hongshaorou/tdmpc2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update readme + dockerfile

Browse Source
This commit is contained in:
Nicklas Hansen
2025-05-13 14:51:16 -07:00
parent 7ec6bc83a8
commit 7992fa193e
2 changed files with 12 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
README.md
View File

@@ -36,19 +36,18 @@ You will need a machine with a GPU and at least 12 GB of RAM for single-task onl
We provide a `Dockerfile` for easy installation. You can build the docker image by running We provide a `Dockerfile` for easy installation. You can build the docker image by running
``` ```
cd docker && docker build . -t <user>/tdmpc2:1.0.0 cd docker && docker build . -t <user>/tdmpc2:1.0.1
``` ```
This docker image contains all dependencies needed for running DMControl, Meta-World, and ManiSkill2 experiments. This docker image contains all dependencies needed for running DMControl. We also provide a pre-built docker image [here](https://hub.docker.com/repository/docker/nicklashansen/tdmpc2/tags/1.0.1/sha256-b07d4e04d4b28ffd9a63ac18ec1541950e874bb51d276c7d09b36135f170dd93).
If you prefer to install dependencies manually, start by installing dependencies via `conda` by running the following command: If you prefer to use `conda` rather than docker, start by running the following command:
``` ```
conda env create -f docker/environment.yaml conda env create -f docker/environment.yaml
pip install gym==0.21.0
``` ```
The `environment.yaml` file installs dependencies required for training on DMControl tasks. Other domains can be installed by following the instructions in `environment.yaml`. The `docker/environment.yaml` file installs dependencies required for training on DMControl tasks. Other domains can be installed by following the instructions in `docker/environment.yaml`.
If you want to run ManiSkill2, you will additionally need to download and link the necessary assets by running If you want to run ManiSkill2, you will additionally need to download and link the necessary assets by running
@@ -62,13 +61,13 @@ which downloads assets to `./data`. You may move these assets to any location. T
export MS2_ASSET_DIR=<path>/<to>/<data> export MS2_ASSET_DIR=<path>/<to>/<data>
``` ```
and restart your terminal. Meta-World additionally requires MuJoCo 2.1.0. We host the unrestricted MuJoCo 2.1.0 license (courtesy of Google DeepMind) at [https://www.tdmpc2.com/files/mjkey.txt](https://www.tdmpc2.com/files/mjkey.txt). You can download the license by running and restart your terminal. Note that Meta-World requires MuJoCo 2.1.0 and `gym==0.21.0` which is becoming increasingly difficult to install. We host the unrestricted MuJoCo 2.1.0 license (courtesy of Google DeepMind) at [https://www.tdmpc2.com/files/mjkey.txt](https://www.tdmpc2.com/files/mjkey.txt). You can download the license by running
``` ```
wget https://www.tdmpc2.com/files/mjkey.txt -O ~/.mujoco/mjkey.txt wget https://www.tdmpc2.com/files/mjkey.txt -O ~/.mujoco/mjkey.txt
``` ```
See `docker/Dockerfile` for installation instructions if you do not already have MuJoCo 2.1.0 installed. MyoSuite requires `gym==0.13.0` which is incompatible with Meta-World and ManiSkill2. Install separately with `pip install myosuite` if desired. Depending on your existing system packages, you may need to install other dependencies. See `docker/Dockerfile` for a list of recommended system packages. Depending on your existing system packages, you may need to install other dependencies. See `docker/Dockerfile` for a list of recommended system packages.
---- ----
@@ -87,7 +86,7 @@ This codebase provides support for all **104** continuous control tasks from **D
| myosuite | myo-key-turn | myosuite | myo-key-turn
| myosuite | myo-key-turn-hard | myosuite | myo-key-turn-hard
which can be run by specifying the `task` argument for `evaluation.py`. Multi-task training and evaluation is specified by setting `task=mt80` or `task=mt30` for the 80-task and 30-task sets, respectively. We also provide basic support for other MuJoCo/Box2d Gymnasium tasks; refer to the `envs` directory for a list of tasks. It should be relatively straightforward to add support for custom tasks by following the examples in `envs`. which can be run by specifying the `task` argument for `evaluation.py`. Multi-task training and evaluation is specified by setting `task=mt80` or `task=mt30` for the 80-task and 30-task sets, respectively. While you generally do not need to access the underlying task IDs or embeddings during training or evaluation of our multi-task models, the mapping from task name to task embedding used in our work can be found [here](https://github.com/nicklashansen/tdmpc2/blob/7ec6bc83a82a5188ca3faddc59aea83f430ab570/tdmpc2/common/__init__.py#L26). As of April 2025, our codebase also provides basic support for other MuJoCo/Box2d Gymnasium tasks; refer to the `envs` directory for a list of tasks. It should be relatively straightforward to add support for custom tasks by following the examples in `envs`.
**Note:** we also provide support for image observations in the DMControl tasks. Use argument `obs=rgb` if you wish to train visual policies. **Note:** we also provide support for image observations in the DMControl tasks. Use argument `obs=rgb` if you wish to train visual policies.

36
docker/Dockerfile
View File

@@ -4,14 +4,14 @@
# https://www.tdmpc2.com # # https://www.tdmpc2.com #
# -------------------------------------- # # -------------------------------------- #
# Build instructions: # # Build instructions: #
# docker build . -t <user>/tdmpc2:1.0.0 # # docker build . -t <user>/tdmpc2:1.0.1 #
# docker push <user>/tdmpc2:1.0.0 # # docker push <user>/tdmpc2:1.0.1 #
# -------------------------------------- # # -------------------------------------- #
# Run: # # Run: #
# docker run -i \ # # docker run -i \ #
# -v <path>/<to>/tdmpc2:/tdmpc2 \ # # -v <path>/<to>/tdmpc2:/tdmpc2 \ #
# --gpus all \ # # --gpus all \ #
# -t <user>/tdmpc2:1.0.0 \ # # -t <user>/tdmpc2:1.0.1 \ #
# /bin/bash # # /bin/bash #
########################################## ##########################################
@@ -42,34 +42,8 @@ RUN conda update conda && \
conda init conda init
SHELL ["/bin/bash", "-c"] SHELL ["/bin/bash", "-c"]
RUN echo "cd /root" >> /root/.bashrc RUN echo "cd /root" >> /root/.bashrc
# image does not include metaworld, maniskill, myosuite
# mujoco 2.1.0 # these can be installed separately; see environment.yaml for details
ENV MUJOCO_GL egl
ENV LD_LIBRARY_PATH /root/.mujoco/mujoco210/bin:${LD_LIBRARY_PATH}
RUN mkdir -p /root/.mujoco && \
wget https://www.tdmpc2.com/files/mjkey.txt && \
wget https://github.com/deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz && \
tar -xzf mujoco210-linux-x86_64.tar.gz && \
rm mujoco210-linux-x86_64.tar.gz && \
mv mujoco210 /root/.mujoco/mujoco210 && \
mv mjkey.txt /root/.mujoco/mjkey.txt && \
find /root/.mujoco -uid 421709 -exec chown root:root {} \; && \
python -c "import mujoco_py"
# gym
RUN pip install gym==0.21.0
# metaworld
RUN pip install git+https://github.com/Farama-Foundation/Metaworld.git@04be337a12305e393c0caf0cbf5ec7755c7c8feb
# maniskill2
ENV MS2_ASSET_DIR /root/data
RUN pip install mani-skill2==0.4.1 && \
cd /root && \
python -m mani_skill2.utils.download_asset all -y
# myosuite (conflicts with meta-world / mani-skill2)
# RUN pip install myosuite
# success! # success!
RUN echo "Successfully built TD-MPC2 Docker image!" RUN echo "Successfully built TD-MPC2 Docker image!"