ray/docker/examples/Dockerfile

# The examples Docker image adds dependencies needed to run the examples

FROM ray-project/deploy

# This updates numpy to 1.14 and mutes errors from other libraries
RUN conda install -y numpy
RUN apt-get install -y zlib1g-dev
RUN pip install gym[atari] opencv-python==3.2.0.8 tensorflow lz4
RUN pip install --upgrade git+git://github.com/hyperopt/hyperopt.git
RUN conda install pytorch-cpu torchvision-cpu -c pytorch
updated Docker files (#171) * updated Docker files * single Docker RUN for apt-get installs and cleanup * stylistic cleanup 2016-12-31 17:21:33 -08:00			`# The examples Docker image adds dependencies needed to run the examples`
Docker builds on Travis (#343) * attempt to build on travis using docker * run tests in foreground * add examples to travis tests * test from current checkout * attempt to fix docker version issues * try build with xenial * attempt docker upgrade * avoid hang on configuration files * matrix osx and linux w/ docker * restore non-test docker builds * fix typo * tuning and cleanup * add missing file * comment cleanup 2016-08-02 17:03:28 -07:00
updated Docker files (#171) * updated Docker files * single Docker RUN for apt-get installs and cleanup * stylistic cleanup 2016-12-31 17:21:33 -08:00			`FROM ray-project/deploy`
[rllib] Fix A3C PyTorch implementation (#2036) * Use F.softmax instead of a pointless network layer Stateless functions should not be network layers. * Use correct pytorch functions * Rename argument name to out_size Matches in_size and makes more sense. * Fix shapes of tensors Advantages and rewards both should be scalars, and therefore a list of them should be 1D. * Fmt * replace deprecated function * rm unnecessary Variable wrapper * rm all use of torch Variables Torch does this for us now. * Ensure that values are flat list * Fix shape error in conv nets * fmt * Fix shape errors Reshaping the action before stepping in the env fixes a few errors. * Add TODO * Use correct filter size Works when `self.config['model']['channel_major'] = True`. * Add missing channel major * Revert reshape of action This should be handled by the agent or at least in a cleaner way that doesn't break existing envs. * Squeeze action * Squeeze actions along first dimension This should deal with some cases such as cartpole where actions are scalars while leaving alone cases where actions are arrays (some robotics tasks). * try adding pytorch tests * typo * fixup docker messages * Fix A3C for some envs Pendulum doesn't work since it's an edge case (expects singleton arrays, which `.squeeze()` collapses to scalars). * fmt * nit flake * small lint 2018-05-30 10:48:11 -07:00
			`# This updates numpy to 1.14 and mutes errors from other libraries`
			`RUN conda install -y numpy`
fix docker build bug (#207) 2017-01-18 23:23:34 -08:00			`RUN apt-get install -y zlib1g-dev`
[rllib] RLlib CLI (#2375) 2018-07-12 19:12:04 +02:00			`RUN pip install gym[atari] opencv-python==3.2.0.8 tensorflow lz4`
[tune] HyperOpt Support (v2) (#1763) 2018-04-04 11:08:26 -07:00			`RUN pip install --upgrade git+git://github.com/hyperopt/hyperopt.git`
[rllib] Fix A3C PyTorch implementation (#2036) * Use F.softmax instead of a pointless network layer Stateless functions should not be network layers. * Use correct pytorch functions * Rename argument name to out_size Matches in_size and makes more sense. * Fix shapes of tensors Advantages and rewards both should be scalars, and therefore a list of them should be 1D. * Fmt * replace deprecated function * rm unnecessary Variable wrapper * rm all use of torch Variables Torch does this for us now. * Ensure that values are flat list * Fix shape error in conv nets * fmt * Fix shape errors Reshaping the action before stepping in the env fixes a few errors. * Add TODO * Use correct filter size Works when `self.config['model']['channel_major'] = True`. * Add missing channel major * Revert reshape of action This should be handled by the agent or at least in a cleaner way that doesn't break existing envs. * Squeeze action * Squeeze actions along first dimension This should deal with some cases such as cartpole where actions are scalars while leaving alone cases where actions are arrays (some robotics tasks). * try adding pytorch tests * typo * fixup docker messages * Fix A3C for some envs Pendulum doesn't work since it's an edge case (expects singleton arrays, which `.squeeze()` collapses to scalars). * fmt * nit flake * small lint 2018-05-30 10:48:11 -07:00			`RUN conda install pytorch-cpu torchvision-cpu -c pytorch`