Framework for abstracting Amiga debuggers and access to AmigaOS libraries and devices.

Overview

Framework for abstracting Amiga debuggers.

This project provides abstration to control an Amiga remotely using a debugger.

The APIs are not yet stable.

I include an end-user ready GUI tool based on this, amigaXfer, as a preview.

amigaXfer

This is a tool for data transfer between an Amiga and another computer using the serial port. No agent required on Amiga's side, as it uses the kickstart rom's debugger to take control of the Amiga.

There's multiple ways to get into this debugger. A simple one is through Workbench's debug menu, present when wb is loaded using loadwb -debug.

Selecting the Debug, RomWack or SAD menu option in Workbench 1.x/2.x/3.x will then enter the debugger and enable amigaXfer usage.

Alternatively, it is possible to bootstrap an Amiga for which no bootable disks are available.

https://rvalles.net/bootstrapping-an-amiga-without-a-bootable-amiga-floppy.html

amigaXfer runs on multiple platforms. Windows binaries are provided in release binary builds. Python 3.8+, PySerial and wxPython are required if running from sources.

It is able to e.g. read/write/compare floppies, install bootblocks, send/receive files and dump the kickstart rom.

Highlights:

  • Uses the kickstart's serial debugger, and thus it does not require an agent.
  • Supports RomWack (AmigaOS 1.x, 2.x) and SAD (AmigaOS 3.x) builtin debuggers.
  • High speed transfers; 512kbps possible on basic 68000 @ 7MHz A500.
  • Can be used to bootstrap an Amiga for which no bootable disks are available.
  • Checksums (CRC32/ISO-HDLC) used throughout to ensure transfer integrity.
You might also like...
In this project, we'll be making our own screen recorder in Python using some libraries.

Screen Recorder in Python Project Description: In this project, we'll be making our own screen recorder in Python using some libraries. Requirements:

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!
A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Note: This is an alpha (preview) version which is still under refining. nn-Meter is a novel and efficient system to accurately predict the inference l

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.
CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices.

CenterFace Introduce CenterFace(size of 7.3MB) is a practical anchor-free face detection and alignment method for edge devices. Recent Update 2019.09.

High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices.

Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer

CondenseNet: Light weighted CNN for mobile devices
CondenseNet: Light weighted CNN for mobile devices

CondenseNets This repository contains the code (in PyTorch) for "CondenseNet: An Efficient DenseNet using Learned Group Convolutions" paper by Gao Hua

Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

TF Watcher TF Watcher is a simple to use Python package and web app which allows you to monitor 👀 your Machine Learning training or testing process o

Comments
  • Crash with AmigaOS 3.2 / 68060 / Fastmem (I-cache)

    Crash with AmigaOS 3.2 / 68060 / Fastmem (I-cache)

    I'm very impressed with this project. Really marvellous and nicely laid out code.

    I am however seeing a crash when starting this on OS 3.2. I'm not sure if its OS3.2, MMULib or my accelerator card that might be causing the issue. The crash happens randomly transferring and running the snippets.

    OS3.2 has romwack.

    My hardware setup is a full 68060 with MMULib and 128Mb of SDRAM.

    Interestingly I can manually create a script and run AllocMem over and over but no issues. I'm happy to help dig into the whys but some hints might be useful.

    My end goal is to simply have a cross development environment with a serial cable.

    opened by terriblefire 23
Releases(1.1.2)
  • 1.1.2(Aug 21, 2022)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 or higher, 32bit or 64bit. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45/46/47 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    If doing the floppyless bootstrap process, as a free and open source alternative to Workbench disks, Nico Bendlin's HelloAmi will boot all the way up to Workbench. He kindly enabled the Workbench's debug menu on my request, which involved some research work on his end.

    Changes

    • SetupDialog
      • Open serial in exclusive mode if possible.
      • Support for interrupting the DEL-sending CrashEntry routine.
    • BootblockTool
      • Remove stale code from debug/optdebug bootblocks.
      • New "noboot" bootblock: Amiga won't boot it. DOS can still access.
    • RomTool
      • Fix: Progressbar progress display was not accurate.
    • Fix: Clear icache on code upload (020+). (Thanks to TerribleFire, issue #1)
    • Improved log output.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them. A makefile is provided.

    Note that this version has changed the assembly files. Re-copy or rebuild.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.1.2_win32.zip(11.72 MB)
  • 1.1.1(Jul 8, 2021)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 or higher, 32bit or 64bit. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45/46/47 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    If doing the floppyless bootstrap process, as a free and open source alternative to Workbench disks, Nico Bendlin's HelloAmi will boot all the way up to Workbench. He kindly enabled the Workbench's debug menu on my request, which involved some research work on his end.

    Changes

    • SetupDialog
      • Detect missing m68k objects.
      • Better UX on connection issues.
    • Bootblock Tool
      • New bootblocks:
        • exitstrap is a hack to actually exit strap's init routine.
        • warmdos is exitstrap + start dos via WarmCapture(). A curiosity.
    • DOS Tool (preview)
      • BUGFIX: Fixed crash with AmigaOS 2.x and setpatch.
      • File transfers can now be interrupted.
    • Improved log output.

    Thanks to Ralf Hoffmann for AmigaOS 2.x issue report and testing fix and Daniel Doran for pre-release testing.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them.

    Note that the assembler files have changed. Current objects are needed for the new library function calling mechanism (related to the fix for the setpatch issue with AmigaOS 2 mentioned above). Re-copy or rebuild.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.1.1_win32.zip(11.72 MB)
  • 1.1.0(May 18, 2021)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 or higher, 32bit or 64bit. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45/46 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    If doing the floppyless bootstrap process, as a free and open source alternative to Workbench disks, Nico Bendlin's HelloAmi will boot all the way up to Workbench. He kindly enabled the Workbench's debug menu on my request, which involved some research work on his end.

    Changes

    • SetupDialog
      • ResetFirst will reboot machine during connection.
        • Writing floppies is slightly faster in this environment, due to less tasks running.
        • DosTool not usable in this environment due to dos.library being not yet initialized.
        • Allows entry via non-critical guru right click.
    • FloppyTool
      • BUGFIX: Fixed tool not working at all and instead spitting FCh ioerr on some machines.
        • Thanks to Michael Kagerbauer for reporting issue and testing fix.
      • Disk2ADF will now retry reads 5 times before giving up.
      • More user friendly IO error reporting.
      • Thanks to Michael Kagerbauer for feedback on old IOERR reporting.
    • BootblockTool
      • Better error reporting.
    • BUGFIX: Fixed issue in workaround for WRITE_BYTE SAD bug (kick v39).
    • Workaround introduced for SAD reboot function ACK bug.
      • SAD doesn't check TSRE after writing ACK to SERDAT; reboot will interrupt ACK on a fast CPU.
      • Don't bother waiting for ACK.
    • Floppyless Bootstrap should now work on all kickstarts.
      • Tested on kickstart 34/37/39/40/45/46.
    • Size SetupDialog/RomTool windows to contents.
      • Thanks to Alexander Fritsch for feedback/screenshots on window sizing issues with some win7 themes.
    • Cleaned up tool startup/cleanup procedures for all tools.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them.

    Note that the assembler files have changed. Current objects are needed for the floppyXfer server bugfix. Re-copy or rebuild.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.1.0_win32.zip(11.61 MB)
  • 1.0.1(Apr 2, 2021)

    amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port with the Amiga.

    Binaries should work on Windows 7 32bit or higher. Any Amiga that has a Serial Port is supported; Kickstart 34/37/39/40/45 tested.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    Changes

    • RomTool
      • Initialization GUI work outside GUI thread issue fixed.
      • Kickstart detection logic is now slightly more clever.
      • Can now be interrupted mid-dumping.
      • Larger transfer blocks, faster dumping.
      • Timer added.
      • Debug text output added.
    • DosTool
      • Target directory can safely contain a trailing slash.
      • Buffer size scales with free RAM, up to 256KB. Faster.
      • Timer added.
    • FloppyTool
      • Progressbar added.
    • UI improvements.
    • Documentation improvements.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built objects from the Windows archive. Else, vasm or phxass will build them.

    Note: Reissued win32 zip, due to an issue unpacking it with win7. It does not appear to like advcomp'd zips.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.0.1-newzip_win32.zip(11.81 MB)
  • 1.0.0(Mar 25, 2021)

    First release of amigaXfer, an easy-to-use GUI tool for lightning fast disk/file transfers on the serial port.

    Binaries should work on Windows 7 32bit or higher.

    Read the README.txt in the archive for multiple methods of entry to the Amiga ROM debugger.

    For other systems, use source code. The dependencies are python 3.8+, pyserial and wxpython. For the 68000 code, it is possible to just copy the built blobs from the Windows archive. Else, vasm or phxass will build them.

    CAREFUL THAT NEWER VERSIONS ARE AVAILABLE. Anyone linking here: Please link the releases page instead of a specific release.

    Source code(tar.gz)
    Source code(zip)
    amigaXfer_1.0.0_win32.zip(11.91 MB)
Owner
Roc Vallès
Roc Vallès
Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Code for running simulations for the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Lin

Matthew Farrell 1 Nov 22, 2022
The codebase for our paper "Generative Occupancy Fields for 3D Surface-Aware Image Synthesis" (NeurIPS 2021)

Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021) Project Page | Paper Xudong Xu, Xingang Pan, Dahua Lin and Bo Dai GOF

xuxudong 97 Nov 10, 2022
Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman*, Gabri

224 Dec 29, 2022
Repository accompanying the "Sign Pose-based Transformer for Word-level Sign Language Recognition" paper

by Matyáš Boháček and Marek Hrúz, University of West Bohemia Should you have any questions or inquiries, feel free to contact us here. Repository acco

Matyáš Boháček 30 Dec 30, 2022
Official Repository for our ECCV2020 paper: Imbalanced Continual Learning with Partitioning Reservoir Sampling

Imbalanced Continual Learning with Partioning Reservoir Sampling This repository contains the official PyTorch implementation and the dataset for our

Chris Dongjoo Kim 40 Sep 18, 2022
mmfewshot is an open source few shot learning toolbox based on PyTorch

OpenMMLab FewShot Learning Toolbox and Benchmark

OpenMMLab 514 Dec 28, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 09, 2022
magiCARP: Contrastive Authoring+Reviewing Pretraining

magiCARP: Contrastive Authoring+Reviewing Pretraining Welcome to the magiCARP API, the test bed used by EleutherAI for performing text/text bi-encoder

EleutherAI 43 Dec 29, 2022
Exploiting a Zoo of Checkpoints for Unseen Tasks

Exploiting a Zoo of Checkpoints for Unseen Tasks This repo includes code to reproduce all results in the above Neurips paper, authored by Jiaji Huang,

Baidu Research 8 Sep 06, 2022
Husein pet projects in here!

project-suka-suka Husein pet projects in here! List of projects mysejahtera-density. Generate resolution points using meshgrid and request each points

HUSEIN ZOLKEPLI 47 Dec 09, 2022
This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled Time Series presented at Causal Analysis Workshop 2021.

signed-area-causal-inference This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled

Will Glad 1 Mar 11, 2022
Fewshot-face-translation-GAN - Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.

Few-shot face translation A GAN based approach for one model to swap them all. The table below shows our priliminary face-swapping results requiring o

768 Dec 24, 2022
Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, Daniel Silva, Andrew McCallum, Amr Ahmed. KDD 2019.

gHHC Code for: Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. Nicholas Monath, Manzil Zaheer, D

Nicholas Monath 35 Nov 16, 2022
Data Augmentation Using Keras and Python

Data-Augmentation-Using-Keras-and-Python Data augmentation is the process of increasing the number of training dataset. Keras library offers a simple

Happy N. Monday 3 Feb 15, 2022
STEAL - Learning Semantic Boundaries from Noisy Annotations (CVPR 2019)

STEAL This is the official inference code for: Devil Is in the Edges: Learning Semantic Boundaries from Noisy Annotations David Acuna, Amlan Kar, Sanj

469 Dec 26, 2022
Gesture Volume Control Using OpenCV and MediaPipe

This Project Uses OpenCV and MediaPipe Hand solutions to identify hands and Change system volume by taking thumb and index finger positions

Pratham Bhatnagar 6 Sep 12, 2022
Deep Face Recognition in PyTorch

Face Recognition in PyTorch By Alexey Gruzdev and Vladislav Sovrasov Introduction A repository for different experimental Face Recognition models such

Alexey Gruzdev 141 Sep 11, 2022
Code for our ACL 2021 paper "One2Set: Generating Diverse Keyphrases as a Set"

One2Set This repository contains the code for our ACL 2021 paper “One2Set: Generating Diverse Keyphrases as a Set”. Our implementation is built on the

Jiacheng Ye 63 Jan 05, 2023
Evaluation framework for testing segmentation networks in PyTorch

Evaluation framework for testing segmentation networks in PyTorch. What segmentation network to choose for next Kaggle competition? This benchmark knows the answer!

Eugene Khvedchenya 37 Apr 27, 2022
Annealed Flow Transport Monte Carlo

Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud

DeepMind 30 Nov 21, 2022