The AEA ml skills demonstrate an interaction between two AEAs trading data.

There are two types of agents:

• The ml data provider which sells training data.
• The ml model trainer which trains a model

Preparation instructions

Dependencies

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

Launch an OEF node

In a separate terminal, launch a local OEF node (for search and discovery).

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

Keep it running for all the following demos.

Demo instructions 1 - no ledger payment:

Create the data provider AEA

In the root directory, create the data provider AEA and enter the project.

aea create ml_data_provider
cd ml_data_provider

Add the ml_data_provider skill and oef connection.

aea add connection fetchai/oef:0.1.0
aea add skill fetchai/ml_data_provider:0.1.0

Alternatively, install the agent directly

In the root directory, fetch the data provider AEA and enter the project.

aea fetch fetchai/ml_data_provider:0.1.0
cd ml_data_provider

The aea fetch command creates the entire agent, including its dependencies for you.

Install the dependencies

The ml data provider uses tensorflow and numpy.

aea install

Run the data provider AEA

aea run --connections oef

Create the model trainer AEA

In a separate terminal, in the root directory, create the model trainer AEA and enter the project.

aea create ml_model_trainer
cd ml_model_trainer

Add the ml_train skill and oef connection.

aea add connection fetchai/oef:0.1.0
aea add skill fetchai/ml_train:0.1.0

Alternatively, install the agent directly

In the root directory, fetch the data provider AEA and enter the project.

aea fetch fetchai/ml_model_trainer:0.1.0
cd ml_model_trainer

Install the dependencies

The ml data provider uses tensorflow and numpy.

aea install

Run the model trainer AEA

aea run --connections oef

After some time, you should see the AEAs transact and the model trainer train its model.

Demo instructions 2 - Fetch.ai ledger payment:

We will now run the same demo but with a real ledger transaction on Fetch.ai test net.

Update the AEA configs

Both in ml_model_trainer/aea-config.yaml and ml_data_provider/aea-config.yaml, replace ledger_apis: {} with the following.

ledger_apis:
  fetchai:
    addr: alpha.fetch-ai.com
    port: 80

Fund the ml model trainer AEA

Create some wealth for your ml model trainer on the Fetch.ai testnet. (It takes a while).

cd ..
python scripts/fetchai_wealth_generation.py --private-key ml_model_trainer/fet_private_key.txt --amount 10000000 --addr alpha.fetch-ai.com --port 80
cd ml_model_trainer

Update the ml model trainer AEA skills config

We tell the ml model trainer skill to use the ledger, by using the following command:

aea config set skills.ml_train.shared_classes.strategy.args.is_ledger_tx True

Run both AEAs

From their respective directories, run both AEAs

aea run --connections oef

Clean up

cd ..
aea delete ml_data_provider
aea delete ml_model_trainer

Communication

This diagram shows the communication between the two agents.

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

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