Posts (page 182)
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7 min readThere are several ways to share memory between Java and Rust. One of the common methods is using the Java Native Interface (JNI) to call Rust functions from Java code. By defining functions in Rust that utilize the extern keyword and then loading the Rust dynamic library in Java using JNI, you can pass data back and forth between the two languages.Another method is using the Foreign Function Interface (FFI) provided by Rust.
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5 min readIn PyTorch, the grad() function is used to calculate the gradient of a tensor with respect to a graph of computations. This function is typically used in conjunction with autograd, which enables automatic differentiation of tensors. When you call grad() on a tensor, PyTorch will compute the gradient by tracing back through the operations that created the tensor, and then calculating the gradients of those operations with respect to the input tensor.
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5 min readIn Hadoop, the shuffle phase starts immediately after the map phase is completed. This phase is responsible for transferring data from the mappers to the reducers by grouping and sorting the output data based on the keys. The shuffle phase plays a crucial role in distributing and organizing the map output data so that it can be processed efficiently by the reducers.
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4 min readTo fetch binary columns from MySQL in Rust, you can use the mysql crate which provides a native MySQL client for Rust. You will first need to establish a connection to the MySQL database using the mysql::Pool object. Then, you can execute a query to fetch the binary columns from the database table.When fetching binary columns, you can use the Row::take method to extract binary data from the result set.
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7 min readTo properly minimize two loss functions in PyTorch, you can combine the two loss functions into a single loss function that you aim to minimize. One common approach is to sum or average the two individual loss functions to create a composite loss function.You can then use an optimizer, such as Stochastic Gradient Descent (SGD) or Adam, to minimize this composite loss function.
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4 min readTo mount Hadoop HDFS, you can use the FUSE (Filesystem in Userspace) technology. FUSE allows users to create a virtual filesystem without writing any kernel code. There are several FUSE-based HDFS mounting solutions available, such as hadoofuse and hadoop-fs. These solutions enable you to mount Hadoop HDFS as a regular filesystem on your local machine, allowing you to interact with HDFS data using standard filesystem operations like ls, cp, and mv.
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7 min readTo get data from Redisearch with Rust, you can use the Redis Rust client library called "redis-rs." First, you will need to add the library as a dependency in your project's Cargo.toml file. Then, you can establish a connection to your Redis server using the client provided by the library.Once you have established a connection, you can execute Redis commands to interact with Redisearch. Specifically, to get data from Redisearch, you will need to use the FT.
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4 min readTo increase GPU memory for PyTorch, you can start by optimizing your code to use memory efficiently. This includes carefully managing tensors, reducing unnecessary operations, and avoiding unnecessary copying of data between CPU and GPU.You can also adjust the batch size of your model or reduce the size of your input data to lower the memory usage.If you have access to a GPU with more memory, you can upgrade your hardware to increase the available GPU memory.
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4 min readTo output the top 100 results in Hadoop, you can use the MapReduce framework to write a custom job that will sort the data and then output only the top 100 results. You can achieve this by implementing a custom partitioner, comparator, and reducer to perform the sorting operation and then use a secondary sort technique to output only the top 100 results.
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5 min readTo import a file from a sibling folder in Rust, you can use the mod keyword to create a module for the sibling folder and then use the use keyword to import the file from that module. For example, if you have a sibling folder named models and a file named user.rs inside that folder, you can import it in your current file like this: mod models; use models::user; fn main() { // Use user module here } This will allow you to access the contents of the user.
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4 min readIn PyTorch, a data loader is a utility that helps with loading and batching data for training deep learning models. To define a data loader in PyTorch, you need to first create a dataset object that represents your dataset. This dataset object should inherit from PyTorch's Dataset class and override the len and getitem methods to provide the size of the dataset and to access individual samples from the dataset, respectively.
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4 min readTo delete an entry from a mapfile in Hadoop, you can use the Hadoop File System (HDFS) command hadoop fs -rmr <path-to-file>. This command will remove the specified entry from the mapfile in the Hadoop file system. Additionally, you can also use Hadoop MapReduce APIs to delete entries programmatically from a mapfile.