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[Feature] Offline datasets: D4RL (pytorch#928)
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vmoens authored Mar 16, 2023
1 parent da88aad commit ce81995
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60 changes: 60 additions & 0 deletions .circleci/config.yml
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Expand Up @@ -474,6 +474,62 @@ jobs:
- store_test_results:
path: test-results

unittest_linux_d4rl_gpu:
<<: *binary_common
machine:
image: ubuntu-2004-cuda-11.4:202110-01
resource_class: gpu.nvidia.medium
environment:
image_name: "nvidia/cudagl:11.4.0-base"
TAR_OPTIONS: --no-same-owner
PYTHON_VERSION: << parameters.python_version >>
CU_VERSION: << parameters.cu_version >>

steps:
- checkout
- designate_upload_channel
- run:
name: Generate cache key
# This will refresh cache on Sundays, nightly build should generate new cache.
command: echo "$(date +"%Y-%U")" > .circleci-weekly
- restore_cache:
keys:
- env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux_libs/scripts_d4rl/environment.yml" }}-{{ checksum ".circleci-weekly" }}
- run:
name: Setup
command: docker run -e PYTHON_VERSION -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_libs/scripts_d4rl/setup_env.sh
- save_cache:

key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux_libs/scripts_d4rl/environment.yml" }}-{{ checksum ".circleci-weekly" }}

paths:
- conda
- env
- run:
# Here we create an envlist file that contains some env variables that we want the docker container to be aware of.
# Normally, the CIRCLECI variable is set and available on all CI workflows: https://circleci.com/docs/2.0/env-vars/#built-in-environment-variables.
# They're available in all the other workflows (OSX and Windows).
# But here, we're running the unittest_linux_gpu workflows in a docker container, where those variables aren't accessible.
# So instead we dump the variables we need in env.list and we pass that file when invoking "docker run".
name: export CIRCLECI env var
command: echo "CIRCLECI=true" >> ./env.list
- run:
name: Install torchrl
command: docker run -e PYTHON_VERSION -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_libs/scripts_d4rl/install.sh
- run:
name: Run tests
command: docker run --env-file ./env.list -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_libs/scripts_d4rl/run_test.sh
- run:
name: Codecov upload
command: |
bash <(curl -s https://codecov.io/bash) -Z -F d4rl-gpu
- run:
name: Post Process
command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_libs/scripts_d4rl/post_process.sh
- store_test_results:
path: test-results


unittest_linux_jumanji_gpu:
<<: *binary_common
machine:
Expand Down Expand Up @@ -1223,6 +1279,10 @@ workflows:
cu_version: cu117
name: unittest_linux_habitat_gpu_py3.8
python_version: '3.8'
# - unittest_linux_d4rl_gpu:
# cu_version: cu117
# name: unittest_linux_d4rl_gpu_py3.8
# python_version: '3.8'
- unittest_linux_jumanji_gpu:
cu_version: cu117
name: unittest_linux_jumanji_gpu_py3.8
Expand Down
18 changes: 18 additions & 0 deletions .circleci/unittest/linux_libs/scripts_d4rl/environment.yml
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@@ -0,0 +1,18 @@
channels:
- pytorch
- defaults
dependencies:
- pip
- pip:
- hypothesis
- future
- cloudpickle
- pytest
- pytest-cov
- pytest-mock
- pytest-instafail
- pytest-rerunfailures
- expecttest
- pyyaml
- scipy
- hydra-core
51 changes: 51 additions & 0 deletions .circleci/unittest/linux_libs/scripts_d4rl/install.sh
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#!/usr/bin/env bash

unset PYTORCH_VERSION
# For unittest, nightly PyTorch is used as the following section,
# so no need to set PYTORCH_VERSION.
# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config.
apt-get update && apt-get install -y git wget gcc g++
#apt-get update && apt-get install -y git wget freeglut3 freeglut3-dev

set -e

eval "$(./conda/bin/conda shell.bash hook)"
conda activate ./env

if [ "${CU_VERSION:-}" == cpu ] ; then
version="cpu"
else
if [[ ${#CU_VERSION} -eq 4 ]]; then
CUDA_VERSION="${CU_VERSION:2:1}.${CU_VERSION:3:1}"
elif [[ ${#CU_VERSION} -eq 5 ]]; then
CUDA_VERSION="${CU_VERSION:2:2}.${CU_VERSION:4:1}"
fi
echo "Using CUDA $CUDA_VERSION as determined by CU_VERSION ($CU_VERSION)"
version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")"
fi


# submodules
git submodule sync && git submodule update --init --recursive

printf "Installing PyTorch with %s\n" "${CU_VERSION}"
if [ "${CU_VERSION:-}" == cpu ] ; then
# conda install -y pytorch torchvision cpuonly -c pytorch-nightly
# use pip to install pytorch as conda can frequently pick older release
# conda install -y pytorch cpuonly -c pytorch-nightly
pip3 install --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu --force-reinstall
else
pip3 install --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cu116 --force-reinstall
fi

# install tensordict
pip install git+https://github.com/pytorch-labs/tensordict.git

# smoke test
python -c "import functorch;import tensordict"

printf "* Installing torchrl\n"
pip3 install -e .

# smoke test
python -c "import torchrl"
6 changes: 6 additions & 0 deletions .circleci/unittest/linux_libs/scripts_d4rl/post_process.sh
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#!/usr/bin/env bash

set -e

eval "$(./conda/bin/conda shell.bash hook)"
conda activate ./env
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