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| # Configuring nx-parallel | ||
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| `nx-parallel` provides flexible parallel computing capabilities, allowing you to control settings like `backend`, `n_jobs`, `verbose`, and more. This can be done through two configuration systems: `joblib` and `NetworkX`. This guide explains how to configure `nx-parallel` using both systems. | ||
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| ## 1. Setting configs using `joblib.parallel_config` | ||
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| `nx-parallel` relies on [`joblib.Parallel`](https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html) for parallel computing. You can adjust its settings through the [`joblib.parallel_config`](https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_config.html) class provided by `joblib`. For more details, check out the official [joblib documentation](https://joblib.readthedocs.io/en/latest/parallel.html). | ||
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| ### 1.1 Usage | ||
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| ```python | ||
| from joblib import parallel_config | ||
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| # Setting global configs | ||
| parallel_config(n_jobs=3, verbose=50) | ||
| nx.square_clustering(H) | ||
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| # Setting configs in a context | ||
| with parallel_config(n_jobs=7, verbose=0): | ||
| nx.square_clustering(H) | ||
| ``` | ||
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| Please refer the [official joblib's documentation](https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_config.html) to better understand the config parameters. | ||
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| Note: Ensure that `nx.config.backends.parallel.active = False` when using `joblib` for configuration, as NetworkX configurations will override `joblib.parallel_config` settings if `active` is `True`. | ||
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| ## 2. Setting configs using `networkx`'s configuration system for backends | ||
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| To use NetworkX’s configuration system in `nx-parallel`, you must set the `active` flag (in `nx.config.backends.parallel`) to `True`. | ||
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| ### 2.1 Configs in NetworkX for backends | ||
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| When you import NetworkX, it automatically sets default configurations for all installed backends, including `nx-parallel`. | ||
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| ```python | ||
| import networkx as nx | ||
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| print(nx.config) | ||
| ``` | ||
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| Output: | ||
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| ``` | ||
| NetworkXConfig( | ||
| backend_priority=[], | ||
| backends=Config( | ||
| parallel=ParallelConfig( | ||
| active=False, | ||
| backend="loky", | ||
| n_jobs=None, | ||
| verbose=0, | ||
| temp_folder=None, | ||
| max_nbytes="1M", | ||
| mmap_mode="r", | ||
| prefer=None, | ||
| require=None, | ||
| inner_max_num_threads=None, | ||
| backend_params={}, | ||
| ) | ||
| ), | ||
| cache_converted_graphs=True, | ||
| ) | ||
| ``` | ||
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| As you can see in the above output, by default, `active` is set to `False`. So, to enable NetworkX configurations for `nx-parallel`, set `active` to `True`. Please refer the [NetworkX's official backend and config docs](https://networkx.org/documentation/latest/reference/backends.html) for more on networkx configuration system. | ||
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| ### 2.2 Usage | ||
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| ```python | ||
| # enabling networkx's config for nx-parallel | ||
| nx.config.backends.parallel.active = True | ||
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| # Setting global configs | ||
| nxp_config = nx.config.backends.parallel | ||
| nxp_config.n_jobs = 3 | ||
| nxp_config.verbose = 50 | ||
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| nx.square_clustering(H) | ||
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| # Setting config in a context | ||
| with nxp_config(n_jobs=7, verbose=0): | ||
| nx.square_clustering(H) | ||
| ``` | ||
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| The configuration parameters are the same as `joblib.parallel_config`, so you can refer to the [official joblib's documentation](https://joblib.readthedocs.io/en/latest/generated/joblib.parallel_config.html) to better understand these config parameters. | ||
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| ### 2.3 How Does NetworkX's Configuration Work in nx-parallel? | ||
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| In `nx-parallel`, there's a `_configure_if_nx_active` decorator applied to all algorithms. This decorator checks the value of `active`(in `nx.config.backends.parallel`) and then accordingly uses the appropriate configuration system (`joblib` or `networkx`). If `active=True`, it extracts the configs from `nx.config.backends.parallel` and passes them in a `joblib.parallel_config` context manager and calls the function in this context. Otherwise, it simply calls the function. | ||
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| ## 3. Comparing NetworkX and Joblib Configuration Systems | ||
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| ### 3.1 Using Both Systems Simultaneously | ||
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| You can use both NetworkX’s configuration system and `joblib.parallel_config` together in `nx-parallel`. However, it’s important to understand their interaction. | ||
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| Example: | ||
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| ```py | ||
| # Enable NetworkX configuration | ||
| nx.config.backends.parallel.active = True | ||
| nx.config.backends.parallel.n_jobs = 6 | ||
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| # Global Joblib configuration | ||
| joblib.parallel_config(backend="threading") | ||
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| with joblib.parallel_config(n_jobs=4, verbose=55): | ||
| # NetworkX config for nx-parallel | ||
| # backend="loky", n_jobs=6, verbose=0 | ||
| nx.square_clustering(G, backend="parallel") | ||
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| # Joblib config for other parallel tasks | ||
| # backend="threading", n_jobs=4, verbose=55 | ||
| joblib.Parallel()(joblib.delayed(sqrt)(i**2) for i in range(10)) | ||
| ``` | ||
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| - **NetworkX Configurations for nx-parallel**: When calling functions within `nx-parallel`, NetworkX’s configurations will override those specified by Joblib. For example, the `nx.square_clustering` function will use the `n_jobs=6` setting from `nx.config.backends.parallel`, regardless of any Joblib settings within the same context. | ||
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| - **Joblib Configurations for Other Code**: For any other parallel code outside of `nx-parallel`, such as a direct call to `joblib.Parallel`, the configurations specified within the Joblib context will be applied. | ||
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| This behavior ensures that `nx-parallel` functions consistently use NetworkX’s settings when enabled, while still allowing Joblib configurations to apply to non-NetworkX parallel tasks. | ||
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| **Key Takeaway**: When both systems are used together, NetworkX's configuration (`nx.config.backends.parallel`) takes precedence for `nx-parallel` functions. To avoid unexpected behavior, ensure that the `active` setting aligns with your intended configuration system. | ||
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| ### 3.2 Key Differences | ||
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| - **Parameter Handling**: The main difference is how `backend_params` are passed. Since, in networkx configurations are stored as a [`@dataclass`](https://docs.python.org/3/library/dataclasses.html), we need to pass them as a dictionary, whereas in `joblib.parallel_config` you can just pass them along with the other configurations, as shown below: | ||
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| ```py | ||
| nx.config.backends.parallel.backend_params = {"max_nbytes": None} | ||
| joblib.parallel_config(backend="loky", max_nbytes=None) | ||
| ``` | ||
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| - **Default Behavior**: By default, `nx-parallel` looks for configs in `joblib.parallel_config` unless `nx.config.backends.parallel.active` is set to `True`. | ||
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| ### 3.3 When Should You Use Which System? | ||
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| When the only networkx backend you're using is `nx-parallel`, then either of the NetworkX or `joblib` configuration systems can be used, depending on your preference. | ||
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| But, when working with multiple NetworkX backends, it's crucial to ensure compatibility among the backends to avoid conflicts between different configurations. In such cases, using NetworkX's configuration system to configure `nx-parallel` is recommended. This approach helps maintain consistency across backends. For example: | ||
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| ```python | ||
| nx.config.backend_priority = ["another_nx_backend", "parallel"] | ||
| nx.config.backends.another_nx_backend.config_1 = "xyz" | ||
| joblib.parallel_config(n_jobs=7, verbose=50) | ||
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| nx.square_clustering(G) | ||
| ``` | ||
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| In this example, if `another_nx_backend` also internally utilizes `joblib.Parallel` (without exposing it to the user) within its implementation of the `square_clustering` algorithm, then the `nx-parallel` configurations set by `joblib.parallel_config` will influence the internal `joblib.Parallel` used by `another_nx_backend`. To prevent unexpected behavior, it is advisable to configure these settings through the NetworkX configuration system. | ||
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| **Future Synchronization:** We are working on synchronizing both configuration systems so that changes in one system automatically reflect in the other. This started with [PR#68](https://github.com/networkx/nx-parallel/pull/68), which introduced a unified context manager for `nx-parallel`. For more details on the challenges of creating a compatibility layer to keep both systems in sync, refer to [Issue#76](https://github.com/networkx/nx-parallel/issues/76). | ||
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| If you have feedback or suggestions, feel free to open an issue or submit a pull request. | ||
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| Thank you :) |
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