freelanceshack.com
-
5 min readTo filter data in pandas by a custom date, you can first convert the date column to a datetime data type using the pd.to_datetime() function. Then, you can use boolean indexing to filter the dataframe based on your custom date criteria. For example, you can create a boolean mask by comparing the date column to your custom date and then use this mask to filter the dataframe.
-
7 min readPyTorch models are typically stored in a file with a ".pth" extension, which stands for PyTorch. These files contain the state_dict of the model, which is a dictionary object that maps each layer in the model to its parameters. This includes weights, biases, and any other learnable parameters. The state_dict can be easily loaded into a PyTorch model using the load_state_dict() function.
-
4 min readTo use a function from a class in Python with pandas, you can create an instance of the class and then call the function using dot notation. For example, if you have a class called MyClass with a function called my_function, you can use it in pandas like this: import pandas as pd class MyClass: def my_function(self): return "Hello, world!" my_instance = MyClass() result = my_instance.my_function() df = pd.
-
5 min readTo rename classes of a trained model in PyTorch, you can modify the model's state_dict by changing the keys corresponding to the class names. You can do this by iterating through the state_dict and renaming the keys appropriately. This can be useful when you want to rename classes for easier interpretation or visualization of the model's output. Additionally, you may need to update the model's configuration or mapping to match the new class names.
-
6 min readIn PyTorch, you can properly reset the GPU memory by using the torch.cuda.empty_cache() function. This function clears the memory cache on the default CUDA device, releasing any unoccupied memory that was previously reserved by PyTorch. By calling this function, you can reclaim memory that is no longer in use, enabling more efficient memory management on the GPU. It is recommended to use this function periodically or whenever you notice that your GPU memory is becoming fragmented or overloaded.
-
4 min readIn pandas, you can count duplicates by using the duplicated() function followed by the sum() function.For example: import pandas as pd data = {'A': [1, 2, 2, 3, 4, 4, 4]} df = pd.DataFrame(data) print(df.duplicated().sum()) This will output the number of duplicates in the DataFrame df. You can also pass specific columns to the duplicated() function if you only want to check for duplicates in those columns.
-
3 min readTo increase the timeout for PyTorch, you can adjust the default timeout value in the torch.distributed.rpc library. This can be done by setting the environment variable TORCH_DISTRIBUTED_RPC_TIMEOUT to a higher value, such as 60 seconds. This will give more time for PyTorch processes to communicate and synchronize with each other before timing out.
-
7 min readTo check if a time-series belongs to last year using pandas, you can extract the year from the time-series data using the dt accessor and then compare it with the previous year. First, make sure the time-series data is of datetime type by converting it if necessary. Then, use the year attribute of the datetime object to extract the year from the data. Compare the extracted year with the current year - 1 to determine if the time-series belongs to last year.
-
5 min readTo get the CUDA compute capability of a GPU in PyTorch, you can use the following code snippet: import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(torch.cuda.get_device_capability(device.index)) This code will print the compute capability of the GPU currently being used by PyTorch for computations.
-
5 min readTo analyze the content of a column value in pandas, you can use various methods and functions available in the pandas library. For example, you can use the str accessor to perform operations on string values in a specific column, such as extracting substrings, counting occurrences of a particular substring, or checking for the presence of a certain pattern.
-
6 min readTo write a custom batched function in PyTorch, you can use the torch.autograd.Function class. This class allows you to define your own custom autograd functions in PyTorch. To create a custom batched function, you need to define a subclass of torch.autograd.Function and implement the forward and backward methods.In the forward method, you define the computation that your custom function performs on the input tensors.