This page shows how to validate the config files config.yaml, db/mysql.yaml and db/postgresql.yaml Parameter sweeps are a really good use case for when this may come in handy. Any config merged into this config structure will be validated against the schema you define. The structure of this post is as follows: Note: The samples and code shared in this post are available here. If the config has a static structure, You can define it using Structured Configs. Structured Configs use Python dataclasses to In this post, I will go over my learnings of both the tools and also talk about gaps I still see present. This kind of mixed-breed config handling approach is very hard to maintain and understand. Features Inspired by the 12-factor application guide Settings management (default values, validation, A modern simfile parsing & editing library for Python 3, Python Decouple: Strict separation of settings from code Decouple helps you to organize your settings so that you can change parameters without having, None: # this line actually runs the checks of pydantic OmegaConf.to_object(cfg) print(OmegaConf.to_yaml(cfg)) # we just print the config and wait a few secs # note that IDEs allow auto-complete for accessing the attributes! time.sleep(cfg.main.sleep)">, int: if port < 1024: raise ValueError(f"Choose a non-privileged port!") GitHub-flavored Markdown & a sane subset of HTML is supported. Some of the indirect advantages of having a good config solution are clean, reliable, and simple application code. we will extend a Structured Config. OmegaConf and Hydra does not support Union type in the config either. The name Hydra comes from its ability to run multiple similar jobs - much like a Hydra with multiple heads. config.yaml is loaded automatically when you run your application. Hydra is a supposedly generic configuration system but they have been very careful to have good affordances for ML in particular. Thank you for this terrific contribution to open source ML! Fortunately, there are 3rd party HOWTOs. The primary Defaults List will come from config.yaml. It would be even great if Hydra integrates nicely with Pydantic as well but at least there is a path to using these two tools together. . BaseModel implementation looks like this: BaseSettings is also another great feature that seeks to abstract away settings/config that is sourced from other sources like an environment variable, init files, etc. An example of such use is as follows, notice fully qualified class name in __target__ field in config: This feature, in my view, should be used sparingly as it tightly couples the config to application-specific objects. This version could be used for non-ML projects where scripts should have minimal side effects. Variable interpolation is supported but a little bit weird because it supports both internal variables (which we just saw) and system environment variables. I find it useful to be aware of the following patterns: For one thing, hydra hijacks working directory by changing it to the current log directory. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Hydra is an open-source Python framework that simplifies the development of research and other complex applications. Overall, configuration solution for training experiments has following requirements: With that in mind, I evaluated a bunch of tools combination of Hydra and Pydantic stood out as a good choice. Interpolation offers a means to refer to an existing config instead of duplicating it. You can have as many such configs as you want. In either case, it requires quite a bit of finagling to get to the streamlined level of functionality that @playertr is talking about. This is limiting in my view. Ability to version config and enforce a schema on the config specification for reliability and robustness. Omitting a default value is equivalent to assigning MISSING to it, although it is sometimes Paths are complicated. It is recommended that you visit the OmegaConf Structured Configs page to learn more later. This can be easily configured using hydra as following: If your application configs are slightly more complex and you might want object representation of your config then Hydra provided structured config option with ConfigStore. Whats defined in, What is the final config used in my run: models output specification needs to be in line with data specification. Ability to organize the configuration of training in a hierarchical fashion acknowledging the dependency and hierarchy of participating components. Each version makes a breaking change to the previous but each concrete implementation implements a method ` def to_next(self) -> Optional[BaseModel]` that cracks at migrating the config to a new specification. Even within ML contexts, I find I often wish to use Hydra but without changing the current working directory, outputting logs of configurations, etc. We have seen that if the name of the config file matches the name of a configs stored in the ConfigStore it will be used to validate the config file automatically. The directory structure of our application now looks like: defaults is a special directive telling Hydra to use db/mysql.yaml when composing the configuration object. To address the warning while maintaining the new behavior, append _self_ to the end of the Defaults List. Being able to store and reuse the final assembled/compiled config and reproduce the experiments reliably. Fill in your email address if you want to get notified when someone replies to your comment. There is lot more complexity in config management that Kubernetes immplements but whats transferable here in my view is: This prototype is a toy example to explore how to handle versioning in the config and provide a migration path for when old version config is provided. title = {Hydra - A framework for elegantly configuring complex applications}, url = {https://github.com/facebookresearch/hydra}, Hierarchical configuration composable from multiple sources, Configuration can be specified or overridden from the command line, Run your application locally or launch it to run remotely, Run multiple jobs with different arguments with a single command. @playertr thanks for the issue. This perhaps is optional if one is not interested in dynamic domain-specific validations - something that can not be achieved in serialized formats like yaml/json. Whenever a breaking change is introduced, a new float version of config is created while the preceding version of config implements a migration path to the following version. Code shown below, represents Model config which has 3 versions v i.e. This is true for any software application however some applications demand higher investment on the config piece than others. However, I often find myself wanting only Hydra's basic configuration support -- including the CLI interface and YAML+StructuredConfig specification -- without creating a new working directory with logs, and without the advance launching, sweeping, and logging functionality. When Hydra composes the final config object it will use the config schemas as specified in the Default Lists. I don't want to confuse potential users by introducing concepts such as workdir-changing and parameter sweeps for simple command-line utilities. You can now choose which database configuration to use and override values from the command line: You can have as many config groups as you need. It's necessary to convert, If your process hangs when started, this may be caused by. to your account. Disabling this functionality through Hydra configuration isnt completely straightforward and the need to do so certainly isnt something many users expect when they first start utilizing Hydra. To work around this, you can initialize the experiment parameter with a default empty file and use W&B Sweep to override those empty configs on each call. # note that IDEs allow auto-complete for accessing the attributes! Unlike the example in the previous page, the Defaults List here is config.yaml and not in the Config class. In this case, you define your config object model as follows: A few things noteworthy to call out here: Once, the config object model is done, one should define ConfigStore and register config objects: Here, group key in config registry indicates mutually exclusive groups. Note hydra only depends on OmegaConf and antlr, which would be needed for your use cases anyway. Once hydra has loaded the config, it will be loaded as dict/dictionary and surfaced as, Where is composition coming from: We will see the Defaults list in the next page. Whats the best approach to validate this? When creating a run, always use latest version of config. The missing bits of config management from Pydra i.e. I want to expose the schema of Model config, how can I do this. Review of recent advances in dealing with data size challenges in Deep Learning . By definition model training configurations are hierarchial. Passing parameters not present in Hydra defaults, Hydra supports passing extra parameters through the command line which aren't present in the default configuration file, by using a. before command. Pro tip: this is handy with an environment variable config system like dotenv. This is useful if there is a one-to-one mapping between the Structured Configs and the YAML files. Eg can the image size be less than 0? or other online means. are defined within a hydra configuration file. Relevant issues: #1937: Conditionally disable the creation of folders in output/multirun via the CLI I have not seen any issue in the use of validator like this but unsure if this is the intended use of validator support. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. Anything users provide must be validated in the application for various things like type-checking, application-specific validation, constraint limits, etc.

literal we have seen in OmegaConf configs before. Hydra also allows you to configure job and run settings, an example of such configuration is located here. 1.1. Looking at joblib, for example, we might want to enable a param sweep from the command line. Its interesting to note that other features of Hydra like multirun and sweeps given they employ multi-processing may interfere with other tools employing MP techniques too. Extension to this is being able to run expression-based interpolations eg list of items to the total item in the list. If you would like to support my work you can tip me via Brave Alternatively, one can also override these settings in the main config.yaml file: Each process gets an int id assigned which the application can recognize. A very general, feature-rich template for rapid and scalable ML experimentation with best practices. More about these can be found here. Value 'fail' could not be converted to Integer, | Config path | Package | _self_ | Parent |, ------------------------------------------------------------------------------, | hydra/output/default | hydra | False | hydra/config |, | hydra/launcher/basic | hydra.launcher | False | hydra/config |, | hydra/sweeper/basic | hydra.sweeper | False | hydra/config |, | hydra/help/default | hydra.help | False | hydra/config |, | hydra/hydra_help/default | hydra.hydra_help | False | hydra/config |, | hydra/hydra_logging/default | hydra.hydra_logging | False | hydra/config |, | hydra/job_logging/default | hydra.job_logging | False | hydra/config |, | hydra/config | hydra | True | |, | base_config | | False | config |, | db/base_mysql | db | False | db/mysql |, | db/mysql | db | True | config |, | config | | True | |, Validating against a schema in the same config group, Validating against a schema from a different config group. The resulting cfg object is a composition of configs from defaults with configs specified in your config.yaml. Note that in some cases it may instead be desirable to add _self_ directly after the schema and before other Defaults List elements. Corollary: these config files are a security risk! Validation framework is a must-have in any config management framework. Without duplication, reuse configurations across modules wherever needed by means such as interpolation, expressions.