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FedML MLOps User Guide

Video Tutorial: https://www.youtube.com/embed/Xgm0XEaMlVQ

Write Once, Run Anywhere: Seamlessly Migrate Your Local Development to the Real-world Edge-cloud Deployment

  • How Does FedML MLOps Platform Work?

  • Local Development and Building MLOps Packages

  • Create Application and Upload Local Packages

  • Install FedML Agent: fedml login $account_id

  • Invite Collaborators, Create a Group and a Project

  • Start Run (Training) via Automated Deployment and Scaling

  • Experimental Tracking via Simplified Project Management

How Does FedML MLOps Platform Work?

image
Figure 1: the workflow describing how MLOps works

MLOps works following the figure above; this workflow is handled by a web UI and thus avoids complex deployment. To begin, please create an account:

Local Development and Building MLOps Packages

After you finish the local developing/debugging of the FedML project using FedML library (e.g., successfully run the example https://doc.fedml.ai/cross-silo/examples/mqtt_s3_fedavg_mnist_lr_example.html), you can now deploy it into the real-world edge-cloud system.

Next, we use the cross-silo project example at https://github.com/FedML-AI/FedML/tree/master/python/examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line to guide you through how to deploy it and collaborate with others’ data silos.

1. Create Application and Upload Local Packages

image

You can use the following FedML CLI to build the package, and then upload the packages as shown in the figure above.

fedml build -t client -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST

Usage: fedml build [OPTIONS]

  Commands for open.fedml.ai MLOps platform

Options:
  -t, --type TEXT            client or server? (value: client; server)
  -sf, --source_folder TEXT  the source code folder path
  -ep, --entry_point TEXT    the entry point of the source code
  -cf, --config_folder TEXT  the config folder path
  -df, --dest_folder TEXT    the destination package folder path
  --help                     Show this message and exit.

The following script uses examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line as an example to illustrate the arguments for building MLOps packages. Please replace related values with your own.

  • Build the deployable package for the client

cd python/examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line

SOURCE=client
ENTRY=torch_client.py
CONFIG=config
DEST=./mlops
fedml build -t client -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST

Note: for ENTRY, please only specify the file name (torch_client.py) under the SOURCE folder; please do not pass the absolute path.

If the script works correctly, the console log should be as follows.

(mnn37) chaoyanghe@Chaoyangs-MBP python % fedml build -t client -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST
Argument for type: client
Argument for source folder: examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line/client
Argument for entry point: torch_client.py
Argument for config folder: examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line/config
Argument for destination package folder: ./mlops
Now, you are building the fedml packages which will be used in the MLOps platform.
The packages will be used for client training and server aggregation.
When the building process is completed, you will find the packages in the directory as follows: ./mlops/dist-packages.
Then you may upload the packages on the configuration page in the MLOps platform to start the federated learning flow.
Building...
You have finished all building process. 
Now you may use ./mlops/client-package.zip to start your federated learning run.
  • Build the deployable package for the server

SOURCE=server
ENTRY=torch_server.py
CONFIG=config
DEST=./mlops
fedml build -t server -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST

Note: for ENTRY, please only specify the file name (torch_server.py) under the SOURCE folder; please do not pass the absolute path.

If the script works correctly, the console log should be as follows.

(mnn37) chaoyanghe@Chaoyangs-MBP python % fedml build -t server -sf $SOURCE -ep $ENTRY -cf $CONFIG -df $DEST
Argument for type: server
Argument for source folder: examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line/server
Argument for entry point: torch_server.py
Argument for config folder: examples/cross_silo/mqtt_s3_fedavg_mnist_lr_example/one_line/config
Argument for destination package folder: ./mlops
Now, you are building the fedml packages which will be used in the MLOps platform.
The packages will be used for client training and server aggregation.
When the building process is completed, you will find the packages in the directory as follows: ./mlops/dist-packages.
Then you may upload the packages on the configuration page in the MLOps platform to start the federated learning flow.
Building...
You have finished all building process. 
Now you may use ./mlops/server-package.zip to start your federated learning run.

The output package is located in mlops folder.

2. Install FedML Agent: fedml login $account_id

After installing the FedML library at your edge devices, you can bind it to MLOps platform with a simple CLI:

fedml login $account_id

Please change $account_id to your own (see the screenshot shown below).

image Figure 2: login your edge devices to our platform

In the edge device, you will see the log as below if the script works correctly. You can also press the “refresh” button in Figure 2 to check the status of your device.

(fedml3.7) chaoyanghe@Chaoyangs-MBP cross_silo % fedml login 302
client None, server None
login as client: True, as server: None
/Users/chaoyanghe/sourcecode/FedML_product/FedML/python/fedml/cli/edge_deployment/client_login.py
(fedml3.7) chaoyanghe@Chaoyangs-MBP cross_silo % Namespace(local_server='127.0.0.1', type='login', user='302', version='release')
Namespace(account_id=302, cloud_region='', config_version='release', current_running_dir='/Users/chaoyanghe/fedml-client', device_id='0xb6ff42da6a7e', local_server='127.0.0.1', log_file_dir='/Users/chaoyanghe/fedml-client/fedml/logs', os_name='MacOS', type='login', user=302, version='release')
Current directory of client agent: /Users/chaoyanghe/fedml-client
client ids:[266]
log file path /Users/chaoyanghe/fedml-client/fedml/logs/fedml-run-0-edge-266-upload.log
login: unique_device_id = @0xb6ff42da6a7e.MacOS
login: edge_id = 266
subscribe: flserver_agent/266/start_train
subscribe: flserver_agent/266/stop_train
subscribe: fl_client/flclient_agent_266/status
Congratulations, you have logged into the FedML MLOps platform successfully!
Your device id is @0xb6ff42da6a7e.MacOS. You may review the device in the MLOps edge device list.

3. Invite Collaborators, Create a Group and a Project

  • 3.1 Invite Collaborators

(1) Copy your invitation link

(2) Send the invitation link to your collaborators via any communication tools (e.g., Slack, Facebook Messenger, Email, etc.)

(3) Your collaborators can open the URL and accept the invitation

Now you and your collaborators should be able to see each other on the My Collaborators page.

image Figure 3: invite your collaborators

  • 3.2 Group Management

After inviting your collaborators, you can Create New Group, input the group name, and select your collaborators into the group.

We support a topology visualization to assist you and your collaborators in checking all collaborative edge devices in that group.

Note that after creation, our platform will add newly bound devices from all members to the group. If you would like to change the members in this group, you will have to create a new group.

image Figure 4: showing the topology of the group

4. Start Run (Training) via Automated Deployment and Scaling

Now let’s create a project and get training started!

  • Create a new project for a specific group or utilize the existing project you created before

  • Open a project and create a new run under the project. By clicking Start, the run will begin.

image Figure 5: create and start a new run

5. Experimental Tracking via Simplified Project Management

Now you are ready to start the training. Enjoy! We provide the following experimental tracking abilities as follows.

  • monitoring device status (including logging in each device) and training progress image

  • visualizing training results image

  • visualizing system performance image

  • model downloading/serving

  • distributed logging image

More advanced features will be supported soon. We appreciate your valuable feedback.