Steps and Workflow.

.gitignore

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+# Created by https://www.toptal.com/developers/gitignore/api/python,pycharm
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+
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+.pdm.toml
+
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+
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+
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+
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+.venv
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+
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.idea/.gitignore

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README.md

-# Sc5 CWL Workflow
-
-
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.ebrains.eu/weidler/sc5-cwl-workflow.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.ebrains.eu/weidler/sc5-cwl-workflow/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!).  Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
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-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.

mpiconfig.yml

+default_nproc: 12
\ No newline at end of file

step1/Dockerfile

+ARG UBUNTU_VERSION=18.04
+ARG ARCH=
+ARG CUDA=11.2
+
+FROM nvidia/cuda${ARCH:+-$ARCH}:${CUDA}.1-cudnn8-runtime-ubuntu${UBUNTU_VERSION} as base
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+# key fix
+RUN sed -i '/developer\.download\.nvidia\.com\/compute\/cuda\/repos/d' /etc/apt/sources.list.d/*
+RUN sed -i '/developer\.download\.nvidia\.com\/compute\/machine-learning\/repos/d' /etc/apt/sources.list.d/*
+RUN apt-key del 7fa2af80
+
+COPY cuda-keyring_1.0-1_all.deb cuda-keyring_1.0-1_all.deb
+RUN dpkg -i cuda-keyring_1.0-1_all.deb
+
+WORKDIR /usr/local/cuda-11.2/lib64
+RUN ln -s libcusolver.so.11 libcusolver.so.10
+WORKDIR /
+
+# install packages
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    curl \
+    git \
+    swig \
+    wget \
+    virtualenv \
+    libgl1-mesa-dev \
+    libgl1-mesa-glx \
+    libglew-dev \
+    libc6-dev \
+    libosmesa6-dev \
+    patchelf \
+    software-properties-common \
+    gfortran \
+    && apt-get install --no-install-recommends -y file g++ gcc make gdb strace ca-certificates \
+    && add-apt-repository -y ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get --no-install-recommends install -y python3.8-dev python3.8 python3-pip \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# INSTALL MPI
+RUN wget -q http://www.mpich.org/static/downloads/3.1.4/mpich-3.1.4.tar.gz
+RUN tar xf mpich-3.1.4.tar.gz
+WORKDIR mpich-3.1.4
+RUN ./configure --disable-fortran --enable-fast=all,O3 --prefix=/usr
+RUN make -j$(nproc)
+RUN make install
+RUN ldconfig
+WORKDIR /
+
+# ENVIRONMENT VARIABLES
+ENV LD_LIBRARY_PATH="/usr/lib/nvidia:$LD_LIBRARY_PATH"
+ENV LD_LIBRARY_PATH="/usr/local/nvidia/lib64:$LD_LIBRARY_PATH"
+ENV LD_LIBRARY_PATH="/usr/local/cuda-11.2/lib64:$LD_LIBRARY_PATH"
+
+# matplotlib
+ENV MPLCONFIGDIR=/var/cache/matplotlib
+
+# SETUP ENV
+ENV VIRTUAL_ENV=/venv
+RUN virtualenv --python=python3.8 $VIRTUAL_ENV
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# INSTALL TOOL
+RUN pip install angorapy==0.7.1 \
+    && pip install mujoco \
+    && pip install imageio \
+    && pip install distance
+
+WORKDIR /
+COPY train.py train.py
+
+# CLEANUP
+RUN pip cache purge
+
+# FINAL
+CMD [ "/bin/bash" ]
+

step1/reinforcement_learning_tool.cwl

+#!/usr/bin/env cwl-runner
+
+# --------------------------------------------------- COMMAND LINE TOOL INFO ----------------------------------------------------
+
+cwlVersion: v1.0
+class: CommandLineTool
+
+# -------------------------------------------------------- BASE COMMAND ---------------------------------------------------------
+
+baseCommand: ['python3', '/train.py']
+
+# ----------------------------------------------------- HINTS/REQUIREMENTS ------------------------------------------------------
+$namespaces:
+  cwltool: http://commonwl.org/cwltool#
+requirements:
+  cwltool:MPIRequirement:
+    processes: $(inputs.workers)
+hints:
+  DockerRequirement:
+    dockerPull: docker-registry.ebrains.eu/sc5/sc5_workflow_rltrain:latest
+  ResourceRequirement:
+    ramMin: 16000
+    outdirMin: 1000
+# --------------------------------------------------------- TOOL INPUTS ---------------------------------------------------------
+
+inputs:
+  env:
+    type: string
+    inputBinding:
+      position: 1
+  pcon:
+    type: string?
+    inputBinding:
+      position: 2
+      prefix: --pcon
+  rcon:
+    type: string?
+    inputBinding:
+      position: 3
+      prefix: --rcon
+  workers:
+    type: int?
+    inputBinding:
+      position: 4
+      prefix: --workers
+  iterations:
+    type: int?
+    inputBinding:
+      position: 5
+      prefix: --iterations
+  horizon:
+    type: int?
+    inputBinding:
+      position: 6
+      prefix: --horizon
+
+# -------------------------------------------------------- TOOL OUTPUTS ---------------------------------------------------------
+
+stdout: output_file.txt
+outputs:
+  out:
+    type: stdout
+  out_data:
+    type: File
+    outputBinding:
+      glob: storage/experiments/*/progress.json

step1/train.py

+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import pprint
+import traceback
+
+import distance
+
+import numpy as np
+
+with np.testing.suppress_warnings() as sup:
+    sup.filter(DeprecationWarning)
+    sup.filter(UserWarning)
+
+    from angorapy.train import run_experiment
+    from angorapy.utilities.defaults import autoselect_distribution
+
+    import tensorflow as tf
+
+    import argparse
+    import logging
+
+    import argcomplete
+    from gym.spaces import Box, Discrete, MultiDiscrete
+
+    from angorapy.configs import hp_config
+    from angorapy.common.policies import get_distribution_by_short_name
+    from angorapy.models import get_model_builder
+    from angorapy.common.const import COLORS
+    from angorapy.utilities.monitoring import Monitor
+    from angorapy.utilities.util import env_extract_dims
+    from angorapy.common.wrappers import make_env
+    from angorapy.common.transformers import StateNormalizationTransformer, RewardNormalizationTransformer
+    from angorapy.agent.ppo_agent import PPOAgent
+
+    from angorapy.environments import *
+
+    from mpi4py import MPI
+
+
+    if __name__ == "__main__":
+
+        tf.get_logger().setLevel('INFO')
+        all_envs = [e.id for e in list(gym.envs.registry.all())]
+
+        # parse commandline arguments
+        parser = argparse.ArgumentParser(description="Train a PPO Agent on some task.")
+
+        # general parameters
+        parser.add_argument("env", nargs='?', type=str, default="LunarLander-v2", help="the target gym environment")
+        parser.add_argument("--architecture", choices=["simple", "deeper", "wider", "shadow"], default="simple",
+                            help="architecture of the policy")
+        parser.add_argument("--model", choices=["ffn", "rnn", "lstm", "gru"], default="ffn",
+                            help=f"model type if architecture allows for choices")
+        parser.add_argument("--distribution", type=str, default=None,
+                            choices=["categorical", "gaussian", "beta", "multi-categorical"])
+        parser.add_argument("--shared", action="store_true",
+                            help=f"make the model share part of the network for policy and value")
+        parser.add_argument("--iterations", type=int, default=5000, help=f"number of iterations before training ends")
+
+        # meta arguments
+        parser.add_argument("--pcon", type=str, default=None, help="config name (utilities/hp_config.py) to be loaded")
+        parser.add_argument("--rcon", type=str, default=None,
+                            help="config (utilities/reward_config.py) of the reward function")
+        parser.add_argument("--cpu", action="store_true", help=f"use cpu only")
+        parser.add_argument("--sequential", action="store_true", help=f"run worker sequentially workers")
+        parser.add_argument("--load-from", type=int, default=None, help=f"load from given agent id")
+        parser.add_argument("--preload", type=str, default=None, help=f"load visual component weights from pretraining")
+        parser.add_argument("--export-file", type=int, default=None, help=f"save policy to be loaded in workers into file")
+        parser.add_argument("--eval", action="store_true", help=f"evaluate additionally to have at least 5 eps")
+        parser.add_argument("--radical-evaluation", action="store_true", help=f"only record stats from seperate evaluation")
+        parser.add_argument("--save-every", type=int, default=0, help=f"save agent every given number of iterations")
+        parser.add_argument("--monitor-frequency", type=int, default=1, help=f"update the monitor every n iterations.")
+        parser.add_argument("--gif-every", type=int, default=0, help=f"make a gif every n iterations.")
+        parser.add_argument("--debug", action="store_true", help=f"run in debug mode (eager mode)")
+        parser.add_argument("--no-monitor", action="store_true", help="dont use a monitor")
+
+        # gathering parameters
+        parser.add_argument("--workers", type=int, default=8, help=f"the number of workers exploring the environment")
+        parser.add_argument("--horizon", type=int, default=2048,
+                            help=f"number of time steps one worker generates per cycle")
+        parser.add_argument("--discount", type=float, default=0.99, help=f"discount factor for future rewards")
+        parser.add_argument("--lam", type=float, default=0.97, help=f"lambda parameter in the GAE algorithm")
+        parser.add_argument("--no-state-norming", action="store_true", help=f"do not normalize states")
+        parser.add_argument("--no-reward-norming", action="store_true", help=f"do not normalize rewards")
+
+        # optimization parameters
+        parser.add_argument("--epochs", type=int, default=3, help=f"the number of optimization epochs in each cycle")
+        parser.add_argument("--batch-size", type=int, default=64, help=f"minibatch size during optimization")
+        parser.add_argument("--lr-pi", type=float, default=1e-3, help=f"learning rate of the policy")
+        parser.add_argument("--lr-schedule", type=str, default=None, choices=[None, "exponential"],
+                            help=f"lr schedule type")
+        parser.add_argument("--clip", type=float, default=0.2, help=f"clipping range around 1 for the objective function")
+        parser.add_argument("--c-entropy", type=float, default=0.01, help=f"entropy factor in objective function")
+        parser.add_argument("--c-value", type=float, default=1, help=f"value factor in objective function")
+        parser.add_argument("--tbptt", type=int, default=16, help=f"length of subsequences in truncated BPTT")
+        parser.add_argument("--grad-norm", type=float, default=0.5, help=f"norm for gradient clipping, 0 deactivates")
+        parser.add_argument("--clip-values", action="store_true", help=f"clip value objective")
+        parser.add_argument("--stop-early", action="store_true", help=f"stop early if threshold of env was surpassed")
+        parser.add_argument("--experiment-group", type=str, default="default", help="experiment group identifier")
+
+        comm = MPI.COMM_WORLD
+        rank = comm.Get_rank()
+        is_root = rank == 0
+
+        # read arguments
+        argcomplete.autocomplete(parser)
+        args = parser.parse_args()
+
+        if args.env not in all_envs:
+            if is_root:
+                indices = np.argsort([distance.levenshtein(w, args.env) for w in all_envs])[:3]
+                print(f"Unknown environment {args.env}. Did you mean one of {[all_envs[i] for i in indices]}")
+            exit()
+
+        # if config is given load it as default, then overwrite with any goal given parameters
+        if args.pcon is not None:
+            try:
+                parser.set_defaults(**getattr(hp_config, args.pcon))
+                args = parser.parse_args()
+                if is_root:
+                    print(f"Loaded Config {args.pcon}.")
+            except AttributeError as err:
+                raise ImportError("Cannot find config under given name. Does it exist in utilities/hp_config.py?")
+
+        if args.debug:
+            tf.config.run_functions_eagerly(True)
+            if is_root:
+                logging.warning("YOU ARE RUNNING IN DEBUG MODE!")
+
+        try:
+            run_experiment(args.env, vars(args), use_monitor=not args.no_monitor)
+        except Exception as e:
+            if rank == 0:
+                traceback.print_exc()

step2/Dockerfile

+FROM python:3.8-slim
+
+# install dependencies
+RUN pip install --no-cache-dir argparse numpy matplotlib
+
+# copy python script, make executable and add to path
+COPY visualization.py .
+RUN chmod +x visualization.py
+
+CMD [ "/bin/bash" ]

step2/analysis_tool.cwl

+cwlVersion: v1.0
+class: CommandLineTool
+
+baseCommand: ['python3', '/visualization.py']
+
+
+hints:
+  DockerRequirement:
+    dockerPull: docker-registry.ebrains.eu/sc5/sc5_workflow_visualization:latest
+  ResourceRequirement:
+    ramMin: 2048
+    outdirMin: 4096
+
+inputs:
+  input_file:
+    type: File
+    inputBinding:
+      position: 1
+  output_file_name:
+    type: string
+    inputBinding:
+      prefix: --output_file
+      position: 2
+
+outputs:
+  plot:
+    type: File
+    outputBinding:
+      glob: $(inputs.output_file_name)

step2/progress.json

+{
+  "rewards": {
+    "mean": [
+      26.58,
+      45.22,
+      60.77
+    ],
+    "stdev": [
+      13.6
+    ],
+    "last_cycle": []
+  },
+  "lengths": {
+    "mean": [
+      26.58
+    ],
+    "stdev": [
+      13.6
+    ],
+    "last_cycle": []
+  },
+  "entropies": [
+    0.6919
+  ],
+  "vloss": [
+    49.9441
+  ],
+  "ploss": [
+    0.0227
+  ],
+  "preprocessors": {}
+}
\ No newline at end of file

step2/visualization.py

+#!/usr/bin/env python3
+
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+import json
+
+parser = argparse.ArgumentParser()
+parser.add_argument('input_file', help='json file containing the progress indicators')
+parser.add_argument('--output_file', default='out.png', help='file where output plot should be written')
+args = parser.parse_args()
+
+fig, ax = plt.subplots(figsize=(12, 6))
+
+with open(args.input_file) as f:
+    data = json.load(f)
+
+plt.plot(data["rewards"]["mean"])
+
+ax.set_xlabel('cycle')
+ax.set_ylabel('reward')
+ax.legend()
+
+plt.savefig(args.output_file)

workflow.cwl

+# -------------------------------------------------------- WORKFLOW INFO --------------------------------------------------------
+
+cwlVersion: v1.0
+class: Workflow
+
+# ------------------------------------------------------- WORKFLOW INPUTS -------------------------------------------------------
+
+inputs:
+  env: string
+  pcon: string
+  rcon: string
+  workers: int          # (32-bit signed)
+  iterations: int
+  horizon: int
+  output_file_name: string
+
+# ------------------------------------------------------- WORKFLOW OUTPUTS ------------------------------------------------------
+
+outputs:
+  output_file:
+    type: File
+    outputSource: reinforcement_learning/out
+  output_progress:
+    type: File
+    outputSource: reinforcement_learning/out_data
+  output_visualization:
+    type: File
+    outputSource: visualization/plot
+
+# ------------------------------------------------------- WORKFLOW STEPS --------------------------------------------------------
+
+steps:
+  reinforcement_learning:
+    run: step1/reinforcement_learning_tool.cwl
+    in:
+      env: env
+      pcon: pcon
+      rcon: rcon
+      workers: workers
+      iterations: iterations
+      horizon: horizon
+    out: [out, out_data]
+  visualization:
+    run: step2/analysis_tool.cwl
+    in:
+      input_file: reinforcement_learning/out_data
+      output_file_name: output_file_name
+    out: [plot]
\ No newline at end of file

workflow_info.yml

+env: CartPole-v1
+pcon: discrete_no_norms
+rcon: default
+workers: 6
+horizon: 512
+iterations: 2
+output_file_name: 'output.png'
+