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fix some typos from README.md

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@ -1,11 +1,11 @@
# Z ML Energy-Based Models
This repository contains adapted code for [OpenAI's Implicit Generation and Generalization in Energy Based Models](https://arxiv.org/pdf/1903.08689.pdf).
This repository contains an adapted code for [OpenAI's Implicit Generation and Generalization in Energy Based Models](https://arxiv.org/pdf/1903.08689.pdf).
## Installing locally
### Install system's requirement
### Install the system's requirement
```bash
brew install gcc@6
@ -31,22 +31,22 @@ source venv/bin/activate
pip install -r requirements.txt
```
Note that this is an adapted requirement file, since the [OpenAI's original](https://github.com/openai/ebm_code_release/blob/master/requirements.txt) is not complete/correct.
Note that this is an adapted requirement file since the [OpenAI's original](https://github.com/openai/ebm_code_release/blob/master/requirements.txt) is not complete/correct.
### Install MuJoCo
Download and install [MuJoCo](https://www.roboti.us/index.html).
You will also need to register for a license, which asks for a machine ID. The documentation in the website is incomplete, so just download the suggested script and run:
You will also need to register for a license, which asks for a machine ID. The documentation on the website is incomplete, so just download the suggested script and run:
```bash
mv getid_osx.dmg getid_osx.dms
./getid_osx.dms
```
### Download pretrained models (exmples)
### Download pre-trained models (exmples)
Download all [pretrained models](https://sites.google.com/view/igebm/home) and unzip into a local folder `cachedir`:
Download all [pre-trained models](https://sites.google.com/view/igebm/home) and unzip into a local folder `cachedir`:
```bash
mkdir cachedir
@ -63,7 +63,7 @@ OpenAI's original code contains [hardcoded constants that only work on Linux](ht
### Parallelization with `mpiexec`
All code supports horovod execution, so model training can be increased substantially by using multiple different workers by running each command.
All code supports [`horovod` execution](https://github.com/horovod/horovod), so model training can be increased substantially by using multiple different workers by running each command.
```
mpiexec -n <worker_num> <command>
```
@ -188,11 +188,12 @@ python ebm_combine.py --task=conceptcombine --exp_size=<exp_size> --exp_shape=<e
## TODO
* Run in docker/kubernetes/Orquestra.
* Run and extract resuts from all examples.
* Make tests/benchamer with `mpiexec`.
* Create an `env` file for constants and parameters.
* Upgrade all the libraries, upgrade to TF2.
* [ ] Run in docker/kubernetes/Orquestra.
* [ ] Run and extract resuts from all examples.
* [ ] Understand `horovod`.
* [ ] Make tests/benchamer with `mpiexec`.
* [ ] Create an `env` file for constants and parameters.
* [ ] Upgrade all the libraries, upgrade to TF2.
---