maxwang7 edited this page May 3, 2017 · 10 revisions

In five minutes, you will be able to upload a program, a dataset, and execute your first run in CodaLab.

First, sign up for a CodaLab account at worksheets.codalab.org, and sign in. Then click on My Home in the top-right navigation bar. This takes to your home worksheet (named something like home-pliang), which you should think of as your home directory.

Now we will run through a simple example in which you will sort a file in CodaLab.

Step 1: Upload files

Create a file on your local computer called a.txt with the following contents (using your favorite editor):


Create another file called sort.py with the following contents:

import sys
for line in sorted(sys.stdin.readlines()):
    print line,

Click on the Upload button on the top of the side panel (this button may not appear if your screen width is too small; increase the width of your screen and it should appear). This pops up a file browser; select a.txt. Repeat the same thing for sort.py.

When each file is uploaded, it is appended to the current worksheet as a bundle. The bundle shows up as a row in a table (later, we'll see how to change the formatting). Click on the row to select it; more information about the bundle will appear on the right side panel, including the metadata as well as the contents of the file.

Each bundle has a 32-character UUID, a globally unique identifier, which can be used to unambiguously refer to that bundle forever. You can send someone the UUID and they will know exactly what you're referring to.

While the contents of the bundle are immutable, you can edit its metadata (name, description, etc.) by clicking on the fields in the side panel.

While a.txt is a dataset and sort.py is a program, from CodaLab's perspective, these are just bundles.

Bundles can also be directories. To upload a directory, zip (or tar) up the directory and upload the zip file. The zip file will be unpacked automatically. If you use the command-line interface (CLI), you can upload directories directly.

Step 2: Run a command

Already CodaLab provides a nice place to share code and data, but the true power of CodaLab is the ability to operate on these bundles. In particular, CodaLab allows you to run arbitrary shell commands, which really gives you the freedom to do whatever computation you want.

To do this, click New Run on the top of the side panel. Select sort.py and a.txt as the dependencies (Step 1), and enter the following command (Step 2):

python sort.py < a.txt

Click Run. This should append a new (run) bundle to the current worksheet, which encapsulates the computation. If you wait a second, the state of the bundle will turn from created to staged to running to ready. You should see the stdout on the right panel show the resulting output:


You have successfully run your first experiment in CodaLab. To explain what just happened, CodaLab just ran your command in a sandbox environment in a docker container. From the point of view of your command, you're sitting in some temporary directory that contains the dependencies you specified. It's like you did this:

$ ls
$ python sort.py < a.txt

All the files and directories that are written to the current directory (including stdout and stderr, which are written as files) are saved as the contents of the newly-created run bundle. The contents are changing as the command is executing, but once the command terminates, the contents of the bundle become immutable. Files that you write elsewhere are not stored. So a common convention in CodaLab is to have your commands simply write into the current directory.

Libraries. If your command depends on custom libraries (e.g., TensorFlow), they need to be installed in the docker image. You can use an existing docker container or build your own docker image.

Parallelism. You can start off multiple runs in parallel, and even ones that depend on previous runs that haven't yet finished. Since CodaLab knows about dependencies, it will wait for all the dependencies of a run to finish before starting the run.

Dependencies. You should think about the current worksheet as your current directory, where you're running commands that generate new bundles. However, you must be explicit about dependencies! CodaLab will run your command only in the presence of the dependencies you explicitly specify, not all the bundles in the current worksheet.

Operations. If you made a mistake, you can kill the process for your bundle by right-clicking on the corresponding row in the table and selecting Kill this run bundle. You can remove non-running bundles by selecting Remove bundle permanently.

Note that whenever you run a bundle or perform an operation, a command is sent to the web terminal (CodaLab> prompt) at the top of the screen. This terminal allows you to use most of the command-line interface (CLI) commands. Type help to get more information.

Step 3: Present your results

So far, your worksheet just contains a table with one bundle per row. But we can customize this view to better document and present our results. To edit the worksheet, click Edit Source button (or hitting e).

You will be taken to an editor in your browser with plain-text markdown source of the worksheet. CodaLab markdown is an extension of markdown that allows you to interleave usual markdown with bundles and formatting directives.

You can edit the source freely. For example, you might edit the source to look like this (note that your worksheet will have different UUIDs):

This is my **first** [CodaLab worksheet](https://worksheets.codalab.org).
I uploaded some bundles:
[dataset a.txt]{0x34a1fa62acc840ec96da98f17dbddf66}
[dataset sort.py]{0xf9fc733b19894eb2a97f6b47f35d7ea0}
Here's my first CodaLab run:
% display table name command /stdout time
[run sort-run -- :sort.py,:a.txt : python sort.py < a.txt]{0x08908cc6cb594b9394ed7ba6a0bd25f6}

The directive % display table ... tells CodaLab to render the bundle as a table with certain columns. For example, the /stdout column tells CodaLab to display the contents of the stdout file inside the bundle. This custom formatting is extremely useful if you are monitoring multiple runs, and you want to print out various metrics such as time, accuracy, number of iterations, etc.

Remember that worksheets are just views on the underlying bundle graph, and the lines that look like [run sort-run ...] are just pointers to a bundle. Therefore, you can re-order, remove, duplicate the bundles in the worksheet, and even move/copy bundles across worksheets as easily as text editing. Note that deleting a reference to a bundle does not actually delete the bundle; it merely detaches it.

Step 4: Finding / browsing content in CodaLab

One of the benefits of CodaLab is it provides a global ecosystem for sharing code, data, and results. That is, once someone puts their content in CodaLab, anyone else can find and build on top of it easily.

Click on Public Home on navigation bar to see the list of executable papers and datasets in CodaLab. Click on My Dashboard to see your own bundles and worksheets.

You can search for bundles and worksheets using keywords. Click on the web terminal (the CodaLab> prompt) at the top of the screen and type in a command for finding bundles:

search <bundle keywords>                    # General form
search mnist                                # Find all bundles matching "mnist"
search .mine .limit=20 created=.sort-       # List your most recent bundles
search .mine .floating                      # List your bundles not on any worksheet
search .last                                # List the latest bundles

and finding worksheets:

wsearch <worksheet keywords>                # General form
wsearch acl2016                             # Find all worksheets matching "acl2016"

Look at the CLI reference for more information.


Congratulations - you have successfully used CodaLab to upload a program, a dataset, and performed a run! In practice, you might have more complex workflows, running commands to preprocess the data, running algorithms with different settings, using CodaLab to manage your runs. If you prefer working from the shell, check out the command-line interface (CLI). CodaLab markdown is a powerful way to create worksheets that document your experiments, either for a private research log or a public executable paper. There are many possibilities!

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