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Implement new Go based Desktop app
This focuses on Windows first, but coudl be used for Mac and possibly linux in the future.
This commit is contained in:
Daniel Hiltgen
committed by
jmorganca
jmorganca
parent
f397e0e988
commit
29e90cc13b
@@ -1,60 +1,72 @@
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# How to troubleshoot issues
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Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on Mac by running the command:
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```shell
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cat ~/.ollama/logs/server.log
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```
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On Linux systems with systemd, the logs can be found with this command:
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```shell
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journalctl -u ollama
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```
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When you run Ollama in a container, the logs go to stdout/stderr in the container:
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```shell
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docker logs <container-name>
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```
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(Use `docker ps` to find the container name)
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If manually running `ollama serve` in a terminal, the logs will be on that terminal.
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Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
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## LLM libraries
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Ollama includes multiple LLM libraries compiled for different GPUs and CPU
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vector features. Ollama tries to pick the best one based on the capabilities of
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your system. If this autodetection has problems, or you run into other problems
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(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
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library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest
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but most compatible is `cpu`. Rosetta emulation under MacOS will work with the
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`cpu` library.
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In the server log, you will see a message that looks something like this (varies
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from release to release):
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```
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Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
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```
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**Experimental LLM Library Override**
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You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass
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autodetection, so for example, if you have a CUDA card, but want to force the
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CPU LLM library with AVX2 vector support, use:
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```
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OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
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```
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You can see what features your CPU has with the following.
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```
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cat /proc/cpuinfo| grep flags | head -1
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```
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## Known issues
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# How to troubleshoot issues
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Sometimes Ollama may not perform as expected. One of the best ways to figure out what happened is to take a look at the logs. Find the logs on **Mac** by running the command:
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```shell
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cat ~/.ollama/logs/server.log
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```
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On **Linux** systems with systemd, the logs can be found with this command:
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```shell
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journalctl -u ollama
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```
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When you run Ollama in a **container**, the logs go to stdout/stderr in the container:
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```shell
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docker logs <container-name>
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```
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(Use `docker ps` to find the container name)
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If manually running `ollama serve` in a terminal, the logs will be on that terminal.
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When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
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- `explorer %LOCALAPPDATA%\Ollama` to view logs
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- `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
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- `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
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- `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
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To enable additional debug logging to help troubleshoot problems, first **Quit the running app from the tray menu** then in a powershell terminal
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```powershell
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$env:OLLAMA_DEBUG="1"
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& "ollama app.exe"
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```
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Join the [Discord](https://discord.gg/ollama) for help interpreting the logs.
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## LLM libraries
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Ollama includes multiple LLM libraries compiled for different GPUs and CPU
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vector features. Ollama tries to pick the best one based on the capabilities of
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your system. If this autodetection has problems, or you run into other problems
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(e.g. crashes in your GPU) you can workaround this by forcing a specific LLM
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library. `cpu_avx2` will perform the best, followed by `cpu_avx` an the slowest
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but most compatible is `cpu`. Rosetta emulation under MacOS will work with the
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`cpu` library.
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In the server log, you will see a message that looks something like this (varies
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from release to release):
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```
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Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
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```
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**Experimental LLM Library Override**
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You can set OLLAMA_LLM_LIBRARY to any of the available LLM libraries to bypass
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autodetection, so for example, if you have a CUDA card, but want to force the
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CPU LLM library with AVX2 vector support, use:
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```
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OLLAMA_LLM_LIBRARY="cpu_avx2" ollama serve
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```
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You can see what features your CPU has with the following.
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```
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cat /proc/cpuinfo| grep flags | head -1
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```
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## Known issues
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* N/A
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46
docs/windows.md
Normal file
46
docs/windows.md
Normal file
@@ -0,0 +1,46 @@
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# Ollama Windows Preview
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Welcome to the Ollama Windows preview.
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No more WSL required!
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Ollama now runs as a native Windows application, including NVIDIA GPU support.
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After installing Ollama Windows Preview, Ollama will run in the background and
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the `ollama` command line is available in `cmd`, `powershell` or your favorite
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terminal application. As usual the Ollama [api](./api.md) will be served on
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`http://localhost:11434`.
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As this is a preview release, you should expect a few bugs here and there. If
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you run into a problem you can reach out on
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[Discord](https://discord.gg/ollama), or file an
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[issue](https://github.com/ollama/ollama/issues).
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Logs will be often be helpful in dianosing the problem (see
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[Troubleshooting](#troubleshooting) below)
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## System Requirements
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* Windows 10 or newer, Home or Pro
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* NVIDIA 452.39 or newer Drivers if you have an NVIDIA card
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## API Access
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Here's a quick example showing API access from `powershell`
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```powershell
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(Invoke-WebRequest -method POST -Body '{"model":"llama2", "prompt":"Why is the sky blue?", "stream": false}' -uri http://localhost:11434/api/generate ).Content | ConvertFrom-json
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```
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## Troubleshooting
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While we're in preview, `OLLAMA_DEBUG` is always enabled, which adds
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a "view logs" menu item to the app, and increses logging for the GUI app and
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server.
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Ollama on Windows stores files in a few different locations. You can view them in
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the explorer window by hitting `<cmd>+R` and type in:
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- `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
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- *app.log* contains logs from the GUI application
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- *server.log* contains the server logs
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- *upgrade.log* contains log output for upgrades
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- `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
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- `explorer %HOMEPATH%\.ollama` contains models and configuration
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- `explorer %TEMP%` contains temporary executable files in one or more `ollama*` directories
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