xebxeb/fasttext-docker repository overview

⁠fastText Docker

Dockerfile and example for Facebook Research's fastText⁠.

⁠Quick Deployments

If you'd like to use a cluster manager to deploy fastText-docker, we have configurations for:

• Kubernetes⁠

See more in deployments⁠

⁠Getting Started

The quickest way to see the fastText classification tutorial with fastText-docker is:

docker pull xebxeb/fasttext-docker
mkdir -p /tmp/data && mkdir -p /tmp/result
docker run --rm -v /tmp/data:/data -v /tmp/result:/result -it xebxeb/fasttext-docker ./classification-example.sh

NOTE: if you ran the above on macOS, the data & results are going to be on your Docker Machine VM. Use a path in /Users/${USER}/ if you want to map to your local system. Or do something like: docker-machine ssh `docker-machine active` ls /tmp/data to see the files in your VM.

⁠Types of Dockerfiles

There are two Dockerfiles, including:

• Dockerfile all-in-one, used for development purposes. Includes the fastText binary, the entire source repository and Python dependencies.
• Dockerfile.binary just for executing the fastText binary. The fasttext binary is the container's entrypoint.

⁠Pulling Prebuilt Images

If you'd like to use the published prebuilt images, you can pull them from DockerHub. NOTE: the latest will always be the devel tag.

docker pull xebxeb/fasttext-docker
docker pull xebxeb/fasttext-docker:devel
docker pull xebxeb/fasttext-docker:binary

⁠Development Container

⁠Building Devel

To build the devel Docker image, simply do a make after cloning or:

docker build -t fasttext .

Building the docker image will create the fasttext binary as well as clone the repository -- all in the root of the container.
There is no entrypoint for the devel container and any of the examples in the fastText⁠ repository will work.

⁠Using Devel

The development container is meant to be interactive, so the best way to use it is probably with a mounted volume and bash.

$ mkdir -p /tmp/data && mkdir -p /tmp/result
$ docker run --rm -it -v /tmp/data:/data -v /tmp/result:/result fasttext /bin/bash
# ./fasttext
usage: fasttext <command> <args>

The commands supported by fasttext are:

  supervised       train a supervised classifier
  test             evaluate a supervised classifier
  predict          predict most likely label
  skipgram         train a skipgram model
  cbow             train a cbow model
  print-vectors    print vectors given a trained model

# ./classification-example.sh
Resolving googledrive.com (googledrive.com)... 216.58.194.33, 2607:f8b0:4000:802::2001
Connecting to googledrive.com (googledrive.com)|216.58.194.33|:443... connected.
HTTP request sent, awaiting response... 302 Moved Temporarily
....
dbpedia_csv/
dbpedia_csv/classes.txt
dbpedia_csv/test.csv
dbpedia_csv/train.csv
dbpedia_csv/readme.txt
make: Nothing to be done for `opt'.
Read 32M words
Progress: 50.2%  words/sec/thread: 1833592  lr: 0.049821  loss: 0.141374  eta: 0h0m

You get the idea... it's a full interactive shell with a mounted volume.

NOTE be sure to use absolute paths in your local mount arguments! And if you are on macOS, be sure that your path is within /Users/ -- otherwise you will map to a path on your Docker Machine VM. See classification-example.sh⁠ for an example.

⁠Binary Container

⁠Building Binary

If you'd just like a pre-built binary of fastText, you can build the binary simply by doing:

make binary

(and modifying the Makefile to your image name if you'd like)

⁠Using Binary

You will likely want to mount a volume with Docker in order to use the binary container because it has an entrypoint of the fasttext binary. For example:

docker run --rm -v /var/path/to/data:/data -v /var/path/to/results:/results test "/result/dbpedia.bin" "/data/dbpedia.test"
docker run --rm -v /var/path/to/data:/data -v /var/path/to/results:/results predict "/result/dbpedia.bin" "/data/dbpedia.test" > "data/dbpedia.test.predict"

See classification-example.sh⁠ for an example using the devel tag. Simply replace that with binary and remove the ./fastText argument to achieve the same result.