We are experiencing critical issues with Dask DataFrames when performing multiple lazy computations involving sort_values, concat, and iloc operations. Our Dask Graph typically exceeds 15 MB in size, and we are encountering the following specific problems:

Mid-Computation Error:
We frequently encounter the error "Requested dask.distributed scheduler but no Client active" during computations when we have over 1 million rows mid-computation. Interestingly, the same operations work fine on smaller datasets.
Post-Sort Operations Error:

After performing sort_values, if there are subsequent functions to be executed before compute(), we receive the error "cannot access local variable ‘divisions’ where it is not associated with a value."

Project Details:

Dask Version: 2024.5.2
Dask Scheduler Compute: 1 core, 1 GB memory
Dask Workers: 3, each with 1 core and 1 GB memory
Input CSV Size: 15 MB

We are seeking an expert with deep experience in Dask and distributed computing to help diagnose and resolve these issues. Your role will involve identifying the root cause of the errors and providing guidance or implementing solutions to ensure that our computations can run efficiently on larger datasets without encountering these problems.

Requirements:
– Proven experience with Dask, particularly in handling large-scale computations and optimizing Dask Graphs.
– Familiarity with distributed computing and memory management in Python.
– Ability to analyze and optimize code to prevent errors related to sort_values, concat, and iloc operations.

Budget: $300

Posted On: August 18, 2024 23:39 UTC
Category: Back-End Development
Skills:Dask

Country: India

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