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DIY_Submission.md

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DIY Submission

This README explains how to submit solutions without relying on GitHub actions. For seasoned participants: This was the primary submission process from the MyoChallenge 2022.

Prerequisites

Install Docker compiler

Install docker following the instructions. Once installed, you can compile the docker containers for the 2 agents with the following scripts:

Note: Do not use sudo privileges, rather manage Docker as a non-root user. Building the docker with root privileges might cause problems during the evalai submission.

Register an account on EvalAI for the team

Install EvalAI Command Line Interface (evalai-cli)

pip install "evalai>=1.3.13"

You might need to install evalai dependencies first:

sudo apt-get install libxml2-dev libxslt-dev

[⚠️ IMPORTANT ⚠️] Each team needs to be registered to obtain a specific token to identify it (see instructions). After registration, it is possible to add the EvalAI account token to via evalai-cli (full list of commands here) with the following command:

# Register the tocken to identify your contribution
evalai set_token <your EvalAI participant token>
# Test that the registration was successful. MyoChallenge needs to be in the list returned
evalai challenges --participant
Clone this repository

Clone this repository to have access to all needed files:

# Clone the repository
git clone https://github.com/MyoHub/myochallenge_2023eval.git
# Enter into the root path
cd myochallenge_2023eval
# Install dependencies and tests
source ./setup.sh

STEP 1: Train your model

The API to interface with the environment is explained in the MyoSuite docs.

More information on the training and customization are provided here

STEP 2: Customize Agent Script

We provide 4 templates to describe how the agent will communicate with the environment during the evaluation.

The random templates are very simple and can be used to understand the general structure of the submission template, as well as test if everything works correctly. We also provide deprl-templates, which show you how the submission for an RL learning framework would look like. In order to use the submission template, you will need to (A) import a policy and (B) obtain an action from each observation. Take a look at the observation function. This function can be helpful to reconstruct the observation vector you used during training from the obs_dict provided during evaluation. You cannot use any other state information for the submission!

When you customize these files to submit with your preferred learning framework, is important to add the dependencies that you need to the appropriate requirements files: Either agent_random.txt or agent_deprl.txt, depending on which template you use. These dependencies are automatically installed when building the docker container. Make sure none of your dependencies defaults to GPU-usage, as the containers are evaluated on CPU-only instances.

Once you have finished customizing the scripts, testing between the agent and environment can be performed by using the scripts below:

  • Manipulation random sh ./test/test_mani_agent_random.sh
  • Locomotion/Chase-Tag random sh ./test/test_loco_agent_random.sh
  • Manipulation deprl sh ./test/test_mani_agent_deprl.sh
  • Locomotion/Chase-Tag deprl sh ./test/test_loco_agent_deprl.sh

Upon successful testing, it is possible to submit the solution following next steps.

STEP 3: Build a docker container with the agent

The evaluation will be based on the model submitted as a docker container. It is possible to build the docker in two ways with either directly docker build (Suggested method) or with docker-compose (Alternative method, this will require to install docker-compose)

Suggested Method: Using `docker build` 
# Compile the container for the Manipulation Agent
docker build -f docker/agent/Dockerfile_Mani . -t myochallengeeval_mani_agent

# Compile the container for the Locomotion/ChaseTag Agent
docker build -f docker/agent/Dockerfile_Loco . -t myochallengeeval_loco_agent
Alternative Method: Using `docker-compose` 
# Compile the container for the Mani Agent
docker-compose -f docker-compose-ManiAgent.yml up --build

# Compile the container for the Locomotion/Chase-Tag Agent
docker-compose -f docker-compose-LocoAgent.yml up --build

Step 4: Upload the docker container on evalAI for evaluation

Push the docker image to EvalAI docker registry (it is possible to get the information about the image and TAG with the command docker images)

evalai push <image>:<tag> --phase <phase_name>

Use --private or --public flag in the submission command to make the submission private or public respectively.

for example, commands to upload agents for Phase 1 might look like:

  • Manipulation Agent : evalai push myochallengeeval_mani_agent:latest --phase myochallenge2023-maniphase2-2105 --public

  • Locomotion/Chase-Tag Agent: evalai push myochallengeeval_loco_agent:latest --phase myochallenge2023-locophase2-2105 --public

For more commands, please refer to evalai-cli documentation for additional commands.