diff --git a/README.md b/README.md index 358a6e6d..d38b6e64 100644 --- a/README.md +++ b/README.md @@ -92,8 +92,6 @@ There are two ways to get GNES, either as a Docker image or as a PyPi package. F ### Run GNES as a Docker Container -We provide GNES as a Docker image to simplify the installation. - ```bash docker run gnes/gnes:alpine-latest ``` @@ -102,11 +100,11 @@ This command downloads the latest GNES image (based on [Alpine Linux](https://al #### Choose the right GNES image -Besides the `alpine` image optimized for the space, we also provide Buster (Debian 10.0) and Ubuntu 18.04-based images. The table below summarizes [all available GNES images and tags](https://cloud.docker.com/u/gnes/repository/docker/gnes/gnes). One can fill in `{ver}` with `latest`, `stable` or `x.x.xx`. `latest` refers to the **latest master** of this repository, which is [mutable and may not always be a stable](./CONTRIBUTING.md#Merging-Process). Therefore, we recommend you to use an official release by changing the `latest` to a version tag, say `v0.0.24`. Or you may simply use `stable` for the latest release. +Besides the `alpine` image optimized for the space, we also provide Buster (Debian 10.0) and Ubuntu 18.04-based images. The table below summarizes [all available GNES images and tags](https://cloud.docker.com/u/gnes/repository/docker/gnes/gnes). One can fill in `{ver}` with `latest`, `stable` or `x.x.xx`. `latest` refers to the **latest master** of this repository, which is [mutable and may not always be a stable](./CONTRIBUTING.md#Merging-Process). We recommend you to use an official release by changing the `latest` to a version tag, say `v0.0.24`. Or you may simply use `stable` for the last release.
GNES image | +Image | Size and layers | Description |
---|---|---|---|
gnes:full-{ver} |
- | based on Ubuntu 16.04; python-3.6.8, cuda-10.0, tf1.14, pytorch1.1, faiss and multiple pretrained models; Heavy but self-contained, useful in testing GNES end-to-endly. |
+ based on Ubuntu 16.04; python-3.6.8, cuda-10.0, tf1.14, pytorch1.1, faiss, multiple pretrained models; Heavy but self-contained, useful in testing GNES end-to-endly. |