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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
please show me how to log training output_name model of the current training run
<wandbot.chat.chat.Chat object at 0x122acae70>
To log a model from your current training run, you can use the `run.log_model` API. This method logs your model files as an artifact and marks it as an output of the W&B run, allowing you to track its lineage.
Here's how you can log your model:
```python
import wandb
import os
# Initialize a W&B run (replace <your-project> and <your-entity>)
run = wandb.init(project="<your-project>", entity="<your-entity>")
# Assume your model is saved to a local directory or file
# For example, if you saved...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
Can multiple users write to the same stream table?
<wandbot.chat.chat.Chat object at 0x12cef8890>
Yes, multiple users with appropriate permissions can contribute to and manage data within Weights & Biases, including tables.
Here's how it works:
* **Access Permissions**: Users with a "Full" seat type in your organization have comprehensive permissions to write, read, and export data for Models or Weave. This includes the ability to create and modify tables[^1]. For Enterprise plans, Identity and Access Management (IAM) allows for configuring user roles at the organization and team level t...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
What are some great resources for me to better understand Weights & Biases usage for Deep Learning?
<wandbot.chat.chat.Chat object at 0x12cef8890>
To better understand Weights & Biases usage for Deep Learning, you can explore several resources:
**1. Official Documentation and Guides**
The main Weights & Biases documentation is a comprehensive resource. Specifically, you can find guides on:
* **How W&B works**: Learn about runs, experiments, artifacts, sweeps, and reports, which are fundamental for tracking deep learning projects[^10].
* **Integrations**: W&B integrates with popular deep learning frameworks like TensorFlow, PyTorch, Ja...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
How can weights & biases help when building llm-powered apps?
<wandbot.chat.chat.Chat object at 0x124614fb0>
Weights & Biases offers several tools to assist in building LLM-powered applications, focusing on tracking, evaluation, and debugging:
1. **W&B Prompts**
* A suite of LLMOps tools designed for developing LLM applications[^1], [^2].
* Helps visualize and inspect the execution flow of your LLMs, analyze inputs and outputs, view intermediate results, and securely store and manage prompts and LLM chain configurations[^1], [^2].
* Includes a **Trace table** for an overview of the ...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
Can I get an enlarged/focused view for charts?
<wandbot.chat.chat.Chat object at 0x11b898260>
Yes, you can get an enlarged or focused view for charts in Weights & Biases, particularly for line plots. W&B allows you to zoom in on specific regions of your dataset without losing data fidelity. When you zoom in on a line plot, W&B dynamically adjusts the bucket sizes used to calculate the minimum, maximum, and average values within each bucket, ensuring you can analyze specific regions and critical points like extreme values or spikes[^1].
By default, W&B divides the x-axis into 1000 dynami...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
is there an example of how to use Launch on Sagemaker using BYOI (Bring your own image)?
<wandbot.chat.chat.Chat object at 0x11b898260>
Yes, you can use W&B Launch on SageMaker with your own Docker image (Bring Your Own Image - BYOI).
Here's how to configure it:
**1. Prerequisites**[^1], [^8]
Before you begin, ensure you have the following AWS resources configured in your preferred AWS region:
* An ECR repository to store your container images.
* One or more S3 buckets to store inputs and outputs for your SageMaker Training jobs.
* An IAM role for Amazon SageMaker that permits SageMaker to run training jobs and interact ...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
how can I get the path to where an artifact is being written?
<wandbot.chat.chat.Chat object at 0x1377a03b0>
You can obtain the local path to an artifact after it has been downloaded.
Here's how you can do it:
1. **Using `artifact.download()`**:
After you've used `run.use_artifact()` to fetch an artifact, you can call the `download()` method on the artifact object. This method downloads all the contents of the artifact and returns the local directory path where the contents are stored[^4], [^5].
```python
import wandb
# Initialize a W&B run
run = wandb.init(project="my-project"...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
does weights and balances have an integration with LangChain?
<wandbot.chat.chat.Chat object at 0x122acae70>
Yes, Weights & Biases has an integration with LangChain, designed to help you visualize and inspect the execution flow of your LLMs, analyze inputs and outputs, view intermediate results, and securely store and manage your prompts and LLM chain configurations[^1], [^2].
Here's how you can integrate LangChain with Weights & Biases:
### Installation
First, install the necessary libraries:
```python
!pip install "wandb>=0.15.4" -qqq
!pip install "langchain>=0.0.218" openai -qqq
```
Ensure your...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
I am logging some metrics using wandb.log method and I have runs grouped by Group. Within that group each run has the metrics logged. While analyzing the runs in the table format on the UI previously before some days it would show average metric of the runs within the group next to the group name, but it's showing basically nothing now. Has anything changed in wandb ?
<wandbot.chat.chat.Chat object at 0x168ca3470>
It sounds like you're referring to the functionality where W&B can display averaged metric values for grouped runs directly in the UI. While the provided context doesn't explicitly detail a recent change that would remove this feature from the run table, it does explain how to visualize average values on plots by using the grouping feature.
To see averaged values in your graphs, you can use the Grouping feature in the table:
1. Navigate to your project page in the W&B App.
2. Locate the run t...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
In the Runs view / Workspace, what button do we push to hide the sidebar with the list of all the run names?
<wandbot.chat.chat.Chat object at 0x124614fb0>
The provided context describes how to expand the run sidebar into a full table view, but it does not explicitly mention a button to hide or collapse the sidebar with the list of all run names.
You can expand the sidebar into a full table by:
- Clicking the **Expand button**[^2].
- Selecting the **table icon** in the upper-right of the Run sidebar[^8].
**Sources**:
- [^2]: [Workspace tab](https://docs.wandb.ai/en/en/guides/models/track/project-page)
- [^8]: [Remove tags from one or more runs](h...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
what are best practices for logging artifacts that optimally wont consume all of my storage.
<wandbot.chat.chat.Chat object at 0x12cef8890>
To optimally manage storage consumption when logging artifacts in Weights & Biases, consider the following best practices:
**Best Practices for Storage Optimization**:
* **Use Reference Artifacts**: Instead of uploading files directly to W&B storage, use reference artifacts to track files saved outside the W&B system (e.g., in Amazon S3, GCS, Azure blobs, or other external storage). This means the artifact only stores metadata like URLs, size, and checksums, while the underlying data remains...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
Can public cloud customers make W&B reports accessible to their colleagues without W&B access?
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Yes, public cloud customers can make W&B reports accessible to their colleagues without W&B access through several methods:
* **Make the project public**: Reports created within a project can be made visible to anyone if the project itself is set to "Public". You can do this by accessing the project page in the Weights & Biases web app, clicking the lock icon in the navigation bar, choosing "Public", and saving the changes[^1], [^2].
* **Share a view-only link**: If making the entire projec...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
How can I stop a run in the web interface?
<wandbot.chat.chat.Chat object at 0x11b898260>
To stop a run in the W&B web interface:
1. Navigate to the project where your run is logging.
2. Select the run you want to stop from within the run selector.
3. Choose the **Overview** tab from the project sidebar.
4. Select the top button located next to the **State** field. The run's state will change from `running` to `Killed`[^1], [^2].
**Sources**:
* [^1]: [Stop a run](https://docs.wandb.ai/en/en/guides/models/track/runs)
* [^2]: [Stop a run](https://docs.wandb.ai/ko/ko/guides/m...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
Can you introduce me wandb? I'm a beginner.
<wandbot.chat.chat.Chat object at 0x124614fb0>
Weights & Biases (W&B) is an AI developer platform designed to help with training, fine-tuning, and leveraging foundation models. It offers tools for machine learning experiment tracking, dataset versioning, and project collaboration, featuring interactive dashboards for visualizing your experiments[^2], [^3], [^4], [^5].
W&B consists of three major components:
* **Models**
* **Weave**
* **Core**[^1]
### Getting Started with W&B
To begin using W&B, follow these steps:
1. **Install the...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
Hello, I am trying to save the gradients as I would with WandbCallback, but with a custom callback:
Is this correct?
class GradientClipperCallback(tf.keras.callbacks.Callback):
def init(self, model):
self.model = model
def on_epoch_end(self, epoch, logs=None):
trainable_vars = self.model.trainable_variables
with tf.GradientTape() as tape:
y_pred = self.model(self.model.inputs, training=False)
gradients = tape.gradient(y_pred, trainable_vars)...
<wandbot.chat.chat.Chat object at 0x12cef8890>
Your custom `GradientClipperCallback` has a few areas that need adjustment to correctly compute and log gradients in a Keras/TensorFlow context.
Here's a breakdown of the issues and a corrected approach:
1. **Constructor Name**: The constructor method should be `__init__` (double underscores), not `init`.
2. **Model Access**: For Keras callbacks, the `set_model` method is where the model is provided to the callback.
3. **Gradient Computation**:
* Gradients are typically computed with ...
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wandbot-v1.3.1_re-test-v56_1k-thinking-resp_flash-lite-stable
en
I have a question about sweeps. How can you constrain relationship between parameters. For example, I now that if `num_layers * hidden_dim` is large, I'll run out of GPU memory. So, also I would like to explore some hyperparameter space, there are some combination I know will fail. `optuna` as a way to do that: you can throw an special exception to cancel a run during a sweep, so that it is not recorded. Is there something similar in W&B, or another way of pruning unwanted combination of hyp...
<wandbot.chat.chat.Chat object at 0x1377a03b0>
The provided context describes how to define a sweep configuration, initialize a sweep, and run a sweep agent, including specifying hyperparameters with ranges or discrete values, and choosing search strategies like grid, random, or Bayesian search[^1][^2][^3][^4][^5][^6][^7][^8][^9][^10][^11][^12].
However, the provided documentation does not contain information on how to constrain relationships between parameters (e.g., `num_layers * hidden_dim` being too large) or how to prune unwanted combi...
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