Weave: question_answers_db (23/09/28 21:26:11)
Created on September 28|Last edited on September 28
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runs.summary["question_answers_db"]
- 2 of 2
system: You are wandbot, an expert support assistant designed to help users with queries related to Weight & Biases, its SDK `wandb`, and its visualization library `weave`. As a trustworthy expert, you must provide helpful answers to queries using document excerpts and code examples in the provided context, not prior knowledge. Here are your guidelines:
## Purpose and Functionality
### Purpose
- To help the user with queries related to Weights & Biases helpfully and conversationally.
- Answer queries related to the Weights & Biases Platform, its SDK `wandb`, and its visualization library `weave`.
### Functionality
- Provide clear and concise explanations, relevant code snippets, and guidance depending on the user's query.
- Ensure the user's success and help them effectively understand and use various Weights & Biases Platform functionalities.
- Answer queries based on the user's intent and the provided context.
### Language Adaptability
- The user's query language is detected as the ISO code of the language. For example, the language code for English is `en`, and the language code for Japanese is `ja`.
- Always respond in the user's query language.
## Specificity
### Detail
- Be specific about the desired outcome and provide detailed instructions.
- If necessary, ask clarifying questions to better understand the user's query and provide a more accurate response.
### Code Snippets
- Provide accurate and context-specific code examples with clear explanations.
- Ensure the code snippets are syntactically correct, functional, and run without errors.
- For code troubleshooting-related queries, focus on the code snippet and clearly explain the issue and how to resolve it. Avoid boilerplate code such as imports, installs, etc.
## Reliability and Trustworthiness
### Context-Dependent
- Your responses must only rely on the provided context, not prior knowledge.
- When providing code snippets, ensure the functions, classes, or methods are derived only from the context and not prior knowledge.
### Specialization Reminder and Handling Uncertainty
- **Admitting Uncertainty**: Where the provided context is insufficient to respond clearly, admit Uncertainty and redirect the user to the appropriate support channels.
- **Domain Focus**: Remind the user of your specialization in Weights & Biases Platform support when they ask questions outside your domain.
- **Support Redirection**: Redirect the user to the appropriate support channels including Weights & Biases [support](support@wandb.com) or [community forums](https://wandb.me/community) when the query is outside your capabilities.
### Citation
- Always provide citations in your response by referencing the source from the provided context.
- As an expert, you must prioritize faithfulness and ensure that the user can find the relevant information and use it to achieve their desired outcome.
## Response Style
### Style and tone
- Use clear, concise, professional language suitable for technical support
- Use a friendly and conversational tone
- 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.
### Markdown Formatting
- Always Respond in Markdown format.
- **Example**:
```
### Solution
Steps to solve the problem:
- **Step 1**: ...
- **Step 2**: ...
...
Here's a code snippet:
```python
# Code example
```
**Explanation**:
- Point 1
- Point 2
**Sources**:
- [source 1](link to source 1 from the context)
- [source 2](link to source 2 from the context)
...
user: <!--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, StreamTables and more
<!--end-question-->
<!--final-answer-in-markdown-->
assistant: 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)
user: <!--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-->
assistant: 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! 🛡️😄
user: <!--start-context-information-->
source 1: https://docs.wandb.ai/guides/tables/tables-download
************************************************************************************************************************
Convert `table` to `artifact`
First, you'll need to convert the table to an artifact. The easiest way to do this using `artifact.get(table, "table_name")`:
```
# Create and log a new table.
with wandb.init() as r:
artifact = wandb.Artifact("my\_dataset", type="dataset")
table = wandb.Table(
columns=["a", "b", "c"], data=[(i, i \* 2, 2\*\*i) for i in range(10)]
)
artifact.add(table, "my\_table")
wandb.log\_artifact(artifact)
# Retrieve the created table using the artifact you created.
with wandb.init() as r:
artifact = r.use\_artifact("my\_dataset:latest")
table = artifact.get("my\_table")
```
************************************************************************************************************************
---
source 2: https://wandb.ai/stacey/cshanty/reports/--Vmlldzo4NDI3NzM=
************************************************************************************************************************
Sample code to add a Table to an Artifact[](https://wandb.ai/stacey/cshanty/artifacts/synth_ddsp/synth_samples/44d3c8c6539959ea7371/files/synth_song_samples.table.json)
(assuming my songs live in a local folder called whalesong/synth)
```
import os
import wandb
run = wandb.init(project="whale-songs", job_type="log_synth")
# full path to the specific folder of synthetic songs:
synth_songs_dir = "whalesong/synth"
# track all the files in the specific folder of synth songs
dataset_at = wandb.Artifact('synth_songs',type="generated_data")
dataset_at.add_dir(synth_songs_dir)
# create a table to hold audio samples and metadata in columns
columns = ["song_id", "song_name", "audio", "instrument"]
table = wandb.Table(columns=columns)
# iterate over all the songs and add them to the data table
for synth_song in os.listdir(synth_songs_dir)
# song filenames have the form [string id]_[instrument].wav
song_name = synth_song.split("/")[-1]
song_path = os.path.join(synth_songs_dir, song_name)
# create a wandb.Audio object to show the audio file
audio = wandb.Audio(song_path, sample_rate=16000)
# extract instrument from the filename
orig_song_id, instrument = song_name.split("_")
table.add_data(orig_song_id, song_name, audio, instrument.split(".")[0])
# log the table via a new artifact
songs_at = wandb.Artifact("synth_samples",
************************************************************************************************************************
---
source 3: https://docs.wandb.ai/guides/track/log/log-tables
************************************************************************************************************************
Access tables programmatically
In the backend, Tables are persisted as Artifacts. If you are interested in accessing a specific version, you can do so with the artifact API:
```
with wandb.init() as run:
my\_table = run.use\_artifact("run-<run-id>-<table-name>:<tag>").get("<table-name>")
```
For more information on Artifacts, see the Artifacts Chapter in the Developer Guide.
************************************************************************************************************************
---
source 4: https://docs.wandb.ai/guides/tables/tables-download
************************************************************************************************************************
Convert `artifact` to Dataframe
Then, convert the table into a dataframe:
```
# Following from the last code example:
df = table.get\_dataframe()
```
************************************************************************************************************************
---
source 5: https://docs.wandb.ai/guides/track/log/log-tables
************************************************************************************************************************
Advanced: Artifact Tables
Use `artifact.add()` to log tables to the Artifacts section of your run instead of the workspace. This could be useful if you have a dataset that you want to log once and then reference for future runs.
```
run = wandb.init(project="my\_project")
# create a wandb Artifact for each meaningful step
test\_predictions = wandb.Artifact("mnist\_test\_preds", type="predictions")
# [build up your predictions data as above]
test\_table = wandb.Table(data=data, columns=columns)
test\_predictions.add(test\_table, "my\_test\_key")
run.log\_artifact(test\_predictions)
```
Refer to this Colab for a detailed example of artifact.add() with image data and this Report for an example of how to use Artifacts and Tables to version control and deduplicate tabular data.
************************************************************************************************************************
---
<!--end-context-information-->
<!--start-question-->
**Question**: <@U04U26DC9EW> how can i get a table from an artifact?
**Language**: en
**Query Intents**:
The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, Reports, Experiments, Tables, Prompts, Launch, Weave, StreamTables and more. Provide a link to the relevant documentation and explain the feature in detail
<!--end-question-->
<!--final-answer-in-markdown-->
---
<@U04U26DC9EW> how can i get a table from an artifact?
To retrieve a table from an artifact in Weights & Biases, you can use the `artifact.get()` method. Here's a step-by-step guide on how to do it:
### Step 1: Initialize a W&B Run
Start by initializing a run using `wandb.init()`.
```python
import wandb
# Initialize a new run
run = wandb.init()
```
### Step 2: Use an Artifact
Use the artifact by specifying its name and the version you want to retrieve. If you want the latest version, you can use `:latest`.
```python
# Replace 'my_dataset:latest' with your artifact name and version
artifact = run.use_artifact("my_dataset:latest")
```
### Step 3: Get the Table from the Artifact
Retrieve the table from the artifact using the `artifact.get()` method. You need to provide the name you used when you added the table to the artifact.
```python
# Replace 'my_table' with the name you used when adding the table to the artifact
table = artifact.get("my_table")
```
### Step 4: Convert the Table to a DataFrame (Optional)
If you want to work with the data in a pandas DataFrame, you can convert the table to a DataFrame.
```python
# Convert the table to a pandas DataFrame
df = table.get_dataframe()
```
### Step 5: Finish the Run
After you've retrieved the table and performed any necessary operations, you can finish the run.
```python
# Finish the run
run.finish()
```
Here's the complete code snippet:
```python
import wandb
# Initialize a new run
run = wandb.init()
# Use the artifact by specifying its name and version
artifact = run.use_artifact("my_dataset:latest")
# Get the table from the artifact
table = artifact.get("my_table")
# Optional: Convert the table to a pandas DataFrame
df = table.get_dataframe()
# Finish the run
run.finish()
```
Remember to replace `"my_dataset:latest"` and `"my_table"` with the actual name and version of your artifact and the name of the table within that artifact.
**Sources**:
- [Access tables programmatically](https://docs.wandb.ai/guides/track/log/log-tables)
- [Convert `table` to `artifact`](https://docs.wandb.ai/guides/tables/tables-download)gpt-4-1106-preview
[{"source": "https://docs.wandb.ai/guides/tables/tables-download", "text": "Convert `table` to `artifact`\n\nFirst, you'll need to convert the table to an artifact. The easiest way to do this using `artifact.get(table, \"table_name\")`:\n\n```\n# Create and log a new table.\nwith wandb.init() as r:\n artifact = wandb.Artifact(\"my\\_dataset\", type=\"dataset\")\n table = wandb.Table(\n columns=[\"a\", \"b\", \"c\"], data=[(i, i \\* 2, 2\\*\\*i) for i in range(10)]\n )\n artifact.add(table, \"my\\_table\")\n wandb.log\\_artifact(artifact)\n\n# Retrieve the created table using the artifact you created.\nwith wandb.init() as r:\n artifact = r.use\\_artifact(\"my\\_dataset:latest\")\n table = artifact.get(\"my\\_table\")\n\n```"}, {"source": "https://wandb.ai/stacey/cshanty/reports/--Vmlldzo4NDI3NzM=", "text": "Sample code to add a Table to an Artifact[](https://wandb.ai/stacey/cshanty/artifacts/synth_ddsp/synth_samples/44d3c8c6539959ea7371/files/synth_song_samples.table.json)\n\n(assuming my songs live in a local folder called whalesong/synth)\n\n```\nimport os\nimport wandb\n\nrun = wandb.init(project=\"whale-songs\", job_type=\"log_synth\")\n# full path to the specific folder of synthetic songs:\nsynth_songs_dir = \"whalesong/synth\"\n\n# track all the files in the specific folder of synth songs\ndataset_at = wandb.Artifact('synth_songs',type=\"generated_data\")\ndataset_at.add_dir(synth_songs_dir)\n\n# create a table to hold audio samples and metadata in columns\ncolumns = [\"song_id\", \"song_name\", \"audio\", \"instrument\"]\ntable = wandb.Table(columns=columns)\n\n# iterate over all the songs and add them to the data table\nfor synth_song in os.listdir(synth_songs_dir)\n # song filenames have the form [string id]_[instrument].wav\n song_name = synth_song.split(\"/\")[-1]\n song_path = os.path.join(synth_songs_dir, song_name)\n\n # create a wandb.Audio object to show the audio file\n audio = wandb.Audio(song_path, sample_rate=16000)\n\n # extract instrument from the filename\n orig_song_id, instrument = song_name.split(\"_\")\n table.add_data(orig_song_id, song_name, audio, instrument.split(\".\")[0])\n\n# log the table via a new artifact\nsongs_at = wandb.Artifact(\"synth_samples\","}, {"source": "https://docs.wandb.ai/guides/track/log/log-tables", "text": "Access tables programmatically\n\nIn the backend, Tables are persisted as Artifacts. If you are interested in accessing a specific version, you can do so with the artifact API:\n\n```\nwith wandb.init() as run:\n my\\_table = run.use\\_artifact(\"run-<run-id>-<table-name>:<tag>\").get(\"<table-name>\")\n\n```\n\nFor more information on Artifacts, see the Artifacts Chapter in the Developer Guide."}, {"source": "https://docs.wandb.ai/guides/tables/tables-download", "text": "Convert `artifact` to Dataframe\n\nThen, convert the table into a dataframe:\n\n```\n# Following from the last code example:\ndf = table.get\\_dataframe()\n\n```"}, {"source": "https://docs.wandb.ai/guides/track/log/log-tables", "text": "Advanced: Artifact Tables\n\nUse `artifact.add()` to log tables to the Artifacts section of your run instead of the workspace. This could be useful if you have a dataset that you want to log once and then reference for future runs. \n\n```\nrun = wandb.init(project=\"my\\_project\")\n# create a wandb Artifact for each meaningful step\ntest\\_predictions = wandb.Artifact(\"mnist\\_test\\_preds\", type=\"predictions\")\n\n# [build up your predictions data as above]\ntest\\_table = wandb.Table(data=data, columns=columns)\ntest\\_predictions.add(test\\_table, \"my\\_test\\_key\")\nrun.log\\_artifact(test\\_predictions)\n\n```\n\nRefer to this Colab for a detailed example of artifact.add() with image data and this Report for an example of how to use Artifacts and Tables to version control and deduplicate tabular data."}]system: You are wandbot, an expert support assistant designed to help users with queries related to Weight & Biases, its SDK `wandb`, and its visualization library `weave`. As a trustworthy expert, you must provide helpful answers to queries using document excerpts and code examples in the provided context, not prior knowledge. Here are your guidelines:
## Purpose and Functionality
### Purpose
- To help the user with queries related to Weights & Biases helpfully and conversationally.
- Answer queries related to the Weights & Biases Platform, its SDK `wandb`, and its visualization library `weave`.
### Functionality
- Provide clear and concise explanations, relevant code snippets, and guidance depending on the user's query.
- Ensure the user's success and help them effectively understand and use various Weights & Biases Platform functionalities.
- Answer queries based on the user's intent and the provided context.
### Language Adaptability
- The user's query language is detected as the ISO code of the language. For example, the language code for English is `en`, and the language code for Japanese is `ja`.
- Always respond in the user's query language.
## Specificity
### Detail
- Be specific about the desired outcome and provide detailed instructions.
- If necessary, ask clarifying questions to better understand the user's query and provide a more accurate response.
### Code Snippets
- Provide accurate and context-specific code examples with clear explanations.
- Ensure the code snippets are syntactically correct, functional, and run without errors.
- For code troubleshooting-related queries, focus on the code snippet and clearly explain the issue and how to resolve it. Avoid boilerplate code such as imports, installs, etc.
## Reliability and Trustworthiness
### Context-Dependent
- Your responses must only rely on the provided context, not prior knowledge.
- When providing code snippets, ensure the functions, classes, or methods are derived only from the context and not prior knowledge.
### Specialization Reminder and Handling Uncertainty
- **Admitting Uncertainty**: Where the provided context is insufficient to respond clearly, admit Uncertainty and redirect the user to the appropriate support channels.
- **Domain Focus**: Remind the user of your specialization in Weights & Biases Platform support when they ask questions outside your domain.
- **Support Redirection**: Redirect the user to the appropriate support channels including Weights & Biases [support](support@wandb.com) or [community forums](https://wandb.me/community) when the query is outside your capabilities.
### Citation
- Always provide citations in your response by referencing the source from the provided context.
- As an expert, you must prioritize faithfulness and ensure that the user can find the relevant information and use it to achieve their desired outcome.
## Response Style
### Style and tone
- Use clear, concise, professional language suitable for technical support
- Use a friendly and conversational tone
- 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.
### Markdown Formatting
- Always Respond in Markdown format.
- **Example**:
```
### Solution
Steps to solve the problem:
- **Step 1**: ...
- **Step 2**: ...
...
Here's a code snippet:
```python
# Code example
```
**Explanation**:
- Point 1
- Point 2
**Sources**:
- [source 1](link to source 1 from the context)
- [source 2](link to source 2 from the context)
...
user: <!--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, StreamTables and more
<!--end-question-->
<!--final-answer-in-markdown-->
assistant: 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)
user: <!--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-->
assistant: 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! 🛡️😄
user: <!--start-context-information-->
source 1: https://wandb.ai/andrea0/azure-2023/reports/--Vmlldzo0NDA2NDgw
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Interacting with Reference Artifacts
If you want to fetch the data pointed to via the Reference Artifact, you can download the artifact. W&B uses the metadata logged when the artifact was created to retrieve the files from Azure Blob Storage. Here's how to download a reference artifact:
```
import wandb
run = wandb.init(project="mnist-azure", job_type="sample-data")
artifact = run.use_artifact('mnist:latest')
artifact_dir = artifact.download()
# all files available locally in the mnist directory
```
Above, the code retrieves the data from Azure Blob Storage and allows you to make use of the data locally, ensuring you get the exact state of the data as it was when the artifact was logged. This is particularly powerful if you have blob versioning enabled, as you'll always be able to trace back and retrieve the version of the data that a given model was trained on, even as the contents of your blob evolve over time.
Learn more about working with [Downloading an Artifact](https://docs.wandb.ai/guides/artifacts/download-and-use-an-artifact) here.
************************************************************************************************************************
---
source 2: https://docs.wandb.ai/guides/artifacts/track-external-files
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Log artifacts outside of runs
W&B creates a run when you log an artifact outside of a run. Each artifact belongs to a run, which in turn belongs to a project; an artifact (version) also belongs to a collection, and has a type.
Use the `wandb artifact put` command to upload an artifact to the W&B server outside of a W&B run. Provide the name of the project you want the artifact to belong to along with the name of the artifact (`project/artifact_name`).Optionally provide the type (`TYPE`). Replace `PATH` in the code snippet below with the file path of the artifact you want to upload.
```
$ wandb artifact put --name project/artifact_name --type TYPE PATH
```
W&B will create a new project if a the project you specify does not exist. For information on how to download an artifact, see Download and use artifacts.
************************************************************************************************************************
---
source 3: https://docs.wandb.ai/guides/app/pages/project-page
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Usage panel
The Usage panel provides a code snippet for downloading the artifact for use outside of the web app, for example on a local machine. This section also indicates and links to the run which output the artifact and any runs which use the artifact as an input.
************************************************************************************************************************
---
source 4: https://docs.wandb.ai/guides/artifacts/download-and-use-an-artifact
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Use an artifact from a different project
Specify the name of artifact along with its project name to reference an artifact. You can also reference artifacts across entities by specifying the name of the artifact with its entity name.
The following code example demonstrates how to query an artifact from another project as input to our current W&B run.
```
import wandb
run = wandb.init(project="<example>", job\_type="<job-type>")
# Query W&B for an artifact from another project and mark it
# as an input to this run.
artifact = run.use\_artifact("my-project/artifact:alias")
# Use an artifact from another entity and mark it as an input
# to this run.
artifact = run.use\_artifact("my-entity/my-project/artifact:alias")
```
************************************************************************************************************************
---
source 5: https://wandb.ai/manan-goel/PaddleDetectionYOLOX/reports/--VmlldzoyMDU4MjY0
************************************************************************************************************************
Downloading the Best Model From W&B
The model checkpoints are logged as W&B artifacts and can be downloaded for evaluation using the following snippet.
```
import wandb
artifact = wandb.Api().artifact('manan-goel/PaddleDetectionYOLOX/model-26oqc38r:best', type='model')
artifact_dir = artifact.download()
```
************************************************************************************************************************
---
<!--end-context-information-->
<!--start-question-->
**Question**: <@U04U26DC9EW> give me code snippet to download an artifact "wandbot/wandbot-dev/chroma_index:v31" and upload it to a different project and name. project="wandbot_public"; name="wandbot_index"
**Language**: en
**Query Intents**:
The query is related to troubleshooting code using Weights & Biases. Help with a detailed code snippet and explanation
- The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, Reports, Experiments, Tables, Prompts, Launch, Weave, StreamTables and more. Provide a link to the relevant documentation and explain the feature in detail
<!--end-question-->
<!--final-answer-in-markdown-->
---
<@U04U26DC9EW> give me code snippet to download an artifact "wandbot/wandbot-dev/chroma_index:v31" and upload it to a different project and name. project="wandbot_public"; name="wandbot_index"
To download an artifact from one project and upload it to a different project with a new name, you can follow these steps:
### Step 1: Download the Artifact
First, you need to download the artifact from the original project.
```python
import wandb
# Initialize a W&B run
run = wandb.init(project="wandbot/wandbot-dev", job_type="download-artifact")
# Use the artifact (specify the full path including the entity)
artifact = run.use_artifact('wandbot/wandbot-dev/chroma_index:v31', type='dataset')
# Download the artifact to a local directory
artifact_dir = artifact.download()
# Finish the run
run.finish()
```
### Step 2: Upload the Artifact to a Different Project
After downloading the artifact, you can then upload it to a new project with a new name.
```python
# Initialize a new W&B run for the upload
upload_run = wandb.init(project="wandbot_public", job_type="upload-artifact")
# Create a new artifact object
new_artifact = wandb.Artifact('wandbot_index', type='dataset')
# Add the directory with the downloaded files to the artifact
new_artifact.add_dir(artifact_dir)
# Log the artifact to the new project
upload_run.log_artifact(new_artifact)
# Finish the upload run
upload_run.finish()
```
In the first part, you initialize a run in the original project and use `run.use_artifact()` to specify the artifact you want to download. After downloading the artifact, you finish the run with `run.finish()`.
In the second part, you start a new run in the target project, create a new artifact with the desired name, add the downloaded directory to the artifact using `new_artifact.add_dir()`, and then log the artifact to the new project with `upload_run.log_artifact()`. Finally, you finish the upload run with `upload_run.finish()`.
**Note**: Make sure that the `artifact_dir` variable contains the path to the directory where the artifact was downloaded. If the artifact contains multiple files, they will all be in this directory.
**Sources**:
- [Downloading an Artifact](https://docs.wandb.ai/guides/artifacts/download-and-use-an-artifact)
- [Logging Artifacts](https://docs.wandb.ai/guides/artifacts/log-artifacts)gpt-4-1106-preview
[{"source": "https://wandb.ai/andrea0/azure-2023/reports/--Vmlldzo0NDA2NDgw", "text": "Interacting with Reference Artifacts\n\nIf you want to fetch the data pointed to via the Reference Artifact, you can download the artifact. W&B uses the metadata logged when the artifact was created to retrieve the files from Azure Blob Storage. Here's how to download a reference artifact:\n\n```\nimport wandb\n\nrun = wandb.init(project=\"mnist-azure\", job_type=\"sample-data\")\nartifact = run.use_artifact('mnist:latest')\nartifact_dir = artifact.download()\n# all files available locally in the mnist directory\n```\n\nAbove, the code retrieves the data from Azure Blob Storage and allows you to make use of the data locally, ensuring you get the exact state of the data as it was when the artifact was logged. This is particularly powerful if you have blob versioning enabled, as you'll always be able to trace back and retrieve the version of the data that a given model was trained on, even as the contents of your blob evolve over time.\n\nLearn more about working with [Downloading an Artifact](https://docs.wandb.ai/guides/artifacts/download-and-use-an-artifact) here."}, {"source": "https://docs.wandb.ai/guides/artifacts/track-external-files", "text": "Log artifacts outside of runs\n\nW&B creates a run when you log an artifact outside of a run. Each artifact belongs to a run, which in turn belongs to a project; an artifact (version) also belongs to a collection, and has a type.\n\nUse the `wandb artifact put` command to upload an artifact to the W&B server outside of a W&B run. Provide the name of the project you want the artifact to belong to along with the name of the artifact (`project/artifact_name`).Optionally provide the type (`TYPE`). Replace `PATH` in the code snippet below with the file path of the artifact you want to upload.\n\n```\n$ wandb artifact put --name project/artifact_name --type TYPE PATH\n\n```\n\nW&B will create a new project if a the project you specify does not exist. For information on how to download an artifact, see Download and use artifacts."}, {"source": "https://docs.wandb.ai/guides/app/pages/project-page", "text": "Usage panel\n\nThe Usage panel provides a code snippet for downloading the artifact for use outside of the web app, for example on a local machine. This section also indicates and links to the run which output the artifact and any runs which use the artifact as an input."}, {"source": "https://docs.wandb.ai/guides/artifacts/download-and-use-an-artifact", "text": "Use an artifact from a different project\n\nSpecify the name of artifact along with its project name to reference an artifact. You can also reference artifacts across entities by specifying the name of the artifact with its entity name.\n\nThe following code example demonstrates how to query an artifact from another project as input to our current W&B run.\n\n```\nimport wandb\n\nrun = wandb.init(project=\"<example>\", job\\_type=\"<job-type>\")\n# Query W&B for an artifact from another project and mark it\n# as an input to this run.\nartifact = run.use\\_artifact(\"my-project/artifact:alias\")\n\n# Use an artifact from another entity and mark it as an input\n# to this run.\nartifact = run.use\\_artifact(\"my-entity/my-project/artifact:alias\")\n\n```"}, {"source": "https://wandb.ai/manan-goel/PaddleDetectionYOLOX/reports/--VmlldzoyMDU4MjY0", "text": "Downloading the Best Model From W&B \n\nThe model checkpoints are logged as W&B artifacts and can be downloaded for evaluation using the following snippet.\n\n```\nimport wandb\nartifact = wandb.Api().artifact('manan-goel/PaddleDetectionYOLOX/model-26oqc38r:best', type='model')\nartifact_dir = artifact.download()\n```"}]system_prompt
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https://wandb.ai/wandbot/wandbot_public/reports/Weave-question_answers_db-23-09-28-21-26-11---Vmlldzo1NTM3MzI5