Contributing to `wandb`

We at Weights & Biases ❤️ open source and welcome contributions from the community! This guide discusses the development workflow and the internals of the wandb client library.

Development workflow

Browse the existing Issues on GitHub to see if the feature/bug you are willing to add/fix has already been requested/reported.
- If not, please create a new issue. This will help the project keep track of feature requests and bug reports and make sure effort is not duplicated.
If you are a first-time contributor, please go to https://github.com/wandb/client and click the "Fork" button in the top-right corner of the page. This will create your personal copy of the repository that you will use for development.
- Set up SSH authentication with GitHub.
- Clone the forked project to your machine and add the upstream repository that will point to the main wandb project:
```
git clone https://github.com/<your-username>/client.git
cd client
git remote add upstream https://github.com/wandb/client.git
```
Develop you contribution.
- Make sure your fork is in sync with the main repository:
```
git checkout master
git pull upstream master
```
- Create a git branch where you will develop your contribution. Use a sensible name for the branch, for example:
```
git checkout -b new-awesome-feature
```
- Hack! As you make progress, commit your changes locally, e.g.:
```
git add changed-file.py tests/test-changed-file.py
git commit -m "Added integration with a new library"
```
- Test and lint your code! Please see below for a detailed discussion.
Proposed changes are contributed through
GitHub Pull Requests.
- When your contribution is ready and the tests all pass, push your branch to GitHub:
```
git push origin new-awesome-feature
```
- Once the branch is uploaded, GitHub will print a URL for submitting your contribution as a pull request. Open that URL in your browser, write an informative title and a detailed description for your pull request, and submit it.
- Please link the relevant issue (either the existing one or the one you created) to your PR. See the right column on the PR page. Alternatively, in the PR description, mention that it "Fixes link-to-the-issue" - GitHub will do the linking automatically.
- The team will review your contribution and provide feedback. To incorporate changes recommended by the reviewers, commit edits to your branch, and push to the branch again (there is no need to re-create the pull request, it will automatically track modifications to your branch), e.g.:
```
git add tests/test-changed-file.py
git commit -m "Added another test case to address reviewer feedback"
git push origin new-awesome-feature
```
- Once your pull request is approved by the reviewers, it will be merged into the main codebase.

Setting up your development environment

We test the library code against multiple python versions and use pyenv to manage those. Install pyenv by running

curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash

To load pyenv automatically, add the following lines to your shell's startup script, such as ~/.bashrc or ~/.zshrc (and then either restart the shell, run exec $SHELL, or source the changed script):

export PYENV_ROOT="$HOME/.pyenv"
export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv virtualenv-init -)"

Then run the following command to set up your environment:

./tools/setup_dev_environment.py

At the first invocation, this tool will set up multiple python environments, which takes some time. You can set up a subset of the target environments to test against, for example:

./tools/setup_dev_environment.py --python-versions 3.7 3.8

The tool will also set up tox, which we use for automating development tasks such as code linting and testing.

Note: to switch the default python version, edit the .python-version file in the repository root.

Mac with the Apple M1 chip

The tensorflow-macos package that is installed on Macs with the Apple M1 chip, requires the h5py package to be installed, which in turn requires hdf5 to be installed in the system. You can install hdf5 and h5py into a pyenv environment with the following commands using homebrew:

$ brew install hdf5
$ export HDF5_DIR="$(brew --prefix hdf5)"
$ pip install --no-binary=h5py h5py

The soundfile package requires the libsndfile package to be installed in the system. Note that a pre-release version of soundfile will be installed. You can install libsndfile with the following command using homebrew:

$ brew install libsndfile

The moviepy package requires the ffmpeg package to be installed in the system. You can install ffmpeg with the following command using homebrew:

$ brew install ffmpeg

The lightgbm package might require build packages cmake and libomp to be installed. You can install cmake and libomp with the following command using homebrew:

$ brew install cmake libomp

Code organization

wandb/
├── ...
├── apis/   # Public api (still has internal api but this should be moved to wandb/internal)
│   ├── ...
│   ├── internal.py
│   ├── ...
│   └── public.py
├── cli/    # Handlers for command line functionality
├── ...
├── integration/    # Third party integration
│   ├── fastai/
│   ├── gym/
│   ├── keras/
│   ├── lightgbm/
│   ├── metaflow/
│   ├── prodigy/
│   ├── sacred/
│   ├── sagemaker/
│   ├── sb3/
│   ├── tensorboard/
│   ├── tensorflow/
│   ├── torch/
│   ├── xgboost/
│   └── ...
├── ...
├── proto/  # Protocol buffers for inter-process communication and persist file store
├── ...
├── sdk/    # User accessed functions [wandb.init()] and objects [WandbRun, WandbConfig, WandbSummary, WandbSettings]
│   ├── backend/    # Support to launch internal process
│   ├── ...
│   ├── interface/  # Interface to backend execution
│   ├── internal/   # Backend threads/processes
│   └── ...
├── ...
├── sweeps/ # Hyperparameter sweep engine (see repo: https://github.com/wandb/sweeps)
└── ...

Building protocol buffers

We use protocol buffers to communicate from the user process to the wandb backend process.

If you update any of the .proto files in wandb/proto, you'll need to run:

make proto

Linting the code

We use black, flake8, and mypy for code formatting and checks (including static type checks).

To reformat the code, run:

tox -e format

To run checks, execute:

tox -e flake8,mypy

Testing

We use the pytest framework. Tests can be found in tests/.

By default, tests are run in parallel with 4 processes. This can be changed by setting the CI_PYTEST_PARALLEL environment variable to a different value.

To run specific tests in a specific environment:

tox -e py37 -- tests/test_public_api.py -k substring_of_test

To run all tests in a specific environment:

tox -e py38

If you make changes to requirements_dev.txt that are used by tests, you need to recreate the python environments with:

tox -e py37 --recreate

Sometimes, pytest will swallow or shorten important print messages or stack traces sent to stdout and stderr (particularly when they are coming from background processes). This will manifest as a test failure with no/shortened associated output. In these cases, add the -vvvv --showlocals flags to stop pytest from capturing the messages and allow them to be printed to the console. Eg:

tox -e py37 -- tests/test_public_api.py -k substring_of_test -vvvv --showlocals

If a test fails, you can use the --pdb -n0 flags to get the pdb debugger attached to the test:

tox -e py37 -- tests/test_public_api.py -k failing_test -vvvv --showlocals --pdb -n0

You can also manually set breakpoints in the test code (breakpoint()) to inspect the test failures.

Overview

Testing wandb is tricky for a few reasons:

wandb.init launches a separate process, this adds overhead and makes it difficult to assert logic happening in the backend process.
The library makes lots of requests to a W&B server as well as other services. We don't want to make requests to an actual server, so we need to mock one out.
The library has many integrations with 3rd party libraries and frameworks. We need to assert we never break compatibility with these libraries as they evolve.
wandb writes files to the local file system. When we're testing we need to make sure each test is isolated.
wandb reads configuration state from global directories such as ~/.netrc and ~/.config/wandb/settings we need to override these in tests.
The library needs to support jupyter notebook environments as well.

To make our lives easier we've created lots of tooling to help with the above challenges. Most of this tooling comes in the form of Pytest Fixtures. There are detailed descriptions of our fixtures in the section below. What follows is a general overview of writing good tests for wandb.

To test functionality in the user process the wandb_init_run is the simplest fixture to start with. This is like calling wandb.init() except we don't actually launch the wandb backend process and instead returned a mocked object you can make assertions with. For example:

def test_basic_log(wandb_init_run):
    wandb.log({"test": 1})
    assert wandb.run._backend.history[0]["test"] == 1

One of the most powerful fixtures is live_mock_server. When running tests we start a Flask server that provides our graphql, filestream, and additional web service endpoints with sane defaults. This allows us to use wandb just like we would in the real world. It also means we can assert various requests were made. All server logic can be found in tests/utils/mock_server.py and it's really straight forward to add additional logic to this server. Here's a basic example of using the live_mock_server:

def test_live_log(live_mock_server, test_settings):
    run = wandb.init(settings=test_settings)
    run.log({"test": 1})
    ctx = live_mock_server.get_ctx()
    first_stream_hist = utils.first_filestream(ctx)["files"]["wandb-history.jsonl"]
    assert json.loads(first_stream_hist["content"][0])["test"] == 1

Notice we also used the test_settings fixture. This turns off console logging and ensures the run is automatically finished when the test finishes. Another really cool benefit of this fixture is it creates a run directory for the test at tests/logs/NAME_OF_TEST. This is super useful for debugging because the logs are stored there. In addition to getting the debug logs you can find the live_mock_server logs at tests/logs/live_mock_server.log.

We also have pytest fixtures that are automatically used. These include local_netrc and local_settings this ensures we never read those settings files from your own environment.

The final fixture worth noting is notebook. This actually runs a jupyter notebook kernel and allows you to execute specific cells within the notebook environment:

def test_one_cell(notebook):
    with notebook("one_cell.ipynb") as nb:
        nb.execute_all()
        output = nb.cell_output(0)
        assert "lovely-dawn-32" in output[-1]["data"]["text/html"]

Finding good test points

The wandb system can be viewed as 3 distinct services:

The user process where wandb.init() is called
The internal process where work is done to format data to be synced to the server
The backend server which listens to graphql endpoints and populates a database

The interfaces are described here:

  Users   .  Shared  .  Internal .  Mock
  Process .  Queues  .  Process  .  Server
          .          .           .
  +----+  .  +----+  .  +----+   .  +----+
  | Up |  .  | Sq |  .  | Ip |   .  | Ms |
  +----+  .  +----+  .  +----+   .  +----+
    |     .    |     .    |      .    |
    | ------>  | -------> | --------> |    1
    |     .    |     .    |      .    |
    |     .    | -------> | --------> |    2
    |     .    |     .    |      .    |
    | ------>  |     .    |      .    |    3
    |     .    |     .    |      .    |

1. Full codepath from wandb.init() to mock_server
   Note: coverage only counts for the User Process and interface code
   Example: tests/wandb_integration_test.py
2. Inject into the Shared Queues to mock_server
   Note: coverage only counts for the interface code and internal process code
   Example: tests/test_sender.py
3. From wandb.Run object to Shared Queues
   Note: coverage counts for User Process
   Example: tests/wandb_run_test.py

Good examples of tests for each level of testing can be found at:

test_metric_user.py: User process tests
test_metric_internal.py: Internal process tests
test_metric_full.py: Full stack tests

Global Pytest Fixtures

All global fixtures are defined in tests/conftest.py:

local_netrc - used automatically for all tests and patches the netrc logic to avoid interacting with your system .netrc
local_settings - used automatically for all tests and patches the global settings path to an isolated directory.
test_settings - returns a wandb.Settings object that can be used to initialize runs against the live_mock_server. See tests/wandb_integration_test.py
runner — exposes a click.CliRunner object which can be used by calling .isolated_filesystem(). This also mocks out calls for login returning a dummy api key.
mocked_run - returns a mocked out run object that replaces the backend interface with a MagicMock so no actual api calls are made.
mocked_module - if you need to test code that calls wandb.util.get_module("XXX"), you can use this fixture to get a MagicMock(). See tests/test_notebook.py
wandb_init_run - returns a fully functioning run with a mocked out interface (the result of calling wandb.init). No api's are actually called, but you can access what apis were called via run._backend.{summary,history,files}. See test/utils/mock_backend.py and tests/frameworks/test_keras.py
mock_server - mocks all calls to the requests module with sane defaults. You can customize tests/utils/mock_server.py to use context or add api calls.
live_mock_server - we start a live flask server when tests start. live_mock_server configures WANDB_BASE_URL point to this server. You can alter or get its context with the get_ctx and set_ctx methods. See tests/wandb_integration_test.py. NOTE: this currently doesn't support concurrent requests so if we run tests in parallel we need to solve for this.
git_repo — places the test context into an isolated git repository
test_dir - places the test into tests/logs/NAME_OF_TEST this is useful for looking at debug logs. This is used by test_settings
notebook — gives you a context manager for reading a notebook providing execute_cell. See tests/utils/notebook_client.py and tests/test_notebooks.py. This uses live_mock_server to enable actual api calls in a notebook context.
mocked_ipython - to get credit for codecov you may need to pretend you're in a jupyter notebook when you aren't, this fixture enables that.

Code Coverage

We use codecov to ensure we're executing all branches of logic in our tests. Below are some JHR Protips™

If you want to see the lines not covered you click on the “Diff” tab. then look for any “+” lines that have a red block for the line number
If you want more context about the files, go to the “Files” tab, it will highlight diffs, but you have to do even more searching for the lines you might care about
If you don't want to use codecov, you can use local coverage (I tend to do this for speeding things up a bit, run your tests then run tox -e cover ). This will give you the old school text output of missing lines (but not based on a diff from master)

We currently have 8 categories of test coverage:

project: main coverage numbers, I don't think it can drop by more than a few percent, or you will get a failure
patch/tests: must be 100%, if you are writing code for tests, it needs to be executed, if you are planning for the future, comment out your lines
patch/tests-utils: tests/conftest.py and supporting fixtures at tests/utils/, no coverage requirements
patch/sdk: anything that matches wandb/sdk/*.py (so top level sdk files). These have lots of ways to test, so it should be high coverage. Currently, target is ~80% (but it is dynamic)
patch/sdk-internal: should be covered very high target is around 80% (also dynamic)
patch/sdk-other: will be a "catch all" for other stuff in wandb/sdk/ target around 75% (dynamic)
patch/apis: we have no good fixtures for this, so until we do, this will get a waiver
patch/other: everything else, we have lots of stuff that isn't easy to test, so it is in this category, currently the requirement is ~60%

Test parallelism

The circleci uses pytest-split to balance unittest load on multiple nodes. In order to do this efficiently every once in a while the test timing file (.test_durations) needs to be updated with:

CI_PYTEST_SPLIT_ARGS="--store-durations" tox -e py37

Functional Testing

TODO: overview of how to write and run functional tests with yea and the yea-wandb plugin.

The yea-wandb plugin for yea uses copies of several components from tests/utils (artifact_emu.py, mock_requests.py, and mock_server.py) to provide a test environment for functional tests. Currently, we maintain a copy of those components in yea-wandb/src/yea_wandb, so they need to be in sync.

If you update one of those files, you need to:

While working on your contribution:
- Make a new branch (say, shiny-new-branch) in yea-wandb and pull in the new versions of the files. Make sure to update the yea-wandb version.
- Point the client branch you are working on to this yea-wandb branch. In tox.ini, search for yea-wandb==<version> and change it to https://github.com/wandb/yea-wandb/archive/shiny-new-branch.zip.
Once you are happy with your changes:
- Bump to a new version by first running make bumpversion-to-dev, committing, and then running make bumpversion-from-dev.
- Merge and release yea-wandb (with make release).
- If you have changes made to any file in (artifact_emu.py, mock_requests.py, or mock_server.py), create a new client PR to copy/paste those changes over to the corresponding file(s) in tests/utils. We have a Github Action that verifies that these files are equal (between the client and yea-wandb). If you have changes in these files and you do not sync them to the client, all client PRs will fail this Github Action.
- Point the client branch you are working on to the fresh release of yea-wandb.

Regression Testing

You can find all the logic in the wandb-testing repo. The main script (wandb-testing/regression/regression.py) to run your regression tests can be found here. Also, the main configuration file (wandb-testing/regression/regression-config.yaml), can be found here.

Example usage:

git clone git@github.com:wandb/wandb-testing.git

cd wandb-testing/regression && python regression.py tests/main/huggingface/ --dryrun

The above script will print all of the huggingface-transformers test configurations. The expected output should look something like this:

########################################
# huggingface-transformers init py37-pt
########################################
########################################
# huggingface-transformers init py37-pt1.4
########################################
########################################
# huggingface-transformers init py37-ptn
########################################

------------------

Good runs:
Failed runs:

In the names of the tests you can see the configurations of the tests:

init is the configuration specified in the test config file.

Some details include:

All the tests are using py37: python-3.7.
Each test uses a different version PyTorch:
- pt: Latests PyTorch release
- pt1.4: Version 1.4 of PyTorch
- ptn: Nightly version of Pytorch

For more details about general usage and how to add new tests see this README.

Live development

You can enter any of the tox environments and install a live dev build with:

source .tox/py37/bin/activate
pip install -e .

There's also a tox dev environment using Python 3, more info here.

TODO: There are lots of cool things we could do with this, currently it just puts us in iPython.

tox -e dev

Library Objectives

Supported user interface

All objects and methods that users are intended to interact with are in the wandb/sdk directory. Any method on an object that is not prefixed with an underscore is part of the supported interface and should be documented.

User interface should be typed using python 3.6+ type annotations. Older versions will use untyped interface.

Arguments/environment variables impacting wandb functions are merged with Settings

wandb.Settings is the main settings object that is passed explicitly or implicitly to all wandb functions.

The primary objective of the design principle is that behavior of code can be impacted by multiple sources. These sources need to be merged consistently and information given to the user when settings are overwritten to inform the user. Examples of sources of settings:

Enforced settings from organization, team, user, project
Settings set by environment variables prefixed with WANDB_, e.g. WANDB_PROJECT=
Settings passed to the wandb.init function: wandb.init(project=)
Default settings from organization, team, project
Settings in global settings file: ~/.config/wandb/settings
Settings in local settings file: ./wandb/settings

Source priorities are defined in wandb.sdk.wandb_settings.Source. Each individual setting of the Settings object is either a default or priority setting. In the latter case, reverse priority is used to determine the source of the setting.

wandb.Settings internals

Under the hood in wandb.Settings, individual settings are represented as wandb.sdk.wandb_settings.Property objects that:

Encapsulate the logic of how to preprocess and validate values of settings throughout the lifetime of a class instance.
Allows for runtime modification of settings with hooks, e.g. in the case when a setting depends on another setting.
Use the update() method to update the value of a setting. Source priority logic is enforced when updating values.
Determine the source priority using the is_policy attribute when updating the property value. E.g. if is_policy is True, the smallest Source value takes precedence.
Have the ability to freeze/unfreeze.

Here's a basic example (for more examples, see tests/wandb_settings_test.py)

from wandb.sdk.wandb_settings import Property, Source


def uses_https(x):
    if not x.startswith("https"):
        raise ValueError("Must use https")
    return True

base_url = Property(
    name="base_url",
    value="https://wandb.com/",
    preprocessor=lambda x: x.rstrip("/"),
    validator=[lambda x: isinstance(x, str), uses_https],
    source=Source.BASE,
)

endpoint = Property(
    name="endpoint",
    value="site",
    validator=lambda x: isinstance(x, str),
    hook=lambda x: "/".join([base_url.value, x]),
    source=Source.BASE,
)

>>> print(base_url)  # note the stripped "/"
'https://wandb.com'
>>> print(endpoint)  # note the runtime hook
'https://wandb.com/site'
>>> print(endpoint._value)  # raw value
'site'
>>> base_url.update(value="https://wandb.ai/", source=Source.INIT)
>>> print(endpoint)  # valid update with a higher priority source
'https://wandb.ai/site'
>>> base_url.update(value="http://wandb.ai/")  # invalid value - second validator will raise exception
ValueError: Must use https
>>> base_url.update(value="https://wandb.dev", source=Source.USER)
>>> print(endpoint)  # valid value from a lower priority source has no effect
'https://wandb.ai/site'

The Settings object:

The code is supposed to be self-documented -- see wandb/sdk/wandb_settings.py :)
Uses Property objects to represent configurable settings.
Clearly and compactly defines all individual settings, their default values, preprocessors, validators, and runtime hooks as well as whether they are treated as policies.
- To leverage both static and runtime validation, the validator attribute is a list of functions (or a single function) that are applied in order. The first function is automatically generated from type annotations of class attributes.
Provides a mechanism to update settings specifying the source (which abides the corresponding Property source logic) via Settings.update(). Direct attribute assignment is not allowed.
Careful Settings object copying.
Mapping interface.
Exposes attribute.value if attribute is a Property.
Has ability to freeze/unfreeze the object.
Settings.make_static() method that we can use to replace StaticSettings.
Adapted/reworked convenience methods to apply settings originating from different source.

Adding a new setting

Add a new type-annotated Settings class attribute.
If the setting comes with a default value/preprocessor/additional validators/runtime hooks, add them to the template dictionary that the Settings._default_props method returns, using the same key name as the corresponding class variable.
- For any setting that is only computed (from other settings) and need/should not be set/updated (and so does not require any validation etc.), define a hook (which does not have to depend on the setting's value) and use "auto_hook": True in the template dictionary (see e.g. the wandb_dir setting).
Add tests for the new setting to tests/wandb_settings_test.py.

Data to be synced to server is fully validated

Calls to wandb.log() result in the dictionary being serialized into a schema'ed data structure. Any non supported element should result in an immediate exception.

All changes to objects are reflected in sync data

When changing properties of objects, those objects should serialize the changes into a schema'ed data structure. There should be no need for .save() methods on objects.

Library can be disabled

When running in disabled mode, all objects act as in memory stores of attribute information, but they do not perform any serialization to sync data.

Detailed walk through of a simple program

Program

1 import wandb
2 run = wandb.init(config=dict(param1=1))
3 run.config.param2 = 2
4 run.log(dict(this=3))

import wandb [line 1]

minimal code should be run on import

wandb.init(...) [line 2]

User Process:
- Calls internal wandb.setup() in case the user has not yet initialized the global wandb state. wandb.setup() is similar to wandb.init() but it impacts the entire process or session. This allows multiple wandb.init() calls to share some common setup.
- Sets up notification and request queues for communicating with internal process
- Spawns internal process used for syncing passing queues and the settings object
- Creates a Run object RunManaged
- Encodes passed config dictionary into RunManaged object
- Sends synchronous protocol buffer request message RunData to internal process
- Wait for response for configurable amount of time. Populate run object with response data
- Terminal (sys.stdout, sys.stderr) is wrapped which sends output to internal process with RunOutput message
- Sets a global Run object for users who use wandb.log() syntax
- Run.on_start() is called to display initial information about the run
- Returns Run object
Internal Process:
- Process initialization
- Wait on notify queue for work
- When RunData message is seen, queue this message to be written to disk wandb_write and sent to cloud wandb_send
- wandb_send thread sends upsert_run graphql http request
- response is populated into a response message
- Spin up internal threads which monitor system metrics
- Queue response message to the user process context

run.config attribute setter [line 3]

User Process:
- Callback on the Run object is called with the changed config item
- Run object callback generates ConfigData message and asynchronously sends to internal process
Internal Process:
- When ConfigData message is seen, queue message to wandb_write and wandb_send
- wandb_send thread sends upsert_run graphql http request

wandb.log(...) [line 4]

User process:
- Log dictionary is serialized and sent asynchronously as HistoryData message to internal process
Internal Process:
- When HistoryData message is seen, queue message to wandb_write and wandb_send
- wandb_send thread sends file_stream data to cloud server

end of program or wandb.finish()

User process:
- Terminal wrapper is shutdown and flushed to internal process
- Exit code of program is captured and sent synchronously to internal process as ExitData
- Run.on_final() is called to display final information about the run

Documentation Generation

The documentation generator is broken into two parts:

generate.py: Generic documentation generator for wandb/ref
docgen_cli.py: Documentation generator for wandb CLI

`generate.py`

The following is a road map of how to generate documentation for the reference. Steps

pip install git+https://github.com/wandb/tf-docs@wandb-docs This installs a modified fork of Tensorflow docs. The modifications are minor templating changes.
python generate.py creates the documentation.

Outputs A folder named library in the same folder as the code. The files in the library folder are the generated markdown.

Requirements

wandb

`docgen_cli.py`

Usage

python docgen_cli.py

Outputs A file named cli.md in the same folder as the code. The file is the generated markdown for the CLI.

Requirements

python >= 3.8
wandb

Deprecating features

Starting with version 1.0.0, wandb will be using Semantic Versioning. The major version of the library will be incremented for all backwards-incompatible changes, including dropping support for older Python versions.

Features currently marked as deprecated will be removed in the next major version (1.0.0).

Marking a feature as deprecated

To mark a feature as deprecated (and to be removed in the next major release), please follow these steps:

Add a new field to the Deprecated message definition in wandb/proto/wandb_telemetry.proto, which will be used to track the to-be-deprecated feature usage.
Rebuild protocol buffers and re-generate wandb/proto/wandb_deprecated.py by running make proto.
Finally, to mark a feature as deprecated, call wand.sdk.lib.deprecate in your code:

from wandb.sdk.lib import deprecate

deprecate.deprecate(
    field_name=deprecate.Deprecated.<new_field_name>,  # new_field_name from step 1
    warning_message="This feature is deprecated and will be removed in a future release.",
)

Adding URLs

All URLs displayed to the user should be added to wandb/sdk/lib/wburls.py. This will better ensure that URLs do not lead to broken links.

Once you add the URL to that file you will need to run:

python tools/generate-tool.py --generate

Files

CONTRIBUTING.md

Latest commit

History

CONTRIBUTING.md

File metadata and controls

Contributing to wandb

Table of Contents

Development workflow

Setting up your development environment

Mac with the Apple M1 chip

Code organization

Building protocol buffers

Linting the code

Testing

Overview

Finding good test points

Global Pytest Fixtures

Code Coverage

Test parallelism

Functional Testing

Regression Testing

Example usage:

Live development

Library Objectives

Supported user interface

Arguments/environment variables impacting wandb functions are merged with Settings

wandb.Settings internals

Adding a new setting

Data to be synced to server is fully validated

All changes to objects are reflected in sync data

Library can be disabled

Detailed walk through of a simple program

Program

import wandb [line 1]

wandb.init(...) [line 2]

run.config attribute setter [line 3]

wandb.log(...) [line 4]

end of program or wandb.finish()

Documentation Generation

generate.py

docgen_cli.py

Deprecating features

Marking a feature as deprecated

Adding URLs

Contributing to `wandb`

`generate.py`

`docgen_cli.py`