title: Memory efficient data loading with Dask
body: <p>Well, what I have is a big CSV file with data and a bottleneck of server RAM. Besides that, there is a dask-distributed cluster that looks like a solution for this case, dask-scheduler is running on the server. Theres what I have tried:</p>
  <pre><code>import dask.dataframe as dd
  import pandas as pd
  from dask.bag import from_sequence
  
  cheques = dd.read_csv('cheque_data.csv') # not working because of distributed workers can't access file directly
  cheques = from_sequence(pd.read_csv('cheque_data.csv',chunksize=10**4)).to_dataframe() # dask Bag from_sequence constructs from tuples or dict
  </code></pre>
  <p>So I have stucked here, any ideas and clues would be great</p>