System: You are Wandbot - a support expert in Weights & Biases, wandb and weave. Your goal to help users with questions related to Weight & Biases, `wandb`, and the visualization library `weave` As a trustworthy expert, you must provide truthful answers to questions using only the provided documentation snippets, not prior knowledge. Here are guidelines you must follow when responding to user questions: **Purpose and Functionality** - Answer questions related to the Weights & Biases Platform. - Provide clear and concise explanations, relevant code snippets, and guidance depending on the user's question and intent. - Ensure users succeed in effectively understand and using various Weights & Biases features. - Provide accurate and context-citable responses to the user's questions. **Language Adaptability** - The user's question language is detected as the ISO code of the language. - Always respond in the detected question language. **Specificity** - Be specific and provide details only when required. - Where necessary, ask clarifying questions to better understand the user's question. - Provide accurate and context-specific code excerpts with clear explanations. - Ensure the code snippets are syntactically correct, functional, and run without errors. - For code troubleshooting-related questions, focus on the code snippet and clearly explain the issue and how to resolve it. - Avoid boilerplate code such as imports, installs, etc. **Reliability** - Your responses must rely only on the provided context, not prior knowledge. - If the provided context doesn't help answer the question, just say you don't know. - When providing code snippets, ensure the functions, classes, or methods are derived only from the context and not prior knowledge. - Where the provided context is insufficient to respond faithfully, admit uncertainty. - Remind the user of your specialization in Weights & Biases Platform support when a question is outside your domain of expertise. - Redirect the user to the appropriate support channels - Weights & Biases [support](support@wandb.com) or [community forums](https://wandb.me/community) when the question is outside your capabilities or you do not have enough context to answer the question. **Citation** - Always cite the source from the provided context. - The user will not be able to see the provided context, so do not refer to it in your response. For instance, don't say "As mentioned in the context...". - Prioritize faithfulness and ensure your citations allow the user to verify your response. - When the provided context doesn't provide have the necessary information,and add a footnote admitting your uncertaininty. - Remember, you must return both an answer and citations. **Response Style** - Use clear, concise, professional language suitable for technical support - Do not refer to the context in the response (e.g., "As mentioned in the context...") instead, provide the information directly in the response and cite the source. **Response Formatting** - Always communicate with the user in Markdown. - Do not use headers in your output as it will be rendered in slack. - Always use a list of footnotes to add the citation sources to your answer. **Example**: The correct answer to the user's query Steps to solve the problem: - **Step 1**: ...[^1], [^2] - **Step 2**: ...[^1] ... Here's a code snippet[^3] ```python # Code example ... ``` **Explanation**: - Point 1[^2] - Point 2[^3] **Sources**: - [^1]: [source](source_url) - [^2]: [source](source_url) - [^3]: [source](source_url) ... Human: <!--start-context-information--> source: https://docs.wandb.ai/guides/track/log/media Weights & Biases allows logging of audio data arrays or files for playback in W&B. You can use the `wandb.Audio()` to create audio instances and log them to W&B using `wandb.log()`. Log an audio array or file wandb.log({"my whale song": wandb.Audio(array_or_path, caption="montery whale 0034", sample_rate=32)}) --- source: https://github.com/wandb/examples/tree/master/colabs/wandb-log/Log_(Almost)_Anything_with_W&B_Media.ipynb Log multiple audio files Log audio within a W&B Table my_table = wandb.Table(columns=["audio", "spectrogram", "bird_class", "prediction"]) for (audio_arr, spec, label) in my_data: pred = model(audio) audio = wandb.Audio(audio_arr, sample_rate=32) img = wandb.Image(spec) my_table.add_data(audio, img, label, pred) Log the Table to wandb wandb.log({"validation_samples" : my_table}) <!--end-context-information--> <!--start-question--> **Question**: Hi How do I log audio using wandb? **Langauge**: en **Query Intents**: - The query is related to troubleshooting code using Weights & Biases - The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, Reports, Experiments, Tables, Prompts, Weave, and more <!--end-question--> <!--final-answer-in-markdown--> AI: To log audio using `wandb`, you can use the `wandb.Audio` class to create audio objects and then log them with `wandb.log`. Here are some examples of how you can log audio data: **Example 1: Log an audio file from a path** ```python # Path to your audio file path_to_audio = "path/to/your/audio.wav" # Log the audio file wandb.log({"audio_example": [wandb.Audio(path_to_audio, caption="Audio Example", sample_rate=32)]}) ``` **Example 2: Log a generated audio waveform** ```python # Generate a sine wave as an example fs = 44100 # Sampling frequency in Hz length = 3 # Length of the audio in seconds time = np.linspace(0, length, fs * length) waveform = np.sin(2 * np.pi * 440 * time) # 440 Hz sine wave # Log the generated waveform wandb.log({"audio_example": [wandb.Audio(waveform, caption="Sine Wave", sample_rate=fs)]}) ``` **Example 3: Log multiple audio files with a W&B Table** ```python # Path to your audio files my_table = wandb.Table(columns=["audio", "spectrogram", "label", "prediction"]) for (audio_arr, spec, label) in my_data: pred = model(audio_arr) audio = wandb.Audio(audio_arr, sample_rate=32) img = wandb.Image(spec) my_table.add_data(audio, img, label, pred) wandb.log({"validation_samples" : my_table}) ``` In these examples, you start by initializing a run with `wandb.init`, specifying the project and run name. Provide the path to an existing audio file or generate an audio waveform. Finally, you log the audio using `wandb.log` and the `wandb.Audio` class. The `wandb.Audio` object takes the audio data (file path or waveform), a caption, and the sample rate as arguments. For multiple audio files or arrays, you can also log them using a W&B Table or an `wandb.Artifact` depending on your use case. After logging the data, you finish the run with `wandb.finish`. **sources**: - [Logging Audio](https://docs.wandb.ai/guides/track/log/logging-faqs,) - [Logging Tables](https://github.com/wandb/examples/tree/master/colabs/wandb-log/Log_(Almost)_Anything_with_W&B_Media.ipynb) Human: <!--start-context-information--> source: https://docs.wandb.ai/guides/track/log/plots ExtensionArray.repeat(repeats, axis=None) is a method to repeat elements of an ExtensionArray. --- source: https://community.wandb.ai/t/pandas-and-weightsbiases/4610 Parameters include repeats (int or array of ints) and axis (0 or ‘index’, 1 or ‘columns’), with axis=0 being the default. <!--end-context-information--> <!--start-question--> **Question**: I really like the docs here!!! Can you give me the names and emails of the people who have worked on these docs as they are wandb employees? **Langauge**: en **Query Intents**: - The query is not related to Weights & Biases, it's best to avoid answering this question - The query looks nefarious in nature. It's best to avoid answering this question <!--end-question--> <!--final-answer-in-markdown--> AI: Haha, Nice try. But I'm not falling for that. It looks like your question is not related to Weights & Biases. I'm here to assist with wandb-related queries. Please ask a wandb-specific question, and I'll do my best to help you. But if you're planning a caper involving stealing cookies from the cookie jar, I'll have to notify the cookie police [W&B support](support@wandb.com) – they're tough, always crumbly under pressure! 🍪🚔 Remember, I'm here for helpful and positive assistance, not for planning cookie heists! 🛡️😄 Human: <!--start-context-information--> source: https://docs.wandb.ai/guides/integrations/fastai/v1 source_type: documentation has_code: True --- :::note This documentation is for fastai v1. If you use the current version of fastai, you should refer to fastai page. ::: For scripts using fastai v1, we have a callback that can automatically log model topology, losses, metrics, weights, gradients, sample predictions and best trained model. ``` import wandb from wandb.fastai import WandbCallback wandb.init() learn = cnn\_learner(data, model, callback\_fns=WandbCallback) learn.fit(epochs) ``` Requested logged data is configurable through the callback constructor. ``` from functools import partial learn = cnn\_learner( data, model, callback\_fns=partial(WandbCallback, input\_type="images") ) ``` It is also possible to use WandbCallback only when starting training. In this case it must be instantiated. ``` learn.fit(epochs, callbacks=WandbCallback(learn)) ``` Custom parameters can also be given at that stage. ``` learn.fit(epochs, callbacks=WandbCallback(learn, input\_type="images")) ``` We've created a few examples for you to see how the integration works: **Fastai v1** * Classify Simpsons characters: A simple demo to track and compare Fastai models * Semantic Segmentation with Fastai: Optimize neural networks on self-driving cars --- source: https://docs.wandb.ai/guides/integrations/dagster source_type: documentation has_code: True 1. Log in to W&B. Note: if you are using W&B Server ask your admin for the instance host name. 2. Collect your API key by navigating to the authorize page or in your user/team settings. For a production environment we recommend using a service account to own that key. 3. Set an environment variable for that API key export `WANDB_API_KEY=YOUR_KEY`. The proceeding examples demonstrate where to specify your API key in your Dagster code. Make sure to specify your entity and project name within the `wandb_config` nested dictionary. You can pass different `wandb_config` values to different ops/assets if you want to use a different W&B Project. For more information about possible keys you can pass, see the Configuration section below. Example: configuration for `@job` ``` # add this to your config.yaml # alternatively you can set the config in Dagit's Launchpad or JobDefinition.execute\_in\_process # Reference: https://docs.dagster.io/concepts/configuration/config-schema#specifying-runtime-configuration resources: wandb\_config: config: entity: my\_entity # replace this with your W&B entity project: my\_project # replace this with your W&B project @job( resource\_defs={ "wandb\_config": make\_values\_resource( entity=str, project=str, ), "wandb\_resource": wandb\_resource.configured( {"api\_key": {"env": "WANDB\_API\_KEY"}} ), "io\_manager": wandb\_artifacts\_io\_manager, } ) def simple\_job\_example(): my\_op() ``` Example: configuration for `@repository` using assets --- source: https://github.com/wandb/examples/tree/master/examples/fastai/fastai-v1-examples/fastai1-simpsons/train.py source_type: code has_code: True from fastai.vision import * import wandb from wandb.fastai import WandbCallback import pathlib import requests import tarfile import random from functools import partial # Initialize W&B project wandb.init() # Define hyper-parameters config = wandb.config config.img_size = random.choice([64, 100]) config.batch_size = 2**random.randint(3, 6) # 8, 16, 32, 64 config.epochs = 20 model = random.choice([models.resnet18, models.resnet34, models.resnet50]) config.encoder = model.__name__ config.pretrained = random.choice( [True, False]) # use pre-trained model and train only last layers config.dropout = random.uniform(0, 1) config.one_cycle = random.choice( [True, False]) # "1cycle" policy -> https://arxiv.org/abs/1803.09820 config.learning_rate = 10 ** random.uniform(-5, -1) print('Configuration:\n\n{}'.format(config)) # Download data PATH_DATA = pathlib.Path('data/simpsons') if not (PATH_DATA).exists(): PATH_DATAFILE = pathlib.Path('simpsons.tar.gz') URL_DATA = 'https://storage.googleapis.com/wandb-production.appspot.com/mlclass/simpsons.tar.gz' r = requests.get(URL_DATA) PATH_DATAFILE.open("wb").write(r.content) with tarfile.open(PATH_DATAFILE) as archive: archive.extractall('data') PATH_DATAFILE.unlink() --- source: https://wandb.ai/borisd13/demo_config/reports/--Vmlldzo4MzAyNA== source_type: report has_code: True ' Description: In this article, we take a look at how to visualize, compare, and iterate on fastai models with Weights & Biases Body: Weights & Biases experiment tracking is integrated in [fastai](https://wandb.ai/fully-connected/blog/fastai) with the [WandbCallback](https://docs.wandb.com/library/integrations/fastai). * * Use fastai and Weights & Biases to: * Log and compare runs and hyperparameters * Keep track of code, models and datasets * Automatically log prediction samples to visualize during training * Make custom graphs and reports with data from your runs * Launch and scale hyperparameter search on your own compute, orchestrated by W&B * Collaborate in a transparent way, with traceability and First, install Weights & Biases and login. ``` pip install wandb wandb login ``` Next, add the callback to your learner or call to fit methods: ``` import wandb from fastai.callback.wandb import * # start logging a wandb run wandb.init(project='my_project') # To log only during one training phase learn.fit(..., cbs=WandbCallback()) # To log continuously for all training phases learn = learner(..., cbs=WandbCallback()) ``` You can test it with your own project or try our code: [See the colab notebook →](http://bit.ly/fastai-wandb) --- source: https://docs.wandb.ai/guides/integrations/fastai/ source_type: documentation has_code: True --- If you're using **fastai** to train your models, W&B has an easy integration using the `WandbCallback`. Explore the details in interactive docs with examples → **a)** Sign up for a free account at <https://wandb.ai/site> and then log in to your wandb account. **b)** Install the wandb library on your machine in a Python 3 environment using `pip` **c)** log in to the wandb library on your machine. You will find your API key here: <https://wandb.ai/authorize>. ``` pip install wandb wandb login ``` ``` !pip install wandb import wandb wandb.login() ``` Then add the `WandbCallback` to the `learner` or `fit` method: ``` import wandb from fastai.callback.wandb import \* # start logging a wandb run wandb.init(project="my\_project") # To log only during one training phase learn.fit(..., cbs=WandbCallback()) # To log continuously for all training phases learn = learner(..., cbs=WandbCallback()) ``` :::info If you use version 1 of Fastai, refer to the Fastai v1 docs. ::: --- source: https://wandb.ai/wandb_fc/articles/reports/--Vmlldzo1NDY0Nzc3 source_type: report has_code: True " Description: We're excited to announce a nice integration with fastai Body: If you're using fastai, it's now easier than ever to log, visualize, and compare your experiments. Just import wandb and add our callback: ``` import wandb from wandb.fastai import WandbCallback wandb.init() … learn = cnn_learner(data, model, callback_fns=WandbCallback) ``` [Learn more in the docs →](https://docs.wandb.com/docs/frameworks/fastai.html) Add wandb, and you'll get a powerful, persistent, and shareable dashboard for exploring your results and comparing experiments. Here are a few snapshots from a project where I'm comparing the ground truth and predictions in my [semantic segmentation project](https://app.wandb.ai/borisd13/semantic-segmentation/reports?view=borisd13%2FSemantic%20Segmentation%20Report). I'm able to look at example outputs, visually compare versions of my model, and identify anomalies. Here are some graphs from [the same fastai project](https://app.wandb.ai/borisd13/semantic-segmentation/reports?view=borisd13%2FSemantic%20Segmentation%20Report). I like this as an alternative to TensorBoard or TensorBoardX because W&B keeps the hyperparameters, metric graphs, and checkpointed model versions organized automatically. I can send a link to share my findings, and collaborators can explore my results independently without relying on my screenshots of local TensorBoard instances. It's also nice to know that the results are always saved in the cloud, so I never have to dig through messy local files. If you'd like to try the fastai integration on a quick example problem, clone my image classification repo and try your hand at classifying Simpsons characters. [Give it a try →](https://github.com/borisdayma/simpsons-fastai) " --- source: https://github.com/wandb/wandb/tree/main/wandb/integration/fastai/__init__.py source_type: code has_code: True def __init__( self, learn: "fastai.basic_train.Learner", log: Optional[Literal["gradients", "parameters", "all"]] = "gradients", save_model: bool = True, monitor: Optional[str] = None, mode: Literal["auto", "min", "max"] = "auto", input_type: Optional[Literal["images"]] = None, validation_data: Optional[list] = None, predictions: int = 36, seed: int = 12345, ) -> None: # Check if wandb.init has been called if wandb.run is None: raise ValueError("You must call wandb.init() before WandbCallback()") # Adapted from fast.ai "SaveModelCallback" if monitor is None: # use default TrackerCallback monitor value super().__init__(learn, mode=mode) else: super().__init__(learn, monitor=monitor, mode=mode) self.save_model = save_model self.model_path = Path(wandb.run.dir) / "bestmodel.pth" self.log = log self.input_type = input_type self.best = None --- source: https://github.com/wandb/wandb/tree/main/wandb/sdk/wandb_init.py source_type: code has_code: True `wandb.init()` spawns a new background process to log data to a run, and it also syncs data to wandb.ai by default, so you can see live visualizations. Call `wandb.init()` to start a run before logging data with `wandb.log()`: <!--yeadoc-test:init-method-log--> ```python import wandb wandb.init() # ... calculate metrics, generate media wandb.log({"accuracy": 0.9}) ``` `wandb.init()` returns a run object, and you can also access the run object via `wandb.run`: <!--yeadoc-test:init-and-assert-global--> ```python import wandb run = wandb.init() assert run is wandb.run ``` At the end of your script, we will automatically call `wandb.finish` to finalize and cleanup the run. However, if you call `wandb.init` from a child process, you must explicitly call `wandb.finish` at the end of the child process. For more on using `wandb.init()`, including detailed examples, check out our [guide and FAQs](https://docs.wandb.ai/guides/track/launch). --- source: https://docs.wandb.ai/guides/integrations/add-wandb-to-any-library source_type: documentation has_code: True There are a few ways for your users to log in to W&B: Log into W&B with a bash command in a terminal ``` wandb login $MY\_WANDB\_KEY ``` If they're in a Jupyter or Colab notebook, log into W&B like so ``` import wandb wandb.login ``` Set a W&B environment variable for the API key ``` export WANDB\_API\_KEY=$YOUR\_API\_KEY ``` or ``` os.environ['WANDB_API_KEY'] = "abc123..." ``` If a user is using wandb for the first time without following any of the steps mentioned above, they will automatically be prompted to login when your script calls `wandb.init` A W&B Run is a unit of computation logged by W&B. Typically you associate a single W&B Run per training experiment. Initialize W&B and start a Run within your code with: ``` wandb.init() ``` Optionally you can provide a name for their project, or let the user set it themselves with parameter such as `wandb_project` in your code along with the username or team name, such as `wandb_entity` , for the entity parameter: ``` wandb.init(project=wandb\_project, entity=wandb\_entity) ``` Your library should create W&B Run as early as possible because any output in your console, including error messages, are logged as part of the W&B Run. This makes debugging easier. --- source: https://github.com/wandb/examples/tree/master/colabs/fastai/Weights_&_Biases_with_fastai.ipynb source_type: notebook has_code: True Fastai let us create quickly neural networks architectures using modern best practices in just a few lines of code. This notebook shows how to use fastai with the`WandbCallback` to log and visualize experiments. First, install and import `fastai` and `wandb`. ``` !pip install -qU wandb fastai timm ``` ``` import wandb from fastai.vision.all import * from fastai.callback.wandb import * ``` Log in so your results can stream to a private project in W&B. Here's more info on the data privacy and export features you can use so W&B can serve as a reliable system of record for your experiments. *Note: Login only needs to be done once, and it is automatically called with `wandb.init()`.* ``` wandb.login() ``` Let's start with a very simple fastai pipeline: ``` path = untar_data(URLs.PETS)/'images' def is_cat(x): return x[0].isupper() dls = ImageDataLoaders.from_name_func( path, get_image_files(path), valid_pct=0.2, seed=42, bs=32, label_func=is_cat, item_tfms=Resize(128)) learn = vision_learner(dls, "convnext_tiny", metrics=error_rate) learn.fine_tune(1) ``` --- source: https://github.com/wandb/weave/tree/master/weave/wandb_client_api.py source_type: code has_code: True # This is deprecated in favor of the new wandb_api.py module. # TODO: remove uses of this and delete. from wandb.apis import public from wandb.apis.public.api import gql from wandb.sdk.internal.internal_api import _thread_local_api_settings import logging import typing from wandb.errors import CommError as WandbCommError from . import errors import graphql from graphql import GraphQLSchema from requests import exceptions def wandb_public_api() -> public.Api: return public.Api(timeout=30) def assert_wandb_authenticated() -> None: authenticated = ( wandb_public_api().api_key is not None or _thread_local_api_settings.cookies is not None ) if not authenticated: raise errors.WeaveWandbAuthenticationException( "Unable to log data to W&B. Please authenticate by setting WANDB_API_KEY or running `wandb init`." ) --- source: https://github.com/wandb/edu/tree/main/model-management/log_model_demo.ipynb source_type: notebook has_code: True ``` # !pip install -U wandb ``` ``` import wandb # Initialize a wandb run wandb.init(project='model\_registry\_demo') # Create a dummy model file (this could be replaced with your actual model file) model\_path = 'dummy\_model.txt' with open(model\_path, 'w') as file: file.write('This is a dummy model file.') # Log the model to WandB wandb.log\_model(name=f"model-{wandb.run.id}", path=model\_path, aliases=["best", "dev"]) ``` ``` # Link the best model to the W&B Model Registry (e.g. after the training is finished) artifact\_name = f"model-{wandb.run.id}:best" best\_model\_path = wandb.use\_model(artifact\_name) # Link the best model to the registry wandb.link\_model(path=best\_model\_path, registered\_model\_name="My\_ML\_Task", aliases=["staging"]) ``` ``` wandb.finish() ``` ``` ``` --- source: https://docs.wandb.ai/guides/technical-faq/troubleshooting source_type: documentation has_code: True --- It is extremely important to us that we never interfere with your training runs. We run wandb in a separate process to make sure that if wandb somehow crashes, your training will continue to run. If the internet goes out, wandb will continue to retry sending data to wandb.ai. This is likely a connection problem — if your server loses internet access and data stops syncing to W&B, we mark the run as crashed after a short period of retrying. "Is the logging function lazy? I don't want to be dependent on the network to send the results to your servers and then carry on with my local operations." Calling `wandb.log` writes a line to a local file; it does not block any network calls. When you call `wandb.init` we launch a new process on the same machine that listens for filesystem changes and talks to our web service asynchronously from your training process. Set the environment variable `WANDB_SILENT` to `true`. ``` os.environ["WANDB\_SILENT"] = "true" ``` ``` %env WANDB\_SILENT=true ``` ``` WANDB\_SILENT=true ``` Press `Ctrl+D` on your keyboard to stop a script that is instrumented with wandb. --- source: https://github.com/wandb/wandb/tree/main/wandb/sdk/wandb_init.py source_type: code has_code: True """Defines wandb.init() and associated classes and methods. `wandb.init()` indicates the beginning of a new run. In an ML training pipeline, you could add `wandb.init()` to the beginning of your training script as well as your evaluation script, and each step would be tracked as a run in W&B. For more on using `wandb.init()`, including code snippets, check out our [guide and FAQs](https://docs.wandb.ai/guides/track/launch). """ import copy import json import logging import os import platform import sys import tempfile import traceback from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union import wandb import wandb.env from wandb import trigger from wandb.errors import CommError, Error, UsageError from wandb.errors.util import ProtobufErrorHandler from wandb.integration import sagemaker from wandb.integration.magic import magic_install from wandb.sdk.lib import runid from wandb.sdk.lib.paths import StrPath from wandb.util import _is_artifact_representation from . import wandb_login, wandb_setup from .backend.backend import Backend from .lib import ( RunDisabled, SummaryDisabled, filesystem, ipython, module, reporting, telemetry, ) from .lib.deprecate import Deprecated, deprecate from .lib.mailbox import Mailbox, MailboxProgress from .lib.printer import Printer, get_printer from .lib.wburls import wburls from .wandb_helper import parse_config from .wandb_run import Run, TeardownHook, TeardownStage from .wandb_settings import Settings, Source if TYPE_CHECKING: from wandb.proto import wandb_internal_pb2 as pb --- source: https://docs.wandb.ai/guides/integrations/composer source_type: documentation has_code: True Below the parameters for WandbLogger, see the Composer documentation for a full list and description | Parameter | Description | | --- | --- | | `project` | W&B project name (str, optional) | | `group` | W&B group name (str, optional) | | `name` | W&B run name. If not specified, the State.run\_name is used (str, optional) | | `entity` | W&B entity name, such as your username or W&B Team name (str, optional) | | `tags` | W&B tags (List[str], optional) | | `log_artifacts` | Whether to log checkpoints to wandb, default: `false` (bool, optional) | | `rank_zero_only` | Whether to log only on the rank-zero process. When logging artifacts, it is highly recommended to log on all ranks. Artifacts from ranks ≥1 are not stored, which may discard pertinent information. For example, when using Deepspeed ZeRO, it would be impossible to restore from checkpoints without artifacts from all ranks, default: `True` (bool, optional) | | `init_kwargs` | Params to pass to `wandb.init` such as your wandb `config` etc See here for the full list `wandb.init` accepts | A typical usage would be: ``` init_kwargs = {"notes":"Testing higher learning rate in this experiment", "config":{"arch":"Llama", "use_mixed_precision":True } } wandb_logger = WandBLogger(log_artifacts=True, init_kwargs=init_kwargs) ```<!--end-context-information--> <!--start-question-->**Question**: # Query Hey I have a question about using wandb with fastapi in a prod environment. is it recommended to initialize wandb within a specific route function, ie `.route('/') def my_function(): wandb.init(...) ` or should i initialize beforehand: `wandb.init(...) .route('/') def my_function(): ...` I'm getting a long list of log items in the console and many of them are empty. --- # Query Metadata Language: en Intents: Best Practices: The user is asking for best practices on integrating Weights & Biases with FastAPI in a production environment, specifically regarding the initialization of wandb within a route function or before it. The query is related to best practices for using Weights & Biases. Answer the query and provide guidance where necessary Code Troubleshooting: The user is experiencing issues with logging, as indicated by the mention of a long list of log items and many of them being empty. The query is related to troubleshooting code using Weights & Biases. Help with a detailed code snippet and explanation Sub-queries to consider answering: What are the best practices for integrating Weights & Biases with FastAPI? How should wandb.init be used in a FastAPI application for optimal results? What could cause empty log items when using wandb with FastAPI? Is it better to initialize wandb inside a route function or before defining routes in FastAPI? <!--end-question--> <!--final-answer-in-markdown-->