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API." -category = "main" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "wandb-0.17.9-py3-none-any.whl", hash = "sha256:475da9aeb921f49352107d940a5d2a67e5bdc980d561fbdaf990309fe8faeaa4"}, {file = "wandb-0.17.9-py3-none-macosx_10_14_x86_64.whl", hash = "sha256:9c056e36f12e1a6e7a03cf2abc806264baad44697bda33b5d901a42dc39f1bf8"}, @@ -3804,7 +5239,7 @@ gitpython = ">=1.0.0,<3.1.29 || >3.1.29" platformdirs = "*" protobuf = [ {version = ">=3.15.0,<4.21.0 || >4.21.0,<6", markers = "python_version == \"3.9\" and sys_platform == \"linux\""}, - {version = ">=3.19.0,<4.21.0 || >4.21.0,<6", markers = "python_version > \"3.9\" or sys_platform != \"linux\""}, + {version = ">=3.19.0,<4.21.0 || >4.21.0,<6", markers = "sys_platform != \"linux\""}, ] psutil = ">=5.0.0" pyyaml = "*" @@ -3829,23 +5264,24 @@ workspaces = ["wandb-workspaces"] [[package]] name = "wcwidth" -version = "0.2.13" +version = "0.6.0" description = "Measures the displayed width of unicode strings in a terminal" -category = "dev" optional = false -python-versions = "*" +python-versions = ">=3.8" +groups = ["dev"] files = [ - {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, - {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, + {file = "wcwidth-0.6.0-py3-none-any.whl", hash = "sha256:1a3a1e510b553315f8e146c54764f4fb6264ffad731b3d78088cdb1478ffbdad"}, + {file = "wcwidth-0.6.0.tar.gz", hash = "sha256:cdc4e4262d6ef9a1a57e018384cbeb1208d8abbc64176027e2c2455c81313159"}, ] [[package]] name = "webdataset" version = "0.2.100" description = "Record sequential storage for deep learning." -category = "main" optional = false python-versions = ">=3.6" +groups = ["main"] +markers = "python_version == \"3.9\"" files = [ {file = "webdataset-0.2.100-py3-none-any.whl", hash = "sha256:f70a8e1f6d4f5268b364bd6f77fe8a1168ea14e7e9ed455d71f8d29585fd86af"}, {file = "webdataset-0.2.100.tar.gz", hash = "sha256:798e30ff700277f0b963dc0395f3b9de4971a67cffc7cb6d0cb9225df7b68e42"}, @@ -3856,42 +5292,68 @@ braceexpand = "*" numpy = "*" pyyaml = "*" +[[package]] +name = "webdataset" +version = "0.2.111" +description = "High performance storage and I/O for deep learning and data processing." +optional = false +python-versions = ">=3.10" +groups = ["main"] +markers = "python_version >= \"3.10\"" +files = [ + {file = "webdataset-0.2.111-py3-none-any.whl", hash = "sha256:57a70eb5d7029303ce2262d900ee3f16443bb5e9cf25f634775ce972859bcee4"}, + {file = "webdataset-0.2.111.tar.gz", hash = "sha256:5b2835386a25601307a9ded9bcc0dbd1e81a9eee017784152528e77dd8619511"}, +] + +[package.dependencies] +braceexpand = "*" +numpy = "*" +pyyaml = "*" + +[package.extras] +dev = ["Pillow", "accelerate", "autoflake", "bandit", "bitsandbytes", "black[jupyter]", "build", "bump2version", "flake8", "icecream", "imageio", "ipykernel", "isort", "jupyter", "jupyterlab", "lmdb", "matplotlib", "mkdocs", "mkdocs-autorefs", "mkdocs-jupyter", "mkdocs-material", "mkdocs-material-extensions", "mkdocs-minify-plugin", "mkdocstrings", "mkdocstrings-python", "msgpack", "mypy", "nbconvert", "notebook", "papermill", "pdm", "peft", "pip", "pre-commit", "pydocstyle", "pytest", "pytest-cov", "pytorch_lightning", "ray[default,tune]", "ruff", "scipy", "setuptools", "torch", "torchvision", "transformers", "twine", "typer", "wheel"] + [[package]] name = "wheel" -version = "0.45.1" -description = "A built-package format for Python" -category = "main" +version = "0.46.3" +description = "Command line tool for manipulating wheel files" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" +groups = ["main"] +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\"" files = [ - {file = "wheel-0.45.1-py3-none-any.whl", hash = "sha256:708e7481cc80179af0e556bbf0cc00b8444c7321e2700b8d8580231d13017248"}, - {file = "wheel-0.45.1.tar.gz", hash = "sha256:661e1abd9198507b1409a20c02106d9670b2576e916d58f520316666abca6729"}, + {file = "wheel-0.46.3-py3-none-any.whl", hash = "sha256:4b399d56c9d9338230118d705d9737a2a468ccca63d5e813e2a4fc7815d8bc4d"}, + {file = "wheel-0.46.3.tar.gz", hash = "sha256:e3e79874b07d776c40bd6033f8ddf76a7dad46a7b8aa1b2787a83083519a1803"}, ] +[package.dependencies] +packaging = ">=24.0" + [package.extras] -test = ["pytest (>=6.0.0)", "setuptools (>=65)"] +test = ["pytest (>=6.0.0)", "setuptools (>=77)"] [[package]] name = "zipp" -version = "3.21.0" +version = "3.23.0" description = "Backport of pathlib-compatible object wrapper for zip files" -category = "main" optional = false python-versions = ">=3.9" +groups = ["main", "dev"] +markers = "python_version == \"3.9\"" files = [ - {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, - {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, + {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"}, + {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"}, ] [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = "^3.9" content-hash = "b2a6cb3d0cfe27ac4c81a32d68a760664ad43b1c40cb39460092a6b9abea0db2" diff --git a/pyproject.toml b/pyproject.toml index 2dfec91..f0b0433 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,9 @@ dependencies = [ ] [project.optional-dependencies] +viz = [ + "fiftyone", +] research = [ "awscli>=1.33.44", "awscli-plugin-endpoint>=0.4", diff --git a/scripts/gbif/job_download_ne_america.sh b/scripts/gbif/job_download_ne_america.sh new file mode 100644 index 0000000..5f743a9 --- /dev/null +++ b/scripts/gbif/job_download_ne_america.sh @@ -0,0 +1,61 @@ +#!/bin/bash +#SBATCH --job-name=download_ne_america +#SBATCH --account=def-drolnick +#SBATCH --time=6:00:00 +#SBATCH --cpus-per-task=4 +#SBATCH --mem=8G +#SBATCH --output=/project/6068129/melabbas/data/ne-america-eccv2024/slurm-%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +set -euo pipefail + +S5CMD="$HOME/bin/s5cmd" +ENDPOINT="https://object-arbutus.cloud.computecanada.ca" +DEST=/project/6068129/melabbas/data/ne-america-eccv2024/wbds/ + +mkdir -p "$DEST" + +echo "Starting download at $(date)" +echo "Destination: $DEST" + +# Build a run-file of only the missing shards (resumable) +RUNFILE=$(mktemp /tmp/s5cmd_runfile_XXXXXX.txt) + +echo "Building run-file of missing files..." +S3_PREFIX="s3://ami-dataset-eccv2024/ami_gbif/fine-grained_classification/wbds" + +$S5CMD --profile ami --endpoint-url "$ENDPOINT" \ + ls "${S3_PREFIX}/ne-america_*" \ + | awk '{print $NF}' \ + | while read fname; do + if [ ! -f "$DEST/$fname" ]; then + echo "cp ${S3_PREFIX}/${fname} $DEST/${fname}" + fi + done > "$RUNFILE" + +N_MISSING=$(wc -l < "$RUNFILE") +echo "Files to download: $N_MISSING" + +if [ "$N_MISSING" -gt 0 ]; then + $S5CMD --profile ami --endpoint-url "$ENDPOINT" \ + --numworkers 32 \ + run "$RUNFILE" +else + echo "All files already present, nothing to download." +fi + +rm -f "$RUNFILE" + +echo "Download complete at $(date)" + +# Count and report +N_TRAIN=$(ls "$DEST"ne-america_train450-*.tar 2>/dev/null | wc -l) +N_VAL=$(ls "$DEST"ne-america_val450-*.tar 2>/dev/null | wc -l) +N_TEST=$(ls "$DEST"ne-america_test450-*.tar 2>/dev/null | wc -l) +TOTAL_GB=$(du -sh "$DEST" | cut -f1) + +echo "train=$N_TRAIN val=$N_VAL test=$N_TEST total=$TOTAL_GB" + +~/bin/notify "ne-america download done" \ + "train=${N_TRAIN}/1583, val=${N_VAL}/187, test=${N_TEST}/375, size=${TOTAL_GB} — $(date)" diff --git a/scripts/gbif/job_download_quebec_vermont.sh b/scripts/gbif/job_download_quebec_vermont.sh new file mode 100644 index 0000000..1668912 --- /dev/null +++ b/scripts/gbif/job_download_quebec_vermont.sh @@ -0,0 +1,27 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=download_quebec_vermont +#SBATCH --cpus-per-task=4 +#SBATCH --mem=8G +#SBATCH --time=1:00:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/download_quebec_vermont_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +DEST="/project/6068129/melabbas/data/quebec_vermont" +ENDPOINT="https://object-arbutus.cloud.computecanada.ca" +SRC="s3://ami-fine-tuning/quebec_vermont" + +export AWS_PROFILE=ami +export AWS_REGION=us-east-1 + +mkdir -p "$DEST" + +echo "Starting download at $(date)" + +s5cmd --endpoint-url "$ENDPOINT" cp --if-size-differ \ + "${SRC}/*" "$DEST/" + +echo "Download completed at $(date)" + +~/bin/notify "download_quebec_vermont: done" "Dataset downloaded to $DEST" diff --git a/scripts/gbif/job_download_vermont_species.sh b/scripts/gbif/job_download_vermont_species.sh new file mode 100644 index 0000000..8fc1562 --- /dev/null +++ b/scripts/gbif/job_download_vermont_species.sh @@ -0,0 +1,18 @@ +#!/bin/bash +#SBATCH --job-name=download_vermont_species +#SBATCH --cpus-per-task=4 +#SBATCH --mem=8G +#SBATCH --time=4:00:00 +#SBATCH --output=download_vermont_species_%j.out +#SBATCH --account=def-drolnick + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +OUT="$BASE_DIR/data/vermont_species.zip" + +echo "Starting download at $(date)" +curl -L -C - --progress-bar \ + "https://api.gbif.org/v1/occurrence/download/request/0030333-260208012135463.zip" \ + -o "$OUT" + +echo "Done at $(date)" +echo "File size: $(du -sh $OUT)" diff --git a/scripts/gbif/job_dwca_inspect_northamerica.sh b/scripts/gbif/job_dwca_inspect_northamerica.sh new file mode 100644 index 0000000..1e24100 --- /dev/null +++ b/scripts/gbif/job_dwca_inspect_northamerica.sh @@ -0,0 +1,64 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=dwca_inspect_northamerica +#SBATCH --cpus-per-task=2 +#SBATCH --mem=32G +#SBATCH --time=3:00:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/dwca_inspect_northamerica_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +DWCA="/home/melabbas/projects/def-drolnick/melabbas/ami-ml/data/northamerica_gbif_download_v1/gbif_inat_northamerica_butterflies_106spp_2081177rec_20260227.zip" +DWCA_TOOLS="/home/melabbas/projects/def-drolnick/melabbas/dwca-tools" +REPORT="/home/melabbas/projects/def-drolnick/melabbas/ami-ml/data/northamerica_gbif_download_v1/dwca_inspection_northamerica_butterflies_106spp_2081177rec_image_counts_20260227.txt" + +cd "$DWCA_TOOLS" +source .venv/bin/activate + +{ +echo "############################################################" +echo "# DwCA Inspection Report" +echo "# Archive : $(basename $DWCA)" +echo "# Generated: $(date)" +echo "############################################################" +echo "" + +echo "============================================================" +echo "=== SECTION 1: summarize files" +echo "============================================================" +echo "" +dwca-tools summarize files "$DWCA" +echo "" +echo "Completed at $(date)" +echo "" + +echo "============================================================" +echo "=== SECTION 2: summarize taxa --image-counts" +echo "============================================================" +echo "" +dwca-tools summarize taxa "$DWCA" \ + --image-counts +echo "" +echo "Completed at $(date)" +echo "" + +echo "============================================================" +echo "=== SECTION 3: summarize taxa --group-by verbatimScientificName --show-mismatched-names --image-counts" +echo "============================================================" +echo "" +dwca-tools summarize taxa "$DWCA" \ + --group-by verbatimScientificName \ + --show-mismatched-names \ + --image-counts +echo "" +echo "Completed at $(date)" +echo "" + +echo "############################################################" +echo "# End of report" +echo "############################################################" +} | tee "$REPORT" + +echo "" +echo "Report saved to: $REPORT" +notify "dwca inspect northamerica done" "3-section inspection report (with image counts) saved to $(basename $REPORT)" diff --git a/scripts/gbif/job_dwca_summarize_files.sh b/scripts/gbif/job_dwca_summarize_files.sh new file mode 100644 index 0000000..1b630d9 --- /dev/null +++ b/scripts/gbif/job_dwca_summarize_files.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=dwca_summarize_files +#SBATCH --cpus-per-task=1 +#SBATCH --mem=8G +#SBATCH --time=0:30:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/dwca_summarize_files_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +DWCA="/home/melabbas/projects/def-drolnick/melabbas/ami-ml/data/vermont_gbif_download_v2/gbif_inat_vermont_butterflies_106spp_2479479rec_20260225.zip" +DWCA_TOOLS="/home/melabbas/projects/def-drolnick/melabbas/dwca-tools" + +cd "$DWCA_TOOLS" +source .venv/bin/activate + +echo "=== dwca-tools summarize files ===" +echo "Archive: $DWCA" +echo "Started at $(date)" +echo "" + +dwca-tools summarize files "$DWCA" + +echo "" +echo "Completed at $(date)" diff --git a/scripts/gbif/job_dwca_summarize_taxa.sh b/scripts/gbif/job_dwca_summarize_taxa.sh new file mode 100644 index 0000000..f833827 --- /dev/null +++ b/scripts/gbif/job_dwca_summarize_taxa.sh @@ -0,0 +1,25 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=dwca_summarize_taxa +#SBATCH --cpus-per-task=2 +#SBATCH --mem=16G +#SBATCH --time=3:00:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/dwca_summarize_taxa_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +DWCA="/home/melabbas/projects/def-drolnick/melabbas/ami-ml/data/vermont_gbif_download_v2/gbif_inat_vermont_butterflies_106spp_2479479rec_20260225.zip" +DWCA_TOOLS="/home/melabbas/projects/def-drolnick/melabbas/dwca-tools" + +cd "$DWCA_TOOLS" +source .venv/bin/activate + +echo "=== dwca-tools summarize taxa ===" +echo "Archive: $DWCA" +echo "Started at $(date)" +echo "" + +dwca-tools summarize taxa "$DWCA" + +echo "" +echo "Completed at $(date)" diff --git a/scripts/gbif/job_dwca_summarize_taxa_verbatim.sh b/scripts/gbif/job_dwca_summarize_taxa_verbatim.sh new file mode 100644 index 0000000..abfcf03 --- /dev/null +++ b/scripts/gbif/job_dwca_summarize_taxa_verbatim.sh @@ -0,0 +1,27 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=dwca_summarize_verbatim +#SBATCH --cpus-per-task=2 +#SBATCH --mem=16G +#SBATCH --time=3:00:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/dwca_summarize_verbatim_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +DWCA="/home/melabbas/projects/def-drolnick/melabbas/ami-ml/data/vermont_gbif_download_v2/gbif_inat_vermont_butterflies_106spp_2479479rec_20260225.zip" +DWCA_TOOLS="/home/melabbas/projects/def-drolnick/melabbas/dwca-tools" + +cd "$DWCA_TOOLS" +source .venv/bin/activate + +echo "=== dwca-tools summarize taxa --group-by verbatimScientificName --show-mismatched-names ===" +echo "Archive: $DWCA" +echo "Started at $(date)" +echo "" + +dwca-tools summarize taxa "$DWCA" \ + --group-by verbatimScientificName \ + --show-mismatched-names + +echo "" +echo "Completed at $(date)" diff --git a/scripts/gbif/job_gbif_download.sh b/scripts/gbif/job_gbif_download.sh new file mode 100755 index 0000000..3d39981 --- /dev/null +++ b/scripts/gbif/job_gbif_download.sh @@ -0,0 +1,35 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=gbif_download +#SBATCH --cpus-per-task=2 +#SBATCH --mem=8G +#SBATCH --time=4:00:00 +#SBATCH --output=gbif_download_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +NAMES_FILE="$BASE_DIR/data/vermont_species_list_v2.txt" +OUTPUT_DIR="$BASE_DIR/data/vermont_gbif_download_v2" +COUNTRY="" # e.g. "US" — leave empty to omit +BBOX="" # e.g. "42.7,-73.5,45.0,-71.5" — leave empty to omit +DATASET_KEY="50c9509d-22c7-4a22-a47d-8c48425ef4a7" + +cd "$BASE_DIR" +source .venv/bin/activate +if [[ -f .env ]]; then set -a; source .env; set +a; fi # loads GBIF_USER/PWD/EMAIL +mkdir -p "$OUTPUT_DIR" + +EXTRA_FLAGS="" +[[ -n "$COUNTRY" ]] && EXTRA_FLAGS="$EXTRA_FLAGS --country $COUNTRY" +[[ -n "$BBOX" ]] && EXTRA_FLAGS="$EXTRA_FLAGS --bbox $BBOX" +[[ -n "$DATASET_KEY" ]] && EXTRA_FLAGS="$EXTRA_FLAGS --dataset-key $DATASET_KEY" + +ami-dataset download-gbif \ + --names-file "$NAMES_FILE" \ + --output-dir "$OUTPUT_DIR" \ + --poll-interval 30 \ + --max-wait 14400 \ + $EXTRA_FLAGS + +echo "Done at $(date)"; ls -lh "$OUTPUT_DIR" diff --git a/scripts/gbif/job_gbif_download_northamerica.sh b/scripts/gbif/job_gbif_download_northamerica.sh new file mode 100644 index 0000000..70dd4b7 --- /dev/null +++ b/scripts/gbif/job_gbif_download_northamerica.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=gbif_download_northamerica +#SBATCH --cpus-per-task=2 +#SBATCH --mem=8G +#SBATCH --time=12:00:00 +#SBATCH --output=gbif_download_northamerica_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +NAMES_FILE="$BASE_DIR/data/vermont_species_list_v2.txt" +OUTPUT_DIR="$BASE_DIR/data/northamerica_gbif_download_v1" +DATASET_KEY="50c9509d-22c7-4a22-a47d-8c48425ef4a7" + +cd "$BASE_DIR" +source .venv/bin/activate +if [[ -f .env ]]; then set -a; source .env; set +a; fi # loads GBIF_USER/PWD/EMAIL +mkdir -p "$OUTPUT_DIR" + +ami-dataset download-gbif \ + --names-file "$NAMES_FILE" \ + --output-dir "$OUTPUT_DIR" \ + --dataset-key "$DATASET_KEY" \ + --country US \ + --country CA \ + --poll-interval 60 \ + --max-wait 21600 + +echo "Done at $(date)"; ls -lh "$OUTPUT_DIR" +notify "gbif northamerica download done" "DwCA for 106 Vermont spp (US+CA, iNat) saved to $OUTPUT_DIR" diff --git a/scripts/job_clean_dataset.sh b/scripts/job_clean_dataset.sh index 592d773..c06544f 100644 --- a/scripts/job_clean_dataset.sh +++ b/scripts/job_clean_dataset.sh @@ -1,31 +1,25 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=clean_dataset -#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=2 +#SBATCH --mem=16G #SBATCH --time=3:00:00 -#SBATCH --partition=long-cpu # Ask for long-cpu job -#SBATCH --cpus-per-task=2 # Ask for 2 CPUs -#SBATCH --mem=300G # Ask for 300 GB of RAM #SBATCH --output=clean_dataset_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script ami-dataset clean-dataset \ ---dwca-file $DWCA_FILE \ ---verified-data-csv $VERIFICATION_RESULTS \ ---life-stage-predictions $LIFESTAGE_RESULTS + --dwca-file $DWCA_FILE \ + --verified-data-csv $VERIFICATION_RESULTS \ + --life-stage-predictions $LIFESTAGE_RESULTS -# Print time taken to execute the script -echo "Time taken to clean the dataset: $SECONDS seconds" +echo "Clean completed at $(date)" diff --git a/scripts/job_copy_data_to_object_storage.sh b/scripts/job_copy_data_to_object_storage.sh index 8a7793f..449c2c2 100644 --- a/scripts/job_copy_data_to_object_storage.sh +++ b/scripts/job_copy_data_to_object_storage.sh @@ -1,28 +1,22 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=upload_dataset -#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=2 +#SBATCH --mem=4G #SBATCH --time=96:00:00 -#SBATCH --partition=long-cpu # Ask for long cpu job -#SBATCH --cpus-per-task=2 # Ask for 2 CPUs -#SBATCH --mem=4G # Ask for 4 GB of RAM #SBATCH --output=upload_dataset_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script aws s3 sync $GLOBAL_MODEL_DIR $GLOBAL_MODEL_OBJECT_STORE --delete -# Print time taken to execute the script -echo "Time taken to upload the dataset: $SECONDS seconds" +echo "Upload completed at $(date)" diff --git a/scripts/job_create_webdataset.sh b/scripts/job_create_webdataset.sh index dc89f18..2a17d3e 100644 --- a/scripts/job_create_webdataset.sh +++ b/scripts/job_create_webdataset.sh @@ -1,40 +1,33 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=create_webdataset -#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=2 +#SBATCH --mem=10G #SBATCH --time=48:00:00 -#SBATCH --partition=unkillable-cpu # Ask for unkillable-cpu job -#SBATCH --cpus-per-task=2 # Ask for 4 CPUs -#SBATCH --mem=10G # Ask for 10 GB of RAM #SBATCH --output=create_webdataset_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script ami-dataset create-webdataset \ ---annotations-csv $TEST_CSV \ ---webdataset-pattern $TEST_WBDS \ ---wandb-run wbds_test \ ---dataset-path $GLOBAL_MODEL_DATASET_PATH \ ---image-path-column image_path \ ---label-column acceptedTaxonKey \ ---columns-to-json $COLUMNS_TO_JSON \ ---resize-min-size 450 \ ---category-map-json $CATEGORY_MAP_JSON \ ---wandb-entity $WANDB_ENTITY \ ---wandb-project $WANDB_PROJECT - + --annotations-csv $TEST_CSV \ + --webdataset-pattern $TEST_WBDS \ + --wandb-run wbds_test \ + --dataset-path $GLOBAL_MODEL_DATASET_PATH \ + --image-path-column image_path \ + --label-column acceptedTaxonKey \ + --columns-to-json $COLUMNS_TO_JSON \ + --resize-min-size 450 \ + --category-map-json $CATEGORY_MAP_JSON \ + --wandb-entity $WANDB_ENTITY \ + --wandb-project $WANDB_PROJECT -# Print time taken to execute the script -echo "Time taken to create the webdataset: $SECONDS seconds" +echo "Webdataset completed at $(date)" diff --git a/scripts/job_delete_images.sh b/scripts/job_delete_images.sh index e0a3502..8e8a783 100644 --- a/scripts/job_delete_images.sh +++ b/scripts/job_delete_images.sh @@ -1,30 +1,24 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=delete_corrupted_images -#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=2 +#SBATCH --mem=4G #SBATCH --time=4:00:00 -#SBATCH --partition=long-cpu # Ask for long-cpu job -#SBATCH --cpus-per-task=2 # Ask for 2 CPUs -#SBATCH --mem=4G # Ask for 4 GB of RAM #SBATCH --output=delete_corrupted_images_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script ami-dataset delete-images \ ---error-images-csv $VERIFICATION_ERROR_RESULTS \ ---base-path $GLOBAL_MODEL_DATASET_PATH + --error-images-csv $VERIFICATION_ERROR_RESULTS \ + --base-path $GLOBAL_MODEL_DATASET_PATH -# Print time taken to execute the script -echo "Time taken to delete the corrupted images: $SECONDS seconds" +echo "Delete completed at $(date)" diff --git a/scripts/job_fetch_images.sh b/scripts/job_fetch_images.sh index 6f04e82..e4f9bff 100644 --- a/scripts/job_fetch_images.sh +++ b/scripts/job_fetch_images.sh @@ -1,31 +1,27 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=fetch_gbif_images -#SBATCH --partition=long-cpu # Ask for long-cpu job -#SBATCH --cpus-per-task=1 # Ask for 1 CPUs -#SBATCH --mem=300G # Ask for 300 GB of RAM +#SBATCH --cpus-per-task=4 +#SBATCH --mem=16G +#SBATCH --time=4:00:00 #SBATCH --output=fetch_gbif_images_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script ami-dataset fetch-images \ ---dataset-path $GLOBAL_MODEL_DATASET_PATH \ ---dwca-file $DWCA_FILE \ ---num-images-per-category 1000 \ ---num-workers 4 \ ---subset-list $ACCEPTED_KEY_LIST + --dataset-path $GLOBAL_MODEL_DATASET_PATH \ + --dwca-file $DWCA_FILE \ + --num-images-per-category 1000 \ + --num-workers 4 \ + --subset-list $ACCEPTED_KEY_LIST -# Print time taken to execute the script -echo "Time taken: $SECONDS seconds" +echo "Fetch completed at $(date)" diff --git a/scripts/job_predict_lifestage.sh b/scripts/job_predict_lifestage.sh index 21c9dbf..ef526ca 100644 --- a/scripts/job_predict_lifestage.sh +++ b/scripts/job_predict_lifestage.sh @@ -1,40 +1,37 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=lifestage_prediction -#SBATCH --ntasks=1 -#SBATCH --time=24:00:00 +#SBATCH --cpus-per-task=4 #SBATCH --mem=16G -#SBATCH --partition=long # Ask for long job -#SBATCH --cpus-per-task=4 # Ask for 4 CPUs -#SBATCH --gres=gpu:1 # Ask or 1 GPU +#SBATCH --time=24:00:00 +#SBATCH --gres=gpu:1 +#SBATCH --partition=gpubase_bynode_b1 #SBATCH --output=lifestage_prediction_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +module load python/3.11 -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +cd "$BASE_DIR" +source .venv/bin/activate -# Keep track of time -SECONDS=0 +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# 4. Launch your script ami-dataset predict-lifestage \ ---verified-data-csv $VERIFICATION_RESULTS_P2 \ ---results-csv $LIFESTAGE_RESULTS_P2 \ ---wandb-run lifestage_prediction_p2 \ ---dataset-path $GLOBAL_MODEL_DATASET_PATH \ ---model-path $LIFESTAGE_MODEL \ ---category-map-json $LIFESTAGE_CATEGORY_MAP \ ---wandb-entity $WANDB_ENTITY \ ---wandb-project $WANDB_PROJECT \ ---log-frequence 25 \ ---batch-size 1024 \ ---num-classes 2 + --verified-data-csv $VERIFICATION_RESULTS_P2 \ + --results-csv $LIFESTAGE_RESULTS_P2 \ + --wandb-run lifestage_prediction_p2 \ + --dataset-path $GLOBAL_MODEL_DATASET_PATH \ + --model-path $LIFESTAGE_MODEL \ + --category-map-json $LIFESTAGE_CATEGORY_MAP \ + --wandb-entity $WANDB_ENTITY \ + --wandb-project $WANDB_PROJECT \ + --log-frequence 25 \ + --batch-size 1024 \ + --num-classes 2 -# Print time taken to execute the script -echo "Time taken to run life stage prediction: $SECONDS seconds" +echo "Life stage prediction completed at $(date)" diff --git a/scripts/job_split_dataset.sh b/scripts/job_split_dataset.sh index 91c7bd4..792c90c 100644 --- a/scripts/job_split_dataset.sh +++ b/scripts/job_split_dataset.sh @@ -1,32 +1,26 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=split_dataset -#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=2 +#SBATCH --mem=6G #SBATCH --time=2:00:00 -#SBATCH --partition=long-cpu # Ask for long-cpu job -#SBATCH --cpus-per-task=2 # Ask for 2 CPUs -#SBATCH --mem=6G # Ask for 6 GB of RAM #SBATCH --output=split_dataset_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script ami-dataset split-dataset \ ---dataset-csv $FINAL_CLEAN_DATASET \ ---split-prefix $SPLIT_PREFIX \ ---max-instances 1000 \ ---min-instances 4 + --dataset-csv $FINAL_CLEAN_DATASET \ + --split-prefix $SPLIT_PREFIX \ + --max-instances 1000 \ + --min-instances 4 -# Print time taken to execute the script -echo "Time taken to split the dataset: $SECONDS seconds" +echo "Split completed at $(date)" diff --git a/scripts/job_train_classifier.sh b/scripts/job_train_classifier.sh index 00bd615..144313f 100644 --- a/scripts/job_train_classifier.sh +++ b/scripts/job_train_classifier.sh @@ -1,42 +1,37 @@ #!/bin/bash -#SBATCH --job-name=test_classifer_training_code -#SBATCH --ntasks=1 -#SBATCH --time=14:00:00 -#SBATCH --mem=48G -#SBATCH --partition=main +#SBATCH --account=def-drolnick +#SBATCH --job-name=train_classifier #SBATCH --cpus-per-task=4 -#SBATCH --gres=gpu:rtx8000:2 -#SBATCH --output=test_classifer_training_code_%j.out +#SBATCH --mem=48G +#SBATCH --time=14:00:00 +#SBATCH --gres=gpu:1 +#SBATCH --partition=gpubase_bynode_b1 +#SBATCH --output=train_classifier_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +module load python/3.11 -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +cd "$BASE_DIR" +source .venv/bin/activate -# Keep track of time -SECONDS=0 +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# 4. Copy your dataset to the compute node +# Copy dataset to compute node for faster I/O cp $SAMPLE_WBDS_LINUX $SLURM_TMPDIR -echo "Time taken to copy the data: $((SECONDS/60)) minutes" - -# 5. Launch your job ami-classification train-model \ ---num_classes 2497 \ ---train_webdataset "$SLURM_TMPDIR/ne-america_train450-{000000..000010}.tar" \ ---val_webdataset "$SLURM_TMPDIR/ne-america_val450-{000000..000015}.tar" \ ---test_webdataset "$SLURM_TMPDIR/ne-america_test450-{000000..000005}.tar" \ ---model_save_directory $TEST_PATH \ ---total_epochs 20 \ ---wandb_entity moth-ai \ ---wandb_project test - -# Print time taken to execute the script -echo "Time taken to train the model: $((SECONDS/60)) minutes" \ No newline at end of file + --num_classes 2497 \ + --train_webdataset "$SLURM_TMPDIR/ne-america_train450-{000000..000010}.tar" \ + --val_webdataset "$SLURM_TMPDIR/ne-america_val450-{000000..000015}.tar" \ + --test_webdataset "$SLURM_TMPDIR/ne-america_test450-{000000..000005}.tar" \ + --model_save_directory $TEST_PATH \ + --total_epochs 20 \ + --wandb_entity moth-ai \ + --wandb_project test + +echo "Training completed at $(date)" diff --git a/scripts/job_verify_images.sh b/scripts/job_verify_images.sh index bd5c38b..fc9c53a 100644 --- a/scripts/job_verify_images.sh +++ b/scripts/job_verify_images.sh @@ -1,34 +1,28 @@ #!/bin/bash +#SBATCH --account=def-drolnick #SBATCH --job-name=verify_gbif_images -#SBATCH --ntasks=1 +#SBATCH --cpus-per-task=16 +#SBATCH --mem=16G #SBATCH --time=24:00:00 -#SBATCH --partition=long-cpu # Ask for long-cpu job -#SBATCH --cpus-per-task=16 # Ask for 16 CPUs -#SBATCH --mem=300G # Ask for 300 GB of RAM #SBATCH --output=verify_gbif_images_%j.out -# 1. Load the required modules -module load miniconda/3 +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" -# 2. Load your environment -conda activate ami-ml +cd "$BASE_DIR" +source .venv/bin/activate -# 3. Load the environment variables outside of python script -set -o allexport -source .env -set +o allexport +if [[ -f .env ]]; then + set -a + source .env + set +a +fi -# Keep track of time -SECONDS=0 - -# 4. Launch your script ami-dataset verify-images \ ---dataset-path $GLOBAL_MODEL_DATASET_PATH \ ---dwca-file $DWCA_FILE \ ---num-workers 16 \ ---results-csv $VERIFICATION_RESULTS \ ---resume-from-ckpt $VERIFICATION_RESULTS \ ---subset-list $ACCEPTED_KEY_LIST + --dataset-path $GLOBAL_MODEL_DATASET_PATH \ + --dwca-file $DWCA_FILE \ + --num-workers 16 \ + --results-csv $VERIFICATION_RESULTS \ + --resume-from-ckpt $VERIFICATION_RESULTS \ + --subset-list $ACCEPTED_KEY_LIST -# Print time taken to execute the script -echo "Time taken to verify images: $SECONDS seconds" +echo "Verify completed at $(date)" diff --git a/scripts/old/job_clean_dataset.sh b/scripts/old/job_clean_dataset.sh new file mode 100644 index 0000000..592d773 --- /dev/null +++ b/scripts/old/job_clean_dataset.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#SBATCH --job-name=clean_dataset +#SBATCH --ntasks=1 +#SBATCH --time=3:00:00 +#SBATCH --partition=long-cpu # Ask for long-cpu job +#SBATCH --cpus-per-task=2 # Ask for 2 CPUs +#SBATCH --mem=300G # Ask for 300 GB of RAM +#SBATCH --output=clean_dataset_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset clean-dataset \ +--dwca-file $DWCA_FILE \ +--verified-data-csv $VERIFICATION_RESULTS \ +--life-stage-predictions $LIFESTAGE_RESULTS + +# Print time taken to execute the script +echo "Time taken to clean the dataset: $SECONDS seconds" diff --git a/scripts/old/job_copy_data_to_object_storage.sh b/scripts/old/job_copy_data_to_object_storage.sh new file mode 100644 index 0000000..8a7793f --- /dev/null +++ b/scripts/old/job_copy_data_to_object_storage.sh @@ -0,0 +1,28 @@ +#!/bin/bash +#SBATCH --job-name=upload_dataset +#SBATCH --ntasks=1 +#SBATCH --time=96:00:00 +#SBATCH --partition=long-cpu # Ask for long cpu job +#SBATCH --cpus-per-task=2 # Ask for 2 CPUs +#SBATCH --mem=4G # Ask for 4 GB of RAM +#SBATCH --output=upload_dataset_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +aws s3 sync $GLOBAL_MODEL_DIR $GLOBAL_MODEL_OBJECT_STORE --delete + +# Print time taken to execute the script +echo "Time taken to upload the dataset: $SECONDS seconds" diff --git a/scripts/old/job_create_webdataset.sh b/scripts/old/job_create_webdataset.sh new file mode 100644 index 0000000..dc89f18 --- /dev/null +++ b/scripts/old/job_create_webdataset.sh @@ -0,0 +1,40 @@ +#!/bin/bash +#SBATCH --job-name=create_webdataset +#SBATCH --ntasks=1 +#SBATCH --time=48:00:00 +#SBATCH --partition=unkillable-cpu # Ask for unkillable-cpu job +#SBATCH --cpus-per-task=2 # Ask for 4 CPUs +#SBATCH --mem=10G # Ask for 10 GB of RAM +#SBATCH --output=create_webdataset_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset create-webdataset \ +--annotations-csv $TEST_CSV \ +--webdataset-pattern $TEST_WBDS \ +--wandb-run wbds_test \ +--dataset-path $GLOBAL_MODEL_DATASET_PATH \ +--image-path-column image_path \ +--label-column acceptedTaxonKey \ +--columns-to-json $COLUMNS_TO_JSON \ +--resize-min-size 450 \ +--category-map-json $CATEGORY_MAP_JSON \ +--wandb-entity $WANDB_ENTITY \ +--wandb-project $WANDB_PROJECT + + +# Print time taken to execute the script +echo "Time taken to create the webdataset: $SECONDS seconds" diff --git a/scripts/old/job_delete_images.sh b/scripts/old/job_delete_images.sh new file mode 100644 index 0000000..e0a3502 --- /dev/null +++ b/scripts/old/job_delete_images.sh @@ -0,0 +1,30 @@ +#!/bin/bash +#SBATCH --job-name=delete_corrupted_images +#SBATCH --ntasks=1 +#SBATCH --time=4:00:00 +#SBATCH --partition=long-cpu # Ask for long-cpu job +#SBATCH --cpus-per-task=2 # Ask for 2 CPUs +#SBATCH --mem=4G # Ask for 4 GB of RAM +#SBATCH --output=delete_corrupted_images_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset delete-images \ +--error-images-csv $VERIFICATION_ERROR_RESULTS \ +--base-path $GLOBAL_MODEL_DATASET_PATH + +# Print time taken to execute the script +echo "Time taken to delete the corrupted images: $SECONDS seconds" diff --git a/scripts/old/job_fetch_images.sh b/scripts/old/job_fetch_images.sh new file mode 100644 index 0000000..6f04e82 --- /dev/null +++ b/scripts/old/job_fetch_images.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#SBATCH --job-name=fetch_gbif_images +#SBATCH --partition=long-cpu # Ask for long-cpu job +#SBATCH --cpus-per-task=1 # Ask for 1 CPUs +#SBATCH --mem=300G # Ask for 300 GB of RAM +#SBATCH --output=fetch_gbif_images_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset fetch-images \ +--dataset-path $GLOBAL_MODEL_DATASET_PATH \ +--dwca-file $DWCA_FILE \ +--num-images-per-category 1000 \ +--num-workers 4 \ +--subset-list $ACCEPTED_KEY_LIST + +# Print time taken to execute the script +echo "Time taken: $SECONDS seconds" diff --git a/scripts/old/job_predict_lifestage.sh b/scripts/old/job_predict_lifestage.sh new file mode 100644 index 0000000..21c9dbf --- /dev/null +++ b/scripts/old/job_predict_lifestage.sh @@ -0,0 +1,40 @@ +#!/bin/bash +#SBATCH --job-name=lifestage_prediction +#SBATCH --ntasks=1 +#SBATCH --time=24:00:00 +#SBATCH --mem=16G +#SBATCH --partition=long # Ask for long job +#SBATCH --cpus-per-task=4 # Ask for 4 CPUs +#SBATCH --gres=gpu:1 # Ask or 1 GPU +#SBATCH --output=lifestage_prediction_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset predict-lifestage \ +--verified-data-csv $VERIFICATION_RESULTS_P2 \ +--results-csv $LIFESTAGE_RESULTS_P2 \ +--wandb-run lifestage_prediction_p2 \ +--dataset-path $GLOBAL_MODEL_DATASET_PATH \ +--model-path $LIFESTAGE_MODEL \ +--category-map-json $LIFESTAGE_CATEGORY_MAP \ +--wandb-entity $WANDB_ENTITY \ +--wandb-project $WANDB_PROJECT \ +--log-frequence 25 \ +--batch-size 1024 \ +--num-classes 2 + +# Print time taken to execute the script +echo "Time taken to run life stage prediction: $SECONDS seconds" diff --git a/scripts/old/job_split_dataset.sh b/scripts/old/job_split_dataset.sh new file mode 100644 index 0000000..91c7bd4 --- /dev/null +++ b/scripts/old/job_split_dataset.sh @@ -0,0 +1,32 @@ +#!/bin/bash +#SBATCH --job-name=split_dataset +#SBATCH --ntasks=1 +#SBATCH --time=2:00:00 +#SBATCH --partition=long-cpu # Ask for long-cpu job +#SBATCH --cpus-per-task=2 # Ask for 2 CPUs +#SBATCH --mem=6G # Ask for 6 GB of RAM +#SBATCH --output=split_dataset_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset split-dataset \ +--dataset-csv $FINAL_CLEAN_DATASET \ +--split-prefix $SPLIT_PREFIX \ +--max-instances 1000 \ +--min-instances 4 + +# Print time taken to execute the script +echo "Time taken to split the dataset: $SECONDS seconds" diff --git a/scripts/old/job_train_classifier.sh b/scripts/old/job_train_classifier.sh new file mode 100644 index 0000000..00bd615 --- /dev/null +++ b/scripts/old/job_train_classifier.sh @@ -0,0 +1,42 @@ +#!/bin/bash +#SBATCH --job-name=test_classifer_training_code +#SBATCH --ntasks=1 +#SBATCH --time=14:00:00 +#SBATCH --mem=48G +#SBATCH --partition=main +#SBATCH --cpus-per-task=4 +#SBATCH --gres=gpu:rtx8000:2 +#SBATCH --output=test_classifer_training_code_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Copy your dataset to the compute node +cp $SAMPLE_WBDS_LINUX $SLURM_TMPDIR + +echo "Time taken to copy the data: $((SECONDS/60)) minutes" + +# 5. Launch your job +ami-classification train-model \ +--num_classes 2497 \ +--train_webdataset "$SLURM_TMPDIR/ne-america_train450-{000000..000010}.tar" \ +--val_webdataset "$SLURM_TMPDIR/ne-america_val450-{000000..000015}.tar" \ +--test_webdataset "$SLURM_TMPDIR/ne-america_test450-{000000..000005}.tar" \ +--model_save_directory $TEST_PATH \ +--total_epochs 20 \ +--wandb_entity moth-ai \ +--wandb_project test + +# Print time taken to execute the script +echo "Time taken to train the model: $((SECONDS/60)) minutes" \ No newline at end of file diff --git a/scripts/old/job_verify_images.sh b/scripts/old/job_verify_images.sh new file mode 100644 index 0000000..bd5c38b --- /dev/null +++ b/scripts/old/job_verify_images.sh @@ -0,0 +1,34 @@ +#!/bin/bash +#SBATCH --job-name=verify_gbif_images +#SBATCH --ntasks=1 +#SBATCH --time=24:00:00 +#SBATCH --partition=long-cpu # Ask for long-cpu job +#SBATCH --cpus-per-task=16 # Ask for 16 CPUs +#SBATCH --mem=300G # Ask for 300 GB of RAM +#SBATCH --output=verify_gbif_images_%j.out + +# 1. Load the required modules +module load miniconda/3 + +# 2. Load your environment +conda activate ami-ml + +# 3. Load the environment variables outside of python script +set -o allexport +source .env +set +o allexport + +# Keep track of time +SECONDS=0 + +# 4. Launch your script +ami-dataset verify-images \ +--dataset-path $GLOBAL_MODEL_DATASET_PATH \ +--dwca-file $DWCA_FILE \ +--num-workers 16 \ +--results-csv $VERIFICATION_RESULTS \ +--resume-from-ckpt $VERIFICATION_RESULTS \ +--subset-list $ACCEPTED_KEY_LIST + +# Print time taken to execute the script +echo "Time taken to verify images: $SECONDS seconds" diff --git a/scripts/test/job_check_species.sh b/scripts/test/job_check_species.sh new file mode 100644 index 0000000..df2f5b0 --- /dev/null +++ b/scripts/test/job_check_species.sh @@ -0,0 +1,71 @@ +#!/bin/bash +#SBATCH --job-name=check_species +#SBATCH --account=def-drolnick +#SBATCH --cpus-per-task=4 +#SBATCH --mem=8G +#SBATCH --time=1:00:00 +#SBATCH --output=/project/6068129/melabbas/data/ne-america-eccv2024/check_species-%j.out +#SBATCH --mail-type=END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +set -euo pipefail + +module load python/3.11 + +python3 - <<'EOF' +import json, urllib.request, concurrent.futures, time + +with open('/project/6068129/melabbas/data/ne-america-eccv2024/metadata/01_ami-gbif_fine-grained_ne-america_category_map.json') as f: + cat_map = json.load(f) + +keys = list(cat_map.keys()) +print(f'Total species: {len(keys)}', flush=True) + +butterfly_families = {'Papilionidae','Pieridae','Nymphalidae','Lycaenidae','Riodinidae','Hesperiidae'} +results = {} + +def fetch(k): + for attempt in range(3): + try: + url = f'https://api.gbif.org/v1/species/{k}' + with urllib.request.urlopen(url, timeout=10) as r: + d = json.load(r) + return k, d.get('canonicalName','?'), d.get('family','unknown'), d.get('order','unknown') + except Exception as e: + time.sleep(1) + return k, '?', 'error', 'error' + +with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor: + futures = {executor.submit(fetch, k): k for k in keys} + done = 0 + for f in concurrent.futures.as_completed(futures): + k, name, fam, order = f.result() + results[k] = {'name': name, 'family': fam, 'order': order} + done += 1 + if done % 100 == 0: + print(f' {done}/{len(keys)}...', flush=True) + +# Save full results +out_path = '/project/6068129/melabbas/data/ne-america-eccv2024/metadata/species_lookup.json' +with open(out_path, 'w') as f: + json.dump(results, f, indent=2) +print(f'Saved to {out_path}', flush=True) + +# Summary +families = {} +for v in results.values(): + fam = v['family'] + families[fam] = families.get(fam, 0) + 1 + +print('\nTop 20 families:') +for fam, cnt in sorted(families.items(), key=lambda x: -x[1])[:20]: + tag = ' <- BUTTERFLY' if fam in butterfly_families else '' + print(f' {fam}: {cnt}{tag}') + +butterfly_count = sum(v for k,v in families.items() if k in butterfly_families) +other_count = sum(v for k,v in families.items() if k not in butterfly_families and k not in ('unknown','error')) +print(f'\nButterflies: {butterfly_count}') +print(f'Moths (approx): {other_count}') +EOF + +~/bin/notify "check_species done" "Species family lookup complete. Results in data/ne-america-eccv2024/metadata/species_lookup.json" diff --git a/scripts/test/job_count_butterfly_images.sh b/scripts/test/job_count_butterfly_images.sh new file mode 100644 index 0000000..87bef38 --- /dev/null +++ b/scripts/test/job_count_butterfly_images.sh @@ -0,0 +1,34 @@ +#!/bin/bash +#SBATCH --job-name=count_butterfly_imgs +#SBATCH --account=def-drolnick +#SBATCH --cpus-per-task=4 +#SBATCH --mem=8G +#SBATCH --time=0:30:00 +#SBATCH --output=/project/6068129/melabbas/data/count_butterfly_images-%j.out +#SBATCH --mail-type=END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +module load python/3.11 + +python3 - <<'EOF' +import tarfile, glob + +shards = sorted(glob.glob('/project/rrg-bengioy-ad/melabbas/northamerica_butterflies_v2/webdataset/**/*.tar', recursive=True)) +print(f'Total shards: {len(shards)}', flush=True) + +totals = {'train': 0, 'val': 0, 'test': 0} +for shard in shards: + split = 'train' if '/train/' in shard else 'val' if '/val/' in shard else 'test' + try: + with tarfile.open(shard) as tf: + count = sum(1 for m in tf.getmembers() if m.name.endswith('.jpg')) + totals[split] += count + except Exception as e: + print(f'Error {shard}: {e}', flush=True) + +for split, count in totals.items(): + print(f'{split}: {count:,}') +print(f'Total: {sum(totals.values()):,}') +EOF + +~/bin/notify "count_butterfly_imgs done" "Image count complete. Results in data/count_butterfly_images-${SLURM_JOB_ID}.out" diff --git a/scripts/test/job_inspect_webdataset.sh b/scripts/test/job_inspect_webdataset.sh new file mode 100644 index 0000000..b5ae68d --- /dev/null +++ b/scripts/test/job_inspect_webdataset.sh @@ -0,0 +1,24 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=inspect_webdataset +#SBATCH --cpus-per-task=2 +#SBATCH --mem=8G +#SBATCH --time=0:30:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/test/inspect_webdataset_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +WBDS_DIR="/project/6068129/melabbas/ami-ml/data/vermont_butterflies/webdataset" +# Category map auto-discovered from WBDS_DIR/*_category_map.json — no need to specify manually +# unless auto-discovery fails (0 or multiple *_category_map.json files found) + +cd "$BASE_DIR" +source .venv/bin/activate + +python src/dataset_tools/inspect_webdataset.py \ + --webdataset-dir "$WBDS_DIR" \ + --split vermont_butterflies_val_verbatim \ + --sort-by count + +~/bin/notify "inspect_webdataset done" "See job output for class distribution: inspect_webdataset_${SLURM_JOB_ID}.out" diff --git a/scripts/test/job_northamerica_fetch_webdataset.sh b/scripts/test/job_northamerica_fetch_webdataset.sh new file mode 100755 index 0000000..d468e81 --- /dev/null +++ b/scripts/test/job_northamerica_fetch_webdataset.sh @@ -0,0 +1,98 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=northamerica_fetch_webdataset +#SBATCH --cpus-per-task=32 +#SBATCH --mem=64G +#SBATCH --time=12:00:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/northamerica_fetch_webdataset_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +# Sharded fetch-and-pack for northamerica_butterflies. +# Replaces the old fetch + webdataset two-step for large datasets. +# Images are downloaded in chunks of CHUNK_SIZE, packed into webdataset tars, +# then deleted — keeping peak disk usage to ~chunk_size images at a time. +# The job is fully resumable: re-submit to continue from the last completed chunk. + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +DATASET="northamerica_butterflies" +SPLITS="$BASE_DIR/data/$DATASET/splits" +WBDS="/project/rrg-bengioy-ad/melabbas/$DATASET/webdataset" # rrg storage (intentional) +TEMP_BASE="${SCRATCH:-/scratch/melabbas}/$DATASET/temp_images" + +LABEL_COL="verbatimScientificName" +IMAGE_PATH_COL="image_path" +URL_COL="identifier" +RESIZE=450 +CHUNK_SIZE=10000 +NUM_WORKERS=32 +MAX_SHARD_SIZE=$((100 * 1024 * 1024)) # 100 MB per shard + +cd "$BASE_DIR" +source .venv/bin/activate + +mkdir -p "$WBDS" "$TEMP_BASE" + +echo "=== northamerica fetch-and-pack started at $(date) ===" + +# --- Train split (saves category map for reuse by val/test) --- +ami-dataset fetch-and-pack \ + --annotations-csv "$SPLITS/train.csv" \ + --temp-dir "$TEMP_BASE/train" \ + --webdataset-dir "$WBDS/train" \ + --split train \ + --label-column "$LABEL_COL" \ + --image-path-column "$IMAGE_PATH_COL" \ + --url-column "$URL_COL" \ + --save-category-map-json "$WBDS/northamerica_butterflies_category_map.json" \ + --max-shard-size "$MAX_SHARD_SIZE" \ + --resize-min-size "$RESIZE" \ + --chunk-size "$CHUNK_SIZE" \ + --num-workers "$NUM_WORKERS" + +echo "=== train done at $(date) ===" +notify "northamerica fetch-and-pack: train done" \ + "Train split packed. Starting val... ($(ls $WBDS/train/*.tar 2>/dev/null | wc -l) shards)" + +# --- Val split --- +ami-dataset fetch-and-pack \ + --annotations-csv "$SPLITS/val.csv" \ + --temp-dir "$TEMP_BASE/val" \ + --webdataset-dir "$WBDS/val" \ + --split val \ + --label-column "$LABEL_COL" \ + --image-path-column "$IMAGE_PATH_COL" \ + --url-column "$URL_COL" \ + --category-map-json "$WBDS/northamerica_butterflies_category_map.json" \ + --max-shard-size "$MAX_SHARD_SIZE" \ + --resize-min-size "$RESIZE" \ + --chunk-size "$CHUNK_SIZE" \ + --num-workers "$NUM_WORKERS" + +echo "=== val done at $(date) ===" + +# --- Test split --- +ami-dataset fetch-and-pack \ + --annotations-csv "$SPLITS/test.csv" \ + --temp-dir "$TEMP_BASE/test" \ + --webdataset-dir "$WBDS/test" \ + --split test \ + --label-column "$LABEL_COL" \ + --image-path-column "$IMAGE_PATH_COL" \ + --url-column "$URL_COL" \ + --category-map-json "$WBDS/northamerica_butterflies_category_map.json" \ + --max-shard-size "$MAX_SHARD_SIZE" \ + --resize-min-size "$RESIZE" \ + --chunk-size "$CHUNK_SIZE" \ + --num-workers "$NUM_WORKERS" + +echo "=== test done at $(date) ===" + +# Summary +TRAIN_SHARDS=$(ls "$WBDS/train"/*.tar 2>/dev/null | wc -l) +VAL_SHARDS=$(ls "$WBDS/val"/*.tar 2>/dev/null | wc -l) +TEST_SHARDS=$(ls "$WBDS/test"/*.tar 2>/dev/null | wc -l) + +echo "Shards: train=$TRAIN_SHARDS val=$VAL_SHARDS test=$TEST_SHARDS" +notify "northamerica fetch-and-pack done" \ + "All 3 splits complete. train=$TRAIN_SHARDS val=$VAL_SHARDS test=$TEST_SHARDS shards in $WBDS" diff --git a/scripts/test/job_test.sh b/scripts/test/job_test.sh new file mode 100644 index 0000000..653feaa --- /dev/null +++ b/scripts/test/job_test.sh @@ -0,0 +1,19 @@ +#!/bin/bash +#SBATCH --job-name=ami_test +#SBATCH --cpus-per-task=1 +#SBATCH --mem=2G +#SBATCH --time=0:05:00 +#SBATCH --output=ami_test_%j.out +#SBATCH --account=def-drolnick + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" + +cd "$BASE_DIR" +source .venv/bin/activate + +echo "=== ami-ml test job ===" +echo "Date: $(date)" +echo "Host: $(hostname)" +echo "Python: $(python --version)" +echo "ami-dataset: $(ami-dataset --version 2>&1 || echo 'not found')" +echo "=== done ===" diff --git a/scripts/test/job_test_fetch_and_pack.sh b/scripts/test/job_test_fetch_and_pack.sh new file mode 100755 index 0000000..0d4aa59 --- /dev/null +++ b/scripts/test/job_test_fetch_and_pack.sh @@ -0,0 +1,75 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=test_fetch_and_pack +#SBATCH --cpus-per-task=4 +#SBATCH --mem=8G +#SBATCH --time=0:30:00 +#SBATCH --output=/project/6068129/melabbas/ami-ml/scripts/test_fetch_and_pack_%j.out + +# Smoke test for fetch-and-pack: 30 rows, chunk_size=10 (3 chunks), 4 workers. +# Verifies: images fetched, shards created, temp dir cleaned, progress file deleted. + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +TEST_CSV="$BASE_DIR/data/test_fap_30rows.csv" +TEMP_DIR="${SCRATCH:-/scratch/melabbas}/test_fetch_and_pack/temp" +OUT_DIR="${SCRATCH:-/scratch/melabbas}/test_fetch_and_pack/webdataset" + +cd "$BASE_DIR" +source .venv/bin/activate + +mkdir -p "$OUT_DIR" "$TEMP_DIR" + +echo "=== fetch-and-pack smoke test started at $(date) ===" + +ami-dataset fetch-and-pack \ + --annotations-csv "$TEST_CSV" \ + --temp-dir "$TEMP_DIR" \ + --webdataset-dir "$OUT_DIR" \ + --split test_fap \ + --label-column verbatimScientificName \ + --image-path-column image_path \ + --url-column identifier \ + --chunk-size 10 \ + --num-workers 4 \ + --max-shard-size $((10 * 1024 * 1024)) \ + --shuffle-images False \ + --save-category-map-json "$OUT_DIR/test_fap_category_map.json" + +echo "" +echo "=== Results ===" +echo "Shards produced:" +ls -lh "$OUT_DIR"/*.tar 2>/dev/null || echo " (none)" + +echo "" +echo "Temp dir contents (should be empty after success):" +find "$TEMP_DIR" -type f 2>/dev/null | head -10 || echo " (empty)" + +echo "" +echo "Progress file (should be gone after success):" +ls "$OUT_DIR"/test_fap_progress.json 2>/dev/null && echo " EXISTS (unexpected)" || echo " absent (correct)" + +echo "" +echo "Category map:" +cat "$OUT_DIR/test_fap_category_map.json" 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); print(f' {len(d)} categories')" || echo " (not found)" + +echo "" +echo "Shard integrity check:" +python3 - < 1 else wds.WebDataset(shards[0]) + for sample in dataset: + count += 1 + print(f" {count} samples readable across all shards — OK") +except Exception as e: + print(f" ERROR: {e}", file=sys.stderr) + sys.exit(1) +PYEOF + +echo "" +echo "=== smoke test finished at $(date) ===" +notify "fetch-and-pack smoke test done" "$(ls "$OUT_DIR"/*.tar 2>/dev/null | wc -l) shards, check job output for integrity results" diff --git a/scripts/test/job_test_new_pipeline.sh b/scripts/test/job_test_new_pipeline.sh new file mode 100644 index 0000000..0576246 --- /dev/null +++ b/scripts/test/job_test_new_pipeline.sh @@ -0,0 +1,191 @@ +#!/bin/bash +#SBATCH --job-name=test_new_pipeline +#SBATCH --account=def-drolnick +#SBATCH --cpus-per-task=8 +#SBATCH --mem=16G +#SBATCH --time=2:00:00 +#SBATCH --output=/project/6068129/melabbas/data/test_new_pipeline-%j.out +#SBATCH --mail-type=END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +set -euo pipefail + +BASE_DIR="/project/6068129/melabbas/ami-ml" +DWCA="$BASE_DIR/data/vermont_butterflies.zip" +OUT="/project/6068129/melabbas/data/test_new_pipeline" +SPLITS="$OUT/splits" +WBDS="$OUT/webdataset" +TEMP="$OUT/temp_images" + +cd "$BASE_DIR" +source .venv/bin/activate + +echo "================================================================" +echo "INPUT: DwCA inspection" +echo "================================================================" +python3 - </dev/null || uv pip install -e ".[viz]" + +echo "Node: $(hostname)" +echo "SSH tunnel: ssh -L ${PORT}:$(hostname):${PORT} melabbas@fir.alliancecan.ca" + +python src/dataset_tools/visualize_webdataset.py \ + --webdataset-pattern "$WBDS/vermont_train_verbatim-{000000..000051}.tar" \ + --category-map-json "$WBDS/vermont_category_map_verbatim.json" \ + --num-samples 500 \ + --port $PORT diff --git a/scripts/vermont/job_vermont_webdataset.sh b/scripts/vermont/job_vermont_webdataset.sh new file mode 100644 index 0000000..76a9012 --- /dev/null +++ b/scripts/vermont/job_vermont_webdataset.sh @@ -0,0 +1,45 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=vermont_webdataset +#SBATCH --cpus-per-task=4 +#SBATCH --mem=16G +#SBATCH --time=2:00:00 +#SBATCH --output=vermont_webdataset_%j.out + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +IMAGES="$BASE_DIR/data/vermont_butterflies/images" +SPLITS="$BASE_DIR/data/vermont_butterflies/splits" +WBDS="$BASE_DIR/data/vermont_butterflies/webdataset" + +cd "$BASE_DIR" +source .venv/bin/activate +mkdir -p "$WBDS" + +ami-dataset create-webdataset \ + --annotations-csv "$SPLITS/train.csv" \ + --dataset-path "$IMAGES" \ + --webdataset-pattern "$WBDS/vermont_train_verbatim-%06d.tar" \ + --image-path-column image_path \ + --label-column verbatimScientificName \ + --resize-min-size 450 \ + --save-category-map-json "$WBDS/vermont_category_map_verbatim.json" + +ami-dataset create-webdataset \ + --annotations-csv "$SPLITS/val.csv" \ + --dataset-path "$IMAGES" \ + --webdataset-pattern "$WBDS/vermont_val_verbatim-%06d.tar" \ + --image-path-column image_path \ + --label-column verbatimScientificName \ + --resize-min-size 450 \ + --category-map-json "$WBDS/vermont_category_map_verbatim.json" + +ami-dataset create-webdataset \ + --annotations-csv "$SPLITS/test.csv" \ + --dataset-path "$IMAGES" \ + --webdataset-pattern "$WBDS/vermont_test_verbatim-%06d.tar" \ + --image-path-column image_path \ + --label-column verbatimScientificName \ + --resize-min-size 450 \ + --category-map-json "$WBDS/vermont_category_map_verbatim.json" + +echo "Webdataset completed at $(date)" diff --git a/scripts/vermont_species/job_vermont_species_clean.sh b/scripts/vermont_species/job_vermont_species_clean.sh new file mode 100644 index 0000000..60225ad --- /dev/null +++ b/scripts/vermont_species/job_vermont_species_clean.sh @@ -0,0 +1,22 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=vermont_species_clean +#SBATCH --cpus-per-task=2 +#SBATCH --mem=32G +#SBATCH --time=1:00:00 +#SBATCH --output=vermont_species_clean_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +DWCA="$BASE_DIR/data/vermont_gbif_download_v2/gbif_inat_vermont_butterflies_106spp_2479479rec_20260225.zip" +SPLITS="$BASE_DIR/data/vermont_butterflies/splits" + +cd "$BASE_DIR" +source .venv/bin/activate + +ami-dataset clean-dataset \ + --dwca-file "$DWCA" \ + --verified-data-csv "$SPLITS/verification_results.csv" + +echo "Clean completed at $(date)" diff --git a/scripts/vermont_species/job_vermont_species_fetch.sh b/scripts/vermont_species/job_vermont_species_fetch.sh new file mode 100644 index 0000000..f521a60 --- /dev/null +++ b/scripts/vermont_species/job_vermont_species_fetch.sh @@ -0,0 +1,28 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=vermont_species_fetch +#SBATCH --cpus-per-task=8 +#SBATCH --mem=32G +#SBATCH --time=24:00:00 +#SBATCH --output=vermont_species_fetch_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +DWCA="$BASE_DIR/data/vermont_gbif_download_v2/gbif_inat_vermont_butterflies_106spp_2479479rec_20260225.zip" +IMAGES="$BASE_DIR/data/vermont_butterflies/images" +MAX_IMAGES_PER_SPECIES=1000 # set to 0 for no limit + +cd "$BASE_DIR" +source .venv/bin/activate +mkdir -p "$IMAGES" + +# Already-downloaded images are skipped automatically (tool checks if file exists) +ami-dataset fetch-images \ + --dwca-file "$DWCA" \ + --dataset-path "$IMAGES" \ + --num-workers 8 \ + --num-images-per-category "$MAX_IMAGES_PER_SPECIES" \ + --subset-key verbatimScientificName + +echo "Fetch completed at $(date)" diff --git a/scripts/vermont_species/job_vermont_species_split.sh b/scripts/vermont_species/job_vermont_species_split.sh new file mode 100644 index 0000000..f9dbbd3 --- /dev/null +++ b/scripts/vermont_species/job_vermont_species_split.sh @@ -0,0 +1,26 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=vermont_species_split +#SBATCH --cpus-per-task=2 +#SBATCH --mem=6G +#SBATCH --time=0:30:00 +#SBATCH --output=vermont_species_split_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +SPLITS="$BASE_DIR/data/vermont_butterflies/splits" + +cd "$BASE_DIR" +source .venv/bin/activate + +ami-dataset split-dataset \ + --dataset-csv "$SPLITS/verification_results_clean.csv" \ + --split-prefix "$SPLITS" \ + --max-instances 1000 \ + --min-instances 0 \ + --test-frac 0.2 \ + --val-frac 0.2 \ + --category-key verbatimScientificName + +echo "Split completed at $(date)" diff --git a/scripts/vermont_species/job_vermont_species_train.sh b/scripts/vermont_species/job_vermont_species_train.sh new file mode 100644 index 0000000..096b5f3 --- /dev/null +++ b/scripts/vermont_species/job_vermont_species_train.sh @@ -0,0 +1,55 @@ +#!/bin/bash +#SBATCH --account=def-drolnick_gpu +#SBATCH --job-name=vermont_species_train +#SBATCH --gres=gpu:1 +#SBATCH --cpus-per-task=4 +#SBATCH --mem=32G +#SBATCH --time=3:00:00 +#SBATCH --output=vermont_species_train_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com +#SBATCH --partition=gpubase_bynode_b1 + +module load python/3.11 + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +WBDS="$BASE_DIR/data/vermont_butterflies/webdataset" +MODELS="$BASE_DIR/data/vermont_butterflies/models" + +cd "$BASE_DIR" +source .venv/bin/activate +mkdir -p "$MODELS" + +if [[ -f .env ]]; then + set -a + source .env + set +a +fi + +# Dynamically resolve shard counts and num_classes +N_TRAIN=$(ls "$WBDS"/vermont_butterflies_train_verbatim-*.tar 2>/dev/null | wc -l) +N_VAL=$(ls "$WBDS"/vermont_butterflies_val_verbatim-*.tar 2>/dev/null | wc -l) +N_TEST=$(ls "$WBDS"/vermont_butterflies_test_verbatim-*.tar 2>/dev/null | wc -l) +NUM_CLASSES=$(python -c "import json; d=json.load(open('$WBDS/vermont_butterflies_category_map.json')); print(len(d))") + +TRAIN_PAT="$WBDS/vermont_butterflies_train_verbatim-{000000..$(printf '%06d' $((N_TRAIN-1)))}.tar" +VAL_PAT="$WBDS/vermont_butterflies_val_verbatim-{000000..$(printf '%06d' $((N_VAL-1)))}.tar" +TEST_PAT="$WBDS/vermont_butterflies_test_verbatim-{000000..$(printf '%06d' $((N_TEST-1)))}.tar" + +echo "Train shards: $N_TRAIN, Val shards: $N_VAL, Test shards: $N_TEST, Num classes: $NUM_CLASSES" + +ami-classification train-model \ + --train_webdataset "$TRAIN_PAT" \ + --val_webdataset "$VAL_PAT" \ + --test_webdataset "$TEST_PAT" \ + --num_classes "$NUM_CLASSES" \ + --model_save_directory "$MODELS" \ + --total_epochs 30 \ + --warmup_epochs 2 \ + --batch_size 128 \ + --random_seed 123 \ + --wandb_entity "hack1996man" \ + --wandb_project "ai_for_leps" \ + --wandb_run_name "vermont_butterflies_verbatim_run2" + +echo "Training completed at $(date)" diff --git a/scripts/vermont_species/job_vermont_species_verify.sh b/scripts/vermont_species/job_vermont_species_verify.sh new file mode 100644 index 0000000..64e6416 --- /dev/null +++ b/scripts/vermont_species/job_vermont_species_verify.sh @@ -0,0 +1,32 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=vermont_species_verify +#SBATCH --cpus-per-task=16 +#SBATCH --mem=128G +#SBATCH --time=24:00:00 +#SBATCH --output=vermont_species_verify_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +DWCA="$BASE_DIR/data/vermont_gbif_download_v2/gbif_inat_vermont_butterflies_106spp_2479479rec_20260225.zip" +IMAGES="$BASE_DIR/data/vermont_butterflies/images" +SPLITS="$BASE_DIR/data/vermont_butterflies/splits" + +cd "$BASE_DIR" +source .venv/bin/activate +mkdir -p "$SPLITS" + +RESUME_FLAG="" +if [[ -f "$SPLITS/verification_results.csv" ]]; then + RESUME_FLAG="--resume-from-ckpt $SPLITS/verification_results.csv" +fi + +ami-dataset verify-images \ + --dwca-file "$DWCA" \ + --dataset-path "$IMAGES" \ + --results-csv "$SPLITS/verification_results.csv" \ + $RESUME_FLAG \ + --num-workers 16 + +echo "Verify completed at $(date)" diff --git a/scripts/vermont_species/job_vermont_species_webdataset.sh b/scripts/vermont_species/job_vermont_species_webdataset.sh new file mode 100644 index 0000000..1de184f --- /dev/null +++ b/scripts/vermont_species/job_vermont_species_webdataset.sh @@ -0,0 +1,47 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --job-name=vermont_species_webdataset +#SBATCH --cpus-per-task=4 +#SBATCH --mem=16G +#SBATCH --time=2:00:00 +#SBATCH --output=vermont_species_webdataset_%j.out +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +IMAGES="$BASE_DIR/data/vermont_butterflies/images" +SPLITS="$BASE_DIR/data/vermont_butterflies/splits" +WBDS="$BASE_DIR/data/vermont_butterflies/webdataset" + +cd "$BASE_DIR" +source .venv/bin/activate +mkdir -p "$WBDS" + +ami-dataset create-webdataset \ + --annotations-csv "$SPLITS/train.csv" \ + --dataset-path "$IMAGES" \ + --webdataset-pattern "$WBDS/vermont_butterflies_train_verbatim-%06d.tar" \ + --image-path-column image_path \ + --label-column verbatimScientificName \ + --resize-min-size 450 \ + --save-category-map-json "$WBDS/vermont_butterflies_category_map.json" + +ami-dataset create-webdataset \ + --annotations-csv "$SPLITS/val.csv" \ + --dataset-path "$IMAGES" \ + --webdataset-pattern "$WBDS/vermont_butterflies_val_verbatim-%06d.tar" \ + --image-path-column image_path \ + --label-column verbatimScientificName \ + --resize-min-size 450 \ + --category-map-json "$WBDS/vermont_butterflies_category_map.json" + +ami-dataset create-webdataset \ + --annotations-csv "$SPLITS/test.csv" \ + --dataset-path "$IMAGES" \ + --webdataset-pattern "$WBDS/vermont_butterflies_test_verbatim-%06d.tar" \ + --image-path-column image_path \ + --label-column verbatimScientificName \ + --resize-min-size 450 \ + --category-map-json "$WBDS/vermont_butterflies_category_map.json" + +echo "Webdataset completed at $(date)" diff --git a/scripts/viz/job_fiftyone_viz.sh b/scripts/viz/job_fiftyone_viz.sh new file mode 100644 index 0000000..e5a7d03 --- /dev/null +++ b/scripts/viz/job_fiftyone_viz.sh @@ -0,0 +1,30 @@ +#!/bin/bash +#SBATCH --account=def-drolnick +#SBATCH --cpus-per-task=4 +#SBATCH --mem=16G +#SBATCH --time=2:00:00 +#SBATCH --job-name=fiftyone_viz +#SBATCH --output=/project/6068129/melabbas/ami-ml/logs/fiftyone_viz_%j.log +#SBATCH --mail-type=BEGIN,END,FAIL +#SBATCH --mail-user=hack1996man@gmail.com + +NODE=$(hostname) +echo "==============================" +echo "Node: ${NODE}" +echo "Port: 5151" +echo "SSH tunnel: ssh -N -J melabbas@fir.alliancecan.ca -L 5151:localhost:5151 melabbas@${NODE}" +echo "Then open: http://localhost:5151" +echo "==============================" + +BASE_DIR="/home/melabbas/projects/def-drolnick/melabbas/ami-ml" +cd "$BASE_DIR" +source .venv/bin/activate + +# Bind the FiftyOne server to all interfaces so it's reachable via SSH tunnel +export FIFTYONE_DEFAULT_APP_ADDRESS="0.0.0.0" + +python src/dataset_tools/visualize_webdataset.py \ + --webdataset-pattern "data/vermont_butterflies/webdataset/vermont_train_verbatim-{000000..000051}.tar" \ + --category-map-json "data/vermont_butterflies/webdataset/vermont_category_map_verbatim.json" \ + --num-samples 500 \ + --port 5151 diff --git a/scripts/viz/launch_fiftyone.sh b/scripts/viz/launch_fiftyone.sh new file mode 100755 index 0000000..8811cbe --- /dev/null +++ b/scripts/viz/launch_fiftyone.sh @@ -0,0 +1,92 @@ +#!/bin/bash +# ============================================================================= +# launch_fiftyone.sh — Submit and monitor a FiftyOne visualization job +# +# Usage: +# bash scripts/launch_fiftyone.sh +# +# What it does: +# 1. Submits job_fiftyone_viz.sh to SLURM +# 2. Waits for the job to start running +# 3. Waits for FiftyOne to finish loading samples +# 4. Prints the exact SSH tunnel command to run on your laptop +# +# Notes: +# - The SLURM job (job_fiftyone_viz.sh) runs for up to 2 hours +# - To visualize vermont_species instead of vermont_butterflies, +# edit job_fiftyone_viz.sh and update --webdataset-pattern and +# --category-map-json before running this script +# ============================================================================= + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +JOB_SCRIPT="${SCRIPT_DIR}/job_fiftyone_viz.sh" +LOG_DIR="${SCRIPT_DIR}/../logs" + +mkdir -p "$LOG_DIR" + +echo "Submitting FiftyOne visualization job..." +SUBMIT_OUT=$(sbatch "$JOB_SCRIPT") +JOB_ID=$(echo "$SUBMIT_OUT" | awk '{print $NF}') +echo "Submitted job ${JOB_ID}" + +# Wait for the job to start running +echo "Waiting for job to start..." +while true; do + STATUS=$(squeue -j "$JOB_ID" -h -o "%T %N" 2>/dev/null) + STATE=$(echo "$STATUS" | awk '{print $1}') + NODE=$(echo "$STATUS" | awk '{print $2}') + + if [[ "$STATE" == "RUNNING" ]]; then + echo "Job is RUNNING on node: ${NODE}" + break + elif [[ -z "$STATUS" ]]; then + echo "ERROR: Job ${JOB_ID} disappeared from queue (may have failed immediately)" + exit 1 + fi + sleep 5 +done + +LOG_FILE="${LOG_DIR}/fiftyone_viz_${JOB_ID}.log" + +# Wait for FiftyOne server to be ready +echo "Waiting for FiftyOne to load samples and start server..." +READY=false +for i in $(seq 1 60); do + if [[ -f "$LOG_FILE" ]]; then + # Check if server is up (no more "Could not connect" and has the remote session banner) + if grep -q "remote sessions" "$LOG_FILE" 2>/dev/null && \ + ! grep -q "Could not connect" "$LOG_FILE" 2>/dev/null; then + READY=true + break + fi + # Also check if the curl endpoint responds + if curl -s --connect-timeout 2 "http://${NODE}:5151/fiftyone" 2>/dev/null | grep -q "version"; then + READY=true + break + fi + fi + sleep 10 +done + +if [[ "$READY" == "false" ]]; then + echo "WARNING: FiftyOne may not have started cleanly. Check log: ${LOG_FILE}" + echo "You can still try the tunnel — it may just need more time." +fi + +echo "" +echo "============================================================" +echo " FiftyOne is running!" +echo " Job: ${JOB_ID} (up to 2 hours)" +echo " Node: ${NODE}" +echo " Log: ${LOG_FILE}" +echo "" +echo " Run this on your LAPTOP to open the UI:" +echo "" +echo " ssh -N -J melabbas@fir.alliancecan.ca -L 5151:localhost:5151 melabbas@${NODE}" +echo "" +echo " Then open: http://localhost:5151" +echo "============================================================" +echo "" +echo "To cancel the job early: scancel ${JOB_ID}" diff --git a/src/dataset_tools/clean_dataset.py b/src/dataset_tools/clean_dataset.py index 924e94b..88308da 100644 --- a/src/dataset_tools/clean_dataset.py +++ b/src/dataset_tools/clean_dataset.py @@ -11,13 +11,20 @@ def _load_data(dwca_file: str, verified_data_csv: str, life_stage_predictions: str): metadata = load_dwca_data(dwca_file) metadata["image_path"] = metadata.apply(get_image_path, axis=1) - verified_data = pd.read_csv(verified_data_csv) - verified_metadata = pd.merge( - verified_data[["image_path", "width", "height", "fetch_date"]], - metadata, - how="inner", - on="image_path", - ) + + if verified_data_csv is not None: + verified_data = pd.read_csv(verified_data_csv) + verified_metadata = pd.merge( + verified_data[["image_path", "width", "height", "fetch_date"]], + metadata, + how="inner", + on="image_path", + ) + else: + # No verify output available — work directly from DwCA metadata. + # Thumbnail filtering will be skipped (no width/height). + verified_metadata = metadata.copy() + if life_stage_predictions is not None: life_stage_preds = pd.read_csv(life_stage_predictions) verified_metadata = pd.merge( @@ -83,6 +90,7 @@ def clean_dataset( thumb_size: int, remove_non_adults: bool, life_stage_predictions: str, + output_csv: str = None, ): verified_metadata = _load_data(dwca_file, verified_data_csv, life_stage_predictions) current_filtered = len(verified_metadata) @@ -98,15 +106,20 @@ def clean_dataset( current_filtered, ignore_dataset_by_key, verified_metadata ) - if remove_tumbnails: + if remove_tumbnails and verified_data_csv is not None: current_filtered, verified_metadata = _remove_thumbnails( current_filtered, thumb_size, verified_metadata ) + elif remove_tumbnails and verified_data_csv is None: + print("Skipping thumbnail filter: no verified-data-csv provided (no width/height).") if remove_non_adults: verified_metadata = _remove_non_adults(current_filtered, verified_metadata) verified_metadata_clean = verified_metadata.copy() - metadata_clean_filename = verified_data_csv[:-4] + "_clean.csv" + if output_csv is not None: + metadata_clean_filename = output_csv + else: + metadata_clean_filename = verified_data_csv[:-4] + "_clean.csv" verified_metadata_clean.to_csv(metadata_clean_filename, index=False) print(f"Clean dataset saved to {metadata_clean_filename}") diff --git a/src/dataset_tools/cli.py b/src/dataset_tools/cli.py index c97fa81..966e599 100644 --- a/src/dataset_tools/cli.py +++ b/src/dataset_tools/cli.py @@ -22,7 +22,7 @@ """ import functools -from typing import Union +from typing import Optional, Union import click @@ -35,6 +35,8 @@ PREDICT_CMD = "predict_cmd" SPLIT_CMD = "split_cmd" WEBDATASET_CMD = "webdataset_cmd" +FETCH_AND_PACK_CMD = "fetch_and_pack_cmd" +DOWNLOAD_GBIF_CMD = "download_gbif_cmd" # This is most useful to automatically test the CLI COMMAND_KEYS = frozenset( @@ -46,6 +48,8 @@ CLEAN_CMD, SPLIT_CMD, WEBDATASET_CMD, + FETCH_AND_PACK_CMD, + DOWNLOAD_GBIF_CMD, ] ) @@ -58,6 +62,8 @@ CLEAN_CMD: "clean-dataset", SPLIT_CMD: "split-dataset", WEBDATASET_CMD: "create-webdataset", + FETCH_AND_PACK_CMD: "fetch-and-pack", + DOWNLOAD_GBIF_CMD: "download-gbif", } # Command help text dictionary @@ -69,6 +75,11 @@ CLEAN_CMD: "Filter out images to ensure quality of training data", SPLIT_CMD: "Split the provided dataset into train, validate and test sets", WEBDATASET_CMD: "Assemble final training set in webdataset format", + FETCH_AND_PACK_CMD: ( + "Fetch images from a split CSV and pack into webdataset shards in chunks, " + "deleting raw images after each chunk to minimise file quota usage" + ), + DOWNLOAD_GBIF_CMD: "Download a GBIF Darwin Core Archive filtered by verbatim scientific names", } @@ -487,7 +498,25 @@ def predict_lifestage_command( context_settings={"show_default": True}, ) @with_dwca_file -@with_verified_data_csv +@click.option( + "--verified-data-csv", + type=str, + default=None, + help=( + "CSV file containing verified image info (output of verify-images). " + "If not provided, the dataset is loaded directly from the DwC-A file and " + "thumbnail filtering is skipped." + ), +) +@click.option( + "--output-csv", + type=str, + default=None, + help=( + "Path for the cleaned output CSV. Required when --verified-data-csv is not " + "provided. When omitted with a verify CSV, defaults to _clean.csv." + ), +) @click.option( "--ignore-dataset-by-key", type=str, @@ -539,6 +568,7 @@ def predict_lifestage_command( def clean_dataset_command( dwca_file: str, verified_data_csv: str, + output_csv: str, remove_duplicate_url: bool, ignore_dataset_by_key: str, remove_tumbnails: bool, @@ -546,6 +576,11 @@ def clean_dataset_command( remove_non_adults: bool, life_stage_predictions: str, ): + if verified_data_csv is None and output_csv is None: + raise click.UsageError( + "--output-csv is required when --verified-data-csv is not provided." + ) + from src.dataset_tools.clean_dataset import clean_dataset clean_dataset( @@ -557,6 +592,7 @@ def clean_dataset_command( thumb_size=thumb_size, remove_non_adults=remove_non_adults, life_stage_predictions=life_stage_predictions, + output_csv=output_csv, ) @@ -778,6 +814,219 @@ def create_webdataset_command( ) +# +# Fetch-and-Pack Command +# +@click.command( + name=COMMANDS[FETCH_AND_PACK_CMD], + help=COMMANDS_HELP[FETCH_AND_PACK_CMD], + context_settings={"show_default": True}, +) +@click.option( + "--annotations-csv", + type=str, + required=True, + help="Path to split CSV file with image URL, path, and label columns.", +) +@click.option( + "--temp-dir", + type=str, + required=True, + help=( + "Directory for temporarily storing raw images during a chunk. " + "Contents are deleted after each chunk is packed." + ), +) +@click.option( + "--webdataset-dir", + type=str, + required=True, + help="Output directory where webdataset .tar shards will be written.", +) +@click.option( + "--split", + type=str, + required=True, + help="Split name (e.g. train, val, test). Used as shard filename prefix.", +) +@click.option( + "--label-column", + type=str, + required=True, + help="CSV column containing the category label.", +) +@click.option( + "--image-path-column", + type=str, + required=True, + help="CSV column containing the relative image file path.", +) +@click.option( + "--url-column", + type=str, + default="identifier", + help="CSV column containing the image download URL.", +) +@click.option( + "--max-shard-size", + type=int, + default=100 * 1024 * 1024, + help="Maximum size of each shard in bytes.", +) +@click.option( + "--resize-min-size", + type=int, + help=( + "Size which the shortest image side will be resized to. " + "If not given, the original image is stored without resizing." + ), +) +@click.option( + "--category-map-json", + type=str, + help=( + "JSON containing the categories id map. If not provided, the category map " + "will be inferred from the annotations CSV. " + "MUST be provided for val and test splits (use the map saved from train)." + ), +) +@click.option( + "--save-category-map-json", + type=str, + help="Path to save the inferred category map JSON (use for the train split).", +) +@click.option( + "--columns-to-json", + type=str, + help="Comma-separated list of CSV columns to embed as JSON metadata per sample.", +) +@click.option( + "--chunk-size", + type=int, + default=10_000, + help="Number of images to fetch and pack per chunk.", +) +@with_num_workers +@click.option( + "--request-timeout", + type=int, + default=30, + help="Timeout in seconds for unresponsive HTTP connections.", +) +@with_random_seed +@click.option( + "--shuffle-images", + type=bool, + default=True, + help="Shuffle the dataset before chunking.", +) +def fetch_and_pack_command( + annotations_csv: str, + temp_dir: str, + webdataset_dir: str, + split: str, + label_column: str, + image_path_column: str, + url_column: str, + max_shard_size: int, + resize_min_size: int, + category_map_json: str, + save_category_map_json: str, + columns_to_json: str, + chunk_size: int, + num_workers: int, + request_timeout: int, + random_seed: int, + shuffle_images: bool, +): + from src.dataset_tools.fetch_and_pack import fetch_and_pack + + fetch_and_pack( + annotations_csv=annotations_csv, + temp_dir=temp_dir, + webdataset_dir=webdataset_dir, + split=split, + label_column=label_column, + image_path_column=image_path_column, + url_column=url_column, + max_shard_size=max_shard_size, + resize_min_size=resize_min_size, + category_map_json=category_map_json, + save_category_map_json=save_category_map_json, + columns_to_json=columns_to_json, + chunk_size=chunk_size, + num_workers=num_workers, + request_timeout=request_timeout, + random_seed=random_seed, + shuffle_images=shuffle_images, + ) + + +# +# Download GBIF Command +# +@click.command( + name=COMMANDS[DOWNLOAD_GBIF_CMD], + help=COMMANDS_HELP[DOWNLOAD_GBIF_CMD], + context_settings={"show_default": True}, +) +@click.argument("names", nargs=-1, required=False, metavar="NAME...") +@click.option( + "--names-file", + default=None, + help="Text file with one species name per line (blank lines and # comments ignored)", +) +@click.option( + "--output-dir", + required=True, + help="Directory to save the downloaded DwC-A zip file", +) +@click.option( + "--dataset-key", + default="50c9509d-22c7-4a22-a47d-8c48425ef4a7", + help="GBIF dataset UUID (default: iNaturalist Research-grade Observations)", +) +@click.option( + "--country", + multiple=True, + help="ISO 3166-1 alpha-2 country code to filter by (e.g. 'US'). Repeat to include multiple countries (e.g. --country US --country CA).", +) +@click.option( + "--bbox", + default=None, + help="Bounding box filter: 'min_lat,min_lng,max_lat,max_lng'", +) +@click.option( + "--poll-interval", + default=30, + type=int, + help="Seconds between GBIF status polls", +) +@click.option( + "--max-wait", + default=3600, + type=int, + help="Maximum seconds to wait for the download to complete", +) +def download_gbif_command( + names: tuple, + names_file: Optional[str], + output_dir: str, + dataset_key: str, + country: tuple, + bbox: Optional[str], + poll_interval: int, + max_wait: int, +): + from src.dataset_tools.download_gbif import download_gbif, read_names_from_file + + all_names = list(read_names_from_file(names_file)) if names_file else [] + all_names.extend(names) + if not all_names: + raise click.UsageError("Provide at least one name via NAME argument(s) or --names-file") + download_gbif(all_names, output_dir, dataset_key, list(country) or None, bbox, poll_interval, max_wait) + + # # # # # # # # # # # # # # # Main CLI configuration # # # # # # # # # # # # # # # diff --git a/src/dataset_tools/download_gbif.py b/src/dataset_tools/download_gbif.py new file mode 100644 index 0000000..608f3a4 --- /dev/null +++ b/src/dataset_tools/download_gbif.py @@ -0,0 +1,167 @@ +"""Download a GBIF Darwin Core Archive filtered by verbatim scientific names.""" + +import os +import time + +from dotenv import load_dotenv + +load_dotenv() + +INAT_DATASET_KEY = "50c9509d-22c7-4a22-a47d-8c48425ef4a7" +TERMINAL_STATUSES = {"SUCCEEDED", "CANCELLED", "KILLED", "FAILED", "SUSPENDED", "FILE_ERASED"} + + +def read_names_from_file(names_file: str) -> list[str]: + """Read one name per line; skip blank lines and #-prefixed comments.""" + names = [] + with open(names_file) as f: + for line in f: + line = line.strip() + if line and not line.startswith("#"): + names.append(line) + return names + + +def bbox_to_wkt(bbox: str) -> str: + """Convert 'min_lat,min_lng,max_lat,max_lng' to a WKT POLYGON string. + + GBIF requires lng,lat order in WKT coordinates. + """ + parts = bbox.split(",") + if len(parts) != 4: + raise ValueError(f"bbox must be 4 comma-separated floats, got: {bbox!r}") + try: + min_lat, min_lng, max_lat, max_lng = [float(p.strip()) for p in parts] + except ValueError: + raise ValueError(f"bbox values must be floats, got: {bbox!r}") + return ( + f"POLYGON((" + f"{min_lng} {min_lat}, " + f"{max_lng} {min_lat}, " + f"{max_lng} {max_lat}, " + f"{min_lng} {max_lat}, " + f"{min_lng} {min_lat}" + f"))" + ) + + +def build_predicate( + names: list[str], + dataset_key: str = INAT_DATASET_KEY, + countries: list[str] | None = None, + bbox: str | None = None, +) -> dict: + """Build a GBIF download predicate dict from the given parameters.""" + if not names: + raise ValueError("names must not be empty") + + predicates = [] + + if len(names) == 1: + predicates.append( + {"type": "equals", "key": "VERBATIM_SCIENTIFIC_NAME", "value": names[0]} + ) + else: + predicates.append( + {"type": "in", "key": "VERBATIM_SCIENTIFIC_NAME", "values": names} + ) + + predicates.append({"type": "equals", "key": "DATASET_KEY", "value": dataset_key}) + predicates.append({"type": "equals", "key": "MEDIA_TYPE", "value": "StillImage"}) + + if countries: + if len(countries) == 1: + predicates.append({"type": "equals", "key": "COUNTRY", "value": countries[0]}) + else: + predicates.append({"type": "in", "key": "COUNTRY", "values": list(countries)}) + + if bbox: + predicates.append({"type": "within", "geometry": bbox_to_wkt(bbox)}) + + return {"type": "and", "predicates": predicates} + + +def submit_download(predicate: dict) -> str: + """Submit a GBIF download request and return the download key.""" + gbif_user = os.environ.get("GBIF_USER") + gbif_pwd = os.environ.get("GBIF_PWD") + gbif_email = os.environ.get("GBIF_EMAIL") + + missing = [name for name, val in [("GBIF_USER", gbif_user), ("GBIF_PWD", gbif_pwd), ("GBIF_EMAIL", gbif_email)] if not val] + if missing: + raise EnvironmentError( + f"Missing required environment variable(s): {', '.join(missing)}. " + "Set them in your .env file or environment before running." + ) + + from pygbif import occurrences as occ + + res = occ.download(predicate, format="DWCA", user=gbif_user, pwd=gbif_pwd, email=gbif_email) + return res[0] + + +def poll_download(key: str, poll_interval: int = 30, max_wait: int = 3600) -> dict: + """Poll GBIF until the download reaches a terminal status. + + Returns the metadata dict on SUCCEEDED; raises on failure or timeout. + """ + from pygbif import occurrences as occ + + start = time.time() + while True: + elapsed = int(time.time() - start) + meta = occ.download_meta(key) + status = meta.get("status", "UNKNOWN") + print(f"[{elapsed}s] status: {status}") + + if status in TERMINAL_STATUSES: + if status == "SUCCEEDED": + return meta + raise RuntimeError( + f"GBIF download {key} ended with non-success status: {status}" + ) + + if elapsed >= max_wait: + raise TimeoutError( + f"GBIF download {key} did not complete within {max_wait}s (last status: {status})" + ) + + time.sleep(poll_interval) + + +def fetch_archive(key: str, output_dir: str) -> str: + """Download the completed GBIF archive zip to output_dir and return its path.""" + from pygbif import occurrences as occ + + os.makedirs(output_dir, exist_ok=True) + occ.download_get(key, path=output_dir) + zip_path = os.path.join(output_dir, f"{key}.zip") + return zip_path + + +def download_gbif( + names: list[str], + output_dir: str, + dataset_key: str = INAT_DATASET_KEY, + countries: list[str] | None = None, + bbox: str | None = None, + poll_interval: int = 30, + max_wait: int = 3600, +) -> str: + """Orchestrate a full GBIF DwC-A download filtered by verbatim scientific names. + + Returns the path to the downloaded zip file. + """ + names = list(dict.fromkeys(names)) # deduplicate, preserve order + print(f"Requesting GBIF download for {len(names)} unique name(s).") + + predicate = build_predicate(names, dataset_key=dataset_key, countries=countries, bbox=bbox) + key = submit_download(predicate) + print(f"Download submitted. Key: {key}") + + poll_download(key, poll_interval=poll_interval, max_wait=max_wait) + + zip_path = fetch_archive(key, output_dir) + print(f"Archive saved to: {zip_path}") + print(f"Hint: ami-dataset fetch-images --dwca-file {zip_path} --dataset-path ") + return zip_path diff --git a/src/dataset_tools/fetch_and_pack.py b/src/dataset_tools/fetch_and_pack.py new file mode 100644 index 0000000..89e27f5 --- /dev/null +++ b/src/dataset_tools/fetch_and_pack.py @@ -0,0 +1,261 @@ +#!/usr/bin/env python +# coding: utf-8 + +""" +Sharded fetch-and-pack pipeline for large datasets. + +Downloads images in chunks, packs them into webdataset shards, then deletes raw +images before fetching the next chunk. At any point only one chunk of raw images +(~chunk_size files) exists on disk alongside the growing set of tars. + +Designed to replace the separate fetch + webdataset steps for datasets where +the combined raw image count would exceed cluster file quota. +""" + +import glob +import json +import os +import shutil +from functools import partial +from multiprocessing import Pool +from typing import Optional + +import pandas as pd +import requests +import webdataset as wds + +from src.dataset_tools.create_webdataset import ( + _create_samples, + _get_category_map, +) +from src.dataset_tools.utils import set_random_seeds + + +def _url_retrieve(url: str, file_path: str, timeout: int): + headers = { + "User-Agent": ( + "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:124.0) " + "Gecko/20100101 Firefox/124.0" + ) + } + r = requests.get(url, timeout=(timeout, timeout * 10), headers=headers) + with open(file_path, "wb") as f: + f.write(r.content) + + +def _fetch_image_for_chunk( + row, + temp_dir: str, + url_column: str, + image_path_column: str, + timeout: int, +): + """Fetch a single image into temp_dir, preserving the relative image_path structure.""" + url = row[url_column] + rel_path = row[image_path_column] + full_path = os.path.join(temp_dir, rel_path) + + if os.path.isfile(full_path): + return # already fetched (e.g. resume within same chunk attempt) + + img_dir = os.path.dirname(full_path) + if not os.path.isdir(img_dir): + try: + os.makedirs(img_dir, exist_ok=True) + except OSError: + print(f"Cannot create directory {img_dir}", flush=True) + return + + try: + _url_retrieve(url, full_path, timeout) + except Exception as e: + print(f"Error fetching {url}: {e}", flush=True) + + +def _load_progress(progress_file: str) -> tuple[list, int]: + """ + Load progress file. + + Returns: + completed: list of completed chunk indices + global_shard_idx: shard index to use for the next chunk + """ + if os.path.isfile(progress_file): + with open(progress_file, "r") as f: + data = json.load(f) + return data.get("completed", []), data.get("global_shard_idx", 0) + return [], 0 + + +def _save_progress(progress_file: str, completed: list, global_shard_idx: int): + with open(progress_file, "w") as f: + json.dump({"completed": completed, "global_shard_idx": global_shard_idx}, f) + + +def fetch_and_pack( + annotations_csv: str, + temp_dir: str, + webdataset_dir: str, + split: str, + label_column: str, + image_path_column: str, + url_column: str, + max_shard_size: int, + resize_min_size: Optional[int], + category_map_json: Optional[str], + save_category_map_json: Optional[str], + columns_to_json: Optional[str], + chunk_size: int, + num_workers: int, + request_timeout: int, + random_seed: int, + shuffle_images: bool, +): + """ + Fetch images from URLs in a split CSV and pack them into webdataset shards + one chunk at a time, deleting raw images after each chunk is packed. + + Args: + annotations_csv: Path to CSV file with columns for url, image_path, label. + temp_dir: Temporary directory to store raw images for the current chunk. + Cleaned up after each chunk. A split-specific subdirectory is created + inside this path. + webdataset_dir: Directory where .tar shards will be written. + split: Name of the split (e.g. "train", "val", "test"). Used as shard + filename prefix: {split}-%06d.tar + label_column: CSV column containing the category label. + image_path_column: CSV column containing the relative image file path. + url_column: CSV column containing the image download URL. + max_shard_size: Maximum size of each shard in bytes. + resize_min_size: If set, resize shortest image side to this value. + category_map_json: Path to existing category map JSON. If None and + save_category_map_json is set, the map is inferred from the CSV. + save_category_map_json: Path to save the inferred category map. + columns_to_json: Comma-separated list of CSV columns to embed as JSON + metadata in each sample. + chunk_size: Number of images to fetch and pack per chunk. + num_workers: Number of parallel download workers. + request_timeout: Per-request timeout in seconds. + random_seed: Random seed for reproducibility (used when shuffling). + shuffle_images: Whether to shuffle the dataset before chunking. + """ + set_random_seeds(random_seed) + + df = pd.read_csv(annotations_csv) + if shuffle_images: + df = df.sample(frac=1, random_state=random_seed).reset_index(drop=True) + + categories_map = _get_category_map( + df, label_column, category_map_json, save_category_map_json + ) + + os.makedirs(webdataset_dir, exist_ok=True) + os.makedirs(temp_dir, exist_ok=True) + + progress_file = os.path.join(webdataset_dir, f"{split}_progress.json") + completed, global_shard_idx = _load_progress(progress_file) + + # Shard filename pattern: e.g. .../train-%06d.tar + pattern = os.path.join(webdataset_dir, f"{split}-%06d.tar") + + total_rows = len(df) + num_chunks = (total_rows + chunk_size - 1) // chunk_size + print( + f"[fetch_and_pack] split={split} total_rows={total_rows} " + f"chunk_size={chunk_size} num_chunks={num_chunks} " + f"resume_from_chunk={len(completed)} global_shard_idx={global_shard_idx}", + flush=True, + ) + + fetch_fn = partial( + _fetch_image_for_chunk, + temp_dir=temp_dir, + url_column=url_column, + image_path_column=image_path_column, + timeout=request_timeout, + ) + + for chunk_idx in range(num_chunks): + if chunk_idx in completed: + print( + f"[fetch_and_pack] chunk {chunk_idx}/{num_chunks - 1} already done, skipping.", + flush=True, + ) + continue + + start = chunk_idx * chunk_size + end = min(start + chunk_size, total_rows) + chunk_df = df.iloc[start:end].copy() + + print( + f"[fetch_and_pack] chunk {chunk_idx}/{num_chunks - 1}: " + f"rows {start}–{end - 1}, shard_start={global_shard_idx}", + flush=True, + ) + + # --- Step 1: fetch images for this chunk --- + rows = [row for _, row in chunk_df.iterrows()] + with Pool(processes=num_workers) as pool: + pool.map(fetch_fn, rows) + + # --- Step 2: pack fetched images into webdataset shards --- + samples_written = 0 + next_shard_idx = global_shard_idx # fallback if nothing written + with wds.ShardWriter( + pattern, maxsize=max_shard_size, start_shard=global_shard_idx + ) as sink: + for sample in _create_samples( + dataset_path=temp_dir, + categories_map=categories_map, + dataset_df=chunk_df, + md_results=None, + label_column=label_column, + image_path_column=image_path_column, + columns_to_json=columns_to_json, + resize_min_size=resize_min_size, + ): + sink.write(sample) + samples_written += 1 + # Capture shard counter before __exit__ destroys it. + # sink.shard is already the *next* shard index (incremented on open). + if samples_written > 0: + next_shard_idx = sink.shard + + global_shard_idx = next_shard_idx + + print( + f"[fetch_and_pack] chunk {chunk_idx}: wrote {samples_written} samples, " + f"next shard idx = {global_shard_idx}", + flush=True, + ) + + # --- Step 3: delete raw images for this chunk --- + for _, row in chunk_df.iterrows(): + fpath = os.path.join(temp_dir, row[image_path_column]) + if os.path.isfile(fpath): + try: + os.remove(fpath) + except OSError as e: + print(f"Warning: could not delete {fpath}: {e}", flush=True) + + # Clean up any empty subdirectories left in temp_dir + for dirpath, dirnames, filenames in os.walk(temp_dir, topdown=False): + if dirpath == temp_dir: + continue + try: + os.rmdir(dirpath) # only removes if empty + except OSError: + pass # directory not empty, leave it + + # --- Step 4: save progress --- + completed.append(chunk_idx) + _save_progress(progress_file, completed, global_shard_idx) + + # All chunks done — remove progress file + if os.path.isfile(progress_file): + os.remove(progress_file) + + print( + f"[fetch_and_pack] complete: split={split} shards in {webdataset_dir}", + flush=True, + ) diff --git a/src/dataset_tools/inspect_webdataset.py b/src/dataset_tools/inspect_webdataset.py new file mode 100644 index 0000000..85c58ba --- /dev/null +++ b/src/dataset_tools/inspect_webdataset.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python +# coding: utf-8 + +""" +Inspect a webdataset: count images, classes, and show class distribution. + +Usage: + python src/dataset_tools/inspect_webdataset.py \ + --webdataset-dir data/vermont_butterflies/webdataset \ + --sort-by count + + python src/dataset_tools/inspect_webdataset.py \ + --webdataset-dir data/vermont_butterflies/webdataset \ + --category-map-json data/vermont_butterflies/webdataset/vermont_category_map_verbatim.json \ + --split train \ + --top-n 20 \ + --output-json /tmp/stats.json +""" + +import argparse +import collections +import glob +import json +import os +import statistics +import tarfile + + +def find_category_map(webdataset_dir, explicit_path=None): + if explicit_path: + return explicit_path + candidates = glob.glob(os.path.join(webdataset_dir, "*_category_map.json")) + if len(candidates) == 1: + print(f"Auto-discovered category map: {candidates[0]}", flush=True) + return candidates[0] + elif len(candidates) == 0: + print("Warning: no *_category_map.json found; showing raw class IDs.", flush=True) + return None + else: + names = [os.path.basename(c) for c in candidates] + print( + f"Warning: multiple category maps found ({names}); pass --category-map-json explicitly.", + flush=True, + ) + return None + + +def inspect_webdataset(webdataset_dir, category_map_json=None, split=None): + map_path = find_category_map(webdataset_dir, category_map_json) + id_to_name = {} + if map_path: + with open(map_path) as f: + name_to_id = json.load(f) + id_to_name = {v: k for k, v in name_to_id.items()} + + shards = sorted(glob.glob(os.path.join(webdataset_dir, "**/*.tar"), recursive=True)) + if split: + shards = [s for s in shards if split in os.path.basename(s)] + + class_counts = collections.Counter() + for i, shard in enumerate(shards): + print(f" Reading shard {i + 1}/{len(shards)}: {os.path.basename(shard)}", flush=True) + try: + with tarfile.open(shard) as tf: + for member in tf.getmembers(): + if member.name.endswith(".cls"): + cls_bytes = tf.extractfile(member).read().strip() + cls_id = int(cls_bytes) + class_counts[cls_id] += 1 + except Exception as e: + print(f" Warning: error reading {shard}: {e}", flush=True) + + return { + "total_images": sum(class_counts.values()), + "num_classes": len(class_counts), + "num_shards": len(shards), + "class_counts": {id_to_name.get(k, str(k)): v for k, v in class_counts.items()}, + } + + +def print_stats(stats, webdataset_dir, split=None, sort_by="count", top_n=None): + total = stats["total_images"] + counts = stats["class_counts"] + + print(f"\nWebdataset: {webdataset_dir}" + (f" [split: {split}]" if split else "")) + print(f"Shards: {stats['num_shards']:,}") + print(f"Images: {total:,}") + print(f"Classes: {stats['num_classes']:,}") + + if not counts: + return + + if sort_by == "name": + sorted_counts = sorted(counts.items(), key=lambda x: x[0]) + else: + sorted_counts = sorted(counts.items(), key=lambda x: x[1], reverse=True) + + if top_n: + sorted_counts = sorted_counts[:top_n] + + print(f"\nClass distribution (sorted by {sort_by}):") + print(f" {'Rank':>4} {'Class':<35} {'Count':>8} {'%':>5}") + print(f" {'-'*4} {'-'*35} {'-'*8} {'-'*5}") + for rank, (name, count) in enumerate(sorted_counts, 1): + pct = 100.0 * count / total if total > 0 else 0.0 + print(f" {rank:>4} {name:<35} {count:>8,} {pct:>5.1f}") + + values = list(counts.values()) + print( + f"\nMin: {min(values):,} Max: {max(values):,} " + f"Mean: {sum(values)/len(values):.1f} " + f"Median: {statistics.median(values):.1f}" + ) + + +def main(): + parser = argparse.ArgumentParser( + description="Inspect a webdataset: count images and show class distribution." + ) + parser.add_argument( + "--webdataset-dir", + required=True, + help="Directory to scan for .tar shards (searched recursively)", + ) + parser.add_argument( + "--category-map-json", + default=None, + help="Optional: JSON mapping class_name -> class_id. Auto-discovered if omitted.", + ) + parser.add_argument( + "--split", + default=None, + help="Optional: filter to a specific split by name (e.g. train, val, test)", + ) + parser.add_argument( + "--output-json", + default=None, + help="Optional: save full stats to a JSON file", + ) + parser.add_argument( + "--sort-by", + choices=["name", "count"], + default="count", + help="Sort class list by name or count (default: count descending)", + ) + parser.add_argument( + "--top-n", + type=int, + default=None, + help="Only print the top N classes (default: all)", + ) + args = parser.parse_args() + + stats = inspect_webdataset( + webdataset_dir=args.webdataset_dir, + category_map_json=args.category_map_json, + split=args.split, + ) + + print_stats( + stats, + webdataset_dir=args.webdataset_dir, + split=args.split, + sort_by=args.sort_by, + top_n=args.top_n, + ) + + if args.output_json: + with open(args.output_json, "w") as f: + json.dump(stats, f, indent=2) + print(f"\nStats saved to: {args.output_json}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/src/dataset_tools/utils.py b/src/dataset_tools/utils.py index 5b7209c..cbd8836 100644 --- a/src/dataset_tools/utils.py +++ b/src/dataset_tools/utils.py @@ -35,6 +35,7 @@ def load_dwca_data(dwca_file: str): "familyKey", "orderKey", "acceptedTaxonKey", + "verbatimScientificName", "lifeStage", "decimalLatitude", "decimalLongitude", diff --git a/src/dataset_tools/visualize_webdataset.py b/src/dataset_tools/visualize_webdataset.py new file mode 100644 index 0000000..98c903b --- /dev/null +++ b/src/dataset_tools/visualize_webdataset.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python +# coding: utf-8 + +""" +Visualize a webdataset using FiftyOne. + +Usage: + python src/dataset_tools/visualize_webdataset.py \ + --webdataset-pattern "data/vermont_butterflies/webdataset/vermont_train_verbatim-{000000..000051}.tar" \ + --category-map-json "data/vermont_butterflies/webdataset/vermont_category_map_verbatim.json" \ + --num-samples 500 \ + --port 5151 + +To access on a remote cluster via SSH tunnel: + ssh -L 5151:NODE_NAME:5151 USERNAME@fir.alliancecan.ca +Then open: http://localhost:5151 +""" + +import argparse +import io +import json +import tempfile +from pathlib import Path + +import fiftyone as fo +import webdataset as wds + + +def load_category_map(json_path: str) -> dict[int, str]: + """Load category map JSON and return a reverse mapping from int label -> species name.""" + with open(json_path, "r") as f: + categories = json.load(f) + # categories maps "Species name" -> int; reverse it + return {v: k for k, v in categories.items()} + + +def build_fiftyone_dataset( + webdataset_pattern: str, + category_map_json: str, + num_samples: int, + dataset_name: str, + temp_dir: str, +) -> fo.Dataset: + """Load samples from a webdataset and create a FiftyOne dataset.""" + int_to_label = load_category_map(category_map_json) + + dataset = fo.Dataset(name=dataset_name, overwrite=True) + samples = [] + temp_path = Path(temp_dir) + + pipeline = ( + wds.WebDataset(webdataset_pattern) + .decode("pil") + .to_tuple("__key__", "jpg", "cls") + ) + + for i, (key, image, cls_label) in enumerate(pipeline): + if i >= num_samples: + break + + # Save image to temp dir + img_path = temp_path / f"{key.replace('/', '_')}.jpg" + image.save(str(img_path), format="JPEG") + + label = int_to_label.get(int(cls_label), str(cls_label)) + + sample = fo.Sample( + filepath=str(img_path), + ground_truth=fo.Classification(label=label), + cls_int=int(cls_label), + key=key, + ) + samples.append(sample) + + if (i + 1) % 100 == 0: + print(f"Loaded {i + 1} samples...", flush=True) + + dataset.add_samples(samples) + print(f"Dataset built with {len(dataset)} samples.", flush=True) + return dataset + + +def main(): + parser = argparse.ArgumentParser( + description="Visualize a webdataset with FiftyOne." + ) + parser.add_argument( + "--webdataset-pattern", + required=True, + help="Glob/brace pattern for tar files, e.g. 'path/to/train-{000000..000051}.tar'", + ) + parser.add_argument( + "--category-map-json", + required=True, + help="Path to category map JSON (maps species name -> int label)", + ) + parser.add_argument( + "--num-samples", + type=int, + default=500, + help="Maximum number of samples to load (default: 500)", + ) + parser.add_argument( + "--port", + type=int, + default=5151, + help="Port for the FiftyOne app (default: 5151)", + ) + args = parser.parse_args() + + with tempfile.TemporaryDirectory() as temp_dir: + print(f"Writing images to temp dir: {temp_dir}", flush=True) + + dataset = build_fiftyone_dataset( + webdataset_pattern=args.webdataset_pattern, + category_map_json=args.category_map_json, + num_samples=args.num_samples, + dataset_name="webdataset_viz", + temp_dir=temp_dir, + ) + + print( + f"Launching FiftyOne app on port {args.port}. " + "Access via SSH tunnel: ssh -L {port}:NODENAME:{port} USER@fir.alliancecan.ca".format( + port=args.port + ), + flush=True, + ) + + session = fo.launch_app(dataset, remote=True, port=args.port) + session.wait() + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock index 71f8985..e17e9c4 100644 --- a/uv.lock +++ b/uv.lock @@ -1,15 +1,15 @@ version = 1 revision = 3 -requires-python = ">=3.9" +requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'linux'", - "python_full_version >= '3.12' and sys_platform != 'linux'", + "python_full_version >= '3.13' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and sys_platform == 'linux'", + "python_full_version >= '3.13' and sys_platform != 'linux'", + "python_full_version == '3.12.*' and sys_platform != 'linux'", "python_full_version == '3.11.*' and sys_platform == 'linux'", "python_full_version == '3.11.*' and sys_platform != 'linux'", - "python_full_version == 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