[WIP][Docs] Getting Started With TVM Tutorial #7612
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| ################################################################################ | ||
| # Installing from TLC Pack | ||
| # ------------------------ | ||
| # TVM is packaged and distributed as part of the volunteer TLCPack community. |
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Can you show the pip install command for tlcpack here?
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The reason I didn't insert this was because there are so many different strings to choose from, and pip nightly isn't available yet (which until we have a 0.8 release would be the preferred way to do this). I think that adding it once our TLCPack story has stabilized is the right thing to do.
| # - Ability to allow the user to mix the two programming styles | ||
| # | ||
| # Relay (or more detailedly, its fusion pass) is in charge of splitting the | ||
| # neural network into small subgraphs, each of which is a task. |
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You introduce task here. Maybe define it?
| # 3. Lower to *TE*, tensor expressions that define the *computational | ||
| # operations* of the neural network. | ||
| # Upon completing the import and high level optimizations, the next step it | ||
| # to decide how to implement the Relay representation to a hardware target. |
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I don't think implement is the correct word here. Lower is what you want, but you define it later on. Maybe 'convert'?
| # The diagram below illustrates the steps a machine model takes as it is | ||
| # transformed with the TVM optimizing compiler framework. | ||
| # | ||
| # .. image:: /_static/img/tvm.png |
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Thanks Chris, please send the binary image to a separate repo(e.g. https://github.com/tlc-pack/web-data) and refer to it using a https link.
We cannot checkin binary data(images) into the code repo
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I'm going to make all of the changes based on suggestions, then will send a new collection of patch-sets up.
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Thanks @hogepodge , it would be great to send changes of each tutorial file(.py) as separate PRs, so they are easier to review, and we can merge each one incrementally. |
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yeah there is definitely a lot here but generally I like the direction this is going. I didn't read all of the tutorials, but here's a few comments. is it possible to provide a render of the tutorials on a GH repo somewhere (or include with this as a temporary patch)?
| # - Ability to allow the user to mix the two programming styles | ||
| # | ||
| # Relay applies several high-level optimization to the model, after which | ||
| # is runs the Relay Fusion Pass. To aid in the process of converting to |
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what's the Relay Fusion pass?
| # Change this target to the correct backend for you gpu. For example: cuda (NVIDIA GPUs), rocm (Radeon GPUS), OpenGL (???). | ||
| tgt_gpu = "cuda" | ||
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| # Recreate the schedule |
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Do you mean the four spaces? I've wrapped this in an "if" block to make it optional. I think this part needs more work, but I'd like to take it up on a larger extension that talks about targeting GPUs
| # In our example, the device code is stored in ptx, as well as a meta | ||
| # data json file. They can be loaded and linked separately via import. | ||
| # | ||
| # The CPU (host) module is directly saved as a shared library (.so). There |
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I guess i'd also generally support a split of tutorials at each point where we could save a meaningful artifact to disk. e.g. after Relay import, after tvm.relay.build.
Co-authored-by: Tristan Konolige <tristan.konolige@gmail.com>
Co-authored-by: Tristan Konolige <tristan.konolige@gmail.com>
Co-authored-by: Tristan Konolige <tristan.konolige@gmail.com>
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New patch set for introduction and installation: #7638 |
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New patch set for tvmc #7640 |
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Closed as this work is covered in the PRs indicated above. |
This patch refactors a number of tutorials, and adds additional documents, to create a Getting Started with TVM Tutorial. The aim of the tutorial is to guide a new user of TVM through the features and architecture of the project. It starts with a basic overview of TVM, followed by some guidance on how to install TVM.
It then works through examples of how to optimize a ResNet-50v2 models, using TVMC and the Python interface. Having given a high level example of what TVM is capable of, it then takes a bottom-up approach of describing TE using vector addition and matrix multiplication, then continues to build on the matrix multiplication example to describe templates, AutoTVM, and Auto Scheduling. Finally, some additional information about targeting GPUs and RPCs is described.