[Verda] Rename the datacrunch backend to verda #3344

Author: peterschmidt85

contributing/BACKENDS.md

@@ -42,7 +42,7 @@ base class. See its docstrings for descriptions of the methods that your class s
 
 Refer to examples:
 - Offline providers:
-  [datacrunch.py](https://github.com/dstackai/gpuhunt/blob/main/src/gpuhunt/providers/datacrunch.py),
+  [verda.py](https://github.com/dstackai/gpuhunt/blob/main/src/gpuhunt/providers/verda.py),
   [aws.py](https://github.com/dstackai/gpuhunt/blob/main/src/gpuhunt/providers/aws.py),
   [azure.py](https://github.com/dstackai/gpuhunt/blob/main/src/gpuhunt/providers/azure.py),
   [lambdalabs.py](https://github.com/dstackai/gpuhunt/blob/main/src/gpuhunt/providers/lambdalabs.py).
@@ -64,7 +64,7 @@ Add your provider in the following places:
 For offline providers, you can add data quality tests under `src/integrity_tests/`.
 Data quality tests are run after collecting offline catalogs to ensure their integrity.
 
-Refer to examples: [test_datacrunch.py](https://github.com/dstackai/gpuhunt/blob/main/src/integrity_tests/test_datacrunch.py),
+Refer to examples: [test_verda.py](https://github.com/dstackai/gpuhunt/blob/main/src/integrity_tests/test_verda.py),
 [test_gcp.py](https://github.com/dstackai/gpuhunt/blob/main/src/integrity_tests/test_gcp.py).
 
 ### 1.6. Submit a pull request
@@ -125,7 +125,7 @@ Then add these models to `AnyBackendConfig*` unions in [`src/dstack/_internal/co
 The script also generates `*BackendStoredConfig` that extends `*BackendConfig` to be able to store extra parameters in the DB. By the same logic, it generates `*Config` that extends `*BackendStoredConfig` with creds and uses it as the main `Backend` and `Compute` config instead of using `*BackendConfigWithCreds` directly.
 
 Refer to examples:
-[datacrunch](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/datacrunch/models.py),
+[verda](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/verda/models.py),
 [aws](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/aws/models.py),
 [gcp](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/gcp/models.py),
 [azure](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/models.py), etc.
@@ -136,7 +136,7 @@ Go to `compute.py` and implement `Compute` methods.
 Optionally, extend and implement `ComputeWith*` classes to support additional features such as fleets, volumes, gateways, placement groups, etc. For example, extend `ComputeWithCreateInstanceSupport` to support fleets.
 
 Refer to examples:
-[datacrunch](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/datacrunch/compute.py),
+[verda](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/verda/compute.py),
 [aws](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/aws/compute.py),
 [gcp](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/gcp/compute.py),
 [azure](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/azure/compute.py), etc.
@@ -146,7 +146,7 @@ Refer to examples:
 Go to `configurator.py` and implement custom `Configurator` logic. At minimum, you should implement creds validation.
 You may also need to validate other config parameters if there are any.
 
-Refer to examples: [datacrunch](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/datacrunch/configurator.py),
+Refer to examples: [verda](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/verda/configurator.py),
 [aws](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/aws/configurator.py),
 [gcp](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/gcp/configurator.py),
 [azure](https://github.com/dstackai/dstack/blob/master/src/dstack/_internal/core/backends/azure/configurator.py), etc.
@@ -195,7 +195,7 @@ For some VM-based backends, the `dstack` team also maintains
 [custom VM images](../scripts/packer/README.md) with the required dependencies
 and `dstack`-specific optimizations.
 
-Examples of VM-based backends include: `aws`, `azure`, `gcp`, `lambda`, `datacrunch`, `tensordock`, etc.
+Examples of VM-based backends include: `aws`, `azure`, `gcp`, `lambda`, `verda`, etc.
 
 #### 3.1.2. Container-based backend compute type
 

contributing/GPUHUNT.md

@@ -65,7 +65,7 @@ Some providers offer extreme flexibility in possible configurations, but not all
 - Filters out if: outdated family, not supported family
 - Queries configuration details to fill CPU, RAM, and GPU information
 
-### DataCrunch
+### Verda
 
 - Just queries all offers via API
 

docs/assets/images/verda-logo.svg

@@ -854,9 +854,9 @@ There are two ways to configure OCI: using client credentials or using the defau
         compartment_id: ocid1.compartment.oc1..aaaaaaaa
     ```
 
-### DataCrunch
+### Verda (formerly DataCrunch)
 
-Log into your [DataCrunch](https://cloud.datacrunch.io/) account, click Keys in the sidebar, find `REST API Credentials` area and then click the `Generate Credentials` button.
+Log into your [Verda](https://console.verda.com/signin) account, click Keys in the sidebar, find `REST API Credentials` area and then click the `Generate Credentials` button.
 
 Then, go ahead and configure the backend:
 
@@ -866,7 +866,7 @@ Then, go ahead and configure the backend:
 projects:
   - name: main
     backends:
-      - type: datacrunch
+      - type: verda
         creds:
           type: api_key
           client_id: xfaHBqYEsArqhKWX-e52x3HH7w8T

docs/docs/concepts/backends.md

@@ -433,17 +433,17 @@ $ dstack offer --gpu H100 --max-offers 10
 Getting offers...
 ---> 100%
 
- #   BACKEND     REGION     INSTANCE TYPE          RESOURCES                                     SPOT  PRICE   
- 1   datacrunch  FIN-01     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 2   datacrunch  FIN-02     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 3   datacrunch  FIN-02     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 4   datacrunch  ICE-01     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 5   runpod      US-KS-2    NVIDIA H100 PCIe       16xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.39   
- 6   runpod      CA         NVIDIA H100 80GB HBM3  24xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.69   
- 7   nebius      eu-north1  gpu-h100-sxm           16xCPU, 200GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.95   
- 8   runpod      AP-JP-1    NVIDIA H100 80GB HBM3  20xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
- 9   runpod      CA-MTL-1   NVIDIA H100 80GB HBM3  28xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
- 10  runpod      CA-MTL-2   NVIDIA H100 80GB HBM3  26xCPU, 125GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
+ #   BACKEND  REGION     INSTANCE TYPE          RESOURCES                                     SPOT  PRICE   
+ 1   verda    FIN-01     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 2   verda    FIN-02     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 3   verda    FIN-02     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 4   verda    ICE-01     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 5   runpod   US-KS-2    NVIDIA H100 PCIe       16xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.39   
+ 6   runpod   CA         NVIDIA H100 80GB HBM3  24xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.69   
+ 7   nebius   eu-north1  gpu-h100-sxm           16xCPU, 200GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.95   
+ 8   runpod   AP-JP-1    NVIDIA H100 80GB HBM3  20xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
+ 9   runpod   CA-MTL-1   NVIDIA H100 80GB HBM3  28xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
+ 10  runpod   CA-MTL-2   NVIDIA H100 80GB HBM3  26xCPU, 125GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
      ...                                                                                                                
  Shown 10 of 99 offers, $127.816 max
 ```

docs/docs/guides/protips.md

@@ -57,17 +57,17 @@ $ dstack offer --gpu H100:1.. --max-offers 10
 Getting offers...
 ---> 100%
 
- #   BACKEND     REGION     INSTANCE TYPE          RESOURCES                                     SPOT  PRICE   
- 1   datacrunch  FIN-01     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 2   datacrunch  FIN-02     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 3   datacrunch  FIN-02     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 4   datacrunch  ICE-01     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
- 5   runpod      US-KS-2    NVIDIA H100 PCIe       16xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.39   
- 6   runpod      CA         NVIDIA H100 80GB HBM3  24xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.69   
- 7   nebius      eu-north1  gpu-h100-sxm           16xCPU, 200GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.95   
- 8   runpod      AP-JP-1    NVIDIA H100 80GB HBM3  20xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
- 9   runpod      CA-MTL-1   NVIDIA H100 80GB HBM3  28xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
- 10  runpod      CA-MTL-2   NVIDIA H100 80GB HBM3  26xCPU, 125GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
+ #   BACKEND  REGION     INSTANCE TYPE          RESOURCES                                     SPOT  PRICE   
+ 1   verda    FIN-01     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 2   verda    FIN-02     1H100.80S.30V          30xCPU, 120GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 3   verda    FIN-02     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 4   verda    ICE-01     1H100.80S.32V          32xCPU, 185GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.19   
+ 5   runpod   US-KS-2    NVIDIA H100 PCIe       16xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.39   
+ 6   runpod   CA         NVIDIA H100 80GB HBM3  24xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.69   
+ 7   nebius   eu-north1  gpu-h100-sxm           16xCPU, 200GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.95   
+ 8   runpod   AP-JP-1    NVIDIA H100 80GB HBM3  20xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
+ 9   runpod   CA-MTL-1   NVIDIA H100 80GB HBM3  28xCPU, 251GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
+ 10  runpod   CA-MTL-2   NVIDIA H100 80GB HBM3  26xCPU, 125GB, 1xH100 (80GB), 100.0GB (disk)  no    $2.99   
      ...                                                                                                                
  Shown 10 of 99 offers, $127.816 max
 ```

docs/docs/reference/cli/dstack/offer.md

@@ -258,18 +258,18 @@ to configure [backends](../../concepts/backends.md) and other [server-level sett
         type:
             required: true
 
-##### `projects[n].backends[type=datacrunch]` { #datacrunch data-toc-label="datacrunch" }
+##### `projects[n].backends[type=verda]` { #verda data-toc-label="verda" }
 
-#SCHEMA# dstack._internal.core.backends.datacrunch.models.DataCrunchBackendConfigWithCreds
+#SCHEMA# dstack._internal.core.backends.verda.models.VerdaBackendConfigWithCreds
     overrides:
         show_root_heading: false
         type:
             required: true
-        item_id_prefix: datacrunch-
+        item_id_prefix: verda-
 
-###### `projects[n].backends[type=datacrunch].creds` { #datacrunch-creds data-toc-label="creds" }
+###### `projects[n].backends[type=verda].creds` { #verda-creds data-toc-label="creds" }
 
-#SCHEMA# dstack._internal.core.backends.datacrunch.models.DataCrunchAPIKeyCreds
+#SCHEMA# dstack._internal.core.backends.verda.models.VerdaAPIKeyCreds
     overrides:
         show_root_heading: false
         type:

docs/docs/reference/server/config.yml.md

@@ -95,9 +95,9 @@ hide:
     </span> -->
 
     <span class="feature-cell" target="_blank">
-        <img width="52" style="margin: 10px -4px;" src="/assets/images/datacrunch-logo.svg">
+        <img width="42" src="/assets/images/verda-logo.svg">
         <h3>
-            DataCrunch
+            Verda
         </h3>
     </span>
 </div>

docs/partners.md

@@ -98,10 +98,10 @@ cloud resources and run the configuration.
 ```shell
 $ dstack apply -f examples/models/wan22/.dstack.yml
 
- #  BACKEND              RESOURCES                                        INSTANCE TYPE   PRICE
- 1  datacrunch (FIN-01)  cpu=30 mem=120GB disk=200GB H100:80GB:1 (spot)   1H100.80S.30V   $0.99
- 2  datacrunch (FIN-01)  cpu=30 mem=120GB disk=200GB H100:80GB:1 (spot)   1H100.80S.30V   $0.99
- 3  datacrunch (FIN-02)  cpu=44 mem=182GB disk=200GB H200:141GB:1 (spot)  1H200.141S.44V  $0.99
+ #  BACKEND         RESOURCES                                        INSTANCE TYPE   PRICE
+ 1  verda (FIN-01)  cpu=30 mem=120GB disk=200GB H100:80GB:1 (spot)   1H100.80S.30V   $0.99
+ 2  verda (FIN-01)  cpu=30 mem=120GB disk=200GB H100:80GB:1 (spot)   1H100.80S.30V   $0.99
+ 3  verda (FIN-02)  cpu=44 mem=182GB disk=200GB H200:141GB:1 (spot)  1H200.141S.44V  $0.99
 
 ---> 100%
 

examples/models/wan22/README.md

@@ -186,7 +186,11 @@ gcp = [
     "dstack[server]",
 ]
 datacrunch = [
-    "datacrunch",
+    "verda; python_version >= '3.10'",
+    "dstack[server]",
+]
+verda = [
+    "verda; python_version >= '3.10'",
     "dstack[server]",
 ]
 kubernetes = [
@@ -211,5 +215,5 @@ nebius = [
     "dstack[server]",
 ]
 all = [
-    "dstack[gateway,server,aws,azure,gcp,datacrunch,kubernetes,lambda,nebius,oci]",
+    "dstack[gateway,server,aws,azure,gcp,verda,kubernetes,lambda,nebius,oci]",
 ]