Task Runner API

Let’s take a deeper dive into the Task Runner API. If you haven’t already, we suggest checking out the Quick Start for a primer on doing a simple experiment on a single node.

The steps to transition from a local experiment to a distributed federation can be understood best with the following diagram.

Overview of a Task Runner experiment distributed across multiple nodes

:

The Task Runner API uses short-lived components in a federation, which is terminated when the experiment is finished. The components are as follows:

The Collaborator uses a local dataset to train a global model and the Aggregator receives model updates from Collaborators and aggregates them to create the new global model.
The Aggregator is framework-agnostic, while the Collaborator can use any deep learning frameworks, such as TensorFlow* or PyTorch*.

For this workflow, you modify the federation workspace to your requirements by editing the Federated Learning plan (FL plan) along with the Python* code that defines the model and the data loader. The FL plan is a YAML file that defines the collaborators, aggregator, connections, models, data, and any other parameters that describe the training.

Federated Learning Plan (FL Plan) Settings

Note

Use the Federated Learning plan (FL plan) to modify the federation workspace to your requirements in an aggregator-based workflow.

In order for participants to agree to take part in an experiment, everyone should know ahead of time both what code is going to run on their infrastructure and exactly what information on their system will be accessed. The federated learning (FL) plan aims to capture all of this information needed to decide whether to participate in an experiment, in addition to runtime details needed to load the code and make remote connections. The FL plan is described by the plan.yaml file located in the plan directory of the workspace.

Configurable Settings

Aggregator
openfl.component.Aggregator Defines the settings for the aggregator which is the model-owner in the experiment. While models can be trained from scratch, in many cases the federation performs fine-tuning of a previously trained model. For this reason, pre-trained weights for the model are stored in protobuf files on the aggregator node and passed to collaborator nodes during initialization. The settings for aggregator include:

init_state_path: (str:path) Defines the weight protobuf file path where the experiment’s initial weights will be loaded from. These weights will be generated with the fx plan initialize command.

best_state_path: (str:path) Defines the weight protobuf file path that will be saved to for the highest accuracy model during the experiment.

last_state_path: (str:path) Defines the weight protobuf file path that will be saved to during the last round completed in each experiment.

rounds_to_train: (int) Specifies the number of rounds in a federation. A federated learning round is defined as one complete iteration when the collaborators train the model and send the updated model weights back to the aggregator to form a new global model. Within a round, collaborators can train the model for multiple iterations called epochs.

write_logs: (boolean) Metric logging callback feature. By default, logging is done through tensorboard but users can also use custom metric logging function for each task.

Collaborator
openfl.component.Collaborator Defines the settings for the collaborator which is the data owner in the experiment. The settings for collaborator include:

delta_updates: (boolean) Determines whether the difference in model weights between the current and previous round will be sent (True), or if whole checkpoints will be sent (False). Setting to delta_updates to True leads to higher sparsity in model weights sent across, which may improve compression ratios.

opt_treatment: (str) Defines the optimizer state treatment policy. Valid options are : ‘RESET’ - reinitialize optimizer for every round (default), ‘CONTINUE_LOCAL’ - keep local optimizer state for every round, ‘CONTINUE_GLOBAL’ - aggregate optimizer state for every round.

Data Loader
openfl.federated.data.loader.DataLoader Defines the data loader class that provides access to local dataset. It implements a train loader and a validation loader that takes in the train dataset and the validation dataset respectively. The settings for the dataloader include:

collaborator_count: (int) The number of collaborators participating in the federation

data_group_name: (str) The name of the dataset

batch_size: (int) The size of the training or validation batch

Task Runner
openfl.federated.task.runner.TaskRunner Defines the model, training/validation functions, and how to extract and set the tensors from model weights and optimizer dictionary. Depending on different AI frameworks like PyTorch and Tensorflow, users can select pre-defined task runner methods.
Assigner
openfl.component.Assigner Defines the task that are sent to the collaborators from the aggregator. There are three default tasks that could be given to each Collaborator:

aggregated_model_validation: (str) Perform validation on aggregated global model sent by the aggregator.

train: (str) Perform training on the global model.

locally_tuned_model_validation: (str) Perform validation on the model that was locally trained by the collaborator.

Each YAML top-level section contains the following subsections:

template: The name of the class including top-level packages names. An instance of this class is created when the plan gets initialized.
settings: The arguments that are passed to the class constructor.
defaults: The file that contains default settings for this subsection. Any setting from defaults file can be overridden in the plan.yaml file.

The following is an example of a plan.yaml:

# Copyright (C) 2020-2021 Intel Corporation
# Licensed subject to the terms of the separately executed evaluation license agreement between Intel Corporation and you.

aggregator :
  defaults : plan/defaults/aggregator.yaml
  template : openfl.component.Aggregator
  settings :
    init_state_path     : save/torch_cnn_mnist_init.pbuf
    best_state_path     : save/torch_cnn_mnist_best.pbuf
    last_state_path     : save/torch_cnn_mnist_last.pbuf
    rounds_to_train     : 10
    log_metric_callback :
      template : src.utils.write_metric


collaborator :
  defaults : plan/defaults/collaborator.yaml
  template : openfl.component.Collaborator
  settings :
    delta_updates    : false
    opt_treatment    : RESET

data_loader :
  defaults : plan/defaults/data_loader.yaml
  template : src.dataloader.PyTorchMNISTInMemory
  settings :
    collaborator_count : 2
    data_group_name    : mnist
    batch_size         : 256

task_runner :
  defaults : plan/defaults/task_runner.yaml
  template : src.taskrunner.PyTorchCNN

network :
  defaults : plan/defaults/network.yaml

assigner :
  defaults : plan/defaults/assigner.yaml
  
tasks :
  defaults : plan/defaults/tasks_torch.yaml

compression_pipeline :
  defaults : plan/defaults/compression_pipeline.yaml

Tasks

Each task subsection contains the following:

function: The function name to call. The function must be the one defined in TaskRunner class.
kwargs: kwargs passed to the function.

Note

See an example of the TaskRunner class for details.

Bare Metal Approach

Note

Ensure you have installed the OpenFL package on every node (aggregator and collaborators) in the federation.

See Install the Package for details.

STEP 1: Create a Workspace

Creates a federated learning workspace on one of the nodes.

STEP 2: Configure the Federation

Ensures each node in the federation has a valid public key infrastructure (PKI) certificate.

Distributes the workspace from the aggregator node to the other collaborator nodes.

STEP 3: Start the Federation

STEP 1: Create a Workspace

Start a Python 3.8 (>=3.6, <3.11) virtual environment and confirm OpenFL is available.
```
$ fx
```
This example uses the keras_cnn_mnist template.

Set the environment variables to use the keras_cnn_mnist as the template and ${HOME}/my_federation as the path to the workspace directory.

$ export WORKSPACE_TEMPLATE=keras_cnn_mnist
$ export WORKSPACE_PATH=${HOME}/my_federation

Decide a workspace template, which are end-to-end federated learning training demonstrations. The following is a sample of available templates:

keras_cnn_mnist: a workspace with a simple Keras CNN model that will download the MNIST dataset and train in a federation.

tf_2dunet: a workspace with a simple TensorFlow CNN model that will use the BraTS dataset and train in a federation.

tf_cnn_histology: a workspace with a simple TensorFlow CNN model that will download the Colorectal Histology dataset and train in a federation.

torch_cnn_histology: a workspace with a simple PyTorch CNN model that will download the Colorectal Histology dataset and train in a federation.

torch_cnn_mnist: a workspace with a simple PyTorch CNN model that will download the MNIST dataset and train in a federation.
See the complete list of available templates.
$ fx workspace create --prefix ${WORKSPACE_PATH}

Create a workspace directory for the new federation project.
```
$ fx workspace create --prefix ${WORKSPACE_PATH} --template ${WORKSPACE_TEMPLATE}
```
Note

You can use your own models by overwriting the Python scripts in the src subdirectory in the workspace directory.
Change to the workspace directory.
```
$ cd ${WORKSPACE_PATH}
```
Install the workspace requirements:
```
$ pip install -r requirements.txt
```
Create an initial set of random model weights.

Note

While models can be trained from scratch, in many cases the federation performs fine-tuning of a previously trained model. For this reason, pre-trained weights for the model are stored in protobuf files on the aggregator node and passed to collaborator nodes during initialization.

The protobuf file with the initial weights is found in ${WORKSPACE_TEMPLATE}_init.pbuf.
$ fx plan initialize
This command initializes the FL plan and auto populates the fully qualified domain name (FQDN) of the aggregator node. This FQDN is embedded within the FL plan so the collaborator nodes know the address of the externally accessible aggregator server to connect to.

If you have connection issues with the auto populated FQDN in the FL plan, you can do one of the following:
OPTION 1: override the auto populated FQDN value with the -a flag.
$ fx plan initialize -a aggregator-hostname.internal-domain.com
OPTION 2: override the apparent FQDN of the system by setting an FQDN environment variable.
$ export FQDN=x.x.x.x
and initializing the FL plan
$ fx plan initialize

Note

Each workspace may have multiple FL plans and multiple collaborator lists associated with it. Therefore, fx plan initialize has the following optional parameters.

Optional Parameters	Description
-p, –plan_config PATH	Federated Learning plan [default = plan/plan.yaml]
-c, –cols_config PATH	Authorized collaborator list [default = plan/cols.yaml]
-d, –data_config PATH	The data set/shard configuration file

STEP 2: Configure the Federation

The objectives in this step:

Ensure each node in the federation has a valid public key infrastructure (PKI) certificate. See OpenFL Public Key Infrastructure (PKI) Solutions for details on available workflows.

Distribute the workspace from the aggregator node to the other collaborator nodes.

On the Aggregator Node:

Setting Up the Certificate Authority

Change to the path of your workspace:
$ cd WORKSPACE_PATH
Set up the aggregator node as the certificate authority for the federation.

All certificates will be signed by the aggregator node. Follow the instructions and enter the information as prompted. The command will create a simple database file to keep track of all issued certificates.
$ fx workspace certify

Run the aggregator certificate creation command, replacing AFQDN with the actual fully qualified domain name (FQDN) for the aggregator node.
$ fx aggregator generate-cert-request --fqdn AFQDN
Note

On Linux*, you can discover the FQDN with this command:
$ hostname --all-fqdns | awk '{print $1}'
Note

You can override the apparent FQDN of the system by setting an FQDN environment variable before creating the certificate.
$ fx aggregator generate-cert-request export FQDN=x.x.x.x
If you omit the --fdqn parameter, then fx will automatically use the FQDN of the current node assuming the node has been correctly set with a static address.
$ fx aggregator generate-cert-request
Run the aggregator certificate signing command, replacing AFQDN with the actual fully qualified domain name (FQDN) for the aggregator node.
$ fx aggregator certify --fqdn AFQDN
Note

You can override the apparent FQDN of the system by setting an FQDN environment variable (export FQDN=x.x.x.x) before signing the certificate.
$ fx aggregator certify export FQDN=x.x.x.x

This node now has a signed security certificate as the aggregator for this new federation. You should have the following files.

File Type

Filename

Certificate chain

WORKSPACE.PATH/cert/cert_chain.crt

Aggregator certificate

WORKSPACE.PATH/cert/server/agg_{AFQDN}.crt

Aggregator key

WORKSPACE.PATH/cert/server/agg_{AFQDN}.key

where AFQDN is the fully-qualified domain name of the aggregator node.

Exporting the Workspace

Export the workspace so that it can be imported to the collaborator nodes.
$ fx workspace export
The export command will archive the current workspace (with a zip file extension) and create a requirements.txt of the current Python*packages in the virtual environment.
The next step is to transfer this workspace archive to each collaborator node.

On the Collaborator Node:

Importing the Workspace

Copy the workspace archive from the aggregator node to the collaborator nodes.

Import the workspace archive.

$ fx workspace import --archive WORKSPACE.zip

where WORKSPACE.zip is the name of the workspace archive. This will unzip the workspace to the current directory and install the required Python packages within the current virtual environment.

For each test machine you want to run as collaborator nodes, create a collaborator certificate request to be signed by the certificate authority.

Replace COL_LABEL with the label you assigned to the collaborator. This label does not have to be the FQDN; it can be any unique alphanumeric label.
$ fx collaborator create -n {COL_LABEL} -d {DATA_PATH:optional}
$ fx collaborator generate-cert-request -n {COL_LABEL}
The creation script will also ask you to specify the path to the data. For this example, enter the integer that represents which MNIST shard to use on this collaborator node. For the first collaborator node enter 1. For the second collaborator node enter 2.

This will create the following files:

File Type

Filename

Collaborator CSR

WORKSPACE.PATH/cert/client/col_{COL_LABEL}.csr

Collaborator key

WORKSPACE.PATH/cert/client/col_{COL_LABEL}.key

Collaborator CSR Package

WORKSPACE.PATH/col_{COL_LABEL}_to_agg_cert_request.zip

On the aggregator node (i.e., the certificate authority in this example), sign the Collaborator CSR Package from the collaborator nodes.
$ fx collaborator certify --request-pkg /PATH/TO/col_{COL_LABEL}_to_agg_cert_request.zip
where /PATH/TO/col_{COL_LABEL}_to_agg_cert_request.zip is the path to the Collaborator CSR Package containing the .csr file from the collaborator node. The certificate authority will sign this certificate for use in the federation.

The command packages the signed collaborator certificate, along with the cert_chain.crt file needed to verify certificate signatures, for transport back to the collaborator node:

File Type

Filename

Certificate and Chain Package

WORKSPACE.PATH/agg_to_col_{COL_LABEL}_signed_cert.zip
On the collaborator node, import the signed certificate and certificate chain into your workspace.
$ fx collaborator certify --import /PATH/TO/agg_to_col_{COL_LABEL}_signed_cert.zip

STEP 3: Start the Federation

On the Aggregator Node:

Start the Aggregator.
$ fx aggregator start

Now, the Aggregator is running and waiting for Collaborators to connect.

On the Collaborator Nodes:

Open a new terminal, change the directory to the workspace, and activate the virtual environment.
Run the Collaborator.
$ fx collaborator start -n {COLLABORATOR_LABEL}
where COLLABORATOR_LABEL is the label for this Collaborator.

Note

Each workspace may have multiple FL plans and multiple collaborator lists associated with it. Therefore, fx collaborator start has the following optional parameters.

Optional Parameters

Description

-p, –plan_config PATH

Federated Learning plan [default = plan/plan.yaml]

-d, –data_config PATH

The data set/shard configuration file
Repeat the earlier steps for each collaborator node in the federation.

When all of the Collaborators connect, the Aggregator starts training. You will see log messages describing the progress of the federated training.

When the last round of training is completed, the Aggregator stores the final weights in the protobuf file that was specified in the YAML file, which in this example is located at save/${WORKSPACE_TEMPLATE}_latest.pbuf.

Post Experiment

Experiment owners may access the final model in its native format. Among other training artifacts, the aggregator creates the last and best aggregated (highest validation score) model snapshots. One may convert a snapshot to the native format and save the model to disk by calling the following command from the workspace:

$ fx model save -i model_protobuf_path.pth -o save_model_path

In order for this command to succeed, the TaskRunner used in the experiment must implement a save_native() method.

Another way to access the trained model is by calling the API command directly from a Python script:

from openfl import get_model
model = get_model(plan_config, cols_config, data_config, model_protobuf_path)

In fact, the get_model() method returns a TaskRunner object loaded with the chosen model snapshot. Users may utilize the linked model as a regular Python object.

Running inside Docker

There are two ways you can run OpenFL with Docker*.

Option 1: Deploy a Federation in a Docker Container
Option 2: Deploy Your Workspace in a Docker Container

Option 1: Deploy a Federation in a Docker Container

Note

You have to built an OpenFL image. See OpenFL with Docker* for details.

Run the OpenFL image.
$ docker run -it --network host openfl

You can now experiment with OpenFL in the container. For example, you can test the project pipeline with the “Hello Federation” bash script.

Option 2: Deploy Your Workspace in a Docker Container

Note

You have to set up a TaskRunner and run fx plan initialize in the workspace directory. See STEP 1: Create a Workspace for details.

Build an image with the workspace you created.
$ fx workspace dockerize
By default, the image is saved as WORKSPACE_NAME_image.tar in the workspace directory.
The image can be distributed and run on other nodes without any environment preparation.
```
docker run -it --rm \
    --network host \
    -v user_data_folder:/home/user/workspace/data \
    ${WORKSPACE_IMAGE_NAME} \
    bash
```
Note

The FL plan should be initialized with the FQDN of the node where the aggregator container will be running.
Generate public key infrastructure (PKI) certificates for all collaborators and the aggregator. See OpenFL Public Key Infrastructure (PKI) Solutions for details.
STEP 3: Start the Federation.