEA based on the network you want to transact. To generate and add a private-public key pair for Fetch.ai use:

aea generate-key fetchai
aea add-key fetchai fet_private_key.txt

Then, create some wealth for your model trainer based on the network you want to transact with. On the Fetch.ai testnet network:

aea generate-wealth fetchai
Alternatively, create wealth for other test networks.

Ledger Config:
In `ml_model_trainer/aea-config.yaml` and `ml_data_provider/aea-config.yaml` replace `ledger_apis: {}` with the following based on the network you want to connect. To connect to Ethereum: 

ledger_apis:
  ethereum:
    address: https://ropsten.infura.io/v3/f00f7b3ba0e848ddbdc8941c527447fe
    chain_id: 3
    gas_price: 50
Alternatively, to connect to Cosmos: 
ledger_apis:
  cosmos:
    address: https://rest-agent-land.prod.fetch-ai.com:443
Wealth:
To generate and add a private-public key pair for Ethereum use: 
aea generate-key ethereum
aea add-key ethereum eth_private_key.txt
On the Ethereum `ropsten` network. 
aea generate-wealth ethereum
Alternatively, to generate and add a private-public key pair for Cosmos use: 
aea generate-key cosmos
aea add-key cosmos cosmos_private_key.txt
On the Cosmos `testnet` network. 
aea generate-wealth cosmos

Update the skill configs

The default skill configs assume that the transaction is settled against the fetchai ledger.

Alternatively, configure skills for other test networks.

Data provider:
Ensure you are in the ml_data_provider project directory. For ethereum, update the skill config of the data provider via the `aea config get/set` command like so: 

aea config set vendor.fetchai.skills. ml_data_provider.models.strategy.args.currency_id ETH
aea config set vendor.fetchai.skills.ml_data_provider.models.strategy.args.ledger_id ethereum
Or for cosmos, like so: 
aea config set vendor.fetchai.skills.ml_data_provider.models.strategy.args.currency_id ATOM
aea config set vendor.fetchai.skills.ml_data_provider.models.strategy.args.ledger_id cosmos
This updates the ml_data_provider skill config (`ml_data_provider/vendor/fetchai/skills/ml_data_provider/skill.yaml`). Model trainer:
Ensure you are in the ml_model_trainer project directory. For ethereum, update the skill config of the ml_model_trainer via the `aea config get/set` command like so: 
aea config set vendor.fetchai.skills.ml_trainer.models.strategy.args.currency_id ETH
aea config set vendor.fetchai.skills.ml_trainer.models.strategy.args.ledger_id ethereum
Or for cosmos, like so: 
aea config set vendor.fetchai.skills.ml_train.models.strategy.args.currency_id ATOM
aea config set vendor.fetchai.skills.ml_train.models.strategy.args.ledger_id cosmos
This updates the ml_nodel_trainer skill config (`ml_model_trainer/vendor/fetchai/skills/ml_train/skill.yaml`).

Run both AEAs

Finally, run both AEAs from their respective directories:

aea run

You can see that the AEAs find each other, negotiate and eventually trade.

Cleaning up

When you're finished, delete your AEAs:

cd ..
aea delete ml_data_provider
aea delete ml_model_trainer


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