From f84ddfd435e423617d174b33f20838efe414bc60 Mon Sep 17 00:00:00 2001 From: Yee Hing Tong Date: Tue, 10 Dec 2024 11:36:19 -0800 Subject: [PATCH 1/4] partial Signed-off-by: Yee Hing Tong --- conda-lock.yml | 19196 ++++++++++++++++ .../advanced_composition/eager_workflows.md | 30 +- monodocs-environment.lock.yaml | 9266 ++++---- 3 files changed, 23776 insertions(+), 4716 deletions(-) create mode 100644 conda-lock.yml diff --git a/conda-lock.yml b/conda-lock.yml new file mode 100644 index 0000000000..b72a8560ef --- /dev/null +++ b/conda-lock.yml @@ -0,0 +1,19196 @@ +# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. 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If you encounter any issues please consider submitting a [bug report](https://github.com/flyteorg/flyte/issues/new?assignees=&labels=bug%2Cuntriaged&projects=&template=bug_report.yaml&title=%5BBUG%5D+). ``` @@ -57,8 +57,7 @@ tasks, static subworkflows, and even other eager subworkflows in an _eager_ fashion such that we can materialize their outputs and use them inside the parent eager workflow itself. -In the `simple_eager_workflow` function, we can see that we're `await`ing -the output of the `add_one` task and assigning it to the `out` variable. If +In the `simple_eager_workflow` function, we can call the `add_one` task and assigning it to the `out` variable. If `out` is a negative integer, the workflow will return `-1`. Otherwise, it will double the output of `add_one` and return it. @@ -67,12 +66,17 @@ the Python integer that is the result of `x + 1` and not a promise. ## How it works -When you decorate a function with `@eager`, any function invoked within it -that's decorated with `@task`, `@workflow`, or `@eager` becomes -an [awaitable](https://docs.python.org/3/library/asyncio-task.html#awaitables) -object within the lifetime of the parent eager workflow execution. Note that -this happens automatically and you don't need to use the `async` keyword when -defining a task or workflow that you want to invoke within an eager workflow. +### Parallels to Python `asyncio` +The eager paradigm was written around Python's native `async` functionality. As such, it follows the same rules and +constructs and if you're used to working with async functions, you should be able to apply the exact same understanding to work with eager tasks. + +In the example above, the tasks `add_one` and `double` are normal Flyte tasks and the functions being decorated are normal Python functions. This means that in the execution of the async task `simple_eager_workflow` will block on each of these functions just like Python would if these were simply just Python functions. + +If you want to run functions in parallel, you will need to use `async` marked tasks, just like you would in normal Python. + +Note that `eager` tasks share the same limitation as Python async functions. You can only call an `async` function inside another `async` function, or within a special handler (like `asyncio.run`). This means that until the `@workflow` decorator supports async workflow function definitions, which is doesn't today, you will not be able to call eager tasks or other async Python function tasks, inside workflows. This functionality is slated to be added in future releases. For the time being, you will need to run the tasks directly, either from FlyteRemote or the Flyte UI. + +Unlike Python async however, when an `eager` task runs `async` sub-tasks in a real backend execution (not a local execution), it is doing real, wall-clock time parallelism, not just concurrency (assuming your K8s cluster is appropriately sized). ```{important} With eager workflows, you basically have access to the Python `asyncio` @@ -85,10 +89,10 @@ We're leveraging Python's native `async` capabilities in order to: 1. Materialize the output of flyte tasks and subworkflows so you can operate on them without spinning up another pod and also determine the shape of the workflow graph in an extremely flexible manner. -2. Provide an alternative way of achieving concurrency in Flyte. Flyte has +2. Provide an alternative way of achieving wall-time parallelism in Flyte. Flyte has concurrency built into it, so all tasks/subworkflows will execute concurrently - assuming that they don't have any dependencies on each other. However, eager - workflows provide a python-native way of doing this, with the main downside + assuming that they don't have any dependencies on each other. However, `eager` + tasks provide a Python-native way of doing this, with the main downside being that you lose the benefits of statically compiled workflows such as compile-time analysis and first-class data lineage tracking. ``` @@ -142,7 +146,7 @@ to check if `out` is negative, but we're also using the `gt_100` task in the ### Loops -You can also gather the outputs of multiple tasks or subworkflows into a list: +You can also gather the outputs of multiple tasks or subworkflows into a list. Keep in mind that in this case, you will need to use an `async` function task, since normal tasks will block. ```{literalinclude} /examples/advanced_composition/advanced_composition/eager_workflows.py :caption: advanced_composition/eager_workflows.py diff --git a/monodocs-environment.lock.yaml b/monodocs-environment.lock.yaml index 432354dd9e..b72a8560ef 100644 --- a/monodocs-environment.lock.yaml +++ b/monodocs-environment.lock.yaml @@ -5,11 +5,11 @@ # available, unless you explicitly update the lock file. # # Install this environment as "YOURENV" with: -# conda-lock install -n YOURENV monodocs-environment.lock.yaml +# conda-lock install -n YOURENV conda-lock.yml # To update a single package to the latest version compatible with the version constraints in the source: -# conda-lock lock --lockfile monodocs-environment.lock.yaml --update PACKAGE +# conda-lock lock --lockfile conda-lock.yml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: -# conda-lock -f monodocs-environment.yaml --lockfile monodocs-environment.lock.yaml +# conda-lock -f monodocs-environment.yaml --lockfile conda-lock.yml version: 1 metadata: content_hash: @@ -53,11 +53,11 @@ package: manager: conda platform: linux-64 dependencies: - 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sha256: 84d94506df497af8bf2aa2012a2175b9bb59b3468000ecf36c527386507cee7c + sha256: 7ef7e147285834f3cdc4779d077b2bc9c781568db266f4ad6bb5b899424927e2 category: main optional: false - name: flytekitplugins-deck-standard diff --git a/monodocs-environment.yaml b/monodocs-environment.yaml index 717a1523c2..80e0769fbf 100644 --- a/monodocs-environment.yaml +++ b/monodocs-environment.yaml @@ -61,7 +61,7 @@ dependencies: - neptune # neptune - pip: - - flytekit>=1.12.1b2 + - flytekit>=1.15.0a0 - readthedocs-sphinx-ext - sphinx-code-include - sphinxext-remoteliteralinclude From 75a924397ebc4790efcfaa2e4f4edef72d413686 Mon Sep 17 00:00:00 2001 From: Yee Hing Tong Date: Tue, 10 Dec 2024 14:28:52 -0800 Subject: [PATCH 3/4] update words Signed-off-by: Yee Hing Tong --- .../advanced_composition/eager_workflows.md | 69 ++++++------------- 1 file changed, 22 insertions(+), 47 deletions(-) diff --git a/docs/user_guide/advanced_composition/eager_workflows.md b/docs/user_guide/advanced_composition/eager_workflows.md index 100f2affc8..94b77af7b4 100644 --- a/docs/user_guide/advanced_composition/eager_workflows.md +++ b/docs/user_guide/advanced_composition/eager_workflows.md @@ -210,60 +210,38 @@ developing your workflows and tasks. (eager_workflows_remote)= -### Remote Flyte cluster execution +### Setting up remote Flyte cluster access Under the hood, `@eager` workflows use the {py:class}`~flytekit.remote.remote.FlyteRemote` -object to kick off task, static workflow, and eager workflow executions. - -In order to actually execute them on a Flyte cluster, you'll need to configure -eager workflows with a `FlyteRemote` object and secrets configuration that -allows you to authenticate into the cluster via a client secret key: - -```{code-block} python -from flytekit.remote import FlyteRemote -from flytekit.configuration import Config - -@eager( - remote=FlyteRemote( - config=Config.auto(config_file="config.yaml"), - default_project="flytesnacks", - default_domain="development", - ), - client_secret_group="", - client_secret_key="", -) -async def eager_workflow_remote(x: int) -> int: - ... +object to kick off task, static workflow, and eager workflow executions. In order to create a `FlyteRemote` instance, `Config.auto()` is run and the resulting config object is passed into `FlyteRemote`. + +This means that you just need to ensure eager workflow pods are run with the environment variables that including any mounted secrets for a client secret. For instance, the following three are sufficient in the most basic setup. + +```{code-block} bash +FLYTE_PLATFORM_URL +FLYTE_CREDENTIALS_CLIENT_ID +FLYTE_CREDENTIALS_CLIENT_SECRET ``` -Where `config.yaml` contains a -[flytectl](https://docs.flyte.org/projects/flytectl/en/latest/#configuration)-compatible -config file and `my_client_secret_group` and `my_client_secret_key` are the -{ref}`secret group and key ` that you've configured for your Flyte -cluster to authenticate via a client key. +See the relevant authentication docs for creating client credentials if using Flyte's internal authorization server. ### Sandbox Flyte cluster execution -When using a sandbox cluster started with `flytectl demo start`, however, the -`client_secret_group` and `client_secret_key` are not required, since the -default sandbox configuration does not require key-based authentication. - -```{literalinclude} /examples/advanced_composition/advanced_composition/eager_workflows.py -:caption: advanced_composition/eager_workflows.py -:lines: 130-145 -``` +When using a sandbox cluster started with `flytectl demo start` no authentication is needed and eager workflows should +just work out of the box. ```{important} When executing eager workflows on a remote Flyte cluster, it will execute the -latest version of tasks, static workflows, and eager workflows that are on -the `default_project` and `default_domain` as specified in the `FlyteRemote` -object. This means that you need to pre-register all Flyte entities that are -invoked inside of the eager workflow. +same version of tasks, static workflows, and eager workflows that are on +the `project` and `domain` as the eager task itself. If an entity is not found, FlyteRemote will attempt to register +it. Please be aware of this and potential naming errors due to difference in folder paths when running in the container. +Future work may be done to allow execution in another project/domain, but reference entities should always be +correctly reflected and invoked. ``` ### Registering and running -Assuming that your `flytekit` code is configured correctly, you will need to +Assuming that your `flytekit` code is configured correctly, you should register all of the task and subworkflows that are used with your eager workflow with `pyflyte register`: @@ -294,7 +272,7 @@ invoked inside of it. ## Eager workflows on Flyte console -Since eager workflows are an experimental feature, there is currently no +Since eager workflows are still in beta, there is currently no first-class representation of them on Flyte Console, the UI for Flyte. When you register an eager workflow, you'll be able to see it in the task view: @@ -325,23 +303,20 @@ eager workflow: ## Limitations -As this feature is still experimental, there are a few limitations that you +As this feature is still in beta, there are a few limitations that you need to keep in mind: -- You cannot invoke {ref}`dynamic workflows `, - {ref}`map tasks `, or {ref}`launch plans ` inside an - eager workflow. - [Context managers](https://docs.python.org/3/library/contextlib.html) will only work on locally executed functions within the eager workflow, i.e. using a context manager to modify the behavior of a task or subworkflow will not work because they are executed on a completely different pod. - All exceptions raised by Flyte tasks or workflows will be caught and raised - as an {py:class}`~flytekit.experimental.EagerException` at runtime. + as an {py:class}`~flytekit.exceptions.eager.EagerException` at runtime. - All task/subworkflow outputs are materialized as Python values, which includes offloaded types like `FlyteFile`, `FlyteDirectory`, `StructuredDataset`, and `pandas.DataFrame` will be fully downloaded into the pod running the eager workflow. This prevents you from incrementally downloading or streaming very large datasets - in eager workflows. + in eager workflows. (Please reach out to the team if you are interested in improving this.) - Flyte entities that are invoked inside of an eager workflow must be registered under the same project and domain as the eager workflow itself. The eager workflow will execute the latest version of these entities. From 5f051d7e756cc93b20a070b62f83cc9d5daedaff Mon Sep 17 00:00:00 2001 From: Eduardo Apolinario Date: Wed, 11 Dec 2024 12:28:43 -0500 Subject: [PATCH 4/4] Bump to flytekit 1.15.0a1 Signed-off-by: Eduardo Apolinario --- monodocs-environment.lock.yaml | 3993 ++++++++++++++++---------------- monodocs-environment.yaml | 2 +- 2 files changed, 1945 insertions(+), 2050 deletions(-) diff --git a/monodocs-environment.lock.yaml b/monodocs-environment.lock.yaml index cee58b76f4..255612295d 100644 --- a/monodocs-environment.lock.yaml +++ b/monodocs-environment.lock.yaml @@ -13,8 +13,8 @@ version: 1 metadata: content_hash: - linux-64: 211880a3c070823b7621aa26fd744fa5790125420f51f6e94992be9a3a09e253 - osx-arm64: 880d77880d7e4891e4671eaa557079d6d0bd8da6691f7284a30cf96b4ed0d85d + linux-64: 36c027b9c08b9276ad838f39a958117cf7d0ba74054a2e975269e0203f7c19cd + osx-arm64: 8683ff78800879a75cdea2d9313e94f8fb75c115a40b1657331e8f130256fc72 channels: - url: conda-forge used_env_vars: [] @@ -129,8 +129,8 @@ package: manager: conda platform: osx-arm64 dependencies: - frozenlist: '>=1.1.0' python: '>=3.9' + frozenlist: '>=1.1.0' url: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_1.conda hash: md5: d736bd1b8904d7593dce4893e58a7881 @@ -187,8 +187,8 @@ package: importlib_resources: '' mako: '' python: '>=3.9' - sqlalchemy: '>=1.3.0' typing_extensions: '>=4' + sqlalchemy: '>=1.3.0' url: https://conda.anaconda.org/conda-forge/noarch/alembic-1.14.0-pyhd8ed1ab_1.conda hash: md5: b54392a3894585367c9c87ea804e2fd1 @@ -218,13 +218,13 @@ package: manager: conda platform: osx-arm64 dependencies: - entrypoints: '' jinja2: '' + toolz: '' + entrypoints: '' + python: '>=3.7' + pandas: '>=0.18' jsonschema: '>=3.0' numpy: '>=0.18' - pandas: '>=0.18' - python: '>=3.7' - toolz: '' url: https://conda.anaconda.org/conda-forge/noarch/altair-4.2.2-pyhd8ed1ab_0.conda hash: md5: afca9c6a93335c55bbc84072011e86dc @@ -302,30 +302,17 @@ package: manager: conda platform: osx-arm64 dependencies: - exceptiongroup: '>=1.0.2' - idna: '>=2.8' python: '>=3.9' sniffio: '>=1.1' + idna: '>=2.8' typing_extensions: '>=4.5' + exceptiongroup: '>=1.0.2' url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.7.0-pyhd8ed1ab_0.conda hash: md5: c88107912954a983c2caf25f7fd55158 sha256: 687537ee3af30f8784986bf40cac30e88138770b16e51ca9850c9c23c09aeba1 category: main optional: false -- name: aom - version: 3.9.1 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda - hash: - md5: 346722a0be40f6edc53f12640d301338 - sha256: b08ef033817b5f9f76ce62dfcac7694e7b6b4006420372de22494503decac855 - category: main - optional: false - name: aom version: 3.9.1 manager: conda @@ -418,9 +405,9 @@ package: manager: conda platform: osx-arm64 dependencies: + typing-extensions: '' argon2-cffi-bindings: '' python: '>=3.9' - typing-extensions: '' url: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-23.1.0-pyhd8ed1ab_1.conda hash: md5: a7ee488b71c30ada51c48468337b85ba @@ -646,12 +633,12 @@ package: platform: linux-64 dependencies: libgcc-ng: '>=12' - libglib: '>=2.80.0,<3.0a0' + libglib: '>=2.74.1,<3.0a0' libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda + url: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-hd4edc92_1.tar.bz2 hash: - 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name: aws-c-cal @@ -758,16 +743,15 @@ package: category: main optional: false - name: aws-c-common - version: 0.9.31 + version: 0.9.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.31-hb9d3cd8_0.conda + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.0-hd590300_0.conda hash: - md5: 75f7776e1c9af78287f055ca34797517 - sha256: 31057d023a4ab78996f15dfefa9b2576da3282953623eeba28934c93baf132bc + md5: 71b89db63b5b504e7afc8ad901172e1e + sha256: d70c478150d2551bf7b200bfa3d7cb8a016471819a58bb7fe18a4e526dae3567 category: main optional: false - name: aws-c-common @@ -783,17 +767,16 @@ package: category: main optional: false - name: aws-c-compression - version: 0.3.0 + version: 0.2.17 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.9.31,<0.9.32.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.0-hf20e7d7_0.conda + aws-c-common: '>=0.9.0,<0.9.1.0a0' + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.17-h4d4d85c_2.conda hash: - 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sha256: b237b7f62c6873d612005d8ca37972516221db80c02cd928d3ce34ae363d8977 + md5: cb932dff7328ff620ce8059c9968b095 + sha256: a4a90f366fe9235be7700306efef999d2d95ebc53e3701efb899ff59eadae6cd category: main optional: false - name: aws-c-http @@ -876,19 +857,18 @@ package: category: main optional: false - name: aws-c-io - version: 0.15.0 + version: 0.13.32 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-cal: '>=0.8.0,<0.8.1.0a0' - aws-c-common: '>=0.9.31,<0.9.32.0a0' - libgcc: '>=13' - s2n: '>=1.5.6,<1.5.7.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.0-h17eb868_2.conda + aws-c-cal: '>=0.6.1,<0.6.2.0a0' + aws-c-common: '>=0.9.0,<0.9.1.0a0' + libgcc-ng: '>=12' + s2n: '>=1.3.49,<1.3.50.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.13.32-he9a53bd_1.conda hash: - md5: bb03f4ce96deea2175fc3ec17b2c1c04 - sha256: ae5496bd0bd161074ffa69329f60e0175bdc3f95db70934a35ab30b792158323 + md5: 8a24e5820f4a0ffd2ed9c4722cd5d7ca + sha256: 14911a1d4b66b5b031129d173fd34cedf852e3698eddd1972cf57974ce280f49 category: main optional: false - name: aws-c-io @@ -906,19 +886,18 @@ package: category: main optional: false - name: aws-c-mqtt - version: 0.11.0 + version: 0.9.3 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.9.31,<0.9.32.0a0' - aws-c-http: '>=0.9.0,<0.9.1.0a0' - aws-c-io: '>=0.15.0,<0.15.1.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h407ecb8_2.conda + aws-c-common: '>=0.9.0,<0.9.1.0a0' + aws-c-http: '>=0.7.11,<0.7.12.0a0' + aws-c-io: '>=0.13.32,<0.13.33.0a0' + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.9.3-hb447be9_1.conda hash: - md5: f9fcf88ac9d34b2bfe70429064d7744c - sha256: 55aef334ecc535a9a02c6af299f4c7a2162ef25c93119cf53671ab4090e957a5 + md5: c520669eb0be9269a5f0d8ef62531882 + sha256: e804a788e6147f4466bd2a24b7d9c327bffbe98bfb7420ddeae47dae41b140d6 category: main optional: false - name: aws-c-mqtt @@ -937,23 +916,22 @@ package: category: main optional: false - name: aws-c-s3 - version: 0.7.0 + version: 0.3.14 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-auth: '>=0.8.0,<0.8.1.0a0' - aws-c-cal: '>=0.8.0,<0.8.1.0a0' - aws-c-common: '>=0.9.31,<0.9.32.0a0' - aws-c-http: '>=0.9.0,<0.9.1.0a0' - aws-c-io: '>=0.15.0,<0.15.1.0a0' - aws-checksums: '>=0.2.0,<0.2.1.0a0' - libgcc: '>=13' - openssl: '>=3.3.2,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.0-hadeddc1_5.conda + aws-c-auth: '>=0.7.3,<0.7.4.0a0' + aws-c-cal: '>=0.6.1,<0.6.2.0a0' + aws-c-common: '>=0.9.0,<0.9.1.0a0' + aws-c-http: '>=0.7.11,<0.7.12.0a0' + aws-c-io: '>=0.13.32,<0.13.33.0a0' + aws-checksums: '>=0.1.17,<0.1.18.0a0' + libgcc-ng: '>=12' + openssl: '>=3.1.2,<4.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.3.14-hf3aad02_1.conda hash: - md5: 429e7497e7f08bc470d2872147d8ef6d - sha256: 4e15267aff3a97b5496e8c6e0f3599f2a104cf6ee791fa3ec6529cb7c7e64df2 + md5: a968ffa7e9fe0c257628033d393e512f + sha256: f986c222f1f22e9e8d4869846a975595f1d0043794b41a5cf30ac53b0e4f4852 category: main optional: false - 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pyarrow: '>=0.15.0' - python: '' + google-cloud-bigquery-storage-core: 2.11.0.* url: https://conda.anaconda.org/conda-forge/noarch/google-cloud-bigquery-storage-2.11.0-pyh6c4a22f_0.tar.bz2 hash: md5: 28ef07a4101ddea28bf766b95fc25128 @@ -4516,18 +4455,19 @@ package: category: main optional: false - name: google-cloud-bigquery-storage-core - version: 2.11.0 + version: 2.27.0 manager: conda platform: linux-64 dependencies: - google-api-core-grpc: '>=1.28.0,<3.0.0dev' - libcst: '>=0.2.5' - proto-plus: '>=1.18.0' - python: '>=3.6' - url: https://conda.anaconda.org/conda-forge/noarch/google-cloud-bigquery-storage-core-2.11.0-pyh6c4a22f_0.tar.bz2 + google-api-core-grpc: '>=1.34.0,<3.0.0dev,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,!=2.10.*' + google-auth: '>=2.14.1,<3' + proto-plus: '>=1.22.0,<2.0.0dev' + protobuf: '>=3.19.5,<5.0.0dev,!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5' + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/google-cloud-bigquery-storage-core-2.27.0-pyhff2d567_0.conda hash: - 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python: '>=3.9' url: https://conda.anaconda.org/conda-forge/noarch/google-cloud-core-2.4.1-pyhd8ed1ab_1.conda hash: md5: 574cda1b3e3c74cab4632659d39fdf07 @@ -4628,8 +4568,8 @@ package: manager: conda platform: osx-arm64 dependencies: - python: '>=3.9' six: '' + python: '>=3.9' url: https://conda.anaconda.org/conda-forge/noarch/google-pasta-0.2.0-pyhd8ed1ab_2.conda hash: md5: 005b9749218cb8c9e94ac2a77ca3c8c0 @@ -4654,8 +4594,8 @@ package: manager: conda platform: osx-arm64 dependencies: - google-crc32c: '>=1.0,<2.0dev' python: '>=3.9' + google-crc32c: '>=1.0,<2.0dev' url: https://conda.anaconda.org/conda-forge/noarch/google-resumable-media-2.7.2-pyhd8ed1ab_2.conda hash: md5: 1792ca195c71d1304b3f7c783a3d7419 @@ -4680,8 +4620,8 @@ package: manager: conda platform: osx-arm64 dependencies: - protobuf: '>=3.20.2,<6.0.0.dev0,!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5' python: '>=3.9' + protobuf: '>=3.20.2,<6.0.0.dev0,!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5' url: https://conda.anaconda.org/conda-forge/noarch/googleapis-common-protos-1.66.0-pyhff2d567_0.conda hash: md5: 4861e30ff0cd566ea6fb4593e3b7c22a @@ -4709,10 +4649,10 @@ package: manager: conda platform: osx-arm64 dependencies: - 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name: hdf5 version: 1.14.3 manager: conda @@ -5341,12 +5287,12 @@ package: manager: conda platform: osx-arm64 dependencies: + certifi: '' python: '>=3.8' - h11: '>=0.13,<0.15' - h2: '>=3,<5' sniffio: 1.* + h2: '>=3,<5' anyio: '>=3.0,<5.0' - certifi: '' + h11: '>=0.13,<0.15' url: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.7-pyh29332c3_1.conda hash: md5: 2ca8e6dbc86525c8b95e3c0ffa26442e @@ -5374,11 +5320,11 @@ package: manager: conda platform: osx-arm64 dependencies: - anyio: '' certifi: '' - httpcore: 1.* idna: '' + anyio: '' python: '>=3.9' + httpcore: 1.* url: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda hash: md5: d6989ead454181f4f9bc987d3dc4e285 @@ -5410,15 +5356,15 @@ package: manager: conda platform: osx-arm64 dependencies: + requests: '' filelock: '' - fsspec: '>=2023.5.0' - packaging: '>=20.9' python: '>=3.9' pyyaml: '>=5.1' - requests: '' - tqdm: '>=4.42.1' + packaging: '>=20.9' + fsspec: '>=2023.5.0' typing-extensions: '>=3.7.4.3' typing_extensions: '>=3.7.4.3' + tqdm: '>=4.42.1' url: https://conda.anaconda.org/conda-forge/noarch/huggingface_hub-0.26.5-pyhd8ed1ab_1.conda hash: md5: 73937038e21117fe401f8ea64fbaeacc @@ -5450,17 +5396,16 @@ package: category: main optional: false - name: icu - version: '75.1' + version: '73.2' manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' libgcc-ng: '>=12' libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda hash: - md5: 8b189310083baabfb622af68fd9d3ae3 - sha256: 71e750d509f5fa3421087ba88ef9a7b9be11c53174af3aa4d06aff4c18b38e8e + md5: cc47e1facc155f91abd89b11e48e72ff + sha256: e12fd90ef6601da2875ebc432452590bc82a893041473bc1c13ef29001a73ea8 category: main optional: false - name: icu @@ -5493,8 +5438,8 @@ package: manager: conda platform: osx-arm64 dependencies: - python: '>=3.6' ukkonen: '' + python: '>=3.6' url: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.3-pyhd8ed1ab_0.conda hash: md5: dd3acd023fc358afab730866a0e5e3f5 @@ -5593,8 +5538,8 @@ package: manager: conda platform: osx-arm64 dependencies: - importlib_resources: '>=6.4.5,<6.4.6.0a0' python: '>=3.9' + importlib_resources: '>=6.4.5,<6.4.6.0a0' url: https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.5-pyhd8ed1ab_1.conda hash: md5: 59561d9b70f9df3b884c29910eba6593 @@ -5681,20 +5626,20 @@ package: manager: conda platform: osx-arm64 dependencies: + packaging: '' + psutil: '' + nest-asyncio: '' __osx: '' appnope: '' - comm: '>=0.1.1' - debugpy: '>=1.6.5' - ipython: '>=7.23.1' + python: '>=3.8' + tornado: '>=6.1' jupyter_client: '>=6.1.12' jupyter_core: '>=4.12,!=5.0.*' + ipython: '>=7.23.1' matplotlib-inline: '>=0.1' - nest-asyncio: '' - packaging: '' - psutil: '' - python: '>=3.8' + debugpy: '>=1.6.5' pyzmq: '>=24' - tornado: '>=6.1' + comm: '>=0.1.1' traitlets: '>=5.4.0' url: https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.5-pyh57ce528_0.conda hash: @@ -5731,19 +5676,19 @@ package: manager: conda platform: osx-arm64 dependencies: + typing_extensions: '' __unix: '' decorator: '' exceptiongroup: '' - jedi: '>=0.16' matplotlib-inline: '' - pexpect: '>4.3' + stack_data: '' pickleshare: '' - prompt-toolkit: '>=3.0.41,<3.1.0' - pygments: '>=2.4.0' python: '>=3.9' - stack_data: '' + pygments: '>=2.4.0' + jedi: '>=0.16' traitlets: '>=5' - typing_extensions: '' + pexpect: '>4.3' + prompt-toolkit: '>=3.0.41,<3.1.0' url: https://conda.anaconda.org/conda-forge/noarch/ipython-8.18.1-pyh707e725_3.conda hash: md5: 15c6f45a45f7ac27f6d60b0b084f6761 @@ -5772,11 +5717,11 @@ package: manager: conda platform: osx-arm64 dependencies: + python: '>=3.9' + traitlets: '>=4.3.1' comm: '>=0.1.3' ipython: '>=6.1.0' jupyterlab_widgets: '>=3.0.13,<3.1.0' - 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jsonschema-specifications: '>=2023.03.6' pkgutil-resolve-name: '>=1.3.10' - python: '>=3.9' + jsonschema-specifications: '>=2023.03.6' referencing: '>=0.28.4' rpds-py: '>=0.7.1' url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_1.conda @@ -6177,14 +6122,14 @@ package: manager: conda platform: osx-arm64 dependencies: - fqdn: '' idna: '' + rfc3339-validator: '' + uri-template: '' + fqdn: '' isoduration: '' + rfc3987: '' jsonpointer: '>1.13' jsonschema: '>=4.23.0,<4.23.1.0a0' - rfc3339-validator: '' - rfc3987: '' - uri-template: '' webcolors: '>=24.6.0' url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-4.23.0-hd8ed1ab_1.conda hash: @@ -6217,14 +6162,14 @@ package: manager: conda platform: osx-arm64 dependencies: - fqdn: '' idna: '' + rfc3339-validator: '' + uri-template: '' + fqdn: '' isoduration: '' jsonpointer: '>1.13' - jsonschema: '>=4.23.0,<4.23.1.0a0' - rfc3339-validator: '' rfc3986-validator: '>0.1.0' - uri-template: '' + jsonschema: '>=4.23.0,<4.23.1.0a0' webcolors: '>=24.6.0' url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.23.0-hd8ed1ab_1.conda hash: @@ -6255,12 +6200,12 @@ package: manager: conda platform: osx-arm64 dependencies: - ipykernel: '' ipywidgets: '' - jupyter_console: '' jupyterlab: '' - nbconvert-core: '' notebook: '' + ipykernel: '' + nbconvert-core: '' + jupyter_console: '' python: '>=3.9' url: https://conda.anaconda.org/conda-forge/noarch/jupyter-1.1.1-pyhd8ed1ab_1.conda hash: @@ -6293,15 +6238,15 @@ package: manager: conda platform: osx-arm64 dependencies: - attrs: '' + pyyaml: '' click: '' + tabulate: '' importlib-metadata: '' - nbclient: '>=0.2' + attrs: '' nbformat: '' python: '>=3.9' - pyyaml: '' sqlalchemy: '>=1.3.12,<3' - tabulate: '' + nbclient: '>=0.2' url: https://conda.anaconda.org/conda-forge/noarch/jupyter-cache-1.0.1-pyhff2d567_0.conda hash: md5: b0ee650829b8974202a7abe7f8b81e5a @@ -6327,9 +6272,9 @@ package: manager: conda platform: osx-arm64 dependencies: + python: '>=3.9' importlib-metadata: '>=4.8.3' jupyter_server: '>=1.1.2' - python: '>=3.9' url: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.2.5-pyhd8ed1ab_1.conda hash: md5: 0b4c3908e5a38ea22ebb98ee5888c768 @@ -6359,13 +6304,13 @@ package: manager: conda platform: osx-arm64 dependencies: - 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readthedocs-sphinx-ext - sphinx-code-include - sphinxext-remoteliteralinclude