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remove github submodule
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.github/workflows/push-docker-image.yml

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# Link to discussion: https://github.com/orgs/community/discussions/25678
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- name: Checkout
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uses: actions/checkout@v3
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with:
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submodules: true
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uses: actions/checkout@v3
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- name: Docker meta
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id: meta
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uses: crazy-max/ghaction-docker-meta@v2

Dockerfile

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@@ -35,8 +35,6 @@ RUN echo "export PATH=\"/opt/conda/bin:/root/.cargo/bin:\$PATH\"" >> /root/.bash
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# Install Python dependencies (The gradual copies help with caching)
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WORKDIR open_diloco
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RUN pip install --pre torchdata --index-url https://download.pytorch.org/whl/nightly/cpu
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COPY hivemind_source hivemind_source
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RUN pip install --no-cache-dir ./hivemind_source
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COPY requirements.txt requirements.txt
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RUN pip install --no-cache-dir -r requirements.txt
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COPY requirements-dev.txt requirements-dev.txt

README.md

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Install python dependencies:
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```bash
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# Hivemind
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cd hivemind_source
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pip install .
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cp build/lib/hivemind/proto/* hivemind/proto/.
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pip install -e ".[all]"
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cd ..
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# Requirements
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pip install -r requirements.txt
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# Others
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pip install --pre torchdata --index-url https://download.pytorch.org/whl/nightly/cpu
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pip install -e ./pydantic_config
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# OpenDiLoCo
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pip install .
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```
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Optionally, you can install flash-attn to use Flash Attention 2.
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This requires your system to have cuda compiler set up.
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```
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# (Optional) flash-attn
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pip install flash-attn==2.5.8
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pip install flash-attn>=2.5.8
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```
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## Docker container
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# Debugging Issues
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1. `hivemind` or `pydantic_config`
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If you are having issues with `hivemind` or `pydantic_config`, the issue could be related to submodules.
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You can clean and reinitialize the submodules from the root of the repository with the following commands:
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```
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git submodule deinit -f .
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git clean -xdf
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git submodule update --init --recursive
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```
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2. `RuntimeError: CUDA error: invalid device ordinal`
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1. `RuntimeError: CUDA error: invalid device ordinal`
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A possible culprit is that your `--nproc-per-node` argument for the torchrun launcher is set incorrectly.
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Please set it to an integer less than equal to the number of gpus you have on your machine.
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3. `torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate...`
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2. `torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate...`
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A possible culprit is that your `--per-device-train-batch-size` is too high.
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Try a smaller value.

hivemind_source

Submodule hivemind_source deleted from ad080ed

pydantic_config

Submodule pydantic_config deleted from 8e19e05

requirements.txt

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transformers~=4.40
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datasets>=2.19.1
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wandb==0.16.4
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wandb>=0.16.4
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cyclopts>=2.6.1
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fsspec[gcs]>=2024.3.1
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torch==2.3.1
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torch>=2.3.1
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hivemind @ git+https://github.com/learning-at-home/hivemind.git@213bff9
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pydantic_config @ git+https://github.com/samsja/pydantic_config.git@8e19e05
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