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[Feature] Offline datasets: D4RL (#928)
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Diff for: ‎.circleci/config.yml

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@@ -474,6 +474,62 @@ jobs:
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- store_test_results:
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path: test-results
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unittest_linux_d4rl_gpu:
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<<: *binary_common
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machine:
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image: ubuntu-2004-cuda-11.4:202110-01
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resource_class: gpu.nvidia.medium
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environment:
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image_name: "nvidia/cudagl:11.4.0-base"
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TAR_OPTIONS: --no-same-owner
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PYTHON_VERSION: << parameters.python_version >>
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CU_VERSION: << parameters.cu_version >>
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steps:
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- checkout
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- designate_upload_channel
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- run:
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name: Generate cache key
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# This will refresh cache on Sundays, nightly build should generate new cache.
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command: echo "$(date +"%Y-%U")" > .circleci-weekly
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- restore_cache:
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keys:
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- env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux_libs/scripts_d4rl/environment.yml" }}-{{ checksum ".circleci-weekly" }}
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- run:
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name: Setup
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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
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- save_cache:
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key: env-v3-linux-{{ arch }}-py<< parameters.python_version >>-{{ checksum ".circleci/unittest/linux_libs/scripts_d4rl/environment.yml" }}-{{ checksum ".circleci-weekly" }}
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paths:
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- conda
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- env
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- run:
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# Here we create an envlist file that contains some env variables that we want the docker container to be aware of.
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# Normally, the CIRCLECI variable is set and available on all CI workflows: https://circleci.com/docs/2.0/env-vars/#built-in-environment-variables.
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# They're available in all the other workflows (OSX and Windows).
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# But here, we're running the unittest_linux_gpu workflows in a docker container, where those variables aren't accessible.
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# So instead we dump the variables we need in env.list and we pass that file when invoking "docker run".
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name: export CIRCLECI env var
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command: echo "CIRCLECI=true" >> ./env.list
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- run:
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name: Install torchrl
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command: docker run -e PYTHON_VERSION -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_libs/scripts_d4rl/install.sh
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- run:
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name: Run tests
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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
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- run:
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name: Codecov upload
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command: |
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bash <(curl -s https://codecov.io/bash) -Z -F d4rl-gpu
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- run:
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name: Post Process
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command: docker run -t --gpus all -v $PWD:$PWD -w $PWD "${image_name}" .circleci/unittest/linux_libs/scripts_d4rl/post_process.sh
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- store_test_results:
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path: test-results
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unittest_linux_jumanji_gpu:
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<<: *binary_common
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machine:
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cu_version: cu117
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name: unittest_linux_habitat_gpu_py3.8
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python_version: '3.8'
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# - unittest_linux_d4rl_gpu:
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# cu_version: cu117
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# name: unittest_linux_d4rl_gpu_py3.8
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# python_version: '3.8'
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- unittest_linux_jumanji_gpu:
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cu_version: cu117
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name: unittest_linux_jumanji_gpu_py3.8

Diff for: ‎.circleci/unittest/linux_libs/scripts_d4rl/environment.yml

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channels:
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- pytorch
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- defaults
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dependencies:
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- pip
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- pip:
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- hypothesis
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- future
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- cloudpickle
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- pytest
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- pytest-cov
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- pytest-mock
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- pytest-instafail
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- pytest-rerunfailures
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- expecttest
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- pyyaml
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- scipy
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- hydra-core

Diff for: ‎.circleci/unittest/linux_libs/scripts_d4rl/install.sh

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#!/usr/bin/env bash
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unset PYTORCH_VERSION
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# For unittest, nightly PyTorch is used as the following section,
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# so no need to set PYTORCH_VERSION.
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# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config.
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apt-get update && apt-get install -y git wget gcc g++
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#apt-get update && apt-get install -y git wget freeglut3 freeglut3-dev
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set -e
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eval "$(./conda/bin/conda shell.bash hook)"
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conda activate ./env
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if [ "${CU_VERSION:-}" == cpu ] ; then
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version="cpu"
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else
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if [[ ${#CU_VERSION} -eq 4 ]]; then
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CUDA_VERSION="${CU_VERSION:2:1}.${CU_VERSION:3:1}"
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elif [[ ${#CU_VERSION} -eq 5 ]]; then
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CUDA_VERSION="${CU_VERSION:2:2}.${CU_VERSION:4:1}"
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fi
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echo "Using CUDA $CUDA_VERSION as determined by CU_VERSION ($CU_VERSION)"
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version="$(python -c "print('.'.join(\"${CUDA_VERSION}\".split('.')[:2]))")"
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fi
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# submodules
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git submodule sync && git submodule update --init --recursive
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printf "Installing PyTorch with %s\n" "${CU_VERSION}"
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if [ "${CU_VERSION:-}" == cpu ] ; then
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# conda install -y pytorch torchvision cpuonly -c pytorch-nightly
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# use pip to install pytorch as conda can frequently pick older release
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# conda install -y pytorch cpuonly -c pytorch-nightly
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pip3 install --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu --force-reinstall
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else
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pip3 install --pre torch --extra-index-url https://download.pytorch.org/whl/nightly/cu116 --force-reinstall
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fi
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# install tensordict
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pip install git+https://github.com/pytorch-labs/tensordict.git
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# smoke test
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python -c "import functorch;import tensordict"
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printf "* Installing torchrl\n"
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pip3 install -e .
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# smoke test
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python -c "import torchrl"

Diff for: ‎.circleci/unittest/linux_libs/scripts_d4rl/post_process.sh

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#!/usr/bin/env bash
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set -e
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eval "$(./conda/bin/conda shell.bash hook)"
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conda activate ./env

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