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
# The following is needed to support TensorFlow 1.14
RUN conda remove -y --force wrapt
RUN pip install -U pip
RUN pip install gym[atari] opencv-python-headless tensorflow lz4 keras pytest-timeout smart_open tensorflow_probability
RUN pip install -U h5py  # Mutes FutureWarnings
RUN pip install --upgrade bayesian-optimization
RUN pip install --upgrade git+git://github.com/hyperopt/hyperopt.git
RUN pip install ConfigSpace==0.4.10
RUN pip install --upgrade sigopt nevergrad scikit-optimize hpbandster lightgbm xgboost torch torchvision
RUN pip install -U tabulate mlflow
RUN pip install -U pytest-remotedata>=0.3.1
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`
Fix tensorflow-1.14 installation in jenkins (#5007) 2019-06-21 11:04:40 -07:00			`# The following is needed to support TensorFlow 1.14`
			`RUN conda remove -y --force wrapt`
[tune/sgd] Fix Jenkins (#5765) 2019-09-27 09:59:08 -07:00			`RUN pip install -U pip`
[rllib] Feature/soft actor critic v2 (#5328) * Add base for Soft Actor-Critic * Pick changes from old SAC branch * Update sac.py * First implementation of sac model * Remove unnecessary SAC imports * Prune unnecessary noise and exploration code * Implement SAC model and use that in SAC policy * runs but doesn't learn * clear state * fix batch size * Add missing alpha grads and vars * -200 by 2k timesteps * doc * lazy squash * one file * ignore tfp * revert done 2019-08-01 23:37:36 -07:00			`RUN pip install gym[atari] opencv-python-headless tensorflow lz4 keras pytest-timeout smart_open tensorflow_probability`
[tune] Tweaks to Trainable and Verbosity (#2889) 2018-10-11 23:42:13 -07:00			`RUN pip install -U h5py # Mutes FutureWarnings`
[tune] Add BayesOpt (#3864) Adds BayesOpt as a Tune suggestion algorithm. 2019-01-31 16:54:17 -08:00			`RUN pip install --upgrade bayesian-optimization`
[tune] HyperOpt Support (v2) (#1763) 2018-04-04 11:08:26 -07:00			`RUN pip install --upgrade git+git://github.com/hyperopt/hyperopt.git`
[hotfix] Docker (#5809) * configspace * reorder 2019-09-30 16:39:00 -07:00			`RUN pip install ConfigSpace==0.4.10`
			`RUN pip install --upgrade sigopt nevergrad scikit-optimize hpbandster lightgbm xgboost torch torchvision`
[tune/sgd] Fix Jenkins (#5765) 2019-09-27 09:59:08 -07:00			`RUN pip install -U tabulate mlflow`
[ci] Change Jenkins to py3 (#5022) * conda3 * integration * add nevergrad, remotedata * pytest 0.3.1 * otherdockers * setup * tune 2019-06-24 21:50:37 -07:00			`RUN pip install -U pytest-remotedata>=0.3.1`
[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`