- Did I ever read this paper?
- Which OSDI 2021 papers did I read?
- Which ones have the word 'Distributed' in their titles?
- How many papers in 2020 were co-authored by Professor Mosharaf Chowdhury?
Well, ask reason.
$ reason
>> # Show all papers.
>> ls
+----------------------------------------------------------+----------------+---------+------+
| title | first author | venue | year |
+============================================================================================+
| Shadowtutor: Distributed Partial Distillation for Mobile | Jae-Won Chung | ICPP | 2020 |
| Video DNN Inference | | | |
|----------------------------------------------------------+----------------+---------+------|
| Efficient Memory Disaggregation with Infiniswap | Juncheng Gu | NSDI | 2017 |
|----------------------------------------------------------+----------------+---------+------|
| Refurbish Your Training Data: Reusing Partially | Gyewon Lee | ATC | 2021 |
| Augmented Samples for Faster Deep Neural Network | | | |
| Training | | | |
|----------------------------------------------------------+----------------+---------+------|
| Finding Consensus Bugs in Etherium via Multi-transaction | Youngseok Yang | OSDI | 2021 |
| Differential Fuzzing | | | |
|----------------------------------------------------------+----------------+---------+------|
| Tiresias: A GPU Cluster Manager for Distributed Deep | Juncheng Gu | NSDI | 2019 |
| Learning | | | |
|----------------------------------------------------------+----------------+---------+------|
| Nimble: Lightweight and Parallel GPU Task Scheduling for | Woosuk Kwon | NeurIPS | 2020 |
| Deep Learning | | | |
+----------------------------------------------------------+----------------+---------+------+
>> # Filter by 'title'. All these are regexes!
>> ls 'Deep Learning$'
+------------------------------------------------------------+--------------+---------+------+
| title | first author | venue | year |
+============================================================================================+
| Tiresias: A GPU Cluster Manager for Distributed Deep | Juncheng Gu | NSDI | 2019 |
| Learning | | | |
|------------------------------------------------------------+--------------+---------+------|
| Nimble: Lightweight and Parallel GPU Task Scheduling for | Woosuk Kwon | NeurIPS | 2020 |
| Deep Learning | | | |
+------------------------------------------------------------+--------------+---------+------+
>> # You may set default filters with `cd`.
>> # BTW, `cd .`, `cd ..`, `cd -`, and `cd` are supported, too.
>> cd 'Deep Learning$'
>> pwd
title matches 'Deep Learning$'
>> # Default filter are automatically applied.
>> # Infiniswap (NSDI'17) is not shown, because its title doesn't match 'Deep Learning$'.
>> ls at NSDI
+------------------------------------------------------------+--------------+---------+------+
| title | first author | venue | year |
+============================================================================================+
| Tiresias: A GPU Cluster Manager for Distributed Deep | Juncheng Gu | NSDI | 2019 |
| Learning | | | |
+------------------------------------------------------------+--------------+---------+------+
>> # Delete Tiresias.
>> ls at NSDI | rm
Removed 1 paper.
>> # Import directly from arXiv and USENIX. This will also download paper PDFs.
>> curl https://arxiv.org/abs/2105.11367
+--------------------------------------------------------+--------------+-------+------+
| title | first author | venue | year |
+======================================================================================+
| FedScale: Benchmarking Model and System Performance of | Fan Lai | arXiv | 2021 |
| Federated Learning | | | |
+--------------------------------------------------------+--------------+-------+------+
>> curl https://www.usenix.org/conference/nsdi21/presentation/you
+------------------------------------------+--------------+-------+------+
| title | first author | venue | year |
+========================================================================+
| Ship Compute or Ship Data? Why Not Both? | Jie You | NSDI | 2021 |
+------------------------------------------+--------------+-------+------+
>> # Modify paper metadata.
>> ls ship | set as Kayak
>> # Or, import manually.
>> touch 'Batch Normalization: Accelerating Deep Network Training by Reducing Internal
Covariate Shift' by 'Sergey Ioffe, Christian Szegedy' at ICML in 2015 as BN @ BatchNorm.pdf
+--------------------------------------------------------------+--------------+-------+------+
| title | first author | venue | year |
+============================================================================================+
| Batch Normalization: Accelerating Deep Network Training by | Sergey Ioffe | ICML | 2015 |
| Reducing Internal Covariate Shift | | | |
+--------------------------------------------------------------+--------------+-------+------+
>> # Open with a PDF viewer (`open`) and edit markdown notes with your editor (`ed`).
>> ls 'Why Not Both' | open | ed
+------------------------------------------+--------------+-------+------+
| title | first author | venue | year |
+========================================================================+
| Ship Compute or Ship Data? Why Not Both? | Jie You | NSDI | 2021 |
+------------------------------------------+--------------+-------+------+
>> # Format your markdown notes into an HTML book, and open it in your browser.
>> ls 'Deep Learning' | printf
+------------------------------------------------------------+--------------+---------+------+
| title | first author | venue | year |
+============================================================================================+
| Tiresias: A GPU Cluster Manager for Distributed Deep | Juncheng Gu | NSDI | 2019 |
| Learning | | | |
|------------------------------------------------------------+--------------+---------+------|
| Nimble: Lightweight and Parallel GPU Task Scheduling for | Woosuk Kwon | NeurIPS | 2020 |
| Deep Learning | | | |
+------------------------------------------------------------+--------------+---------+------+
printf will create an HTML book from your notes.
Invoking reason will start a new command prompt. It accepts unix-like commands that instead work on research papers in your paperbase.
Works now:
lsfilters and prints papers in table format. Default columns are title, first author(by1), venue(at), and year(in).cdadds an AND filter to the default set of filters (which is empty upon startup).pwdshows the current default filter set bycd.touchcreates a new entry in your paperbase.curlimports papers from the web, e.g. arXiv or usenix.org. It also downloads paper PDFs if available. Also experimentally supports downloading raw PDF urls and inferring metadata fields.rmremoves entries from your paperbase.setsets paper attributes, including custom labels that can also be used to give colors to papers inls.printfcreates an HTML page of your notes usingmdbook.openopens the paper with your PDF viewer (configurable, defaults to zathura).edopens your editor (configurable, defaults to vim), in which you can edit your notes.wccounts the number of papers.manplus a command will print documentation for that command.exitor Ctrl-d quitsreason.
Not yet, but hopefully soon (Contributions are more than welcome!):
grepreturns a list of papers whose notes contain the query string that you specify.sortsorts papers by given columns.statprints the metadata and notes of papers.topprints out a summary of your paperbase.
You can grab binaries from Releases, or you can run cargo install reason-shell.
reason currently supports Linux and MacOS. Windows is excluded because the owner does not currently own a Windows machine.
In order to share data between multiple platforms, users are encouraged to locate reason metadata, PDF files, and markdown notes in a location synced by cloud storage services such as Google Drive. I use the official Google Drive app on MacOS and Insync on Linux. This offers an extra benefit - you can read PDFs with your iPad, also synced with the cloud storage.
If you already have reason, run man man to view the top-level documentation.
If you're just exploring whether to use reason, take a look at the man directory.
The configuration file is kept at ~/.config/reason/config.toml. If not present, reason will generate one populated with default settings.
For more information, open reason and run man config, or read man/config.md.