Step 8: Parallel Computing
Running computations and processing data in parallel.
How to Accelerate Deep Lake Workflows with Parallel Computing
Deep Lake enables you to easily run computations in parallel and significantly accelerate your data processing workflows. This example primarily focuses on parallel dataset uploading.
Parallel computing use cases such as dataset transformations can be found in this tutorial.
Parallel compute using Deep Lake has two core steps:
Define a function or pipeline that will run in parallel and
Evaluate the function using the appropriate inputs and outputs.
Defining the parallel computing function
The first step is to define a function that will run in parallel by decorating it using @deeplake.compute
. In the example below, file_to_deeplake
converts data from files into Deep Lake format, just like in Step 2: Creating Hub Datasets Manually. If you have not completed Step 2, please download and unzip the example image classification dataset below:
In all functions decorated using @deeplake.compute
, the first argument must be a single element of any input iterable that is being processed in parallel. In this case, that is a filename file_name
, because file_to_deeplake
reads image files and populates data in the dataset's tensors.
The second argument is a dataset sample sample_out
, which can be operated on using similar syntax to dataset objects, such as sample_out.append(...)
, sample_out.extend(...)
, etc.
The function decorated using @deeplake.compute
must return sample_out
, which represents the data that is added or modified by that function.
Executing the parallel computation
To execute the parallel computation, you must define the dataset that will be modified.
Next, you define the input iterable that describes the information that will be operated on in parallel. In this case, that is a list of files files_list
:
You can now create the tensors for the dataset and run the parallel computation using the .eval
syntax. Pass the optional input arguments to file_to_deeplake
and skip the first two default arguments file_name
and sample_out
.
The input iterable files_list
and output dataset ds
is passed to the .eval
method as the first and second argument respectively.
Additional parallel computing use cases such as dataset transformations can be found in this tutorial.
Congrats! You just created a dataset using parallel computing! 🎈
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