Git clone this repository, and cd into directory for remaining commands
git clone https://github.com/openai/gpt-2.git && cd gpt-2
Then, follow instructions for either native or Docker installation.
All steps can optionally be done in a virtual environment using tools such as virtualenv or conda.
Install tensorflow 1.12 (with GPU support, if you have a GPU and want everything to run faster)
pip3 install tensorflow==1.12.0
or
pip3 install tensorflow-gpu==1.12.0
Install other python packages:
pip3 install -r requirements.txt
Download the model data
python3 download_model.py 124M
python3 download_model.py 355M
python3 download_model.py 774M
python3 download_model.py 1558M
Build the Dockerfile and tag the created image as gpt-2:
docker build --tag gpt-2 -f Dockerfile.gpu . # or Dockerfile.cpu
Start an interactive bash session from the gpt-2 docker image.
You can opt to use the --runtime=nvidia flag if you have access to a NVIDIA GPU
and a valid install of nvidia-docker 2.0.
docker run --runtime=nvidia -it gpt-2 bash
| WARNING: Samples are unfiltered and may contain offensive content. |
|---|
Some of the examples below may include Unicode text characters. Set the environment variable:
export PYTHONIOENCODING=UTF-8
to override the standard stream settings in UTF-8 mode.
To generate unconditional samples from the small model:
python3 src/generate_unconditional_samples.py | tee /tmp/samples
There are various flags for controlling the samples:
python3 src/generate_unconditional_samples.py --top_k 40 --temperature 0.7 | tee /tmp/samples
To check flag descriptions, use:
python3 src/generate_unconditional_samples.py -- --help
To give the model custom prompts, you can use:
python3 src/interactive_conditional_samples.py --top_k 40
To check flag descriptions, use:
python3 src/interactive_conditional_samples.py -- --help