Dataset:v2
id
question
ground_truth
notes
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
`@app.route('/')
def my_function():
wandb.init(...)
`
or should i initialize beforehand:
`wandb.init(...)
@app.route('/')
def my_function():
...`
I'm getting a long list of log items in the console and many of them are empty.
When integrating `wandb` (Weights & Biases) with a FastAPI application, it's important to consider the nature of your application and how you're using `wandb`. FastAPI is an asynchronous web framework, and initializing `wandb` within a route function could lead to multiple initializations if the route is hit multiple times, which is not ideal.
Here are some guidelines to help you decide where to place the `wandb.init()` call:
1. **One-time Initialization**: If you need to track metrics across ...
The answer clearly explains the recommended practices for integrating wandb with a FastAPI application, offering both one-time initialization at the application start and per-request initialization within a route function, if necessary. The answer also mentions the possible reasons for seeing many empty log items in the console, such as multiple initializations or incorrect usage within an asynchronous environment, which directly corresponds to the user's observations.
Hey with wandb is it possible to link from the sweep api?
best_run = sweep.best_run()
I have the best run but it isn't clear to me how to link through the api. the UI works fine but trying to get the api working also. I have been reading the docs but missing something.
Yes, with `wandb`, you can link to the best run from a sweep using the API. The `best_run()` method of a sweep object will return the best run based on the metric defined in the sweep configuration. Once you have the best run object, you can use its attributes to access various details about the run, including its URL.
Here's an example of how you can retrieve the best run from a sweep and then get the URL for that run:
```python
import wandb
# Initialize the API
api = wandb.Api()
# Replace ...
This answer correctly explains how to use the W&B API to get the best run from a sweep and access its URL.
Explain how I can version datasets with Weights & Biases. How do I create a new version of a dataset? How does Weights & Biases work with versions? What are some best practices for versioning datasets?
Versioning datasets with Weights & Biases (W&B) is an essential practice for maintaining reproducibility and traceability in machine learning workflows. W&B provides a feature called Artifacts, which allows you to version datasets, models, and other types of files. Here's how you can create a new version of a dataset and work with versions in W&B:
### Creating a New Version of a Dataset with W&B:
1. **Install and Log In**:
Make sure you have W&B installed (`pip install wandb`) and you are l...
The answer correctly summarizes the information from the documents, focusing on how to version datasets with Weights & Biases. It provides a step-by-step guide for creating a new version of a dataset using Artifacts in W&B, describes how W&B works with versions, and outlines best practices for versioning datasets. The answer's content does not deviate from the information provided in the documents and aligns well with the concepts of dataset versioning as outlined in W&B's features.
Hi, can anybody help me with this issue? wandb.sdk.service.service.ServiceStartTimeoutError: Timed out waiting for wandb service to start after 30.0 seconds. Try increasing the timeout with the `_service_wait` setting. I have tried to set 300 seconds, reinstalled wandb as well. but I still get this error
The `wandb.sdk.service.service.ServiceStartTimeoutError` indicates that the Weights & Biases (W&B) service is taking longer than expected to start. This can be due to a variety of reasons, such as network issues, system resource constraints, or problems with the W&B installation.
Here are some steps you can take to troubleshoot and potentially resolve the issue:
1. **Increase the Service Start Timeout:**
You've already tried increasing the timeout to 300 seconds, which is a good first step....
This requires more data to debug and probably a support ticket. The problem is with starting the wandb service, which is poorly documented.
The answer provides a comprehensive list of troubleshooting steps for dealing with the `ServiceStartTimeoutError` in wandb, which correctly addresses the user's issue. It takes into consideration both documentation and general best practices, such as increasing the timeout, checking network connectivity, system resources, reinstalling wandb, and using W&B ...
what is the difference between artifact.add_file and wandb.save
`artifact.add_file` and `wandb.save` are both methods used in the context of Weights & Biases (W&B) to save and track files, but they serve different purposes and are used in different scenarios.
`artifact.add_file`:
- This method is used to add a specific file to a W&B `Artifact`, which is a versioned folder-like structure used to organize and keep track of data, models, and other files.
- When you use `artifact.add_file`, you are explicitly adding a file to an artifact that you intend to log ...
The answer correctly identifies the distinction between `artifact.add_file` and `wandb.save` in the context of Weights & Biases (W&B) based on the provided document. It explains that `artifact.add_file` is for adding files to a versioned W&B Artifact with versioning and deduplication capabilities, while `wandb.save` is for syncing run-specific files with the W&B server. The answer also provides correct example usage for each method, aligning with the usage and intention described in the document...
Hi I have created an academic team, however I don't have admin rights in my own group and cannot access admin features at all
If you've created an academic team on W&B (Weights & Biases) but find yourself without admin rights, it's possible that there was an error during the team creation process, or perhaps the admin rights were not correctly assigned to your account. Here's what you can do to resolve the issue:
1. **Contact Support**: Reach out to W&B support directly. They can help you verify your account status and rectify any issues with your team's setup. You can contact them through the support chat on the W&B ...
The answer provides appropriate troubleshooting steps and suggests that the user contact the support team which is the right way to resolve this issue and solve the user's problem here.
How can I add prediction-related data to a W&B run days after my training run (i.e. not in the same process/moment that I did the training)? Is it possible to link prediction data to an external tool such as BigQuery and still get rich visualizations in W&B?
To add prediction-related data to a W&B run after your training run, you can create a new run for inference and associate it with the previous training run using W&B's API. Here's how you can do it:
1. **Initialize a New Run for Inference**: Start a new W&B run for the inference phase. You can set the `group`, `job_type`, or `tags` to link it logically with your training run.
```python
import wandb
# Initialize a new W&B run for inference
inference_run = wandb.init(
project="your_project_...
While this does not take into account updating values or Tables and artifacts the answer sufficient given the context.
The answer correctly outlines the steps for adding prediction-related data to a W&B run after training has been completed. It aligns with the documented functionality, suggesting to initialize a new run, log predictions, associate with the training run if needed, and finally finish the run. The answer also correctly addresses the possibility of integrating with BigQuery and log...
On my laptop, wandb is configured to log in to my company's wandb. However, I want to use my personal wandb account for my personal projects. Even after wandb login --relogin, it will still try to go to company.wandb.ai instead of api.wandb.ai. How can I solve this?
Even better, is there a good way to handle two wandb accounts? I cannot give details about how I set my company wandb.
To switch between your company's W&B instance and the public W&B cloud, you need to specify the host you're logging into. This is because your company might be using a private instance of W&B, which has a different host address than the public W&B cloud.
Here's how you can log in to your personal account on the public W&B cloud:
1. Open a terminal on your laptop.
2. Run the following command to log in to the public W&B cloud:
```bash
wandb login --relogin --host=https://api.wandb.ai
```
This...
The provided answer suggests editing the shell configuration files to handle multiple wandb accounts with aliases, which is not secure as hardcoding the API keys exposes them. The user specifically mentioned that they cannot give details about how they set their company's wandb, which implies they may not be able to share the wandb instance URL or other settings publicly. The correct approach would be to use environment variables to switch between the different wandb configurations without hardc...
I want to add a new columns to a wandb.Table with an operation involving other columns, what operations can I use and how to actually do this?
In `wandb.Table`, you can add new columns by performing operations on existing columns using standard Python operations and then appending the results as a new column. The operations you can perform on the columns depend on the data types of the columns you're working with. For numerical columns, you can perform arithmetic operations such as addition, subtraction, multiplication, division, and more complex mathematical functions provided by Python libraries like `math` or `numpy`.
Here's a step...
The provided answer accurately represents how to programmatically add a new column to a wandb.Table by performing operations with other columns using Python and then using the 'add_column' method to append the result.
However, it's unclear whether the intention of the question was to do it via UI. It might be prudent to ask a follow-up question in such cases and clarify the intent of the user.
Hey there, I just recently starting using the `WANDB_WATCH="all"` environment variable and I'm trying to find an intelligent way of viewing all these different values on the dashboard. Is there some type of high-level view? In particular I'd like to see how gradients/activations vary across layers for the `llama` model. How do I see outliers across the model weights? Are there any references/examples that you could point me to?
When you set `WANDB_WATCH="all"`, Weights & Biases (W&B) logs histograms of both gradients and parameters for each layer of your model. This can generate a lot of data, especially for large models like `llama`. To view and analyze this data effectively on the W&B dashboard, you can use several features:
1. **Custom Charts**: You can create custom charts on the W&B dashboard to visualize gradients and parameters. This allows you to focus on specific layers or aspects of the model that are of int...
The answer is consistent with the provided document, highlighting using the 'WANDB_WATCH' environment variable and offering a detailed explanation of utilizing the Weights & Biases dashboard to view gradients, parameters, and potential outliers in model weights.
Wandb ignores --configs flag when running training script. Running a script I want to specify what config YAML to use. The documentation states you can specify what configuration file to load using the `--configs` flag, however running
`python myscript.py --configs new-configs.yaml`
on the following script
`import wandb
wandb.init()
print(wandb.config)`
I can see that the configuration contained in `config-defaults.yaml` is being loaded instead. Why is this happening?
The `--configs` flag is not a built-in feature of the `wandb` library or the `wandb.init()` function. The `wandb` library automatically loads configurations from a file named `config-defaults.yaml` if it exists, as mentioned in the documentation. However, it does not natively recognize the `--configs` flag to specify a different configuration file.
If you want to load a different configuration file, you have a couple of options:
1. **Manually specify the configuration in your script**: You can...
The answer correctly identifies that the `--configs` flag is not a built-in feature of the wandb library and provides alternative methods to load a different YAML configuration file into `wandb.init`. The user-provided code snippet demonstrates how to manually read a YAML file and pass the configurations to wandb, which aligns with the official documentation that suggests using a `config-defaults.yaml` or passing values directly to the `wandb.init` config argument.
I want to submit a wandb Report to a conference, can I somehow anonymize the datA?
As of the last update I have, W&B (Weights & Biases) does not provide a direct feature specifically for anonymizing data in reports for the purpose of conference submissions. However, there are some steps you can take to anonymize your data before submitting it to a conference:
1. **Anonymous Mode**: If you're sharing code and want others to run it and see the results without creating a W&B account, you can use Anonymous Mode by setting `wandb.init(anonymous="allow")`. This allows for the creat...
The answer provides accurate advice on anonymizing data for conference submission by utilizing Anonymous Mode and other steps while highlighting that direct anonymization features for reports are not specified.
i am trying to figure out the best way to organize/name/group my wandb runs when doing hyperparam tuning with cross-validation.
ideally, i'd like to be able to see the average score across all the runs that are part of a CV set. and then see the best CV score across the whole hyperparam sweep. (i am using optuna, not wandb, to manage the hyperparam sweep)
is there a recommended approach for this?
When using W&B (Weights & Biases) in conjunction with Optuna for hyperparameter tuning with cross-validation, you can organize your runs effectively by leveraging W&B's features such as projects, groups, tags, and notes. Here's a recommended approach to achieve your goals:
1. **Projects**: Use a dedicated project for your hyperparameter sweep. This will allow you to compare all runs within the same context.
```python
wandb.init(project="your_hyperparam_sweep_project")
```
2. **Groups*...
The answer provides a strategy for organizing and naming runs in Weights & Biases (W&B) when doing hyperparameter tuning with cross-validation and using Optuna for hyperparameter sweeps. It accurately utilizes information from the provided document by suggesting the use of projects, groups, tags, and notes, which are W&B features described in the document. It also provides correct code snippets to illustrate the usage of these features in Python. There is no contradiction with the document, and ...
can you tell why this error occurred while uploading the training results to the server:
wandb: ERROR Error while calling W&B API: run lee086824/Graduate_Project/4iolqdaz was previously created and deleted; try a new run name (Response [409]>)
The error message you're encountering indicates that the run with the ID `4iolqdaz` under the project `Graduate_Project` in the workspace `lee086824` was previously created and then deleted. When a run is deleted from Weights & Biases (W&B), its ID is marked as used and cannot be reused for a new run. This is to maintain the integrity of the data and ensure that run IDs are unique within a project.
The HTTP response code `409` is a conflict error, which in this context means that there's a conf...
The explanation correctly describes the error message in question. It provides steps to resolve the issue by not specifying a run ID manually, which aligns with the guidance given in the documentation. It provides the correct context and solution for the 409 error encountered when trying to reuse a run ID that was previously created and deleted.
why am I getting a table instead of confusion matrix chart using wandb.log({"confusion_matrix_normalized": wandb.plot.confusion_matrix(
y_true=y_test,
probs=y_probas,
class_names=text_labels
)})
When using `wandb.log` to log a confusion matrix, you should typically see a confusion matrix chart in your Weights & Biases dashboard. If you're getting a table instead of a chart, there could be a few reasons for this:
1. **Data Format Issues**: Ensure that `y_test` and `y_probas` are in the correct format. `y_test` should be a list of true class indices, and `y_probas` should be a 2D list or array of probabilities for each class.
2. **Class Names Mismatch**: The `class_names` parameter shou...
Although the answer is correct the retrieved document does not have any mention of the confusion matric documentation or why a table would be rendered instead of a confusion matrix chart as the user is experiencing.
The answer incorrectly suggests that recent API changes or visualization limitations could be factors not supported by any information in the document.
Furthermore, it incorrectly proposes updating the library without evidence from the document that this step is necessary. The con...
How can I store data versions of large datasets using artefacts and tables? Such as millions of videos?
Storing large datasets, such as millions of videos, using Weights & Biases (wandb) artifacts and tables requires careful planning due to the size and complexity of the data. Here are some steps and considerations for managing large datasets with wandb:
1. **Chunking the Data**: Instead of trying to upload the entire dataset at once, break it down into smaller, manageable chunks. For example, you could divide the videos into sets of thousands or tens of thousands, depending on their size.
2. **...
The answer provides a systematic approach for storing and managing large datasets, like videos, using Weights & Biases (wandb) artifacts and tables. The provided steps align with wandb's features like chunking data into manageable pieces, creating artifacts for chunks, using references to cloud storage, utilizing version control, employing deduplication, and setting retention policies. This efficient approach complies with wandb's capabilities for handling large volumes of data.
how to make runs in a sweep parallel
To make runs in a sweep parallel, you can launch multiple agents on different machines or within the same machine if it has multiple CPUs. Each agent will execute a run independently, allowing you to perform parallel experimentation. Here's how you can do it:
### Parallelize on Multiple Machines
1. **Set up your environment**: Ensure that the `wandb` library is installed on each machine you want to use for the sweep.
2. **Launch agents**: On each machine, open a terminal and run the following...
The answer accurately explains how to run sweeps in parallel, both on multiple machines and on a multi-CPU machine, by launching multiple agents. It also provides an alternative approach for Jupyter Notebooks. This information is faithful to the recommendations given in the provided documentation
Can I copy runs from one project from a team to another non-team project from? I can see all the runs in my wandb folder but I don't know how to sync those runs to another project.
I need this because I have used all free tracked hours provided by weights and biases for the team entity and now I wish to transfer those runs to my personal project. There is an option for moving those runs as mentioned here (https://docs.wandb.ai/guides/runs/manage-runs) using GUI, but now I can not access those ru...
If you've used all the free tracked hours provided by Weights & Biases for your team entity and are unable to access the runs through the GUI because you're being redirected to the billing page, you may need to use the Weights & Biases API to programmatically move or copy the runs to your personal project.
Here's a general approach you can take using the W&B API:
1. **Install the W&B Python package** (if not already installed):
```bash
pip install wandb
```
2. **Authenticate your W&B...
While the provided answer does not fully address the user's issue of being unable to access the runs due to being redirected to the billing page, it does offer alternative methods, such as downloading the runs via API and re-uploading them to a personal project, which is the correct approach in this scenario.
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