[Q]: Batch Deletion Of Logged Images

Introduction

During the training process, logging images extensively is a common practice. However, when the storage space is full of images, deleting the entire runs might not be the most desirable solution. Instead, deleting a significant portion of the logged images, such as those logged every 10 epochs, can be a more efficient way to clean up the storage. In this article, we will explore a more efficient way to achieve this using the internal GQL API.

Understanding the Current Approach

The current approach uses the run.scan_history method to scan the history of the run and delete the files one at a time. This method is inefficient because it deletes files individually, which can take a long time to complete. The code snippet provided is as follows:

history = run.scan_history(page_size=10000)
for h in history:
    if h["_step"] % 1000 == 0 and not h["_step"] % 10000 == 0:
        for obj in h.values():
            match obj:
                case {"_type": "image-file", "path": path}:
                    file = run.file(path)
                    file.delete()

This approach has several limitations:

• It deletes files one at a time, which can be time-consuming.
• It uses a page_size of 10,000, which may not be sufficient for large runs.
• It does not take advantage of the internal GQL API, which can provide more efficient and scalable solutions.

Using the Internal GQL API

The internal GQL API provides a more efficient way to delete files in bulk. We can use the run.files method to retrieve a list of files and then delete them in batches. Here's an example code snippet:

import gql

query = gql.gql(
    """
    query {
        files {
            path
        }
    }
    """
)

result = run.execute_query(query)

files = result["files"]

# Delete files in batches of 1000
for i in range(0, len(files), 1000):
    batch = files[i:i+1000]
    delete_query = gql.gql(
        """
        mutation {
            deleteFiles(input: {paths: []}) {
                deleted
            }
        }
        """
    )
    delete_query.variables = {"paths": [file["path"] for file in batch]}
    run.execute_mutation(delete_query)

This approach has several advantages:

• It deletes files in batches, which can be more efficient than deleting files one at a time.
• It uses the internal GQL API, which can provide more efficient and scalable solutions.
• It can handle large runs with ease.

Optimizing the Query

To further optimize the query, we can use the run.files method with a filter clause to retrieve only the files that match the desired criteria. For example:

query = gql.gql(
    """
    query {
        files(filter: {step: {_gt: 1000, _lt: 10000}}) {
            path
        }
    }
    """
)

This query retrieves only the files that were logged between steps 1000 and 10000, which be a more efficient way to delete the desired files.

Conclusion

Q: What is the best way to delete logged images in bulk?

A: The best way to delete logged images in bulk is to use the internal GQL API. This provides a more efficient way to delete files in bulk, rather than deleting files one at a time.

Q: How can I use the internal GQL API to delete logged images?

A: To use the internal GQL API to delete logged images, you can use the run.files method to retrieve a list of files and then delete them in batches. Here's an example code snippet:

import gql

query = gql.gql(
    """
    query {
        files {
            path
        }
    }
    """
)

result = run.execute_query(query)

files = result["files"]

# Delete files in batches of 1000
for i in range(0, len(files), 1000):
    batch = files[i:i+1000]
    delete_query = gql.gql(
        """
        mutation {
            deleteFiles(input: {paths: []}) {
                deleted
            }
        }
        """
    )
    delete_query.variables = {"paths": [file["path"] for file in batch]}
    run.execute_mutation(delete_query)

Q: How can I optimize the query to delete only the desired files?

A: To optimize the query to delete only the desired files, you can use the run.files method with a filter clause. For example:

query = gql.gql(
    """
    query {
        files(filter: {step: {_gt: 1000, _lt: 10000}}) {
            path
        }
    }
    """
)

This query retrieves only the files that were logged between steps 1000 and 10000.

Q: What are the advantages of using the internal GQL API to delete logged images?

A: The advantages of using the internal GQL API to delete logged images include:

• Efficiency: Deleting files in bulk is more efficient than deleting files one at a time.
• Scalability: The internal GQL API can handle large runs with ease.
• Flexibility: You can use the run.files method with a filter clause to retrieve only the desired files.

Q: What are the limitations of using the internal GQL API to delete logged images?

A: The limitations of using the internal GQL API to delete logged images include:

• Complexity: Using the internal GQL API requires a good understanding of GraphQL and the internal API.
• Error handling: You need to handle errors that may occur during the deletion process.
• Dependence on the internal API: The internal GQL API may change over time, which may affect your code.

Q: How can I troubleshoot issues with deleting logged images using the internal GQL API?

A: To troubleshoot issues with deleting logged images using the internal GQL API, you can:

• Check the error messages: Error messages can provide valuable information about what went wrong.
• Use the `run.execute_query method: This method allows you to execute a query and retrieve the results.
• Use the run.execute_mutation method: This method allows you to execute a mutation and retrieve the results.

Q: What are some best practices for deleting logged images using the internal GQL API?

A: Some best practices for deleting logged images using the internal GQL API include:

• Use the run.files method with a filter clause: This allows you to retrieve only the desired files.
• Delete files in batches: This can improve efficiency and scalability.
• Handle errors: You need to handle errors that may occur during the deletion process.