The Dataset viewer window on the readme page of the dataset works, Data Studio reports the following error on the /viewer link: Server error while post-processing the rows. Please report the issue. Error code: TransformRowsProcessingError Exception: RuntimeError Message: Previous task error: I/O error: I/O error: Permission denied (os error 13) Traceback: Traceback (most recent call last): File "/src/libs/libapi/src/libapi/rows_utils.py", line 37, in _transform_row transformed_row[featureName] = get_cell_value( ~~~~~~~~~~~~~~^ dataset=dataset, ^^^^^^^^^^^^^^^^ ...<9 lines>... hf_token=hf_token, ^^^^^^^^^^^^^^^^^^ ) ^ File "/src/libs/libcommon/src/libcommon/viewer_utils/features.py", line 426, in get_cell_value return image( dataset=dataset, ...<9 lines>... json_path=json_path, ) File "/src/libs/libcommon/src/libcommon/viewer_utils/features.py", line 115, in image image = PILImage.open(f) File "/usr/local/lib/python3.14/site-packages/PIL/Image.py", line 3650, in open prefix = fp.read(16) File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 1307, in read return super().read(length) ~~~~~~~~~~~~^^^^^^^^ File "/usr/local/lib/python3.14/site-packages/fsspec/spec.py", line 1897, in read out = self.cache._fetch(self.loc, self.loc + length) File "/usr/local/lib/python3.14/site-packages/fsspec/caching.py", line 234, in _fetch self.cache = self.fetcher(start, end) # new block replaces old ~~~~~~~~~~~~^^^^^^^^^^^^ File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 1253, in _fetch_range return b"".join(self.fs._read_xet(self._xet_file_data, self.size, start=start, end=end)) ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 1126, in _read_xet group = session.new_download_stream_group(**xet_download_group_kwargs(xet_file_data, headers)) RuntimeError: Previous task error: I/O error: I/O error: Permission denied (os error 13) Link: https://huggingface.co/datasets/DominicTWHV/Discord-Scams/viewer

Muốn đẩy dữ liệu realtime từ server xuống trình duyệt mà không dựng WebSocket? Server-Sent Events (SSE) là chuẩn HTTP có sẵn: server giữ một kết nối mở và gửi từng dòng dữ liệu xuống, trình duyệt nhận bằng EventSource. Trong Flask bạn chỉ cần một generator, stream_with_context() và response text/event-stream — kèm cái bẫy RuntimeError mà hầu như ai cũng gặp lần đầu.

When a PyTorch job runs out of memory on a Mac, the error ends with a piece of advice: RuntimeError: MPS backend out of memory (MPS allocated: 18.50 GiB, other allocations: 448.00 KiB, max allowed: 18.72 GiB). Tried to allocate 512.00 MiB on shared pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure). I wanted to know what following that advice does, so I filled MPS memory in 512 MiB steps on my 48 GB M5 Pro (macOS 26.5.2, PyTorch 2.14.0), once per setting, under a guard that stops the process when swap grows by 2 GB. Two things came out of it. On this Mac the default limit is higher than the RAM, so a job that outgrows memory slides into swap and never gets to this error. And lowering the limit takes two variables, not one. ## Where "max allowed" comes from `max allowed` is the limit: `PYTORCH_MPS_HIGH_WATERMARK_RATIO` times what `torch.mps.recommended_max_memory()` returns. On my Mac that is 37.44 GiB, and 0.5 × 37.44 = 18.72 GiB, the number in the error above. `MPS allocated` matched `torch.mps.current_allocated_memory()` in my log, the memory held by tensors. To see your own base number: import torch print(torch.mps.recommended_max_memory() / 2**30, "GiB") ## Why a 48 GB Mac swaps first PyTorch's documentation gives the default high ratio as 1.7. On my Mac that puts the limit at about 63.6 GiB, more memory than the machine has. This is what each setting did: setting | what stopped the fill | tensors held | swap during the run ---|---|---|--- default (1.7) | the guard | 38.5 GiB | 6.1 → 12.0 GB 0.0 | the guard | 36.0 GiB | 5.1 → 9.8 GB 1.0, low 1.0 | the guard | 34.5 GiB | 4.6 → 8.7 GB 0.8, low 0.8 | PyTorch, max allowed 29.95 GiB | 29.5 GiB | no change 0.5, low 0.5 | PyTorch, max allowed 18.72 GiB | 18.5 GiB | no change With the default, with 0.0 and with 1.0, swap started to grow once the process passed 33.5 to 36.2 GB, and PyTorch never raised: its limit was further away than the end of the RAM. So `0.0`, the advice inside the error, changed nothing I could see here. The limit was already out of reach, and nothing in PyTorch stopped the run. ## Setting it: two variables Setting only the high ratio fails at the first MPS call: $ PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.8 python fill.py RuntimeError: invalid low watermark ratio 1.4 The low ratio defaults to 1.4 on Apple Silicon and may not be above the high one (unless the high one is 0.0, which turns the limit off). Set both: PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.8 PYTORCH_MPS_LOW_WATERMARK_RATIO=0.8 python run.py From Python, set them before the first tensor goes to `mps`. PyTorch reads them when MPS starts, not at `import torch`, and ignores changes made after that: import os os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.8" os.environ["PYTORCH_MPS_LOW_WATERMARK_RATIO"] = "0.8" import torch The documentation calls the low ratio a soft limit and says that "setting 0.0 disables adaptive commit and garbage collection". I set it equal to the high one and did not measure it on its own. ## Picking a value The limit should stay below the memory your Mac really has free, with your usual apps open. On mine 1.0 (37.44 GiB) was already too much, and 0.8 (29.95 GiB) gave a clean error with no swap. After a clean error you can lower the batch size or the resolution and try again; a run sliding into swap slows down the whole Mac first. ## A trap when you test this My first run filled the tensors with `torch.ones`. With the default setting it reached the 46 GiB cap I had set, with no error and no swap at all: macOS compresses pages of identical values almost to nothing. Model weights don't compress like that, so a memory test on a Mac needs random data. ## When the model really doesn't fit A clean error only tells you the model doesn't fit. For a pipeline of several models, loading only the models of the current stage does more than any ratio, which is why I wrote stageload. The fill script and the traces of every run above are in its repository, in bench/results/2026-10-13-mps-watermark.

Learn why Python raises "dictionary changed size during iteration," how it differs from list mutation, and three safe ways to delete dictionary keys.

punkx.org/apl Site useless in Pale Moon (latest: 35.0.0 32bit) because wasm engine will not start but starts and functions well in most other browsers: Firefox (on same system), Chrome, Edge, Brave(64bit), etc. Failure appears to be a compatibility problem. Please advise. System: MXLinux V23.6 32bit fully up-to-date. CODE: RuntimeError: Aborted(CompileError: wasm validation error: at offset 5396: expected function bodies). Build with -sASSERTIONS for more info. [moderator note - edited for legibility] * * *

Changes since 1.12.0 Known issues Web RuntimeError: memory access out of bounds error (YouTrack issue). Workaround is to disable the snapshot cache: ComposeUiFlags.useSnapshotCache = false Highli...

I see this error **fairly often** , so I tried to narrow it down as far as I could with a few experiments: * * * I think the **corrupt classifier checkpoint and the fallback retraining are probably the most useful place to start** , rather than the CLIP inference itself. Your log has this sequence: classifier checkpoint cannot be loaded → corrupt file is moved aside → classifier is retrained on CPU for 60 epochs → Space finishes starting → first ZeroGPU request → ZeroGPU worker tries to initialize real CUDA → RuntimeError: No CUDA GPUs are available The interesting part is that the CUDA error happens inside ZeroGPU’s `worker_init`, **before your GPU function gets to the CLIP work**. I made a set of small ZeroGPU test Spaces and progressively removed pieces of the startup path. Eventually I could reproduce essentially the same worker-init failure with nothing more than a single CPU `backward()` in the long-lived Space process: x = torch.tensor(2.0, requires_grad=True) (x * x).backward() Then, on the first later `@spaces.GPU` call, the worker failed at real CUDA initialization with: spaces/zero/wrappers.py -> worker_init torch.init(...) torch.Tensor([0]).cuda() torch._C._cuda_init() RuntimeError: No CUDA GPUs are available The same `backward()` executed in a **separate Python subprocess** did _not_ break the later ZeroGPU worker. So my first two choices would be: 1. **Replace/regenerate the corrupt classifier checkpoint in the repository** , so the Space does not retrain it during startup. 2. If runtime retraining is intentional, **run the training/backward pass in a separate Python process** , let that process exit, then load the resulting checkpoint in the main app process. For example, structurally: if classifier_needs_training: subprocess.run( [sys.executable, "train_classifier.py"], check=True, ) load_classifier() # Later: # @spaces.GPU inference I would try the valid-checkpoint route first because it is the smallest change. Also, if the repaired checkpoint is only written to the Space’s runtime filesystem, remember that normal Space disk is ephemeral across restarts. If you want the fix to survive a rebuild/restart, replace the bad file in the repo or use persistent storage: Spaces storage documentation. What I tested (click for more details) Why CPU backward() and ZeroGPU can interact badly (click for more details) What I would change in TerraID (click for more details) A separate ZeroGPU/CLIP point for after the worker issue is fixed (click for more details) About the `ValueError: Invalid file descriptor: -1` (click for more details) One final caution: the `worker_init -> No CUDA GPUs are available` stack is not unique to one root cause. Other ZeroGPU Spaces have produced a very similar stack from other mistakes, for example a GPU path that was not correctly decorated: MMAudio ZeroGPU discussion. So I would not diagnose this from the stack trace alone. What makes the startup-training explanation much more interesting here is that the posted log already shows an unexpected retraining pass immediately before the first GPU request, and a minimal CPU `backward()` was enough to reproduce the same later ZeroGPU worker failure in isolation. For this particular Space, **a valid classifier checkpoint that eliminates startup retraining is the lowest-cost test I would try first**.

It looks like the main issue is with the ZeroGPU/CUDA initialization rather than your classifier itself. The Space starts correctly, but when the ZeroGPU worker tries to initialize CUDA, it gets “No CUDA GPUs are available”. I’d first check that the Space is actually configured to use ZeroGPU and that the GPU function is properly decorated with `@spaces.GPU`. The corrupted checkpoint is a separate issue, but your code already seems to detect and retrain it. The `Invalid file descriptor: -1` errors look like secondary asyncio shutdown errors rather than the main cause.

Hello everyone, this is my first post on this forum. I made a pottery dating machine. The space is called ‘terraID’. However, whenever I click the ‘Analyze’ button, I keep getting the error that there are No CUDA GPUs are available. I tried everything(yes, import Spaces is the first line of the app.py). Is there anyone who knows how this problem can be solved?There is also a ValueError that I can’t quite figure out. My username is ‘Stormpie’. Thanks in advance for the help! ===== Application Startup at 2026-09-07 08:25:26 ===== # [notice] A new release of pip is available: 26.1.2 → 26.2.1 [notice] To update, run: pip install --upgrade pip # MUSEUM AI v5.2 # Starting ZeroGPU-compatible Space Device at startup: CPU CLIP: lazy-loaded on ZeroGPU request Imported 490 memory examples from museum_memory.json FAISS memory rebuilt: 490 vectors Could not load classifier: PytorchStreamReader failed reading zip archive: unsupported multidisk archive. This is an internal miniz error. If you are seeing this error, there is a high likelihood that your checkpoint file is corrupted. This can happen if the checkpoint was not saved properly, was transferred incorrectly, or the file was modified after saving. Corrupt classifier moved to: museum_classifier_v5.pt.corrupt Training classifier from existing memory… Epoch 001/60 val=11.7% Epoch 010/60 val=21.3% Epoch 020/60 val=27.7% Epoch 030/60 val=30.9% Epoch 040/60 val=35.1% Epoch 050/60 val=40.4% Epoch 060/60 val=44.7% Classifier checkpoint saved: museum_classifier_v5.pt Classifier getraind. Validation accuracy: 45.7%. Training: 396, validation: 94. ### Memory **Totaal:** 490 voorbeelden 0. **ca. 5300-2000 v.Chr.** — 64 1. **2000-0 v.Chr** — 73 2. **0-400 n.Chr** — 79 3. **400-1200 n.Chr** — 73 4. **1200-1500 n.Chr** — 35 5. **1500-1700 n.Chr** — 73 6. **1700-1899 n.Chr** — 52 7. **1900-nu n.Chr** — 41 **FAISS vectors:** 490 **Validation accuracy:** 45.7% * Running on local URL: http://0.0.0.0:7860, with SSR (Node proxy → Python :7861) Exception ignored in: <function BaseEventLoop.__del_ _ at 0x7fb288bc09a0> Traceback (most recent call last): File “/usr/local/lib/python3.12/asyncio/base_events.py”, line 732, in __del_ _ self.close() File “/usr/local/lib/python3.12/asyncio/unix_events.py”, line 68, in close super().close() File “/usr/local/lib/python3.12/asyncio/selector_events.py”, line 104, in close self._close_self_pipe() File “/usr/local/lib/python3.12/asyncio/selector_events.py”, line 111, in _close_self_pipe self._remove_reader(self._ssock.fileno()) File “/usr/local/lib/python3.12/asyncio/selector_events.py”, line 298, in _remove_reader key = self._selector.get_key(fd) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 190, in get_key return mapping[fileobj] ~~~~~~~^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 71, in __getitem_ _ fd = self._selector._fileobj_lookup(fileobj) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 225, in _fileobj_lookup return _fileobj_to_fd(fileobj) ^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 42, in _fileobj_to_fd raise ValueError(“Invalid file descriptor: {}”.format(fd)) ValueError: Invalid file descriptor: -1 Exception ignored in: <function BaseEventLoop.__del_ _ at 0x7fb288bc09a0> Traceback (most recent call last): File “/usr/local/lib/python3.12/asyncio/base_events.py”, line 732, in __del_ _ self.close() File “/usr/local/lib/python3.12/asyncio/unix_events.py”, line 68, in close super().close() File “/usr/local/lib/python3.12/asyncio/selector_events.py”, line 104, in close self._close_self_pipe() File “/usr/local/lib/python3.12/asyncio/selector_events.py”, line 111, in _close_self_pipe self._remove_reader(self._ssock.fileno()) File “/usr/local/lib/python3.12/asyncio/selector_events.py”, line 298, in _remove_reader key = self._selector.get_key(fd) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 190, in get_key return mapping[fileobj] ~~~~~~~^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 71, in __getitem_ _ fd = self._selector._fileobj_lookup(fileobj) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 225, in _fileobj_lookup return _fileobj_to_fd(fileobj) ^^^^^^^^^^^^^^^^^^^^^^^ File “/usr/local/lib/python3.12/selectors.py”, line 42, in _fileobj_to_fd raise ValueError(“Invalid file descriptor: {}”.format(fd)) ValueError: Invalid file descriptor: -1 * To create a public link, set `share=True` in `launch()`. Traceback (most recent call last): File “/usr/local/lib/python3.12/site-packages/spaces/zero/wrappers.py”, line 148, in worker_init torch.init(nvidia_uuid) File “/usr/local/lib/python3.12/site-packages/spaces/zero/torch/patching.py”, line 417, in init torch.Tensor([0]).cuda() File “/usr/local/lib/python3.12/site-packages/torch/cuda/__init_ _.py”, line 478, in _lazy_init torch._C._cuda_init() RuntimeError: No CUDA GPUs are available

`assert isinstance(flag, bool)` reads like a check. Under `python -O` it is not there at all, and nothing at the call site says so. The usual advice is to stop writing safety checks as asserts. There is a better answer: let the module refuse to be imported in the mode where its checks evaporate. if not __debug__: raise RuntimeError("this module's checks are asserts; refusing to run with -O") $ python useguard.py guard loaded, gate(True) = True $ python -O useguard.py; echo $? RuntimeError: this module's checks are asserts; refusing to run with -O 1 Five lines, and the failure moves from the approval to the import. A deployment that will not start is a different kind of problem from a gate that quietly stopped gating. ## What the runtime will and will not tell you Two different questions get confused here, and they have different answers. **What has this image been imported under?** The optimization level is stamped into the bytecode cache filename, so the artifacts accumulate: $ python use.py && python -O use.py && python -OO use.py $ ls __pycache__ m.cpython-314.opt-1.pyc m.cpython-314.opt-2.pyc m.cpython-314.pyc That is real evidence and it survives the process that made it. It is also easy to lose: $ rm -rf __pycache__ && PYTHONDONTWRITEBYTECODE=1 python -O use.py $ ls __pycache__ (no such directory) A read-only image layer does the same thing. So the artifact answers a forensic question, and answers it only sometimes. **What is this process running under right now?** That one is reliable: $ python probe.py sys.flags.optimize = 0 | __debug__ = True $ python -O probe.py sys.flags.optimize = 1 | __debug__ = False question | source | can it be missing? ---|---|--- what has been imported | `__pycache__/*.opt-N.pyc` | yes: `PYTHONDONTWRITEBYTECODE`, or a read-only layer what is running now | `sys.flags.optimize`, `__debug__` | no The guard uses the second one, which is why it works in an image with no `__pycache__` at all. ## Why "just don't use asserts" is the weaker rule It is advice to every future author of every line in the module, and review is what enforces it. The import guard is one line. The interpreter enforces that one, including over asserts nobody has written yet. It also fails in the right direction. Someone deploying with `-O` for speed gets a crash with a sentence explaining the conflict, at startup, in their own terminal. The alternative is that the same person deploys successfully and finds out later, from an action nobody approved. ## What it does not do It does not make asserts a good way to express a safety invariant. If the check matters, `if not isinstance(...): raise` is still better, and the guard is what you put on top of the module while the asserts are still in it. It also stops at the module boundary. A module with the guard is safe under `-O`; a module in the same process without it is not, and nothing coordinates the two. Both of those cost more than five lines. This costs five. _Measured on CPython 3.14.4 (Windows) and cross-checked against 3.14.6 on macOS by @vinhnguyenthanhdn, who pointed out the`__pycache__` artifact in the first place. The three-file listing and the `PYTHONDONTWRITEBYTECODE` result are reproduced from runs on both._

generate_tiles.py has worked fine throughout the transition from mapnik 0.7 to mapnik 2.x in gentoo linux. Now I have switched to debian (wheezy) and I am getting the following error when I run generate_tiles.py: RuntimeError: Failed to find font face 'unifont Medium' in FontSet 'book-fonts' in map '.../osm.xml' If I comment out the book-fonts section in fontset-settings.xml.inc, I get: RuntimeError: Failed to find font face 'unifont Medium' in FontSet 'bold-fonts' in map '.../osm.xml' However, using tilelite creates the tiles just fine. $ ls `python -c "import mapnik2;print mapnik2.fontscollectionpath"` total 9048 drwxr-xr-x 2 root root 4096 2014-10-11 09:10 . drwxr-xr-x 13 root root 4096 2014-10-11 20:48 .. -rw-r--r-- 1 root root 611212 2011-02-27 07:37 DejaVuSans-BoldOblique.ttf -rw-r--r-- 1 root root 672300 2011-02-27 07:37 DejaVuSans-Bold.ttf -rw-r--r-- 1 root root 580168 2011-02-27 07:37 DejaVuSansCondensed-BoldOblique.ttf -rw-r--r-- 1 root root 631992 2011-02-27 07:37 DejaVuSansCondensed-Bold.ttf -rw-r--r-- 1 root root 576004 2011-02-27 07:37 DejaVuSansCondensed-Oblique.ttf -rw-r--r-- 1 root root 643852 2011-02-27 07:37 DejaVuSansCondensed.ttf -rw-r--r-- 1 root root 345204 2011-02-27 07:37 DejaVuSans-ExtraLight.ttf -rw-r--r-- 1 root root 235848 2011-02-27 07:37 DejaVuSansMono-BoldOblique.ttf -rw-r--r-- 1 root root 313856 2011-02-27 07:37 DejaVuSansMono-Bold.ttf -rw-r--r-- 1 root root 241972 2011-02-27 07:37 DejaVuSansMono-Oblique.ttf -rw-r--r-- 1 root root 333636 2011-02-27 07:37 DejaVuSansMono.ttf -rw-r--r-- 1 root root 611556 2011-02-27 07:37 DejaVuSans-Oblique.ttf -rw-r--r-- 1 root root 720012 2011-02-27 07:37 DejaVuSans.ttf -rw-r--r-- 1 root root 332036 2011-02-27 07:37 DejaVuSerif-BoldItalic.ttf -rw-r--r-- 1 root root 341072 2011-02-27 07:37 DejaVuSerif-Bold.ttf -rw-r--r-- 1 root root 331128 2011-02-27 07:37 DejaVuSerifCondensed-BoldItalic.ttf -rw-r--r-- 1 root root 316440 2011-02-27 07:37 DejaVuSerifCondensed-Bold.ttf -rw-r--r-- 1 root root 338140 2011-02-27 07:37 DejaVuSerifCondensed-Italic.ttf -rw-r--r-- 1 root root 330012 2011-02-27 07:37 DejaVuSerifCondensed.ttf -rw-r--r-- 1 root root 338776 2011-02-27 07:37 DejaVuSerif-Italic.ttf -rw-r--r-- 1 root root 363200 2011-02-27 07:37 DejaVuSerif.ttf No book font, but there are bold fonts. These are all the dejavu fonts installed (with their version numbers): ttf-dejavu 2.33-3 ttf-dejavu-core 2.33-3 ttf-dejavu-extra 2.33-3 I've seen a few people having this problem around the web, but no solutions offered.

Hi everyone, I’m learning Python and experimenting with AI APIs for practice. I’m currently trying code like this: import os from google import genai api_key = os.getenv("GEMINI_API_KEY") if not api_key: raise RuntimeError("GEMINI_API_KEY is not set.") client = genai.Client(api_key=api_key) I’d like to learn how to properly set up and use an API without accidentally exposing my key. Is there a legitimate free tier or trial available for beginners? Any tips for setting up the API key securely would also be appreciated.

I have managed to get my previous update cursor to work without any hiccups. All I have done is change the fields the script needs to look for. The error I am getting is: RuntimeError: A column was specified that does not exist. The column exists in the attribute table the runtime error is on line 52 of the script (for row in cursor) : Here is the script #Script to add in a new period field and base the values on the existing HER period column import arcpy #Adjust these two lines: fc = arcpy.GetParameterAsText(0) fields = ['PeriodRang','PeriodFrom'] #Dictionary of old and new value. Add all your values here, instead of many if/elifs remap = { 'Palaeolithic':'Palaeolithic', 'Later Prehistoric':'Prehistoric', 'Prehistoric':'Prehistoric', 'Mesolithic':'Mesolithic', 'Late Mesolithic to Late Bronze Age':'Mesolithic', 'Neolithic':'Neolithic', 'modern':'Modern', 'Unknown':'Undated', 'Undated':'Undated' } with arcpy.da.UpdateCursor(fc, fields) as cursor: for row in cursor: if row[0] in remap: #row[0] is the value of periodRang field. This if, is checking whether the value exist in the remap dictionary row[1] = remap[row[0]] cursor.updateRow(row) ```

This isn’t a lab experiment—it’s a scalable, saltwater-based process that doesn’t compete for farmland. The study shows... Read more

460 million for green energy? Yes. But the real money is in the UK’s FDI rules — which now require pre-clearance for eve... Read more

I am trying to export some test products into CADRG format using GDAL 3.13. I got a .tif file from the FAA VFR Raster Charts page. Issue When I run the following code I get the error: RuntimeError: Only IC=NC (uncompressed), IC=C3/M3 (JPEG) and IC=C8 (JPEG2000) allowed with NITF CreateCopy method. Test Code import os from osgeo import gdal # OsGeo4W os.environ["OSGEO4W_ROOT"] = str("OSGeo4W") # GDAL os.environ["GDAL_DATA"] = str(os.path.join(os.environ["OSGEO4W_ROOT"], "apps/gdal/share/gdal")) os.environ["GDAL_DRIVER_PATH"] = str(os.path.join(os.environ["OSGEO4W_ROOT"], "apps/gdal/lib/gdalplugins")) # PROJ os.environ["PROJ_DATA"] = str(os.path.join(os.environ["OSGEO4W_ROOT"], "share/proj")) INPUT_DIR = Path("maps/") OUTPUT_DIR = Path("maps/temp_output") FILE = "Atlanta SEC" gdal.UseExceptions() file_tif = str(FILE + ".tif") file_vrt = str(INPUT_DIR, FILE + ".vrt") file_dir = Path(OUTPUT_DIR) print("Opening file...") ds = gdal.Open(str(file_tif)) print("Translating to VRT with RGB expansion...") rgb = gdal.Translate( str(file_vrt), ds, options=gdal.TranslateOptions( format="VRT", rgbExpand="rgb", ), ) print("Translating to NITF format...") gdal.Translate( str(file_dir), rgb, options=gdal.TranslateOptions( format="NITF", creationOptions=[ "PRODUCT_TYPE=CADRG", "SCALE=500000", "SERIES_CODE=LF", "IC=C4" ], ), ) print("Done.") If I remove IC, it works without issue, but I was trying to figure out why it won't work since the GDAL Documentation states: C4 means Vector Quantized (VQ) compression, and is only available when PRODUCT_TYPE=CADRG. I want to make sure that the output file is actually CADRG format, since it should be Vector Quantized compression. The error seems to be counter to the documentation. Additional Information This issue also arises using the CLI version of GDAL. EDIT --- IC=C4 is confirmed not required to get the correct compression Added issue on github: (#14709) Issue will be fixed in PR #14710

Repro steps: Install .NET 10 Preview 4 with wasm-tools workload Create new Blazor Web App with interactive WebAssembly rendering enabled Add MSBuild properties to the Client project to enable brows...