ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS". UNIVERSITE PARIS SUD, INRIA, CHALEARN, AND/OR OTHER ORGANIZERS OR CODE AUTHORS DISCLAIM ANY EXPRESSED OR IMPLIED WARRANTIES.
You can download this starting kit by clicking on the green button "Clone or download" on top of this GitHub repo, then "Download ZIP". You'll have this whole starting kit by unzipping the downloaded file.
Another convenient way is to use git clone:
cd <path_to_your_directory>
git clone https://github.com/zhengying-liu/autodl_starting_kit_stable.git
(If you are an experienced user of GitHub, feel free to fork this repo and clone your own repo instead)
Then you can begin participating to the AutoCV/AutoDL challenge by carefully reading this README.md file.
As new features and possible bug fixes will be constantly added to this starting kit, you are invited to get latest updates before each usage by running
cd path/to/autodl_starting_kit_stable/
git pull
(or by syncing your fork if you forked this repo)
To make your own submission to AutoCV/AutoDL challenge, you need to modify the
file model.py in AutoDL_sample_code_submission/, which implements the logic
of your algorithm. You can then test it on your local computer using Docker,
in the exact same environment as on the CodaLab challenge platform. Advanced
users can also run local test without Docker, if they install all the required
packages,
see the Dockerfile.
If you are new to docker, install docker from https://docs.docker.com/get-started/. Then, at the shell, run:
cd path/to/autodl_starting_kit_stable/
docker run -it -v "$(pwd):/app/codalab" -p 8888:8888 evariste/autodl:cpu
The tag cpu indicates that this image only supports usage of CPU (instead of
GPU). The option -v "$(pwd):/app/codalab" mounts current directory
(autodl_starting_kit_stable/) as /app/codalab. If you want to mount other
directories on your disk, please replace $(pwd) by your own directory.
The option -p 8888:8888 is useful for running a Jupyter notebook tutorial
inside Docker.
The backend on CodaLab runs a slightly different Docker image
evariste/autodl:gpu
who has Nvidia GPU supports. Both Docker images have installed packages such as
tensorflow-gpu=1.13.1 (or tensorflow=1.13.1 for cpu), torch=1.1.0,
keras=2.2.4, CUDA 10, cuDNN 7.5, etc. If you want to
run local test with Nvidia GPU support, please make sure you have
installed nvidia-docker and run
instead
nvidia-docker run -it -v "$(pwd):/app/codalab" -p 8888:8888 evariste/autodl:gpu
Make sure you use enough RAM (at least 4GB). If the port 8888 is occupied,
you can use other ports, e.g. 8899, and use instead the option -p 8899:8888.
You will then be able to run the ingestion program (to produce predictions)
and the scoring program (to evaluate your predictions) on toy sample data.
In the AutoCV/AutoDL challenge, these two programs will run in parallel to give
real-time feedback (with learning curves). So we provide a Python script to
simulate this behavior:
python run_local_test.py
Then you can view the real-time feedback with a learning curve by opening the
HTML page in AutoDL_scoring_output/.
The full usage is
python run_local_test.py -dataset_dir='AutoDL_sample_data/miniciao' -code_dir='AutoDL_simple_baseline_models/linear'
or
python run_local_test.py -dataset_dir='AutoDL_public_data/Munster' -code_dir='AutoDL_sample_code_submission'
You can change the argument dataset_dir to other datasets (e.g. the five
public datasets we provide). On the other hand,
you can also modify the directory containing your other sample code
(model.py).
We provide a tutorial in the form of a Jupyter notebook. When you are in your docker container, enter:
jupyter-notebook --ip=0.0.0.0 --allow-root &
Then copy and paste the URL containing your token. It should look like something like that:
http://0.0.0.0:8888/?token=82e416e792c8f6a9f2194d2f4dbbd3660ad4ca29a4c58fe7
and select tutorial.ipynb in the menu.
We provide 5 public datasets for participants. They can use these datasets to:
- Explore data (e.g. using
data_browser.py, see next section); - Do local test for their own algorithm;
- Enable meta-learning. We also provide a script to facilitate the data downloading process. The usage is:
python download_public_datasets.pyNote that this can take a few minutes, depending on your connection.
WARNING: to be run outside of a Docker container.
We provide a script for visualizing random examples of a given dataset:
python data_browser.py -dataset_dir=AutoDL_sample_data/miniciaoYou can change the dataset name miniciao to that of any other dataset
(e.g. Munster, Chucky, Pedro, etc.).
As all datasets are formatted into TFRecords, this script actually provides a way to easily see what their code receives as examples (and labels), especially for the participants who are not familiar with this format.
You may have following questions:
- How is a submission handled and evaluated on CodaLab? How is it implemented?
- What are ingestion program and scoring program? What do they do?
To answer these questions, you
can find a flow chart (evaluation-flow-chart.png) in the repo:
If you still want more details, you can refer to the source code at
- Ingestion Program:
AutoDL_ingestion_program/ingestion.py - Scoring Program:
AutoDL_scoring_program/score.py
Zip the contents of AutoDL_sample_code_submission(or any folder containing
your model.py file) without the directory structure:
cd AutoDL_sample_code_submission/
zip -r mysubmission.zip *
then use the "Upload a Submission" button to make a submission to the competition page on CodaLab platform.
Tip: to look at what's in your submission zip file without unzipping it, you can do
unzip -l mysubmission.zip
If you run into bugs or issues when using this starting kit, please create issues on the Issues page of this repo. Two templates will be given when you click the New issue button.
If you have any questions, please contact us via: autodl@chalearn.org