Update README.md

README.md

@@ -5,7 +5,7 @@
 </p>
 
 <p align="center">
-  <em><strong>LogiX</strong>: Logging for Interpretable and Explainable AI <br></em>
+  <em><strong>LogIX</strong>: Logging for Interpretable and Explainable AI <br></em>
 </p>
 
 <div align="center">
@@ -22,22 +22,22 @@ pip install logix-ai
 ```
 
 ## Usage
-LogiX is designed with the belief that diverse logs generated by neural networks, such as
+LogIX is designed with the belief that diverse logs generated by neural networks, such as
 gradient and activation, can be utilized for analyzing and debugging data, algorithms,
-and other aspects. To use LogiX, users simply adhere to a two-stage workflow:
+and other aspects. To use LogIX, users simply adhere to a two-stage workflow:
 
 1. **Logging**: Extract and save various logs (e.g. per-sample gradient, activation) to disk.
 2. **Analysis**: Load logs from disk and perform custom analysis (e.g. influence function).
 
 ### Logging
-Logging with LogiX is as simple as adding one `with` statement to the existing
-training code. LogiX automatically extracts user-specified logs using PyTorch hooks, and
+Logging with LogIX is as simple as adding one `with` statement to the existing
+training code. LogIX automatically extracts user-specified logs using PyTorch hooks, and
 saves it to disk using a memory-mapped file.
 
 ```python
 import logix
 
-run = logix.init(project="my_project") # initialze LogiX
+run = logix.init(project="my_project") # initialze LogIX
 logix.setup({"log": "grad", "save": "grad", "statistic": "kfac"}) # set logging config
 logix.watch(model) # add your model to log
 
@@ -77,12 +77,12 @@ frameworks (e.g. Lightning) in the future!
 
 ```python
 from transformers import Trainer, Seq2SeqTrainer
-from logix.huggingface import patch_trainer, LogiXArguments
+from logix.huggingface import patch_trainer, LogIXArguments
 
-logix_args = LogiXArguments(project, config, lora=True, ekfac=True)
-LogiXTrainer = patch_trainer(Trainer)
+logix_args = LogIXArguments(project, config, lora=True, ekfac=True)
+LogIXTrainer = patch_trainer(Trainer)
 
-trainer = LogiXTrainer(logix_args=logix_args, # pass LogiXArguments as TrainingArguments
+trainer = LogIXTrainer(logix_args=logix_args, # pass LogIXArguments as TrainingArguments
                         model=model,
                         train_dataset=train_dataset,
                         *args,
@@ -101,11 +101,11 @@ Please check out [Examples](/examples) for more advanced features!
 Logs from neural networks are difficult to handle due to the large size. For example,
 the size of the gradient of *each* training datapoint is about as large as the whole model. Therefore,
 we provide various systems support to efficiently scale neural network analysis to
-billion-scale models. Below are a few features that LogiX currently supports:
+billion-scale models. Below are a few features that LogIX currently supports:
 
 - **Gradient compression** (compression ratio: 1,000-100,000x)
 - **Memory-map-based data IO**
-- **CPU offloading of logs**
+- **CPU offloading of statistics**
 
 ## Compatability
 | DistributedDataParallel| Mixed Precision| Gradient Checkpointing | torch.compile  | FSDP           |
@@ -115,20 +115,20 @@ billion-scale models. Below are a few features that LogiX currently supports:
 ## Contributing
 
 We welcome contributions from the community. Please see our [contributing
-guidelines](CONTRIBUTING.md) for details on how to contribute to LogiX.
+guidelines](CONTRIBUTING.md) for details on how to contribute to LogIX.
 
 ## Citation
 To cite this repository:
 
 ```
 @software{logix2024github,
   author = {Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Minsoo Kang, Youngseog Chung, Kewen Zhao},
-  title = {{LogiX}: Scalable Logging and Analysis Tool for Neural Networks},
+  title = {{LogIX}: Scalable Logging and Analysis Tool for Neural Networks},
   url = {http://github.com/sangkeun00/logix},
   version = {0.0.1},
   year = {2024},
 }
 ```
 
 ## License
-LogiX is licensed under the [Apache 2.0 License](LICENSE).
+LogIX is licensed under the [Apache 2.0 License](LICENSE).