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*.pt | ||
*.mp4 | ||
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guide/book | ||
guide/book |
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# Downloading the Argoverse 2 Datasets | ||
Our datasets are available for download from [AWS S3](https://aws.amazon.com/s3/). For the best experience, we highly recommend using the open-source [s5cmd](https://github.com/peak/s5cmd) tool to transfer the data to your local filesystem (additional info available [here](https://aws.amazon.com/blogs/opensource/parallelizing-s3-workloads-s5cmd/)). Please note that an AWS account is not required to download the datasets. | ||
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### Installing `s5cmd` | ||
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`s5cmd` can be easily installed with the following script: | ||
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```bash | ||
#!/usr/bin/env bash | ||
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export INSTALL_DIR=$HOME/.local/bin | ||
export PATH=$PATH:$INSTALL_DIR | ||
export S5CMD_URI=https://github.com/peak/s5cmd/releases/download/v1.4.0/s5cmd_1.4.0_$(uname | sed 's/Darwin/macOS/g')-64bit.tar.gz | ||
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mkdir -p $INSTALL_DIR | ||
curl -sL $S5CMD_URI | tar -C $INSTALL_DIR -xvzf - s5cmd | ||
``` | ||
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Note that it will install `s5cmd` in your local bin directory. You can always change the path if you prefer installing it in another directory. | ||
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### Downloading Datasets | ||
Once `s5cmd` is installed installed, downloading a dataset is as easy as running the following (using the sensor dataset as an example): | ||
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```bash | ||
s5cmd --no-sign-request cp "s3://argoai-argoverse/av2/sensor/*" target-directory | ||
``` | ||
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The command will download all S3 objects to the target directory (for example, `target-directory` can be `/home/av2/sensors/`). Given the size of the dataset, it might take a couple of hours depending on the network connectivity. | ||
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When the download is finished, the dataset is ready to use! | ||
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### Dataset S3 Locations | ||
```bash | ||
s3://argoai-argoverse/av2/sensor/ | ||
s3://argoai-argoverse/av2/lidar/ | ||
s3://argoai-argoverse/av2/motion-forecasting/ | ||
s3://argoai-argoverse/av2/tbv/ | ||
``` |
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# Installation | ||
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We _highly_ recommend using `conda` with the `conda-forge` channel for package management. | ||
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## Install `conda` | ||
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You will need to install `conda` on your machine. We recommend to install the `conda-forge` version of `conda` found at https://github.com/conda-forge/miniforge#install. | ||
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## Install `av2` | ||
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Simply run: | ||
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```bash | ||
bash install.sh | ||
``` | ||
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which will install _all_ of the necessary dependencies in a conda environment named `av2`. | ||
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To activate your environment (i.e., update your system paths), run: | ||
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```bash | ||
conda activate av2 | ||
``` | ||
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## FAQ | ||
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> Why manage dependencies in `conda` instead of `pip`? | ||
`conda` enables package management outside of the `python` ecosystem. This enables us to specify all necessary dependencies in `environment.yml`. Further, gpu-based packages (e.g., `torch`) are handled better through `conda`. | ||
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> Why `conda-forge`? | ||
`conda-forge` is a community-driven channel of conda recipes. It includes a large number of packages which can all be properly tracked in the `conda` resolver allowing for consistent environments without conflicts. |
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